perfume diffusion experiment

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How to Demonstrate Diffusion with Hot and Cold Water

We all need some space sometimes, right that’s true down to a molecular level. molecules don’t like to stay too close together and will try to move to less crowded areas. that process is called diffusion and we will explore all about it in this simple but revealing experiment., article contents.

What is Diffusion?

Have you ever smelled your neighbor’s lunch on your way home? Or smelled someone’s perfume minutes after that person was gone? You experienced the diffusion!

Diffusion is a movement of particles from the area of high concentration to an area of low concentration. It usually occurs in liquids and gases.

Let’s get some complex-sounding terminology out of the way. When talking about diffusion, we often hear something about the concentration gradient (or electrical gradient if looking at electrons). Gradient just means a change in the quantity of a variable over some distance. In the case of concentration gradient, a variable that changes is the concentration of a substance. So we can define the concentration gradient as space over which the concentration of our substance changes.

For example, think of the situation when we spray the air freshener in the room. There is one spot where the concentration of our substance is very high (where we sprayed it initially) and in the rest of the room it is very low (nothing initially). Slowly concentration gradient is diffusing – our freshener is moving through the air. When the concentration gradient is diffused, we reach equilibrium – the state at which a substance is equally distributed throughout a space.

It’s important to note that particles never stop moving , even after the equilibrium is reached. Imagine two parts of the room divided by a line. It may seem like nothing is happening, but particles from both sides are moving back and forth. It’s just that it is an equal probability of them moving from left to right as it’s from right to left. So we can’t notice any net change.

Diffusion is a type of passive transport . That means it doesn’t require energy to start. It happens naturally, without any shaking or stirring.

There is also a facilitated diffusion which happens in the cell membranes when molecules are transported with the help of the proteins.

You may remember hearing about Osmosis and think about how is this different from it. It is actually a very similar concept. Osmosis is just a diffusion through the partially permeable membrane. We talked about it more in our Gummy Bear Osmosis Experiment so definitely check it out.

What causes Diffusion?

Do particles really want to move somewhere less crowded? Well, no, not in the way we would think of it. There is no planning around, just the probability.

All fluids are bound to the same physical laws – studied by Fluid mechanics , part of the physics. We usually think of fluids as liquids, but in fact, air and other types of gas are also fluids ! By definition , fluid is a substance that has no fixed shape and yields easily to external pressure.

Another property of the fluids is that they flow or move around. Molecules in fluids move around randomly and that causes collisions between them and makes them bounce off in different directions.

This random motion of particles in a fluid is called Brownian motion . It was named by the biologist Robert Brown who observed and described the phenomenon in 1827. While doing some experiments with pollen under the microscope, he noticed it wiggles in the water. He concluded that pollen must be alive. Even though his theory was far off, his observation was important in proving the existence of atoms and molecules.

Factors that influence Diffusion

There are several factors that influence the speed of diffusion. The first is the extent of the concentration gradient . The bigger the difference in concentration over the gradient, the faster diffusion occurs.

Another important factor is the distance over which our particles are moving. We can look at it as the size of a container. As you may imagine, with the bigger distance, diffusion is slower, since particles need to move further.

Then we have characteristics of the solvent and substance. The most notable is the mass of the substance and density of the solvent . Heavier molecules move more slowly; therefore, they diffuse more slowly. And it’s a similar case with the density of the solvent. As density increases, the rate of diffusion decreases. It’s harder to move through the denser solvent, therefore our molecules slow down.

And the last factor we will discuss is the temperature . Both heating and cooling change the kinetic energy of the particles in our substance. In the case of heating, we are increasing the kinetic energy of our particles and that makes them move a lot quicker. So the higher the temperature, the higher the diffusion rate.

We will demonstrate the diffusion of food coloring in water and observe how it’s affected by the difference in temperature. Onwards to the experiment!

Materials needed for demonstrating Diffusion

• 2 transparent glasses – Common clear glasses will do the trick. You probably have more than needed around the house. We need one for warm water and one for cold water so we can observe the difference in diffusion.
• Hot and cold water – The bigger the difference in temperature in two glasses, the bigger difference in diffusion will be observed. You can heat the water to near boiling or boiling state and use it as hot water. Use regular water from the pipe as “cold water”. That is enough difference to observe the effects of temperature on diffusion.
• Food coloring – Regular food coloring or some other colors like tempera (poster paint) will do the trick. Color is required to observe the diffusion in our solvent (water). To make it more fun, you can use 2 different colors. Like red for hot and blue for cold.

Instructions for demonstrating diffusion

We have a video on how to demonstrate diffusion at the start of the article so you can check it out if you prefer a video guide more. Or continue reading instructions below if you prefer step by step text guide.

• Take 2 transparent glasses and fill them with the water . In one glass, pour the cold water and in the other hot water. As we mentioned, near-boiling water for hot and regular temperature water from the pipe will be good to demonstrate the diffusion.
• Drop a few drops of food coloring in each cup . 3-4 drops are enough and you should not put too much food color. If you put too much, the concentration of food color will be too large and it will defuse too fast in both glasses. 
• Watch closely how the color spreads . You will notice how color diffuses faster in hot water. It will take longer to diffuse if there is more water, less food color and if the water is cooler.

What will you develop and learn

• What is diffusion and how it relates to osmosis
• Factors that influence diffusion
• What is Brownian motion
• How to conduct a science experiment
• That science is fun! 😊

If you liked this activity and are interested in more simple fun experiments, we recommend exploring all about the heat conduction . For more cool visuals made by chemistry, check out Lava lamp and Milk polarity experiment . And if you, like us, find the water fascinating, definitely read our article about many interesting properties of water .

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Perfume and Flavor Engineering: A Chemical Engineering Perspective

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In the last two decades, scientific methodologies for the prediction of the design, performance and classification of fragrance mixtures have been developed at the Laboratory of Separation and Reaction Engineering. This review intends to give an overview of such developments. It all started with the question: what do we smell? The Perfumery Ternary Diagram enables us to determine the dominant odor for each perfume composition. Evaporation and 1D diffusion model is analyzed based on vapor-liquid equilibrium and Fick’s law for diffusion giving access to perfume performance parameters. The effect of matrix and skin is addressed and the trail of perfumes analyzed. Classification of perfumes with the perfumery radar is discussed. The methodology is extended to flavor and taste engineering. Finally, future research directions are suggested.

1. Looking Back: The Beginning of Perfume Engineering at LSRE. What Do We Smell?

In the late 1990s, research on perfume engineering at the Laboratory of Separation and Reaction Engineering (LSRE) started with one of our members (AER) and a posdoc student, Vera Mata. She was had PhD in porous media and wanted to be an entrepreneur. She was interested in perfumes and we wanted to answer, with engineering tools, the question: What do we smell? Can we predict it?

A perfume, according to Jean Carles [ 1 ], is a liquid mixture of top notes (first impact, fresh), middle notes (main perfume character) and base notes (long-lasting) in solvents (ethanol, water, matrix). The structure of a perfume is shown in Figure 1 as Carles’s pyramid.

The structure of a perfume represented by Carles’ pyramid.

It should be noted that perfumed products appear in our everyday lives as fine fragrances (Chanel No. 5), air care (candles), fabric care (detergents), personal care (shampoos), personal wash (bar soaps), home care (dish wash), etc.

The Flavors and Fragrances (F&F) industry has a large palette of essential oils and fragrances (~10 4 ) to formulate products, mainly developed by perfumers. At the time we started research in this area, formulation by trial and error (~1000 tests) implied long development times (1–3 years) and production costs (perfumery raw materials, PRM, can cost from $10 up to $50,000 per kg). The F&F industry involved businesses of $26 bn in 2017 [ 2 ]. The market share is around 33% in the USA, 28% in Western Europe, 28% in Japan and China and 11% in the rest of the World. More than 78% of the market in 2017 was shared by the top 10: Givaudan, Firmenich, IFF, Symrise, Mane SA, Frutarom, Takasago, Sensient Flavors, Robertet SA, T. Hasegawa.

Perfume engineering is a branch of product engineering that caters to consumer needs for a specific application or market by providing a new valuable product, following the path: needs, ideas, selection and manufacturing [ 3 ]. The product classification of Raquel Costa et al. [ 4 ] is: commodities, specialty chemicals, formulated products, devices, virtual chemical products, bio-based products and technology-based consumer goods. Perfumes can be easily recognized as formulated products. Product engineering appeared with the shift in the evolution of the chemical industry from bulk chemicals to specific added-value products (electronics, flavors, coatings, fragrances, etc.).

Perfume engineering involves disciplines of thermodynamics, transport phenomena and psychophysics ( Figure 2 ) to predict the odor of mixtures of fragrances, evaporation/release of fragrances, diffusion and performance of perfumes, as well as to predict odor detection thresholds and classify perfumes into olfactory families (perfumery radar).

Chemical engineering today: ChE = M 2 P 2 E [ 5 ] and perfume engineering as a branch of product engineering [ 6 ].

2. The Perception of Odors

The pyramid structure of a perfume mentioned above considers top notes as those giving the initial impact of fragrance; typically citrus, with green notes lasting 15–30 min on the skin. The middle notes are typically spicy, leather, or floral, giving the perfume character (body), and lasting 3–4 h on the skin. Base notes are typically amber or musk, giving the substantivity of the fragrance, and lasting more than 4 h on the skin. These notes, together with solvents (ethanol, water, matrix), stabilizers, colorants, and UV filters, constitute the perfume.

The odor perception, from vapor to the nose, was addressed by Richard Axel [ 7 ] and Linda B. Buck [ 8 ], who won the Nobel Prize in Physiology or Medicine in 2004 “for their discoveries of odorant receptor and the organization of the olfactory system”. Odorants in the air bind to odorant receptors; the odorant receptor cells in the nasal epithelium are activated and send electric signals, which are relayed in glomeruli of the olfactory bulb and transmitted to higher regions of the brain.

The process for the perception of a perfume can be divided into four steps: first the evaporation of the perfume, followed by diffusion in the air until the olfactory system is reached, where there is the perception of odor intensity and odor character . The first two steps are the domain of chemical engineering and the last two belong to psychophysics.

