Sage Journals: Discover world-class research

Abstract

Fibrous textiles readily absorb and desorb ambient odors. However, information on the composition and dynamic analysis of tobacco smoke and hot pot odors on fabrics during desorption is limited. This study used gas chromatography–mass spectrometry to analyze the desorption components of cotton, wool, and polyester fabrics exposed to these two odors, respectively. Then, a dynamic diffusion fabric structure model demonstrated the effect of airflow velocity and fabric porosity on nicotine desorption. Furthermore, we proposed mass diffusion coefficients with different molecular weights. The results showed that cotton fabrics with tobacco smoke released many low molecular weight compounds, while with wool fabrics significantly fewer compounds were detected than for the other two fabrics. Notably, 3-ethenylpyridine, a marker of tobacco smoke, was not detected in wool fabrics. For hot pot odor, cotton fabrics released more hexanal, nonanal, and anethole than wool and polyester, while wool fabrics released many β-pinenes. The numerical results of the dynamic model showed that the air inlet velocity significantly affects the nicotine concentration in the fabric. Meanwhile, the concentration of nicotine in fabrics with lower porosity decreased faster. A lower mass diffusion coefficient will cause odors to remain in the fabric. This study aimed at the composition and the dynamics of odor in fabrics and offers essential information and simple models for reducing unnecessary washing of textiles and odor resistance textile design.

Keywords

Desorption components dynamic model gas chromatography–mass spectrometry mass diffusion coefficient porosity tobacco and hot pot odor

Introduction

Tobacco smoke and hot pot odor are the two common annoying ambient odors that can be easily absorbed and desorbed by fabrics. Tobacco smoke desorbed by textiles is also defined as “thirdhand smoke.” It can cause health problems for non-smokers.¹ The textile is a carrier for tobacco smoke to human skin during the process.^2,3 Hot pot, a popular Chinese food, often retains odors in fabrics for a long time. In addition to causing people to be uncomfortable, it can lead to unscheduled washing behaviors, resulting in additional carbon and chemical emissions. Harmful compounds in odors can accumulate in textiles, be slowly released, and continue to cause harm to the human body.⁴ Therefore, there is a need for a simple and low-cost deodorization technology to remove tobacco smoke and hot pot odor from textiles. The study of tobacco smoke and hot pot odor from various fabrics has made some progress in analyzing odor components and the difference between fabrics.^5–8 Nonetheless, the fabric odor desorption research is minimal. The desorption process is also a dynamic process influenced by odorous molecules, fiber properties, fabric structure, and the external environment. Information on the composition and dynamics of tobacco smoke and hot pot odors from fabrics during desorption is still lacking.^9,10

The ability of a fabric to absorb volatile compounds is related to the physical and chemical properties of the fibers that make it up.^3,10,11 Natural fibers absorb ambient odors more capably than synthetic fibers and take longer to desorb.^7,11,12 At the same time, the adsorption behavior of volatile compounds by fabrics is related to the compounds’ content, structure, and characteristics, such as polarity and aromaticity.^6,13,14 McQueen has successively investigated the body odor intensity of cotton, wool, and polyester. The results showed that polyester has the highest odor intensity. It is concluded that the fiber moisture absorption is negatively correlated to the body odor strength from fabric. The lipophilicity of polyester increases the adsorption of volatile compounds on its surface.^15,16

Furthermore, the adsorption and desorption of volatile compounds by fabrics are related to the mass, volume, surface area of the fabric,¹⁷ and air velocity, temperature, and humidity.^9,17,18 Petrick studied the nicotine adsorption and desorption kinetics in cotton, nylon, polyester, and other materials. They found that nicotine desorption in the fabric is influenced by relative humidity (RH), air exchange rate, and fabric type.¹⁴

Existing models of desorption dynamics suggested that desorption time and fabric volume fraction (i.e. porosity) affect odor deposition and release from textiles. Some studies suggested that porous materials with larger pore sizes may have higher adsorption rates for tobacco smoke.¹⁹ Besides, external boundary conditions (airflow velocity, ventilation time, etc.) can also significantly affect the odor concentration in the fabric.¹⁸ The fabric has a porous structure, and the airflow speeds up the desorption of odorous compounds by the Johannes Diderik van der Waals force. That viscosity significantly affects the airflow rate, and time will affect the effect of air velocity.¹⁸ Although a few studies have been performed on the initial and boundary conditions of the fibrous porous material, there are still some general porous material discussions.^20–22 All the results show that the boundary and initial conditions significantly impact the mass transfer inside the porous media. Zhu has Engineered Water Nanostructures (EWNS) that take advantage of the Reactive Oxygen Species (ROS, which are made up of hydroxyl and superoxide radicals), to remove odors produced by smoking, cooking, and sweating.²³ Current studies have not systematically considered the effects of these factors on the desorption of odor compounds from fabrics.¹⁴

The literature review showed that an odor deposition and desorption dynamic model and analysis of the fibrous porous media are still unavailable. The fiber and fabric structure properties, such as fiber diameter and volume fraction effect, are unclear. The tobacco and hot pot components vary depending on the sample preparation methods.