2.1. Odor Thresholds

The Odor Detection Threshold (ODT) is the minimum concentration of an odorant that can be detected by humans, or, according to ASTM (method E 679-91), is the concentration of an odorous chemical at which the physiological effect elicits a response for 50% of the panelists. Human ODT in the air can vary by order of magnitudes from 0.3 ppb for t-butyl mercaptan, 40 ppb for menthol, 870 ppb for formaldehyde, 15,000 ppb for acetone and 141,000 ppb for methanol [ 9 , 10 , 11 ]. Recalling a story of a Ph.D. student working on sweetening gasoline and using t-butyl mercaptan in our lab; when getting on the crowded bus to go home at rush hour, a void space was nevertheless created around the student! The Odor Recognition Threshold (ORT) is the lowest odorant concentration at which its recognition becomes possible. ODT can be measured in the laboratory by a panel using an olfactometer as shown in Figure 3 .

Measurement of ODT by a panel using an olfactometer at LSRE.

2.2. Odor Intensity Models

The simplest way to quantify the smell of a fragrance molecule i is the Odor Value ( OV i ) [ 12 ] defined as the ratio of its concentration in the gas-phase C i g and its odor detection threshold ODT i , i.e.,

A more realistic measure of the smell which, somehow, considers the saturation of the sensor (nose) is the odor intensity ψ i defined, according to Stevens Power Law [ 13 ], as

where n i is the power law exponent.

2.3. Odor Character Model for a Mixture—The Strongest Component Model

The simplest model to quantify the smell from a multicomponent mixture is to say that we smell the component with the higher odor value or higher intensity, i.e.,

This is the idea behind the Strongest Component Model (SCM).

2.4. Sensory Dose/Response Curve

The sensory dose/response curve is a qualitative measure by panel members of the odor intensity for different concentrations in the liquid mixture. The scale of intensity goes from extremely weak (1) to strong (7). Figure 4 shows a dose/response curve indicating the threshold and the saturation.

Sensory dose/response curve with a scale of odor intensity.

Researchers from Kao Corporation [ 14 ] developed a database for 314 perfumery raw materials (PRM) with Odor Intensity Standard Curves (OISC) showing the relationship between PRM odor intensity and gas concentration. They used the Labeled Magnitude Scale (LMS) of Green [ 15 , 16 ] with a range 0–100 with a quasi-logarithmic scale between labels: barely detectable (bd), 1.4; weak (w), 6.1; moderate (M), 17.2; strong (s), 35.4; very strong (vs), 53.3; and strongest imaginable, 100. Figure 5 shows OISC for manznate, melonal, caryophyllene and cis-3-hexenyl isobutyrate. This treatment is an extension of our methodology in the sense that it links values of odor intensity to the sensory evaluation of panelists.

Odor Intensity Standard Curves in LMS scale versus the logarithm of fragrance concentration in air (Reprinted with permission from Ind. Eng. Chem. Res . 2019, 58, 15036−15044. Copyright 2019, American Chemical Society).

2.5. Evaporation of Perfumes: Modeling Vapor-Liquid Equilibrium (VLE)

The first step to get the concentration of the fragrance in the gas-phase C i g is to relate the liquid composition given by the mole fraction x i with the partial pressure of component i in the gas-phase P i = y i P with the Raoult’s law:

where y i is the mole fraction of species i in the gas phase, P is the pressure, P i s a t is the vapor pressure of species i and γ i is the activity coefficient of species i in the liquid phase. Taking into account the ideal gas law, the concentration of component i in the gas phase is

and its odor value is:

where M i is the molecular weight. The key point is the prediction of vapor composition using group-contribution methods (UNIFAC, UNIFAC-D, ASOG, A-UNIFAC, COSMO-SAC) for calculating the activity coefficients [ 17 , 18 ]. The prediction of the sensorially-dominant odor was compared with the evaluation of perfumers with a correlation r 2 = 0.806 [ 19 ].

2.6. Prediction of Odor Detection Threshold (ODT)

The task of predicting ODT is not easy, but a simplified vision of the process of odor perception will be helpful. We will consider that odorant molecules evaporate and reach our nose ( P i s a t ), dissolve in the mucus layer ( C i , W ) and bind to specific olfactive receptors (OR) located in the cilia ( K O W ); above a certain concentration (ODT) are detected. A sketch, as shown in Figure 6 , aids in understanding.

Simplified steps in odor detection: air and odorant molecules (C), mucus (B) and nasal epithelium (A).

Introducing partition equilibrium between the concentration of odorant in air and mucus (assumed as water) K a i r / m u c u s ∝ K a i r / w a t e r = P i s a t C i , W R T and equilibrium between the odorant concentrations in the mucus and bio-phase (assumed as octanol) K m u c u s / b i o p h a s e ∝ K o c t a n o l , w a t e r the ODT becomes:

Data from literature for 121 odorant molecules were correlated by

with a regression factor od r 2 = 0.77 [ 20 ].

3. Perfumery Ternary Diagram (PTD)

An engineering tool to predict the smell of a liquid mixture was first presented by Mata and Rodrigues [ 21 , 22 , 23 , 24 ] and can be illustrated for the simple case of a mixture of one top note (A), one middle note (B) and one base note (C) in a solvent ethanol (S). The idea comes from engineering ternary diagrams using mixture compositions on a solvent-free basis and working with odor values (or intensity) for each component A, B, and C. For each liquid composition, the odor values (or intensity) are calculated and using the Strongest Component Model we can map the triangle in regions with different dominant odors. This is the Perfumery Ternary Diagram (PTD), shown in Figure 7 . The distance from a point in the triangle to the AB line is the composition of C on a solvent-free basis x C ′ = x C x A + x B + x C where x A , x B and x C are the compositions in the whole perfume mixture. Similarly, the distance from a point to AC gives the composition of B and the distance to the line BC gives the composition of A. The lines separating odor zones are the Perfumery Binary Lines (PBL) where the OV of the two adjacent components are equal.

The perfumery ternary diagram: combining perfume pyramid structure with the ternary phase diagram.

The idea was extended to quaternary (PQD) and quinary mixtures (PQ2D) by Teixeira [ 25 ] to allow visualization, but can be applied to a mixture of any number of components. What is needed is just:

• (i) a database of vapor pressure for perfumery raw materials (PRM);
• (ii) a database of ODT for PRM and
• (iii) a tool to calculate activity coefficients for PRM.

The PTD tool allows us to analyze the effect of non-ideality of perfume mixtures on the odor zones as well as the description of smell with odor value or intensity from Steven′s law. The effect of base notes and fixatives can be easily visualized as illustrated in Figure 8 . By changing the base note from vanillin (left) to tonalide (right) there is no region where we smell tonalide; instead, we smell ethanol.

The effect of base note on odor zones on odor zones: left A—limonene (squares); B—geraniol (triangles); C—vanillin (circles); S—ethanol (losange); right —the base note C tonalide is not perceived and ethanol is (Reprinted with permission from AIChEJ, 2009, 55, 15. John Wiley and Sons).

4. Diffusion and Performance of Perfumes

4.1. perfume performance.

In perfumery, the performance of a perfume is evaluated using the words “ impact ” (immediate olfactory odor sensation), “ tenacity ” (persistence of fragrance after some time near the source), “ diffusion ” (efficacy of perfume at some distance from the source) and “ volume ” (effectiveness of a perfume over time and distance). In chemical engineering terms, all we need is a model allowing the calculation of the concentration of perfume components as a function of time and distance, C i ( z , t ) , and then converting that information into odor value or intensity. This is shown in Figure 9 from Teixeira [ 26 ].

Perfume performance parameters (Reprinted with permission from AIChEJ, 2013, 59, 15. John Wiley and Sons).

The top notes will be perceived first (blooming phase) followed by the middle notes (development phase) and then base notes will eventually show up in the lasting phase as sketched in Figure 10 .

Blooming, development and lasting phases.

4.2. A Simple 1D Diffusion Model

A 1D perfume diffusion model [ 27 ] is developed for the gas–liquid system involving a mass balance in a volume element of the gas phase with thickness Δz ( Figure 11 )

with initial condition y i = y i 0 = 0 and boundary conditions at t > 0

Diffusion tube with volume element of thickness Δz.

In the above equations, t is the time variable, z is the axial space coordinate, D i,air is the diffusivity of species i in the air. Equation (10) represents the equilibrium at the gas-liquid interface. For the liquid phase the mass balance is:

with initial condition n i = n i 0 or x i = x i 0 , x i = x i 0 , where n i is the number of moles of species i in the liquid phase, and A lg is the area of the liquid/gas interface.

The odor evolution near the source for a perfume mixture containing limonene (A), geraniol (B) and vanillin (C) is shown in Figure 12 .

Time evolution of OV near the source- we smell limonene first and geraniol after 1 h ( a ) and iso-OV lines in a plot distance vs. time ( b ). Reprinted with permission from Chem. Eng. Sci . 2009, 64, 2570–2580, 2009, Elsevier.

Evaporation lines are shown in PTD and PQD diagrams at z = 0 (near the source) for a mixture of limonene (A), geraniol (B), vanillin (C) and ethanol (S). Depending on the initial perfume composition the smell follow different paths; for example, if we start with the mixture P1 we smell limonene first and then geraniol ( Figure 13 ).

Evaporation lines of a perfume mixture near the source in PTD and PQD. Reprinted with permission from Chem. Eng. Sci . 2009, 64, 2570–2580, Elsevier.

Diffusion models from a fixed source have been extended to account for the radial space dimension [ 28 , 29 ].

5. Perfume Classification and Perfumery Radar

Perfumery raw materials are classified in various categories by experts and are not usual for people. A classification of fragrances may have different layers related to emotions (culture, memories), look (color, texture), sensations (cool, dry, fatty, warm), aromas (floral, citrus, fruity, woody). There are classifications of fragrances, such as The Fragrance Wheel by Michael Edwards [ 30 ] or the Drom fragrance circle [ 31 ]. Most typical descriptors used in fragrance classification use a decreasing order in the percentage in an in-house database of 2000 odorants: floral, rose, diffusive, fruit, violet, green, musk, woody, herbaceous, citrus, spice, jasmine, amber, honey, liquor, marine, leather moss, tobacco. Examples of databases are those of Brechbill [ 32 ], Surburg and Panten [ 33 ], The Good Scents Company [ 34 ] and AIHA [ 35 ]. The 19 descriptors were used to choose eight olfactory families to be represented in a radar plot—the perfumery radar (PR)—to reduce the arbitrariness of perfume classification. The Perfumery Radar (PR) methodology [ 36 ] involves several steps:

• (i) Classification of pure fragrances in j = 8 olfactory families: citrus, fruity, floral, green, herbaceous, musk, oriental, woody;
• (ii) prediction of the odor intensity for each fragrance i , OV i ;
• (iii) calculation of the odor value for each family O V j = ∑ i = 1 N w i j O V i , and normalization O V j ′ = O V j ∑ j = 1 L O V j ;
• (iv) plotting the perfumery radar.

The weights w i j presented are needed when one PRM is allocated to more than one family. The PR can be validated using GC-MS analysis of perfumes, family odor intensity model and comparison with headspace and perfume classifications. Examples of PR for feminine perfumes, such as L’Air du temps (Nina Ricci) and Addict-Eau de Toillete (Dior), are shown in Figure 14 , together with company classification Floral and Floral–Green and Oriental and Oriental–Floral, respectively.

Perfumery radar of perfumes: left —L’ Air du temps (Nina Ricci); right —Addict-Eau de Toillete (Dior) (Reprinted (adapted) with permission from Perfumery radar: a predictive tool for perfume family classification, Ind. Eng. Chem. Res. 2010. Copyright 2010, American Chemical Society).

It is possible to combine the Perfumery Radar with a diffusion model and evaluate the evolution of perfume performance as shown in Figure 15 for Gloria (Cacherel) at t = 0 and t = 60 s. It is interesting to note that this perfume was classified by experts from various companies as oriental–woody (Osmoz and Fragrantica.com), oriental–fresh (Scent Direct), oriental–woody–floral (iPerfumer, Givaudan), floral–woody–amber (SFP), amber–rose (LT&TS), floral–oriental (Perfume Intelligence). The perfumery radar correctly predicts the olfactory families of several commercial perfumes. It is flexible to inserting new PRM in the database and uses scientific tools to predict the odor space instead of relying on the sensorial perception of people.

Perfumery radar of Gloria (Cacherel) at time t = 0 ( left ) and after t = 60 s ( right ). (Reprinted (adapted) with permission from Perfumery radar: a predictive tool for perfume family classification, Ind. Eng. Chem. Res. , 2010. Copyright 2010, American Chemical Society).

6. The Effect of Matrix and Skin

6.1. effect of matrix (glycerine, dipropylene glycol, skin lotion).

It is expected that the odor of a liquid fragrance mixture will be influenced by the matrix in which it is present [ 37 , 38 , 39 ]: ethanol in a perfume, glycerine, dipropylene glycol in cosmetic matrices. The matrix effect was analyzed in our lab to study the release of Origanum. majorana L. from glycerine, dipropylene glycol and from a topical formulation –skin lotion. The supercritical fluid extraction with CO 2 of the aerial part of O. majorana was carried out at 40 °C and 10 MPa. The extract was incorporated in the matrix in 0.01% ( v / v ). Dynamic headspace (DHS) coupled to GC-FID/MS was used to measure species concentration over time. The odor profile in the presence of glycerine was initially characterized by the fast release of β-caryophyllene, sabinene, p-cymene, limonene, myrcene, linalyl acetate and β-phellandrene followed by a high decrease in the next 5 h. The fast release is due to the hydrophobic nature of these compounds with low LogP ow , i.e., low affinity with the polar solvent glycerine. For example, the LogP ow is 4.5 for limonene and 3.8 for linalyl acetate.

The odor intensity of the fragrance compounds was lower in dipropylene glycol (DPG) as a consequence of its high retention ability. Sabinene was the most released compound and after 2 h the headspace contained residual amounts of sabinene, myrcene, limonene, β-phellandrene, γ-terpinene and β-caryophyllene. DPG can be a good solvent to prolong the perceived scent of a fragranced product. In respect to the dipropylene glycol, the dominant odor changed as the time increased: the mixture started to smell like linalyl acetate (odor described as sweet, citrus, floral and woody) and then changed to myrcene (odor described as peppery, terpene, spicy and balsam). Despite the lower headspace amounts of myrcene compared to the remaining fragrance compounds, it will be more perceived due to its low ODT value (4.5 × 10 −2 mg/m 3 ).

When the matrix is a skin lotion, the initial headspace is reached in sabinene, myrcene, p-cymene, β-phellandrene and γ-terpinene and then a sharp decrease of concentration with time. The most retained components over time were linalyl acetate and β-caryophyllene but terpinen-4-ol increased after 5 h.

6.2. The Effect of Skin

The effect of the skin was studied in our lab with a Franz cell using skin prepared from pig ears according to the protocol described elsewhere [ 40 ]. The Franz cell shown in Figure 16 contains a donor compartment where the fragrance mixture is placed and a receptor compartment separated from the donor by the membrane (skin). It allows the study of the permeation of components through the skin and retention on the skin. At the same time, the odor in the gas phase in the donor compartment can be followed. The Franz cell is equivalent to the Wicke–Kallenbach diffusion cell known in Chemical Reaction Engineering [ 41 ].

Franz cell for permeation studies of fragrance mixtures (Reprinted with permission from Elsevier, Int. Journal of Biological Macromolecules 2020, 147, 150–159).

The modeling of the system sketched in Figure 17 involves a mass balance for the donor liquid solution

with the initial condition (t = 0) C i , d ( 0 ) = C i , d , 0 . In the above equation, A is the membrane area, K p , i is the permeation coefficient of component i through the skin, V d is the volume of the donor compartment, C i , d and C i , r are the concentrations in the donor and receptor compartments and D i , m i x is the diffusivity of i in the gas phase above the liquid in the donor chamber.

Sketch of the Franz cell system.

The mass balance in the gas phase above the liquid in the donor compartment is:

with initial condition C i g ( z , 0 ) = 0 . The boundary conditions are:

and at z = 0 the equilibrium at the gas–liquid interface

The mass balance for the receptor compartment is:

The permeation coefficients calculated from infinite-dose experiments were 1.08 × 10 −5 cm/h, 8.25 × 10 −6 cm/h and 2.15 × 10 −3 cm/h for α-pinene, limonene and linalool, respectively. In all experiments, the fragrances were diluted in ethanol. This is illustrated in Figure 18 for the linalool experiment, infinite-dose.

Cumulative amount of linalool in the receptor compartment versus time for the infinite-dose experiment (Reprinted (adapted) with permission from Evaporation and permeation of fragrance applied to the skin, Ind. Eng. Chem. Res., 2019, 58, 9644–9650. Copyright 2019, American Chemical Society).

The model allowed at the same time to follow the concentration in the headspace above the donor solution. Ultimately it would be important to separate the adsorption of each fragrance onto the skin. The release is affected by the interaction between fragrances and ethanol and also by the vapor pressure of the species. Vapour pressure and permeation coefficients in the skin were measured by Almeida et al. [ 42 ] for 14 PRM: camphor, carvacrol, carvone, citronellol, eucalyptol, eugenol, geraniol, limonene, linalool, menthol, menthone, tonalide, vanillin and α-pinene. Various models have been proposed to address dermal absorption of fragrances and drugs [ 43 , 44 , 45 , 46 ].

7. The Trail of Perfumes

The trail of perfumes or sillage is something we deal with in our everyday life. It describes the scented trail left by the fragrance wearer. It is determined by how long a fragrance travels away and diffuses around the wearer [ 47 ]. We tackled this problem by first analyzing the diffusion of fragrances released from a moving source [ 48 ]. First, we started with a 1D model considering molecular diffusion of a fragrance molecule (α-pinene) in the air as the only mass transport mechanism. Considering an impermeable boundary condition and constant release of the fragrance μ i m a s s = k i M i C i g the gas concentration in a semi-infinite medium is:

The mass evaporation rate can be calculated as

where the mass transfer coefficient k i takes into account film contributions from gas and liquid sides. The validation was performed in a diffusion tube, and a system was developed to move the scented source along the axial direction ( Figure 19 ). Results showed that experimental data fitted well with the numerical simulation, suggesting this model as a valid tool to describe the trail of a fragrance released from a moving source for low Reynolds number of the order of 10 ( Figure 20 ).

Diffusion tube and moving source.

Simulated and experimental gas concentration profiles of a-pinene versus distance at t = 100 s of a source moving at 1.34 × 10 −2 m/s and Dα-pin = 6.04 × 10 −6 m 2 /s (Reprinted with permission from AIChEJ 2018, 64, 2890–2897, John Wiley and Sons).

In the case of a person walking at the speed of 1.34 m/s in a room or corridor inside a building, 3D models are required and mass transport of the perfume to the surrounding air will be dominated by turbulent diffusion or eddy diffusion Dt which is two orders of magnitude higher than molecular diffusion. For the 3D case the solution is:

where the source path is r 0 ( t ) = [ x 0 ( t ) , y 0 ( t ) , z 0 ( t ) ] T , r 0 ( t ) = [ x 0 ( t ) , y 0 ( t ) , z 0 ( t ) ] T and r 1 ( t ) = [ x 0 ( t ) , y 0 ( t ) , − z 0 ( t ) ] T .

As an example for an initial position of the moving source r 0 (0, 0, 1.50 m) is shown in Figure 21 a, and the concentration profiles at t = 200 s for a person with a fragrance α-pinene moving at 1.34 m/s are shown in Figure 21 b.

3D model- moving source at 1.50 m ( a ) and concentration profiles for 3 values of diffusivity evaluated at z = 1.60 m and t = 200 s ( b ) (Reprinted with permission from AIChEJ 2018, 64, 2890–2897. John Wiley and Sons).

These models can help the fragrance industry to achieve the desired trail of fragrance more quickly and efficiently. The search for devices which can be used to measure the sillage of fragranced products continues [ 49 ].

8. Flavor Engineering

The methodology developed for perfume engineering can be extended to taste engineering or flavor engineering. We previously defined the odor detection threshold as the minimum gas phase concentration at which an odorant is detected by the nose; and the odor value OV as the ratio between the concentration of odorant in gas-phase divided by the ODT. Similarly, we define the flavor detection threshold FDT i as the lowest liquid concentration of component i at which it is detected by the retronasal route; the flavor value FV i is then the ratio between the gas phase concentration and its FDT.

The idea is to predict the sensory quality of flavored products based on their gas phase composition with the help of psychophysical models and olfactory descriptors. The first tested case of flavored products was fruit juices (peach, lemon, mango and pineapple). For each fruit juice, the headspace gas phase composition was measured by chromatography. The tests were extended to binary and ternary mixtures of fruit juices [ 50 ]. Odor and flavor radars were constructed with families of fruity, sweet, green, woody, fresh, spicy, citrus, fatty, ripe tones and validated by a sensory evaluation of consumers as shown in Figure 22 for peach juice. It should be noted that the headspace of peach juice contained various compounds identified as ethyl butyrate (fruity, sweet, spicy) isoamyl acetate (sweet, fruity, ripe), benzaldehyde (woody, fruity, sweet), ethyl hexanoate (sweet, fruity, green), limonene (citrus), linalool (citrus, sweet, woody). When one component is allocated to just one family, the weight is 1; when allocated to three families the weights are 0.6, 0.3 and 0.1. The ODTs for the six components are 3.35 × 10 −4 mg/m 3 for ethyl butyrate, 4.99 × 10 −1 for isoamyl acetate, 6 for benzaldehyde, 1.5 × 10 −2 for ethyl hexanate, 6.19 × 10 −1 for limonene and 9.33 × 10 −3 for linalool. The flavor detection thresholds FDT i (mg/kg) for the same six components are 1.8 × 10 −3 , 5.7 × 10 −2 , 5.3 × 10 −1 , 8.0 × 10 −3 , 2.1 × 10 −1 and 3.3 × 10 −3 , respectively.