In this study, we first detected the desorption components of cotton, wool, and polyester fabrics after exposure to tobacco smoke and hot pot odor. Then, we analyzed the concentration changes of nicotine, a typical substance in tobacco smoke, based on a fiber density function and a dynamic model of diffusion in porous materials. The model also discussed the effects of boundary conditions, fabric fiber volume fraction, and odorous molecular diffusivity.

Experimental and dynamic models

Materials

Table 1 shows the detailed parameters of fabrics used in this study. Cotton, wool, and polyester specimens were purchased from the Grace Group, Hangzhou, China. To enrich as many odor molecules as possible in the fabric, terry towel fabrics were used. Hongshuangxi cigarette (the tobacco tar 11 mg, sourced from Shanghai Tobacco Group Co., Ltd., Shanghai, China) and Haidilao hot pot soup base (the net content is 150 g per bag, sourced from Yihai Food Co., Ltd., Shanghai, China) were utilized for the experiments of adsorption.

Table 1.

Experimental fabric information.

Fabric	Texture	Fabric thickness (mm)	Weight (g/m²)	Specimen size (mm)
Cotton	Terry towel fabric	3.79	612.7	70 × 100
Wool	Twill woven	1.03	316.3	70 × 100
Polyester	Terry towel fabric	3.35	584.5	70 × 100

Samples preparation and testing

Fabrics were prewashed and dried three times using program 5A in an Electrolux Wascator FOM71 CLS standard washing machine (Electrolux Laundry Systems, Ljungby, Sweden) according to ISO 6330-2021.²⁴ The fabrics were cut to a preset size, and then the STIK CTH1-150B constant temperature and humidity box (STIK Co., Ltd., Shanghai, China) was used, kept for 24 h in a standard environment (20 ± 2 °C, 65 ± 5% RH) to balance humidity and temperature. In order to eliminate interference from other factors, all samples were tested three times, and the median value was taken as the experimental result.

Tobacco odor adsorption and analytical protocol

As Figure 1 shows, three pieces of fabric were hung equidistantly at the top of a self-made closed suspended adsorption chamber (50 cm × 50 cm × 50 cm). Three cigarettes were lit simultaneously at the bottom. After the cigarettes had burnt entirely, the samples were left for 1 h to absorb the tobacco smoke fully. After completion, the tester quickly put the middle part (4 cm × 7 cm) of the samples into the headspace bottles. The fume hood was the device where the experiments of adsorption were performed. Agilent 5977B gas chromatography–mass spectrometry (GC-MS; Agilent Technologies Co., Ltd., Shanghai, China) was utilized for analysis, and the conditions were as follows: the injector temperature was 250 °C and the initial temperature of the oven was 40 °C, held for 2 min, rising to 280 °C at the rate of 15 °C/min, held for 45 min, and then the operating temperature was consistent with the initial temperature. The carrier gas was helium (purity >99.999%) at a constant flow rate of 1.0 mL/min.

Figure 1.

Schematic of the experiment.

The separations were completed using a DB-WAX column (30 m × 0.25 mm, 0.25 μm), and the mass spectrometer conditions were as follows: ionization mode, electron-impact (EI), MSD transmission line temperature keeping consistent with the final temperature (280 °C), scanning mode for a full scan, and the range of 25–400 m/z.

Hot pot odor adsorption and analytical protocol

Add a bag of hot pot soup base and 750 mL of purified water to the heating device simultaneously at the bottom of the adsorption chamber. After heating the hot pot to boiling, the samples were left for 1 h to fully absorb the hot pot odor. The subsequent operation was the same as described above. The conditions of gas chromatography–mass spectrometry (GC-MS) were as follows: the injector temperature was 250 °C and the initial temperature of the oven was 35 °C, held for 3 min, rising to 100 °C at the rate of 3 °C/min held for 3 min, then rising to 160 °C at the rate of 3 °C/min held for 3 min; after rising to 270 °C at the rate of 12 °C/min, the operating temperature was consistent with the initial temperature.

The same column was used for the separations. The mass spectrometer conditions were as follows: ionization mode, EI, MSD transmission line temperature keeping consistent with the final temperature (270 °C), scanning mode for a full scan, and the range of 33–450 m/z.

Dynamic model of odors in fabric

After the fabric adsorbs, the human body or the environment odor is not caused by a single component of the odor emission.²⁵ The fabric absorbs tobacco smoke, emitting highly complex tobacco odor components, and in order to simplify the model, we selected nicotine, one of the markers of tobacco smoke, for model calculations.