Odor and flavor radars for peach juice (experimental; shaded area–predicted). Reprinted with permission from Ind. Eng. Chem. Res. 2018, 57, 8115−8123 Copyright 2018, American Chemical Society).

A review on the biotechnological production of non-volatile flavors has been published recently by Paulino et al. [ 51 ]. Methodologies to advance the understanding of flavor chemistry have been proposed by Menis-Henrique [ 52 ] and the development of a model mouth discussed in detail by Panda et al. [ 53 ]. Encapsulation of flavors/aromas in food applications has been discussed by Gupta et al. [ 54 ].

9. Looking Ahead

More than two decades have past since research on perfume engineering started at LSRE. Figure 23 shows the timeline and the main contributors to the developments of perfume design: perfumery ternary diagram (PTD) and extension to quaternary and quinary systems (PQD and PQ2D), prediction of odor thresholds and VLE, performance of perfumes—1D and 3D diffusion models—and trail of perfumes, classification of perfumes—perfumery radar, effect of matrix and skin, extension to flavor/taste engineering. We have not listed many trainees coming from France, Poland, Spain or Brazil.

Contributors for research in Perfume Engineering started by Alírio Rodrigues and Vera Mata at LSRE.

Looking ahead our research directions are focused on:

• (i) The importance of diffusivity on the trail of perfumes or sillage

Brahms and Benaim [ 47 ] consider that in a perfume there are top notes and base notes and the heart (middle) notes come both from top and base notes molecules with fast diffusivity (sprinters) or slow diffusivity (long runners). This is an interesting view that requires a closer look at the measurements of diffusivities [ 55 , 56 , 57 ] and molecular design.

This reference to the importance of diffusivity goes back to the work of Mookerjee et al. [ 58 , 59 ] on the Aura of Aroma. The Aura of Aroma ® (AoA) technology consists of sampling the molecules from the air surrounding a liquid fragrance by Solid-Phase Micro-Extraction (SPME). The SPME needle should be as close as possible to the source, without ever touching it, and the sampling period is typically comprised between ½–1 h. In one of the first references to the AoA, the authors state that it is almost exclusively dependent on the species diffusivity, and independent from its “boiling point, molecular weight, odour threshold, or odour value”. Nevertheless, our previous studies suggest that VLE is expected to have an important role in the AoA. To test this hypothesis, the composition of the vapor phase in equilibrium with a reconstituted orchid (dendrobium superbum orchid) liquid fragrance was computed by applying modified Raoult’s Law and estimating the activation coefficients of all the species through the UNIFAC model (details can be found on the Supplementary Materials ). The results were then compared with the composition of the AoA reported by Mookerjee et al. for this fragrance ( Table 1 ).

Comparison between the Aura of Aroma ® measured for a reconstituted orchid (dendrobium superbum orchid) liquid fragrance and the gas-phase composition estimated from VLE.

As stated by Mookerjee et al., the composition of the AoA is considerably different from the composition of the liquid fragrance. For instance, the relative amounts of benzyl acetone, benzyl acetate linalool and 2-Tridecanone increase from the fragrance to the AoA (1.5, 26.0, 15.5 and 275 times, respectively). On the other hand, the relative amounts of oxyphenolon, 2-pentadecanone and ethyl myristate decrease (by 0.1, 0.5 and 0.6) from the liquid fragrance to the AoA. The gas phase composition values estimated from the VLE were slightly different, but respected the same general trends. The largest deviations relative to the AoA were observed for Linalool, 2-Tridecanone and 2-Pentadecanone. Through this rather simple approach, one is led to believe that, although VLE calculations alone do not describe the AoA accurately, they allow us to predict if the relative amount of a species in the gas phase is expected to increase or decrease relative to the liquid phase. Including the effect of the compound’s diffusivity would expectedly improve the preliminary estimations obtained through the VLE.

A closer look at this topic is required using tools to track perfume composition and sillage [ 60 ]. Related to this topic is the area of olfactive marketing where CFD tools are important.

• (ii) Formulation of perfumes and fragranced products

The methodology of perfumery ternary diagram (PTD) was a pioneering idea that can be extended to fragrance mixtures of N components to find compositions delivering a certain smell. It can be further elaborated to include the effect of skin on the evaporation of perfumes. The Perfumery Radar can be extended to other areas (wines) and the methodology extended to taste/flavor engineering.

Zhao et al. [ 61 ] applied a model for the prediction of intensity and character of fragrances, across three main consumer touchpoints for the laundry process. They used headspace gas chromatography data with psychophysical models (Steven’s power law) and tested with a trained sensory panel. The authors claim it is the first time that the approach was validated under realistic conditions for a multi-component perfume mixture diluted in a structured product base. The formulation of fragrance products to be applied in detergents is described in the patent by Teixeira et al. [ 62 ] where specific terminology (experimental velocity—distance traveled 60 cm divided by the speed of diffusivity— time needed for olfactory detection at 60 cm of the mixing point) is used to classify fragrance ingredients.

Fragranced products include microcapsules of polyurethane-urea with perfume as a core material for textile applications such as perfumed suits [ 63 ] or eco-friendly microcapsules based on chitosan and Arabic gum [ 64 ], a research area at LSRE. Microencapsulation of fragrances is also a way of increasing the life of fragranced products [ 65 ].

It is important to say that the engineering tools developed in our group have some challenges to overcome: the prediction of the odor of chiral molecules and the prediction of the odor quality of a molecule, which may change with its concentration. However, having a database with the descriptors of enantiomers allow the use of the engineering tools presented, and VLE methods such as COSMO-SAC [ 18 ] may help in predicting the gas phase composition of chiral molecules from a liquid mixture.

It should be said that many odorant molecules are chiral. Leffingwell database collects more than 400 enantiomers and their odors; about 60% of the pairs have similar smells and 40% smell differently. One example is limonene: R-limonene smells orange and S-limonene smells lemon; another example is carvone: R-carvone smells mint and S-carvone smells like caraway. Several attempts have been made over the years to predict the smell of chiral molecules starting with the “shape” theory: “lock and key” between the odorant and receptor based on Pauling and Delbruck idea [ 66 ]. The shape alone does not explain reality, otherwise, virtually all pairs should smell different. Later, the pros and cons of the vibrational theory of Wright [ 67 , 68 ] and Dyson [ 69 ], later taken by Turin [ 70 ] and reviewed by Meierhenrich et al. [ 71 ] were also considered. Other researchers, such as Brookes et al., tried to correlate odor with molecular flexibility claiming that flexibility allows left and right-handed molecules to be distinguished [ 72 ].

Another point to consider is when the odor quality changes with odorant concentration as already reported by perfumers. One example is indole which smells floral at low concentrations and putrid at high concentrations. Tentative explanation pointed out shifts in the patterns of glomeruli activated by the odorant although other reports see no shift in location and simply increase in the number of glomeruli activated at low concentration of odorant [ 73 ]. Nevertheless, the engineering tools presented are still useful for predicting the odor from a liquid mixture if a database with descriptors of chiral molecules are available.

• (iii) Artificial intelligence (AI) and perfume design

The advent of fast computing, data digitalization, cloud data storage systems, and several other tools from Industry 4.0 is changing several industrial paradigms. Among these technologies, Artificial Intelligence (AI) is presented as the possibility for machines imitating the intelligence and behavior of humans. AI is a key technology that can make use of the increasing big databases to extract useful information and disrupt the way that products are designed and developed. The concern about the potential of AI has also been an increasing issue in the literature [ 74 ]. Big-data is an issue that is becoming common in several sectors. Modern technology has allowed the generation and storage of a huge amount of data, which represents a potential to be explored.

The perfume industry is not an exception in this process, and it is possible to note a recent movement in this field towards the application of AI in perfume development. For instance, predicting the relationship between the structure of a molecule and its odor (quantitative structure-odor relationship, QSOR), is a difficult task [ 75 , 76 ]. One of the first reports in the direction is presented by Zhang, L. et al. [ 77 ], an AI model was trained to learn fragrance molecule classification, building a computer-aided molecular screening tool. The proposed model demonstrated a remarkable accuracy in performing the screening. Yu et al. [ 78 ] present a comprehensive review about the application of computer-based strategies in the design of experimental designs to flavor and sensory analysis. The referred work shows the potential of these techniques, including Artificial Neural Networks (ANN), for food flavor applications.

This topic has also caught the interest of multi-billionaire companies, such as ALPHABET. Sanchez-Lengeling et al. [ 79 ] developed graph neural networks, in ALPHABET’s subsidiary, Google, to quantify the relationship between molecular structure and odor (quantitative structure-odor relationship, QSOR).

AI is already in the fragrances business, the International Business Machines (IBM) working together with Symrise and O Boticário launched the project Phylira, to develop an AI-based tool to create perfumes using their big databases. The support from IBM on predicting natural language descriptions of mono-molecular odorants was published recently [ 80 ]. In this same line, Givaudan launched, in 2019, the project Carto, an AI-powered tool that brings science and technology to the development of new perfume formulation. It uses the proprietary ingredients ‘Odour Value Map’ to maximize the olfactive performance in the final formula.

It is interesting to mention the DREAM Olfaction Prediction Challenge-a crowd-sourced competition to develop models that can predict how a molecule smells from its physical and chemical features. Results from the DREAM Consortium were published recently in Science [ 81 ].

Most recently, a step toward a more complex application of AI in the Flavour and Fragrance field was presented by Zhang, X. et al. [ 82 ] and Santana et al. [ 83 ]. In Zhang’s work, the authors proposed a strategy to develop AI models to be applied in an optimization problem that searches to identify a fragrance formulation to deliver a certain odor. On the other hand, Santana’s work presents one of the firsts application reports of a more sophisticated AI tool, Deep Learning (DNN), to address dynamic-related problems found in the perfume formulation. The DNNs are a group of machine learning techniques that use types of more complex architectures of artificial neural networks to solve difficult problems. The authors proposed a framework that makes use of DNN models and meta-heuristic optimization (stochastic optimization algorithm) to systematically formulate fragrances, considering their behavior through time and space.

This research line driven by Idelfonso Nogueira (LSRE) aims in the future to build a novel smart Cyber–Physical System (CPS) for the on-demand design and production of perfumes. It will be based on current emerging technologies: systems automation, artificial intelligence and real-time optimization (RTO), and get them to work harmoniously in a CPS enabled by the Internet of Things (IoT).

Acknowledgments

One of us (A.E.R.) acknowledges the contribution of Miguel A. Teixeira for the preparation of a short course on perfume engineering given at COBEQ 2014 in Florianopolis which helped in the writing of this manuscript.

Supplementary Materials

The following are available online, Table S1: Physical properties of the studied compounds; Table S2: UNIFAC sub-groups frequency table for the studied compounds.

Author Contributions

Conceptualization, A.E.R.; writing—original draft preparation, A.E.R. and R.P.V.F.; writing—review and editing, A.E.R., R.P.V.F. and I.N.; supervision, A.E.R. All authors have read and agreed to the published version of the manuscript.

This work was financially supported by the Base Funding-UIDB/50020/2020 of the Associate Laboratory LSRE-LCM-funded by national funds through FCT/MCTES (PIDDAC). Financial support of NORTE-01-0145-FEDER-000006f FCT–Fundação para a Ciência e Tecnologia under the CEEC Institucional program is also acknowledged.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Perfume diffusion.

3 comments:

24/29 the only things you missed was that we used devices you did not state how your hypothesis was correct good luck on your next lab report

the only things you missed was that we used devices you did not state how your hypothesis was correct good luck on your next lab report

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Diffusion Demonstration

March 29, 2021 By Emma Vanstone Leave a Comment

Imagine pulling a delicious cake out of an oven, the smell slowly spreads around the room and then through the house. This is diffusion! The lovely cake smelling particles move from where there are lots of them ( high concentration ) to where there are less of them ( low concentration ). Diffusion can be quite a slow process as the movement of particles is random. One very easy diffusion demonstration is to pour squash or food colouring into a glass of water and watch as the colour spreads through the glass.

Diffusion is the movement of a substance from an area of high concentration to an area of low concentration until its concentration becomes equal throughout the available space.

This video shows diffusion in action .

Diffusion Demonstration with Squash and Water

Squash or juice in water is a great demonstration of diffusion. You can see the squash starts in one area and then starts to move from areas of high squash concentration to areas of low concentration. Eventually the squash spreads throughout the whole glass and the colour becomes paler and even throughout. ​

I used food colouring in the images below to make the process easier to see.

Diffusion using food colouring and water

What is diffusion?

Diffusion is the movement of a substance from an area of high concentration to an area of low concentration.

Diffusion occurs in gases and liquids. Particles in gases and liquids move around randomly, often colliding with each other or whatever container they are in. When they collide they change direction which means eventually they spread out through the whole available space.

Examples of Diffusion

Diffusion in humans.

Oxygen diffuses from the alveoli of the lungs into red blood cells. This is because the concentration of oxygen in the alveoli is high and the concentration of oxygen in the red blood cells is low. Red blood cells have very thin cell walls which allows oxygen to diffuse easily in and out of them.

Diffusion in plants

Plants use carbon dioxide fro the air for photosynthesis. Carbon dioxide enters the leaves through small holes called stomata in the underside of the leaf. Spongy cells called mesophyll cells allow gases to diffuse easily in and out of the leaf. The stomata can open and close so the plant doesn’t lose too much water.

More diffusion demonstrations

Making a cup of tea is another great diffusion demonstration. This diffusion activity using different shaped tea bags is great fun.

Another example of movement of substances important for living things is osmosis .

Last Updated on November 3, 2021 by Emma Vanstone

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Examining the Science Behind an Observation: The Behavior of Perfume Particles

The science behind an observation, particularly in relation to the behavior of perfume particles, is fascinating. When you apply perfume, the particles are dispersed into the air, becoming airborne due to their small size and lightweight nature. The high alcohol content in the perfume aids in the evaporation process, allowing your scent to travel. Heat also increases the rate of evaporation; that’s why pulse points, which are warmer due to blood circulation, are key areas for applying perfume. Thus, the scent travels from these warmer areas into the surrounding atmosphere. Over time, the perfume molecules mix with air molecules and spread out in a process known as diffusion. Therefore, even from a distance, people can smell your perfume because the scent particles have traveled through the air.

Is Perfume Filling a Room Diffusion?

Perfume filling a room is indeed a type of diffusion. This happens because perfume is generally a liquid that evaporates into the air as tiny particles. These particles are known as volatile organic compounds (VOCs) and are responsible for creating the scent that we perceive as perfume.

This decrease occurs because the particles spread out throughout the room, meaning they’re less dense in any particular area. It’s this gradual decrease in concentration that leads to the perception of the perfume scent becoming weaker over time.

How Long Does Perfume Diffusion Last in a Room?

• Perfume diffusion lasts in a room based on a few factors, including:
• The type of perfume or fragrance used
• The amount of perfume or fragrance used
• The size of the room
• The ventilation in the room
• On average, perfume diffusion can last anywhere from a few hours to a few days, depending on these factors.

Understanding the science behind fragrance diffusion can shed light on why some perfumes take longer to spread than others. The way perfume particles move and interact with each other plays a crucial role in how quickly the scent will reach it’s maximum spread. Let’s dive deeper into this fascinating topic.

Why Do Perfume Particles Take Long to Spread?

Perfume particles take a long time to spread due to the nature of their molecular structure. Perfumes are made up of complex mixtures of aroma compounds that are dissolved in a solvent. These aroma compounds are typically large and relatively heavy molecules that tend to stick together, making it difficult for them to move through the air and disperse evenly.

Another factor that affects the spread of perfume particles is the method of application. When we spray perfume onto our skin or clothing, the particles are released into the air in a concentrated burst. This causes the molecules to collide with each other and create a thick, dense cloud of fragrance that takes time to dissipate. The amount of fragrance sprayed also plays a role, as a larger amount of perfume will result in a denser cloud that takes longer to spread out.

Higher temperatures and low humidity can cause perfume molecules to evaporate more quickly, while strong air currents can disperse the particles more quickly.

Factors such as room size, air flow, and method of application all play a role, as do individual differences in body chemistry and scent sensitivity. While it may take some time for a perfume to fully spread out and be appreciated by those around us, the end result is often worth the wait as the complex and delicate scents of the perfume gradually reveal themselves to our senses.

Understanding the difference between physical and chemical changes can help us identify the underlying reactions that occur in our daily lives. While physical changes involve a change in the form or state of a substance, chemical changes involve the creation of new substances with different properties. So, is spraying perfume a chemical change? Let’s explore this question further.

Is Spraying Perfume a Chemical Change?

Spraying perfume is a common activity that many people engage in, whether for aesthetic reasons, social purposes or personal smell preferences. Perfume, as we know it, is made up of a mixture of different chemical compounds that are volatile in nature, meaning they easily evaporate at room temperature. When sprayed, these compounds are released into the air, creating a pleasant scent that can be detected by our olfactory system.

A chemical change, on the other hand, occurs when the bonds within the molecules themselves are broken or formed, resulting in a compound with a different chemical composition. Such changes are typically irreversible and often accompanied by a shift in energy, such as heat or light. For example, when wood burns in a fire, it undergoes a chemical change known as combustion, and the resulting products, such as ash and smoke, have different chemical properties from the original wood.

The molecular makeup of the fragrance remains constant and unchanged.

What Are Some Common Chemicals That Are Found in Perfumes and How Do They Affect the Body?

Perfumes contain a variety of chemicals such as alcohols, essential oils, and synthetic fragrance compounds. The effects of these chemicals on the body vary depending on the individual’s sensitivity and the amount and type of chemical. Some chemicals may cause allergies or headaches, while others aren’t harmful unless ingested in large quantities. Overall, the effects of perfumes on the body are still being studied and may vary from person to person.

Opening a bottle of perfume isn’t just a simple act of unscrewing the cap. It involves a fascinating process of gaseous molecules diffusing from a region of high concentration to a relatively low concentration. This means that the moment you open the bottle of perfume, a complex chemical reaction takes place, causing a beautiful aroma to fill the air.

What Happened When You Open the Bottle of Perfume?

This means that the molecules of the scent of the perfume are dispersed from the inside of the bottle to the surrounding air where there’s relatively lower concentration of the same molecules. The perfume bottle is sealed to prevent the scent from dissipating naturally.

However, when the bottle is opened, the pressure inside the bottle is reduced due to the formation of a pressure gradient, which draws the gaseous molecules out of the bottle and into the surrounding air. The scent molecules attach to the air molecules and travel through the air to reach our nose or olfactory neurons.

The diffusion of molecules from the perfume bottle creates a unique scent that we can detect as soon as we open the bottle. The scent of the perfume fills the space around the bottle as soon as it’s opened.

Opening a bottle of perfume can also cause some of the volatile compounds to evaporate, which can alter the scent of the perfume over time. These changes in the scent notes are dependent on the chemical properties of the fragrance and the surrounding environment, such as temperature and humidity. For this reason, it’s important to store perfumes in a cool and dark place to prevent unnecessary evaporation and changes in the scent.

The scent may change over time due to the volatility of some compounds, which can be affected by storage conditions.

How Do Differences in the Chemical Composition of Different Perfume Ingredients Impact the Scent Created When the Bottle Is Opened?

• Top notes are typically made up of lighter molecules that evaporate quickly. They provide the initial burst of scent when the bottle is opened, but disappear relatively quickly.
• Heart notes are made up of heavier molecules that last longer than top notes. They emerge after the top notes have dissipated and provide the main body of the scent.
• Base notes are the heaviest molecules in a perfume, and they last the longest. They provide the foundation for the scent and can linger for hours or even days after the perfume is applied.
• The chemical composition of each individual perfume ingredient contributes to the overall scent in a unique way, and small differences in the formula can result in vastly different fragrances.
• For example, adding a small amount of lavender essential oil to a citrus-based perfume can create a more complex, nuanced scent.
• The amount of each ingredient used in a perfume formula also impacts the final scent. A higher concentration of a certain ingredient will result in a stronger presence of that scent note in the perfume.