The adsorption and diffusion of odorous molecules in fabrics are related to fabric structure, airflow velocity, distribution coefficient between fabric and air, temperature, and humidity.^9,17,18,26 The adsorption and diffusion processes of fabrics in a one-dimensional thickness direction can be described by equation (1):²⁷

\begin{matrix} ε \frac{\partial c}{\partial t} + ρ \frac{\partial c}{\partial t} + v \frac{\partial c}{\partial x} = R + S - D \frac{\partial^{2} c}{\partial x^{2}} \end{matrix}

(1)

In the equation, $ε (\partial c / \partial t)$ is the local acceleration of the odorous molecules in the pores of the fabric, $ε$ is the porosity of the fabric, which is affected by the fabric structure and fiber diameter, and $v (\partial c / \partial x)$ is the velocity of airflow, $- D (\partial^{2} c / \partial x^{2})$ is the diffusion gradient of odorous molecules in the fabric, and $D$ is the diffusion coefficient of odorous molecules. The structure and temperature of the odorous molecules can influence $D$ . $R + S$ is the chemical reaction and deposits on the fiber’s surface. In one-dimensional direction, the velocity field $u$ can be calculated using equation (2):^27,28

\begin{matrix} v = \frac{dx}{dt} = \frac{r_{c}^{2}}{8 μ x} (\frac{1}{2} ρ u^{2}) \end{matrix}

(2)

where $r_{c}$ is the average flow radius of the fluid in the fabric, given based on the fiber orientation probability density function (equation (3)):²⁹

\begin{matrix} r_{c} = \frac{r}{τ} = \frac{π r_{fb}}{τ \sqrt{{\hat{v}}_{fb}}} e^{\frac{{\hat{v}}_{fb}}{2 π^{2}}} \end{matrix}

(3)

The above equation shows that the average flow radius in a fabric depends on the fiber volume fraction ${\hat{v}}_{fb}$ , the fiber radius $r_{fb}$ , and the tortuosity $τ$ .

In addition, the porosity $ε$ can be calculated from equation (4):

\begin{matrix} ε = 1 - {\hat{V}}_{fb} = \frac{ρ_{fab}}{ρ_{fb}} W_{fb} = \frac{M_{fab} {\hat{M}}_{fb}}{V_{fab} ρ_{fb}} \end{matrix}

(4)

When the air quality is negligible, ${\hat{M}}_{fb} = (M_{fab} / M_{fb}) \approx 1$ , and $M_{fb}$ is approximately equal to $M_{fab}$ . The average porosity is related to fiber density $ρ_{fb}$ , fabric volume $V_{fab}$ , fabric mass $M_{fab}$ , and fiber mass fraction ${\hat{M}}_{fb}$ .

Equation (1) is solved in the one-dimensional thickness direction of the cotton fabric, as shown in Figure 2. The initial substance concentration in the fabric is 100%, and the inlet velocity is $v$ . The inlet concentration $c_{inlet} = 0$ and $c_{outlet} = c$ are the parameters to be solved numerically, the number of grids is 100, and the calculation time range is 0–1800. We discussed the initial velocity of airflow $(v = 1, 10, and 100 m / s)$ , the fiber volume fraction (0.1, 0.3, 0.6), and different diffusivity on the diffusion process of odorous molecules in the fabric. This study used the general form boundary PDE (Partial Differential Equation) in COMSOL 5.5 to obtain numerical solutions. The numerical calculation results of the odor concentration in the fabric were all standardized using equation (5):

\begin{matrix} c % = \frac{c}{c_{\max}} \times 100 \end{matrix}

(5)

where $c %$ is the percentage of residual odor concentration in the fabric, $c$ is the result of numerical calculation, and $c_{\max}$ is the maximum concentration in the calculation area. The basic parameters are shown in Table 2.

Figure 2.

Schematic of the effect of airflow on nicotine concentration in fabric.

Table 2.

Parameters for the calculation.^a

$L$	0.1	Fabric length $(m)$
$ρ_{fab}$	290	Fabric density $(kg / m^{3})$
$l_{fab}$	3.79 × 10⁻³	Fabric thickness $(m)$
$ε$	0.3	Fabric average porosity
$r_{c}$	4.14 × 10⁻⁶	Average capillary flow radius $(m)$
$ρ_{cotton}$	1550	Fiber density $(kg / m^{3})$ ³⁰
$r_{fb}$	1.33 × 10⁻⁵	Fiber diameter $(m)$ ³⁰
$D$	7.1 × 10⁻⁶	Mass diffusion coefficient (30 °C) $(m^{2} / s)$ ³¹
$τ$	7.6	Tortuosity (1)²⁷
$ρ_{f}$	1.16	Fluid density (30 °C) $(kg / m^{3})$
$μ_{f}$	1.872 × 10⁻⁵	Viscosity (30 °C) $(Pa \cdot s)$
$T$	30	Temperature $(^{°} C)$

Parameters in the table measured and collected from cotton fabric.

Results and discussion

Desorption components of tobacco odor and influencing factors

Figure 3 and Table 3 show the desorption components of fabrics exposed to tobacco smoke. The results showed that all three fabrics released nicotine, phenol, and 2-isopropyl-2,3-dimethylbutyronitrile. However, the types of compounds detected in the wool fabric were significantly lower than in the other two. Cotton and polyester fabrics released many be harmful or irritating substances, such as furfural, 3-ethenylpyridine (3-EP) and dioctyl diphosphate. Cotton fabrics desorbed many low molecular weight compounds with high polarity and volatility, such as furfural, 3-EP, and benzonitrile, which is consistent with the previous studies.^8,32 At the same time, consistent with the previous research, the odorous molecules released by wool fabrics are more extensive and less diverse.^33–35 Nicotine and 3-EP are often considered the marker compounds of thirdhand smoke,³⁶ but 3-EP was not detected in wool fabrics.