Understanding the complex interactions between particles and their environment is essential in developing fragrances that are effective and appealing to consumers. The study of this fascinating observation has opened up new avenues of research in the fields of chemistry and nanotechnology, where scientists are continually pushing the boundaries of what’s possible with particles and their properties. By delving deeper into the behavior of perfume particles, we can gain a deeper understanding of the fundamental principles that govern the physical world around us and improve our lives in countless ways.

Gillian Page, perfume enthusiast and the creative mind behind our blog, is a captivating storyteller who has devoted her life to exploring the enchanting world of fragrances.

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Examples of Diffusion in Chemistry

10 Diffusion Examples

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Diffusion is the movement of atoms, ions, or molecules from an area of higher concentration to one of lower concentration. The transport of matter continues until equilibrium is reached and there is a uniform concentration through the material.

Examples of Diffusion

• Diffusion is the movement of particles from higher concentration to lower concentration.
• Diffusion continues until equilibrium is reached. At equilibrium, concentration is the same throughout the sample.
• Familiar examples of diffusion are the transport of perfume when it is sprayed in a room or the movement of food coloring in a glass of water.
• Perfume is sprayed in one part of a room, yet soon it diffuses so that you can smell it everywhere.
• A drop of food coloring diffuses throughout the water in a glass so that, eventually, the entire glass will be colored.
• When steeping a cup of tea, molecules from the tea cross from the tea bag and diffuse throughout the cup of water.
• When shaking salt into water, the salt dissolves and the ions move until they are evenly distributed.
• After lighting a cigarette, the smoke spreads to all parts of a room.
• After placing a drop of food coloring onto a square of gelatin, the color will spread to a lighter color throughout the block.
• Carbon dioxide bubbles diffuse from an open soda, leaving it flat.
• If you place a wilted celery stick in water, water will diffuse into the plant, making it firm again.
• Water diffuses into cooking noodles, making them bigger and softer.
• A helium balloon deflates a little bit every day as helium diffuses through the balloon into the air.
• If you place a sugar cube in water, the sugar will dissolve and evenly sweeten the water without having to stir it.

Simple Diffusion Experiment

See diffusion for yourself with this simple experiment.

• 2 water glasses
• Baby oil or vegetable oil
• Food coloring
• Fill a glass mostly full of water.
• In a second glass, add a bit of oil and some drops of food coloring. You can use multiple colors of food coloring, but take care to avoid mixing them.
• Stir together the oil and food coloring so that you break the drops into smaller ones.
• Pour the oil and food coloring into the water glass. The food coloring drops into the water and diffuses into it.

Expand upon this project by comparing the rate of diffusion in hot water versus cold water. If you use different colors of food coloring, explore color theory and see what you get when two different colors mix. For example, red and blue make purple, yellow and blue make green, and so on. Can you explain why food coloring diffuses in the water, but no into the oil?

Diffusion vs Other Transport Processes

Diffusion, together with osmosis and facilitated diffusion, are types of passive transport processes. What this means is that energy is not required for these processes to occur. They are thermodynamically favorable and driven by chemical potential or Gibbs free energy.

In contrast, active transport processes require the input of energy to occur. Active transport includes primary (direct) active transport and secondary (indirect) active transport. The first uses energy molecules as transport mediators. The second couples molecule movement with a thermodynamically favorable transport.

Types of Diffusion

There are several types of diffusion, including:

• Anisotropic diffusion enhances high gradients.
• Atomic diffusion occurs in solids.
• Bohm diffusion involves plasma transport across magnetic fields.
• Eddy diffusion involves turbulent flow.
• Knudsen diffusion is diffusion of a gas through long pores where wall collisions occur.
• Molecular diffusion is movement of molecules from high concentration to low concentration.
• Barr, L.W. (1997). "Diffusion in Materials". DIMAT 96 . Scitec Publications. 1: 1-9.
• Bromberg, S.; Dill, K.A. (2002). Molecular Driving Forces: Statistical Thermodynamics in Chemistry and Biology . Garland Science. ISBN 0815320515.
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• Differences Between Osmosis and Diffusion
• What Is Diffusion in Chemistry?
• Topics Typically Covered in Grade 11 Chemistry
• Simple Candy Osmosis Experiment
• Chemistry Vocabulary Terms You Should Know
• How Reverse Osmosis Works
• Graham's Law Example: Gas Diffusion-Effusion
• Molecular Weight Definition
• Osmosis Definition in Chemistry
• The Major Laws of Chemistry
• Mercury Facts
• Defining Active and Passive Transport
• The Blue Bottle Chemistry Demonstration
• What Is the Difference Between Diffusion and Effusion?
• Chemistry Timeline
• Osmotic Pressure and Tonicity

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Perfume and flavor engineering: a chemical engineering perspective.

1. Looking Back: The Beginning of Perfume Engineering at LSRE. What Do We Smell?

2. the perception of odors, 2.1. odor thresholds, 2.2. odor intensity models, 2.3. odor character model for a mixture—the strongest component model, 2.4. sensory dose/response curve, 2.5. evaporation of perfumes: modeling vapor-liquid equilibrium (vle), 2.6. prediction of odor detection threshold (odt), 3. perfumery ternary diagram (ptd), 4. diffusion and performance of perfumes, 4.1. perfume performance, 4.2. a simple 1d diffusion model, 5. perfume classification and perfumery radar, 6. the effect of matrix and skin, 6.1. effect of matrix (glycerine, dipropylene glycol, skin lotion), 6.2. the effect of skin, 7. the trail of perfumes, 8. flavor engineering, 9. looking ahead, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Rodrigues, A.E.; Nogueira, I.; Faria, R.P.V. Perfume and Flavor Engineering: A Chemical Engineering Perspective. Molecules 2021 , 26 , 3095. https://doi.org/10.3390/molecules26113095

Rodrigues AE, Nogueira I, Faria RPV. Perfume and Flavor Engineering: A Chemical Engineering Perspective. Molecules . 2021; 26(11):3095. https://doi.org/10.3390/molecules26113095

Rodrigues, Alírio E., Idelfonso Nogueira, and Rui P. V. Faria. 2021. "Perfume and Flavor Engineering: A Chemical Engineering Perspective" Molecules 26, no. 11: 3095. https://doi.org/10.3390/molecules26113095

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Real Diffusion Experiment (for Home or School)

Introduction: Real Diffusion Experiment (for Home or School)

As part of my research and my physics degrees I've been studying a lot about diffusion and diffusion-related subjects. After a while it finally hit me that I learned about diffusion before - in my highschool biology class. I thought about the experiments they showed us, and something didn't make sense. So I searched YouTube for diffusion demonstrations, and they looked pretty much the same - someone drops a bit of dye into a large water-filled beaker, and after a few minutes the entire beaker is colored.

At this point I realized how big the misconception about diffusion is! Most diffusion demos are completely wrong !

As I'll show you soon enough, diffusion on these scales takes weeks to happen! All of these demos in fact show a process called 'convection' in which the dye mixes due to currents and swirls in the liquid, not due to diffusion.

So, in this instructable I'll first try to convince you that there's something wrong with these experiments, and that we should re-evaluate how we demonstrate diffusion to student. Then, I'll show you how you can perform diffusion experiments the right way (there's more than one way, of course!). Finally, I'll discuss some of the consequences of the results, which can actually teach us a lot about the world we're living in.

My hope is that - if I convince you that the typical diffusion demos are wrong - you spread the word! Teach the ones you can! And on the other hand, if you think that I'm wrong here - I'd love to hear your opinion, see your experimental data, and talk about it!

I've been waiting to make an instructable about this subject for a while now, but I never really got to it. Finally, the science fair contest motivated me getting it done and posting this article :) I hope you like it!

I made a video about this project for those who like watching narrated videos

If you have any questions or comments, I'd love to hear all of them!

Step 1: what's wrong with typical diffusion demos.

In the diffusion demonstrations we're used to seeing, the main things that cause the dye to mix are not diffusion. It is swirls and currents in the liquid, a process called convection. Most commonly, a drop of dye is injected into a large water-filled beaker, and the audience watch as the color mixes (see the GIF I attached of such experiment I performed myself). The spread of the dye is said to be due to diffusion. However, this is not true. You can clearly see currents and swirls in the liquid (convection).

There are many things that cause the convection. First, the beakers are often wide open and so any currents in the air are transferred to the water, causing them to swirl. Next, since the top of the beaker is open, there's evaporation of water happening (see the drawing I attached). This means that the top of the water container becomes cooler than the bottom. Since cold water is slightly denser, it tends to sink, which leads to currents and swirls again. Finally, these experiments are often done with warm water in intent to show that diffusion is temperature-dependent. However, everything I just mentioned is also enhanced with the increased temperature! The difference between the beaker's temperature and the rest of the room is bigger, and so the water develops an even steeper temperature gradient, which makes everything even worse!

Diffusion, as it turns out, can be very very slow. Humans are used to seeing big things - things on the scale of a mm (1/25") are already pretty small for the human eye. However, diffusion is extremely inefficient at these sizes! Diffusion is fast and efficient only on the scale of microns and smaller, and if you follow along, you'll see exactly why!

This should not be discouraging - the fact that diffusion is slow on large scales - but quick on small scales - explains so much of the world around us, including a lot of biological phenomena, and I'll elaborate on that in the final section.

I'm not trying to say that diffusion experiments are impossible to see and demonstrate, I'm just saying that the most common form of diffusion demos is wrong! There are ways to do it right!

Step 2: Experimental Setup

We need to make sure that convection doesn't happen in our experiment. Here are the things that helped me get it done. I tried skipping some of these, but it didn't work :)

• Use a thin container. Glass test tubes or other things with similar proportions could work. These are pretty cheap, I bought mine from AliExpress.
• We should make sure that when we inject the dye, it doesn't swirl right from the start. To do that, I used salt water (5% salt) instead of tap water. This made them heavier and so the dye floated on them. It doesn't change anything for diffusion (why? you can ask your students questions like this one! let me know if you want the answer), but it helps with the initiating the experiment in a controlled manner.
• Let all of the liquids rest at room temperature before starting. If they have different temperatures, it'll cause convection.
• Inject the dye gently to the top of the container so that it floats at the top. Avoid dropping it from a distance.
• Use plastic wrap or a cork cap to seal the test tube after you initiate the experiment. This will help fight the evaporation and air currents from messing with your experiment.
• Finally, this experiment is best done in a constant environment where the temperature is pretty constant over time. If you want to film it, a good place would be inside a cabinet or a closet.

I used a dye called Fluorescein which is very common in laboratories (often used for diffusion experiments). However, food coloring or ink work perfectly fine. If it's water soluble and has a strong color, it should be fine.