Figure 3.

Chromatogram results of fabrics exposed to tobacco smoke.

Table 3.

Compounds released from various fabrics exposed to tobacco smoke.

RT^a (min)	Compounds^b	CAS#^c	Mr^d	Peak area ratio (%)
RT^a (min)	Compounds^b	CAS#^c	Mr^d	Cotton	Wool	Polyester
4.8	Furfural, C₅H₄O₂	98-01-1	96.08	12.3	/	11.6
5.1	Furfuryl alcohol, C₅H₆O₂	98-00-0	98.10	3.44	/	0.91
5.4	Phenol, C₆H₆O	108-95-2	94.11	0.09	0.73	0.06
6.6	3-EP, C₇H₇N	1121-55-7	105.14	9.6	/	7.97
6.8	Benzonitrile, C₇H₅N	100-47-0	103.12	/	/	1.11
8.8	Naphthalene, C₁₀H₈	91-20-3	128.17	0.33	/	3.63
9.6	1-Phenyl-5-methyl tetrazole, C₈H₈N₄	14213-16-2	160.18	0.8	/	/
10.5	1,8-Cyclotetradecynyl, C₁₄H₂₀	1540-80-3	188.31	/	/	0.12
10.6	Diisooctyl diphosphate, C₁₆H₃₆O₇P₂	70729-86-1	402.40	0.43	/	4.06
10.7	Nicotine, C₁₀H₁₄N₂	54-11-5	162.23	5.5	8.84	0.34
11.8	2-Isopropyl-2,3-dimethylbutyronitrile, C₉H₁₇N	259-882-2	139.24	2.28	3.26	1.5
13.7	2,6-Dimethyl-1-heptane, C₉H₁₈	3074-78-0	126.24	3.5	7.11	/

GC retention time.

Only compounds with a matching quality of over 80 of the NIST 2.0 are reported.

CAS number.

Relative molecular weight.

The moisture content of fabrics influences the tobacco smoke absorbing ability.³⁷ The hygroscopicity of fibers is related to the strength of polar groups in the molecules that make up the fibers, the crystallinity of the aggregate structure of the fiber, and the bonding strength of polar groups and water.³⁸ It is concluded that the free hydroxyl groups in cotton fibers can be bonded with nicotine and its derivatives through hydrogen bonds, making it easier for cotton fibers to adsorb polar compounds.³⁹ The lower odor release ability of wool fibers may be related to the potential ionic interactions between odor compounds and functional groups such as amino and carboxyl groups. The high odor release ability of polyester fabric may be related to its low odor absorption ability and lack of functional groups that can interact with odor compound ions.⁴⁰ Compared to polyester, wool and cotton fibers are more hygroscopic, so they absorb more polar compounds.^38,41,42 In contrast, non-polar compounds like naphthalene bind more easily to non-polar substrates such as polyester. For example, the study of Yao on cotton, polyester, and wool found that wool has a higher absorption rate of decanal and cyclohexanone, but a lower release rate. This may be due to the interaction of a large number of amino acids with various oily substances through hydrogen bonding, sulfur–sulfur interaction, COOH–NH₂ chain, and phosphorus–hydrogen interaction. The high adsorption of ethylbenzene and methyl butyrate on polyesters may be due to the non-covalent interaction of these compounds through moderate strength (dipole–dipole force, hydrogen–sulfur bond). At the same time, cotton has a lower ability to absorb and release odor, which may be due to the effect of hydrogen bonds.⁴³ The presence of acidic groups and amino groups in wool provides additional adsorption sites for odor molecules, acidic and peptide sites can form bonds with odors, odor emission is reduced, and adding 20% wool to the polyester fabric can significantly reduce the odor emission intensity of the polyester fabric. Polar compounds such as furfural, 3-EP, and benzonitrile are more desorbed from cotton and wool fabrics. At the same time, polyester emits more non-polar compounds such as naphthalene. This is consistent with the above conclusion.⁴⁰ Polar compounds such as furfural, 3-EP, and benzonitrile are more desorbed from cotton and wool fabrics. At the same time, polyester emits more non-polar compounds such as naphthalene. This is consistent with the above conclusion.

Fabric structure, usually through the thickness and density of odor adsorption on the fabric, has an impact.⁸ For example, Noble³⁷ found that the weight gain of the different structures of wool fiber was different, and the heavier structure of Gabardine gained the most weight. In this study, the result of the chromatogram shows that the wool fabric has fewer peaks than the other two fabrics, which may be influenced by fabric structure.

Desorption components of hot pot odor and influencing factor

Figure 4 and Table 4 present the desorption components of the fabric exposed to hot pot odor. The results showed that all three fabrics released hexanal, myrcene, β-pinene, nonanal, and anethole. However, the linalyl ester, (−)-4-terpineol, the typical compound of hot pot odor, was not found in cotton fabrics.⁷ Cotton fabrics released more hexanal, nonanal, and anethole than wool and polyester. Wool fabrics released much more β-pinene than the other two fabrics.

Figure 4.

Chromatogram results of fabrics exposed to hot pot odor.

Table 4.

Compounds released from various fabrics exposed to hot pot odor.

RT^a (min)	Compounds^b	CAS#^c	Mr^d	Peak area ratio (%)
RT^a (min)	Compounds^b	CAS#^c	Mr^d	Cotton	Wool	Polyester
6.5	Hexanal, C₆H₁₂O	66-25-1	100.16	5.14	2.58	1.61
10.9	Linalyl ester, C₁₀H₁₈O	78-70-6	154.25	/	0.11	0.87
11.9	(−)-4-Terpineol, C₁₀H₁₈O	20126-76-5	154.25	/	0.04	0.01
13.5	Benzaldehyde, C₇H₆O	100-52-7	106.12	/	3.31	/
16.9	Laurene, C₁₀H₁₆	123-35-3	136.23	0.42	1.14	0.73
17.1	4-Methyl-2-propyl-1-pentanol, C₉H₂₀O	54004-41-0	144.25	/	/	11.7
20.6	β-Pinene, C₁₀H₁₆	18172-67-3	136.23	7.32	34.82	6.93
20.8	Nonanal, C₉H₁₈O	124-19-6	142.24	13.44	3.69	4.91
23.9	4-Methyl phenyl pentanone, C₁₂H₁₆O	1671-77-8	176.26	1.69	/	0.1
29.9	Aniseed, C₁₀H₁₂O	104-46-1	148.20	1.93	/	/
29.9	Anethole, C₁₀H₁₂O	4180-23-8	148.20	3.74	1.69	0.94

^aGC retention time.

Only compounds with a matching quality of over 80 of the NIST 2.0 are reported.

CAS number.

Relative molecular weight.

The adsorption and desorption of hot pot odor on the fabric’s surface are a process affected by many factors. First, polar compounds tend to bind to reactive polar sites. Polar compounds are thereby more easily adsorbed on the fiber with polar groups. In addition, the nanoscale water film formed on the fiber surface also dissolves odorous molecules.^8,14,23,36 Assuming that a large amount of water vapor condenses on the surface of the fiber during the above process, it may hinder the adsorption of polar compounds on the surface of the fibers with more non-polar groups.^7,44

The hydroxyl groups (–OH) in the long cellulose chain of cotton fiber have a strong polarity and can bind to odorous molecules through weak interactions.⁴⁵ Wool fibers can form hydrogen bonds with nitrogen atoms in nicotine and 3-EP through carboxyl and amino groups.³⁸

In addition, the physical properties of fibers can also affect the results of odor adsorption.¹⁹ There are many micropores and lumens in cotton fibers and the scale layer of wool fibers also increases the surface area for adsorbing odorous molecules. The surface of polyester fibers is smooth, long, and hydrophobic, and the chemical adhesion of odorous molecules on its surface is low.^34,46

Numerical simulation of the dynamic model

Figure 5 shows the percentage distribution of nicotine concentration over time at the midpoint of the fabric thickness. The nicotine concentration in the fabric gradually decreases with time. After 1800 s of ventilation, when $v_{inlet}$ is 1 m/s, the nicotine concentration in the fabric is still about 10%, and when the $v_{inlet}$ is 50 m/s, the nicotine concentration distribution in the fabric thickness direction almost reaches 0. In the case of $v_{inlet}$ reaching 100 m/s, the nicotine content was close to 0% after ventilation for 700 s. The inlet velocity of airflow significantly influences the nicotine concentration in the fabric.

Figure 5.

Effect of inlet velocity on the concentration of nicotine over time in the thickness direction.

Figure 6 shows the percentage distribution of nicotine concentration in the direction of fabric thickness at 10, 60, 900, and 1800 s, respectively. At the situation of $t = 10$ and $v_{inlet} = 100 m / s$ , the reduction in the concentration in the direction of fabric thickness was more pronounced than at the other velocities. Nevertheless, in the case, $t = 60$ , the effect of high-velocity airflow was better than other low-velocity airflows, the change of nicotine curve at both low-velocity airflow was gentle, and the removal efficiency was lower compared with $v_{inlet} = 100 m / s$ . After 900 s of ventilation, for the condition of $v_{inlet} = 100 m / s$ , the nicotine concentration in the thickness direction within the fabric had decreased to 0, while for $v_{inlet} = 1 m / s$ , it was still unable to reach 0 even after the 1800 s.

Figure 6.

Effect of inlet velocity on the percentage distribution of nicotine concentration in the thickness direction.