Step 3: Data Capture

Capturing the data is important if we want to have a quantitative understanding of the phenomena. It will also let us see the diffusive behavior as a function of time even though things are moving slowly (see the GIF I attached - that's 48 hours!).

• You want the capture data with a nice clear background. I found that using a black paper works well, but it often depends on the type of food coloring or dye you're using.
• You also want to capture images with constant lighting and camera settings. For that reason, I kept the experiment running inside a closet with a fixed light source :) sunrise & sunset can interfere with your data.
• I found a really nice app called 'Open Camera' (thanks Orit !). It allows you to take timelapse images, set the image resolution, and fix the focus / exposure so it doesn't change automatically. You can also save the data to a google drive folder which means you can check how things are going without opening the closet and having the risk of a ruined experiment. You shouldn't take more than an image every 5-10 minutes. Nothing happens that fast anway, the experiment will probably be running for days.
• Before initiating the experiment, take an image with something of a known size. For example, taking a picture of a ruler would be useful. You'll see more about why this is needed in step 6.
• Initiate the experiment and wait. Take the time and follow the images over your google-drive folder. Try to avoid opening the closet while the experiment is running!

Step 4: Data Analysis Software - 'Tracker' (free Academic Software)

There are many ways analyze the experimental data. I found Tracker can be used in so many physics experiments that it's worth getting to know. It's available in many different languages (not only English), so young students from all over the globe can use it.

Download the Tracker software here . There's an online version but it doesn't work well.

An alternative to 'Tracker' is a software called 'ImageJ' or 'Fiji' (basically the same). It works great too, and has some advanced options too.

To start analyzing your videos, import them. Tracker accepts videos of many formats, but also sequences of images. Note that sequences of images need be named in a fixed format with a incrementing numbers. For example, Img001, Img002, Img003... are good file names (see first image)

You'll often want to rotate the image so that the direction you're interested in is horizontal. To do that, right-click the video, and press filters -> new -> rotate. Rotate the image in the desired direction (see second image).

I've also written a code python to analyze a sequence of images automatically , more about that (file included) in the data-analysis step.

Step 5: Calibrate Pixels to Physical Units

We took images or videos of the real world, but the software has no way of knowing what we're looking at, what's it's size, and how often images were taken. We need to calibrate both space (distances) and time to physical units. You'll need to do this even if you analyze the data in a different software.

To Convert Pixels to Distance Units (GIF #1):

• Select the 'calibration tools' from the toolbar.
• Add a new calibration stick.
• Align it along a known distance. For example, I took a picture of a ruler.
• Calibrate the measured distance. I'm using meters, but you can change to any units you like by pressing the 'Coordinate system' tab -> 'Units...' and setting your preferred system of units.

To Calibrate Time (GIF #2):

• Right-click the video (anywhere on the screen), and press 'Clip Settings'
• Set the frame rate (FPS) or the time interval between images in a sequence (dt). I analyzed images that were taken every 30 minutes, so I set 'dt' to 1800 seconds.

You can set the coordinate system (where x, y = 0) and its orientation on the screen by pressing the coordinate axes tool in the toolbar (see third image).

That's it, from this point on your measurements will be in physical units.

Step 6: Measure the Diffusion Process Over Time

I'm including here 3 different types of analysis. I'll list them in order of complexity, the first one being the easiest one to use but also the least accurate, and the last one being the most complex and accurate method of analysis.

First Method - 'By Eye' (GIF #1):

The food coloring (or whatever ink or chemical you're using as dye) colors the water. We can look for the point where it is no longer visible, and track it's position over time.

• In the 'Tracker' software, press 'Track' -> 'New' -> 'Point Mass'.
• Hold 'Shift' and use the mouse choose the point at which the paint is not longer visible. Each time you click, the software will move on to the next frame.
• You can go back and edit points if you like. You can also decide to skip multiple frames in each click by changing the 'step size' at the bottom. This can be useful especially when things change slowly.
• Keep going until you went through all of the video / image sequence.

Second Method - Intensity Profile (GIF #2):

The previous method lacks some accuracy. 'The point where the dye is not longer visible' is not well defined, and depends on the person analyzing the data. A more robust way of analyzing the data is by looking at the intensity profile of the image. Brighter regions have higher intensity than darker regions. We can measure in Tracker as well.

• Add a new Track of a 'Line Profile' type.
• Use Shift to place it along the direction of the diffusion process.
• A window will open on the right side of the screen showing the intensity as a function of distance. Define a point in the intensity profile that you want to track. For example, 'the point where intensity is equal to 50'.
• Measure it's position over time. You'll need to write down the time and position of each point manually (you can write it into an Excel sheet). Students can do this in pairs to save time. I realize this can be time consuming if you go through all of the captured frames, but analyzing about 20-30 frames should be plenty! Adjust the 'step size' so you skip through more than one image at a time.

Third Method (GIF #3):

This method is basically an upgrade of the previous one. I wrote a python code that analyzes the data automatically. It runs through each image and measures the intensity profile along a selected region. It does a few extra things like removing the background noise and such. Also, I used a green dye so it analyzes the green channel of an RGB image, but you can make a small modification to the code to analyze other colors or all of them combined.

• Run the code and analyze your images.
• You'll end up with all of the intensity profiles. All that is left is to track a selected point along the profile. Say, 50 gray points above the background. Define a threshold that would work for your images.
• For each profile, calculate it's distance from the threshold, that is: abs(profile - threshold). The smallest value of this vector will be the point where the profile is equal to the intensity threshold you've chosen, so the easiest way to find it is by looking for: min(abs(profile - threshold). I've attached MATLAB code that does all of this, plots the profiles, and saves them as images.

Attachments

Step 7: how fast are things moving.

Now that we have tracked the diffusion process over time, we can start the final part of the experiment. In this part we will try to answer questions about the rate at which diffusion occurs.

By looking at the images we've aquired we already have an intuitive feeling for it - diffusion starts off pretty fast, but then, as time passes, it slows down. My experiment was running for 48 hours, and the test tube was far from well mixed. The typical distance the dye I used propagated was about 1cm (less than 1/2"). This is very slow, and very typical for diffusion in water!

I made a GIF of the time dependence of the intensity profile for the first 48 hours of the experiment. We can see that the profile changes very rapidly at first, but then it slows down. This is what we see in the images too, so that's a good sign the analysis works :) I then defined the point where the front of the intensity profile reaches a value of 50 gray points above the background intensity, and marked it with an orange circle on each of the profiles (see third method in the previous step for details). I called this point 'x_D' (D for diffusion).

Finally, I plotted x_D as a function of time (see the graph I attached). x_D is shown with orange markers. There's also a blue line on the graph. This graph describes a theoretical fit to the data. Diffusion has a very precise physical formulation which matches reality to very high accuracy. It suggests that diffusion should occur at a rate that scales as the square root of time. In other words, x_D should scale as: x_D ~ sqrt(D * t), where 'D' is the diffusion coefficient of the dye in water and 't' is time. So, I tried to fit the x_D data to a function of the form x_D = sqrt(D * t). The fit is very good, so it seems that diffusion does scale as the square root of time, as expected! I could also use the fitted function to get an estimate for the diffusion coefficient, and found that it is of the order of 4 * 10^-6 [cm^2/sec]. This is very close to the real value of the dye I used (5.5 * 10^-6 [cm^2/sec]). This difference was expected since I could have defined x_D slightly differently and end up with other results. Measuring the exact diffusion coefficient takes a little more effort than what I did here, but for an estimate and order-of-magnitudes this is perfectly fine.

Step 8: Conclusions

We saw that x_D scales as x_D ~ sqrt(D * t). We can now ask, if we wanted for the dye to reach a point x_D away from the source of the dye, how long should we wait? This is answered by inverting the equation: t_D = (x_D ^2)/D. This seems mondane - nothing special, right? But this equaion dictates so much in biology and life. For example, have you ever wondered why cells are small? Why don't we see huge elephant-sized cells? One of the main reasons for that is that cells depend on diffusion to obtain nutrients. If cells were too big, diffusion would become inefficient. Using the diffusion coefficient we found, we see that diffusion will take about 40 minutes to pass just 1mm (1/25.4"), but it would take less than a second to pass a distance of 10 microns , a typical distance to travel when thinking about cells. For instance, when you exercise, your muscle cells need constant supply of oxygen. If the cells were too big (1mm sounds small, right?), diffusion would become inefficient and the oxygen supply wouldn't reach the inside of the cells fast enough. [the sizes-GIF was created base on Learn Genetics ]

To conclude,

We saw that diffusion experiments need careful attention and a lot of patience. I found that the best way to demonstrate this phenomenon is by capturing a video. You can do that with the students if you want to take this into the class-room. Another option would be to initiate the experiment on one day and looking at the results the next day. You'll see the dye has started to mix into the water.

On large scales, diffusion takes a very long time (over a mm or 1/25.4 of an inch is already considered large!), but on very small scales, such as the sizes of cells (a few microns), diffusion is a very efficient way to move things around. This explains a lot about biological processes and other physical phenomena. I think that once you develop intuition for the process and its time-scales, you can appreciate so many things about the world around us.

I hope you found this topic as interesting as I find it! And if you're in the world of teaching, I hope you spread the word! There's a huge misconception about diffusion due to wrongful demonstrations, and it's our job to make things right :)

If you like my instructable and want to see more, you're welcome

To visit my instructables page and my website.

By the way, if you want to support my projects - subscribing to my new YouTube channel is currently the best way to do that! :)

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• Science Clarified

Diffusion is the movement of molecules from a region of high concentration to one of low concentration. If you have ever opened a bottle of cologne or perfume, you have witnessed diffusion. Molecules of the scent escape from the container, where they are present in very high concentration. They spread outward in every direction to regions where they are in low concentration. Your nose is able to detect the smell of the cologne or perfume even if you are quite a distance from the bottle that has been opened.

Diffusion occurs in all states of matter: solid, liquid, and gas. It occurs rapidly enough to be observable in a reasonable period of time, however, only in liquids and gases.

You can demonstrate diffusion easily in your home. Fill a glass with water. Then add 10 drops of ink (any color) to the water very carefully. The ink sinks to the bottom of the glass because it is more dense than water. Place the glass in a place where it will not be disturbed and make observations of it every day. Over time, the colored ink at the bottom of the glass spreads upward. It moves from a region of high concentration to one of low concentration.

Eventually, the water in the glass is the same shade: a grey, light blue, or pink throughout. The original black, blue, or red ink has been diluted with water to produce the paler shade. Diffusion eventually stops because no region of high ink concentration remains. The concentration of ink and water is the same throughout the glass. That rule applies to all cases of diffusion. When differences in concentration no longer exist, diffusion stops.