The odor molecule mass diffusivity coefficient depends on the molecular structure, molecular mass, and molecular diameter.⁴⁷ Figure 7 shows the estimated results when the mass diffusivity coefficient of the odor molecule is larger and smaller than the nicotine used in this study by one order of magnitude, including $D_{1} = 10^{- 7}$ , $D_{2} = 10^{- 6}$ , and $D_{3} = 10^{- 5}$ . When the mass diffusion coefficient of odor molecules decreased to 10⁻⁷(D₁), the difference in concentration distribution in the fabric was more significant than that of nicotine. Even after ventilation for 1800 s, the lower mass diffusion coefficients still resulted in odor residues in the second half of the fabric. However, the concentration distribution of odor in the front half of the fabric showed that the concentration of molecules with smaller mass diffusion coefficients was generally lower than those with larger mass diffusion coefficients.

Figure 7.

The numerical results under different diffusion coefficients.

Table 5 shows the paired t-test of nicotine concentration percentage with different mass diffusion coefficients at t = 1800 s, where ${c_{D}}_{1}, {c_{D}}_{2} {c_{D}}_{3}$ represent the nicotine concentration percentage with different mass diffusion coefficients. It shows that the mass diffusivity coefficient has a significant effect at t = 1800 s, with the t-statistic values of −14.98, −4.65, and 19.72, separately. All the three t-statistics are much larger than the t-critical value. After 1800 s of ventilation, the distribution of odorous molecules with larger mass diffusion coefficients was flatter in the overall thickness direction of the fabric, and the overall concentration residue in the thickness direction was still higher.

Table 5.

Paired t-test of nicotine concentration percentage with different mass diffusion coefficients at t = 1800 s.

$α = 0.05$	${c_{D}}_{1} - {c_{D}}_{2}$	${c_{D}}_{1} - {c_{D}}_{3}$	${c_{D}}_{2} - {c_{D}}_{3}$
Hypothesized mean difference	0	0	0
P, two-tailed	$1.35 \times 10^{- 40}$	$4.55 \times 10^{- 6}$	$5.76 \times 10^{- 61}$
t-Critical, two-tailed	1.97	1.97	1.97
t-Statistic	−14.98	−4.65	19.72

Figure 8 shows the effect of porosity $(ε_{1} = 0.9, ε_{2} = 0.7, and ε_{3} = 0.4)$ on the concentration of nicotine in the fabrics. Table 6 shows the t-test of nicotine concentration percentage with different fabric porosity at $t = 1800 s$ , shows that the concentration percentage $c_{ε_{2}} - c_{ε_{3}}$ is much smaller than the $c_{ε_{1}} - c_{ε_{2}}$ and $c_{ε_{1}} - c_{ε_{3}}$ . Comparing with the P-value of $[c_{ε_{1}} - c_{ε_{2}}] = 1.53 \times 10^{- 81}$ and $[c_{ε_{1}} - c_{ε_{3}}] = 1.99 \times 10^{- 85}$ , $[c_{ε_{2}} - c_{ε_{3}}]$ is only $4.52 \times 10^{- 4}$ . Meanwhile, the nicotine concentration in the fabrics with lower fiber volume fraction decreased faster.

Figure 8.

Effect of porosity on nicotine concentration in fabric.

Table 6.

t-Test of nicotine concentration percentage with different fabric porosity at $t = 1800 s$ .

$α = 0.05$	$c_{ε_{1}} - c_{ε_{2}}$	$c_{ε_{1}} - c_{ε_{3}}$	$c_{ε_{2}} - c_{ε_{3}}$
Hypothesized mean difference	0	0	0
P, two-tailed	$1.53 \times 10^{- 81}$	$1.99 \times 10^{- 85}$	$4.52 \times 10^{- 4}$
t-Critical, two-tailed	1.96	1.96	1.96
t-Statistic	−21.55	−22.20	−3.52

The fabric-specific surface area (A_ssa, 1/m, equation (6)) is a helpful tool to partly understand how the fabric structure and fiber properties influence the desorption process:

A_{ssa} = \frac{π d_{f} l_{fb} N}{V_{fab}}

(6)

where $d_{f}$ is the fiber diameter, $l_{fb}$ is the fiber length, $N$ is the fiber amount, and $V_{fab}$ is the fiber volume fraction. When the fiber type and the number of fibers are given, as the fiber volume fraction $V_{fab}$ increases, the porosity decreases and the ability for odor deposition will also decrease, which matches the numerical results in Figure 8.

Conclusion

In this study, the desorption components of cotton, wool, and polyester fabrics after absorbing tobacco smoke and hot pot odors were examined, respectively, using GC-MS and other instruments. Subsequently, the effects of different boundary conditions and fiber volume fractions on the odor concentration in the fabrics were investigated based on a dynamic diffusion model with nicotine as an example. The numerical results of the diffusion coefficients were also discussed. The following conclusions can be drawn:

For tobacco smoke, cotton released many low molecular weight compounds such as furfural, 3-EP, and benzonitrile. Compared to cotton and polyester, wool fabrics released larger molecular weight and significantly fewer types of odors. 3-EP, considered a signature compound for thirdhand smoke, was not detected in wool fabrics.

For hot pot odor, cotton fabrics released more hexanal, nonanal, and anethole than wool and polyester. In contrast, wool fabrics released much more β-pinene than the other two. The result may be related to the fact that the large amount of water vapor generated during heating dramatically affects the adsorption and desorption of the fabrics.

The dynamic model’s numerical results showed that the nicotine concentration in the inner part of the fabric decreased faster in fabrics with lower fiber volume fractions. A lower mass diffusion coefficient will result in a concentration residual in the second half of the fabric. The airflow’s inlet velocity greatly influences nicotine concentration in the fabric. When the fabric’s porosity was similar, the nicotine concentration in the fabric was also similar.

This study aimed at the composition and the dynamics of odor in fabrics offers essential information and simple models for prediction to reduce unnecessary washing of textiles and odor resistance textile design. In addition, the reaction rate and other key parameters can be determined based on the transient numerical calculation results of odor and the kinetic model.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors acknowledge the support of Shanghai Engineering Research Center for Clean Production of Textile Chemistry. The authors also would like to acknowledge the illuminating discussions with the engineer Mr Guan Xuewei from Wuxi Little Swan Company Limited.

References

Pozuelos

Jacob

Schick

, et al. Adhesion and removal of third hand smoke from indoor fabrics: A method for rapid assessment and identification of chemical repositories. Int J Environ Res Public Health 2021; 18(7): 3592.

Torres

Merino

Paton

, et al. Biomarkers of exposure to secondhand and thirdhand tobacco smoke: Recent advances and future perspectives. Int J Environ Res Public Health 2018; 15(12): 2693.

Wang

Bao

, et al. Impacts of texture properties and airborne particles on accumulation of tobacco-derived chemicals in fabrics. J Hazard Mater 2019; 369: 108–115.

Licina

Morrison

Beko

, et al. Clothing-mediated exposures to chemicals and particles. Environ Sci Tech 2019; 53(10): 5559–5575.

Borujeni

Yaghmaian

Naddafi

, et al. Identification and determination of the volatile organics of third-hand smoke from different cigarettes and clothing fabrics. J Environ Health Sci Eng 2022; 20: 53–63.

Richter

Laing

Bremer

PJ.

Binding and release of odor compounds from textiles: Changing fiber selection for apparel. Text Res J 2021; 91(7–8): 709–716.

Yuan

Zhong

, et al. Evaluation of adsorption and desorption of chafing dish odor on woolen fabric. IOP Conf Ser Mater Sci Eng 2018; 392(3): 032005.

Chien

Y-C

Chang

C-P

Liu

Z-Z.

Volatile organics off-gassed among tobacco-exposed clothing fabrics. J Hazard Mater 2011; 193: 139–148.

Cao

Liu

Zhang

SPME-based Ca-history method for measuring SVOC diffusion coefficients in clothing material. Environ Sci Tech 2017; 51(16): 9137–9145.

10.

Obendorf

Liu

Leonard

, et al. Effects of aroma chemical vapor pressure and fiber morphology on the retention of aroma chemicals on cotton and poly (ethylene terephthalate) fabrics. J Appl Polymer Sci 2006; 99(4): 1720–1723.

11.

Saini

Rauert

Simpson

, et al. Characterizing the sorption of polybrominated diphenyl ethers (PBDEs) to cotton and polyester fabrics under controlled conditions. Sci Total Environ 2016; 563: 99–107.

12.

Cheng

C-Y

Huang

S-S

Yang

C-M

, et al. Detection of third-hand smoke on clothing fibers with a surface acoustic wave gas sensor. Biomicrofluidics 2016; 10(1): 011907.

13.

Salloum

Chefetz

Hatcher

PG.

Phenanthrene sorption by aliphatic-rich natural organic matter. Environ Sci Tech 2002; 36(9): 1953–1958.

14.

Petrick

Destaillats

Zouev

, et al. Sorption, desorption, and surface oxidative fate of nicotine. Phys Chem Chem Phys 2010; 12(35): 10356–10364.

15.

McQueen

Laing

Brooks

, et al. Odor intensity in apparel fabrics and the link with bacterial populations. Text Res J 2007; 77(7): 449–456.

16.

McQueen

Laing

Delahunty

, et al. Retention of axillary odour on apparel fabrics. J Text Inst 2008; 99(6): 515–523.

17.

Lang

Indoor deposition and the protective effect of houses against airborne pollution. Roskilde: Risoe National Laboratory, 1995.

18.

Saini

Okeme

Mark Parnis

, et al. From air to clothing: Characterizing the accumulation of semi-volatile organic compounds to fabrics in indoor environments. Indoor Air 2017; 27(3): 631–641.

19.

Branton

Bradley

RH.

Effects of active carbon pore size distributions on adsorption of toxic organic compounds. Adsorption 2011; 17(2): 293–301.

20.

Deng

Zhang

Modeling VOCs emission/sorption with variable operating parameters and general boundary conditions. Environ Pollut 2021; 271: 116315.

21.

Hinrichsen

Ódor

Roughening transition in a model for dimer adsorption and desorption. Phys Rev Lett 1999; 82: 1205.

22.