Osmosis is diffusion through a membrane. The membrane acts as a barrier between two solutions of different concentration. One substance (usually water) travels from an area of high concentration to one of low concentration. Osmosis can be compared to the examples of diffusion given above involving perfume and ink. In those cases, no barrier was present to separate perfume from air or ink from water. Diffusion took place directly between two materials.

In contrast, a barrier is always present with osmosis. That barrier is usually called a semipermeable membrane because it allows some kinds of materials to pass through, but not others.

The most familiar example of osmosis through a semipermeable membrane may be a living cell. Cells contain semipermeable membranes

that act something like a plastic baggy holding cell contents inside. The cell membrane is not a solid material, however, but a thin sheet containing many tiny holes. (Imagine a self-sealing sandwich bag—its surface dotted with minuscule holes—then filled with water.) The holes allow small molecules and ions (such as molecules of water and sodium ions) to pass through, but trap larger molecules (such as proteins) inside the cell.

[ See also Dialysis ]

User Contributions:

Comment about this article, ask questions, or add new information about this topic:.

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Pure and impure substances: diffusion in terms of the particle model

Alongside traditional demonstrations of diffusion, we've found some more unusual practical activities and added a few helpful tips.

Visit the practical work page to access all resources and lists focussing on practical work in secondary science:  www.nationalstemcentre.org.uk/sciencepracticals

Quality Assured Category: Physics Publisher: Longman

Chapter five of this resource provides a series of student experiments designed to reinforce the concept of diffusion. There are both teacher notes and student worksheets here to use although teachers may want to produce their own updated student worksheets based on these.

Activity two (coloured crystals in solid gelatin) is particularly recommended since it is so visible. The experiment works equally well in petri dishes of agar where it is probably easier to observe and where holes can be made with straws instead of cork borers. However, the other three activities are worth doing too and taken together this would be a good hands on lesson for students to experience diffusion for themselves.

Perfumes and Smelling

Quality Assured Category: Science Publisher: Association for Science Education (ASE)

Perfume is often used to teach diffusion since if you can smell it then the particles must have travelled to your nose. If you have plenty of time, you might like to work through this whole sequence of these activities where students extract their own scents and develop their literacy skills by writing for a teenage magazine.

However you could base a just single lesson around this resource, finishing off a topic on diffusion and the particle model with students using steam distillation to extract the smelly molecules in orange peel. Students will enjoy developing their practical skills and teachers could challenge them to explain how they can smell their resulting product.

Usefully two versions of the student sheets are provided. Choose the version most suitable for how you want to run the lesson and the ability of your students.

Diffusion with jelly cubes

In this experiment from the Nuffield Foundation cubes of alkaline cubes of agar impregnated with indicator gradually change colour once they are placed in acid solution.

The experiment can be used exactly as it stands as an investigation into the relationship between diffusion rate and size. It will extend and reinforce work that students have already done on diffusion and help them to develop their numeracy and graph drawing skills and would make a very good investigation. Towards the bottom of the page you’ll find a downloadable student worksheet.

However, you could use a cut down version of this resource just to demonstrate diffusion by preparing cubes all of the same size and allowing students to observe the colour change when placed in acid.

Diffusion in liquids

Diffusion occurs when particles can move and it’s good to remind students that this means it will happen in liquids as well as gases. In this lovely experiment students will really enjoy observing the bright yellow lead iodide (as used in the yellow lines on roads) that forms when lead nitrate and potassium iodide meet and react.  

The student worksheet isn’t really necessary since the experiment is so straightforward, but teachers are advised to show students how to set the experiment up and talk through what they might expect to happen before they start work.  It’s worth spelling out to students that they should take care not to disturb or jolt the experiment once it is running.

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Perfume and Flavor Engineering: A Chemical Engineering Perspective

Affiliation.

• 1 Laboratory of Separation and Reaction Engineering, LSRE-LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal.
• PMID: 34067262
• PMCID: PMC8196857
• DOI: 10.3390/molecules26113095

In the last two decades, scientific methodologies for the prediction of the design, performance and classification of fragrance mixtures have been developed at the Laboratory of Separation and Reaction Engineering. This review intends to give an overview of such developments. It all started with the question: what do we smell? The Perfumery Ternary Diagram enables us to determine the dominant odor for each perfume composition. Evaporation and 1D diffusion model is analyzed based on vapor-liquid equilibrium and Fick's law for diffusion giving access to perfume performance parameters. The effect of matrix and skin is addressed and the trail of perfumes analyzed. Classification of perfumes with the perfumery radar is discussed. The methodology is extended to flavor and taste engineering. Finally, future research directions are suggested.

Keywords: classification of perfumes; effect of matrix; evaporation and diffusion of perfumes; flavor engineering; flavors and fragrances; perfume engineering; perfume performance; perfumery radar; perfumery ternary diagram; trail of perfumes.

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Conflict of interest statement

The authors declare no conflict of interest.

The structure of a perfume…

The structure of a perfume represented by Carles’ pyramid.

Chemical engineering today: ChE =…

Chemical engineering today: ChE = M 2 P 2 E [5] and perfume…

Measurement of ODT by a…

Measurement of ODT by a panel using an olfactometer at LSRE.

Sensory dose/response curve with a…

Sensory dose/response curve with a scale of odor intensity.

Odor Intensity Standard Curves in…

Odor Intensity Standard Curves in LMS scale versus the logarithm of fragrance concentration…

Simplified steps in odor detection:…

Simplified steps in odor detection: air and odorant molecules (C), mucus (B) and…

The perfumery ternary diagram: combining…

The perfumery ternary diagram: combining perfume pyramid structure with the ternary phase diagram.

The effect of base note…

The effect of base note on odor zones on odor zones: left A—limonene…

Perfume performance parameters (Reprinted with…

Perfume performance parameters (Reprinted with permission from AIChEJ, 2013, 59, 15. John Wiley…

Blooming, development and lasting phases.

Diffusion tube with volume element…

Diffusion tube with volume element of thickness Δz.

Time evolution of OV near…

Time evolution of OV near the source- we smell limonene first and geraniol…

Evaporation lines of a perfume…

Evaporation lines of a perfume mixture near the source in PTD and PQD.…

Perfumery radar of perfumes: left…

Perfumery radar of perfumes: left —L’ Air du temps (Nina Ricci); right —Addict-Eau…

Perfumery radar of Gloria (Cacherel)…

Perfumery radar of Gloria (Cacherel) at time t = 0 ( left )…

Franz cell for permeation studies…

Franz cell for permeation studies of fragrance mixtures (Reprinted with permission from Elsevier,…

Sketch of the Franz cell…

Sketch of the Franz cell system.

Cumulative amount of linalool in…

Cumulative amount of linalool in the receptor compartment versus time for the infinite-dose…

Diffusion tube and moving source.

Simulated and experimental gas concentration…

Simulated and experimental gas concentration profiles of a-pinene versus distance at t =…

3D model- moving source at…

3D model- moving source at 1.50 m ( a ) and concentration profiles…

Odor and flavor radars for…

Odor and flavor radars for peach juice (experimental; shaded area–predicted). Reprinted with permission…

Contributors for research in Perfume…

Contributors for research in Perfume Engineering started by Alírio Rodrigues and Vera Mata…

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Why is perfume an example of diffusion, why is perfume an example of diffusion: unraveling the olfactory science behind fragrance dispersion, introduction.

The enchanting journey of perfume from the bottle to the senses exemplifies the scientific phenomenon of diffusion. This article explores why perfume is a compelling example of diffusion, unraveling the molecular dynamics that allow fragrance molecules to disperse and create a captivating olfactory experience.

Defining Diffusion in Perfumery

Molecular movement, - random motion of molecules.

Diffusion involves the spontaneous movement of molecules from an area of higher concentration to an area of lower concentration, driven by their inherent kinetic energy.

- Gas-Phase Diffusion in Perfumery

In the context of perfume, diffusion primarily occurs in the gas phase, where volatile fragrance molecules disperse through the air.

Volatile Components in Perfume

Ethereal fragrance molecules, - low molecular weight compounds.

Perfume contains volatile organic compounds with low molecular weights, facilitating their ability to evaporate and diffuse into the surrounding air.

- Essential Oils and Aromatic Compounds

Aromatic compounds, such as those found in essential oils, contribute to the volatility of perfume and play a key role in diffusion.

Application on Pulse Points and Skin

Interaction with body heat *, - temperature-driven diffusion.

Applying perfume to pulse points on the skin capitalizes on body heat, promoting the diffusion of fragrance molecules as they respond to temperature variations.

- Enhanced Dispersion from Warm Areas

Pulse points, being warmer areas of the body, expedite the diffusion process, allowing the fragrance to disperse more effectively into the surrounding air.

Role of Air Circulation

Aiding in diffusion *, - airflow and dispersion.

Natural or induced air currents play a role in aiding diffusion by carrying fragrance molecules away from the application site, contributing to a wider scent radius.

- Creating a Fragrance Trail

The concept of a fragrance trail is a manifestation of diffusion, as the scent lingers and disperses along the path of the wearer's movement.

Olfactory Reception and Diffusion

Interaction with olfactory receptors *, - airborne molecules and olfactory cells.

Diffused fragrance molecules enter the nasal cavity, interacting with olfactory receptors that detect specific scents and transmit signals to the brain.

- Perception of Scent Notes

The sequential perception of top, middle, and base notes in a fragrance is a result of the differential rates of diffusion and evaporation of the various aromatic components.

Diffusion in Various Environmental Conditions

Influence of temperature and humidity *, - temperature as a catalyst.

Higher temperatures generally accelerate diffusion, intensifying the dispersal of fragrance molecules.

- Humidity's Impact on Evaporation

Humidity can affect the rate of evaporation, influencing the diffusion process by either slowing it down or allowing for more gradual dispersal.

In conclusion, perfume serves as a captivating example of diffusion, where volatile fragrance molecules disperse in the air and engage the olfactory senses. From the application on pulse points, where body heat aids in diffusion, to the influence of environmental factors like temperature and humidity, the journey of perfume is intricately tied to the principles of molecular movement. Understanding perfume as an example of diffusion adds a scientific layer to the appreciation of its enchanting and ephemeral nature, where the artistry of fragrance creation converges with the physics of molecular dispersion.

Perfume Nez

Perfume Nez is a haven to the fragrance lover. Join us as we explore fragrances together, their constituent parts, their scent profiles and the brand bests.

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