Zhou

Wang

Feng

, et al. Effects of fast-desorbed gas on the propagation characteristics of outburst shock waves and gas flows in underground roadways. Process Safe Environ Protect 2018; 119: 295–303.

23.

Zhu

Liu

Ding

, et al. A novel method for textile odor removal using engineered water nanostructures. RSC Adv 2019; 9(31): 17726–17736.

24.

ISO 6330: 2021. Textiles—domestic washing and drying procedures for textile testing.

25.

McQueen

Vaezafshar

Odor in textiles: A review of evaluation methods, fabric characteristics, and odor control technologies. Text Res J 2020; 90(9–10): 1157–1173.

26.

Pugliese

Jespersen

Pernov

, et al. Chemical analysis and origin of the smell of line-dried laundry. Environ Chem 2020; 17(5): 355–363.

27.

Liang

Zhang

Wang

Capillary flow in cotton fabric based on fiber orientation probability density function. J Natur Fib. Epub ahead of print 20 September 2021. DOI: 10.1080/15440478.2021.1967829.

28.

Trabi

Ouali

McHale

, et al. Capillary penetration into inclined circular glass tubes. Langmuir 2016; 32(5): 1289–1298.

29.

Pan

Analytical characterization of the anisotropy and local heterogeneity of short fiber composites: Fiber fraction as a variable. J Compos Mater 1994; 28(16): 1500–1531.

30.

Morton

Hearle

An introduction to fibre structure. In: Morton

Hearle

JWS

(eds) Physical Properties of Textile Fibres. 4th ed. Amsterdam: Elsevier, 2008, pp. 1–81.

31.

John

Coburn

Liu

, et al. Effect of temperature and humidity on the gas–particle partitioning of nicotine in mainstream cigarette smoke: A diffusion denuder study. J Aerosol Sci 2018; 117: 100–117.

32.

Ueta

Saito

Teraoka

, et al. Determination of volatile organic compounds for a systematic evaluation of third-hand smoking. Anal Sci 2010; 26(5): 569–574.

33.

Lide

DR.

CRC handbook of chemistry and physics. Boca Raton, FL: CRC Press, 2004, p. 85.

34.

Cieślak

Schmidt

Świercz

, et al. Fibers susceptibility to contamination by environmental tobacco smoke markers. Text Res J 2014; 84(8): 840–853.

35.

Tichenor

BA.

Characterizing sources of indoor air pollution and related sink effects. West Conshohocken, PA: ASTM International, 1996.

36.

Piadé

D’André

Sanders

EB.

Sorption phenomena of nicotine and ethenylpyridine vapors on different materials in a test chamber. Environ Sci Tech 1999; 33(12): 2046–2052.

37.

Noble

RE.

Environmental tobacco smoke uptake by clothing fabrics. Sci Total Environ 2000; 262(1): 1–3.

38.

Mather

Wardman

RH.

The chemistry of textile fibres. London: Royal Society of Chemistry, 2015.

39.

Senthilkumar

Ghanty

Kolandaivel

, et al. Hydrogen-bonded complexes of nicotine with simple alcohols. Int J Quantum Chem 2012; 112(16): 2787–2793.

40.

Wang

, et al. Quantitative and sensory evaluation of odor retention on polyester/wool blends. Text Res J 2019; 89(13): 2729–2738.

41.

Vollhardt

KPC

Schore

. Organic Chemistry; Structure and Function. London: Macmillan International Higher Education, 2014.

42.

Urbańczyk

GW.

Nauka o włóknie. Warszawa: Wydawnictwa Naukowo-Techniczne, 1985.

43.

Yao

Laing

Bremer

, et al. Measuring textile adsorption of body odor compounds using proton-transfer-reaction mass spectrometry. Text Res J 2015; 85(17): 1817–1826.

44.

Boraphech

Thiravetyan

Trimethylamine (fishy odor) adsorption by biomaterials: Effect of fatty acids, alkanes, and aromatic compounds in waxes. J Hazard Mater 2015; 284: 269–277.

45.

Needles

HL.

Textile fibers, dyes, finishes, and processes: A concise guide. New York: Noyes Publications, 1986.

46.

Cieslak

New approach to environmental tobacco smoke exposure and its relation to reemission processes. Int J Occup Med Environ Health 2006; 19(2): 92.

47.

Yin

Zhang

Pore-scale prediction of the effective mass diffusivity of heterogeneous shale structure using the lattice Boltzmann method. Int J Heat Mass Transfer 2019; 133: 976–985.

Tobacco and Hot Pot Odor Adsorbed by Cotton,Wool,and Polyester Fabrics: Desorption Components and Dynamic Analysis

Abstract

Keywords

Introduction

Experimental and dynamic models

Materials

Samples preparation and testing

Tobacco odor adsorption and analytical protocol

Hot pot odor adsorption and analytical protocol

Dynamic model of odors in fabric

Results and discussion

Desorption components of tobacco odor and influencing factors

Desorption components of hot pot odor and influencing factor

Numerical simulation of the dynamic model

Conclusion

Footnotes

Declaration of conflicting interests

Funding

References