The use of Monte Carlo simulation to evaluate the optical properties of polyester fabric treated with titanium dioxide nanopigments / Laleh Asadi in COLORATION TECHNOLOGY, Vol. 139, N° 1 (02/2023)  

[article]  inCOLORATION TECHNOLOGY > Vol. 139, N° 1 (02/2023) . - p. 28-44 Titre : The use of Monte Carlo simulation to evaluate the optical properties of polyester fabric treated with titanium dioxide nanopigments Type de document : texte imprimé Auteurs : Laleh Asadi, Auteur ; Ali Shams Nateri, Auteur Année de publication : 2023 Article en page(s) : p. 28-44 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Caractérisation Coefficient de diffusion Un coefficient de diffusion est une grandeur caractéristique du phénomène de diffusion de la matière. Le coefficient de diffusion mesure le rapport entre le flux molaire dû à la diffusion moléculaire, et le gradient de concentration de l'espèce chimique considérée (ou, plus généralement, de la variable d'effort entraînant cette diffusion), comme formulé par la loi de Fick. Dioxyde de titane Enduction textile Fibres textiles -- Propriétés optiques Lumière -- Diffusion Mie, Diffusion de Monte-Carlo, Méthode de Pigments Pigments inorganiques Spectroscopie de réflectance Textiles et tissus -- Propriétés optiques Index. décimale : 667.3 Teinture et impression des tissus Résumé : The current study utilises Monte Carlo simulation and Mie scattering theory to estimate the reflectance spectra of fabric coated with titanium dioxide nanopigments of various diameters and concentrations. Image processing was carried out and experimental data were gathered to evaluate the performance of Monte Carlo simulation. The distribution and location of the nanopigments on the surface of fabric were determined using the Monte Carlo method. Reflection of the fabric was calculated based on Monte Carlo simulation with the partitive mixing method and Mie theory. According to the experimental and simulation results, the reflectance of coated samples was increased by increasing the concentration and number of titanium dioxide nanoparticles. There was a good match between the results obtained by Monte Carlo simulation and the experimental results. For coated samples (dTiO2: 500 nm), the root mean square error between measured and predicted reflectance by the Monte Carlo and partitive mixing method and by Monte Carlo and Mie theory was 0.022 and 0.0078, respectively. The results indicate that the performance of the Monte Carlo and Mie method was better than that of the Monte Carlo and partitive mixing method. According to t-test analysis, there was no statistically significant difference between the experimental data and Monte Carlo simulation. Note de contenu : - EXPERIMENTAL : Materials - Polyester fabric coating - Characterisation - MODEL DESCRIPTION : MC and the partitive mixing method - MC and the Mie method - Table 1 : For polyester fabric, the percentages of uncoated surface and surface coated with titanium dioxide (TiO2) based on the image processing system - Table 2 : CIELab and colour difference (∆E) values for the uncoated (raw fabric) sample and samples coated with titanium dioxide (TiO2) - Table 3 : Scattering coefficient values for titanium dioxide nanopigments with average diameters of 30, 50, 150, 250 and 500 nm - Table 4 : Forward and backward light scattering of titanium dioxide (TiO2) nanoparticles based on Mie theory - Table 5 : Forward and backward light scattering of titanium dioxide (TiO2) nanoparticles based on image processing - Table 6 : Percentages of empty and occupied particles using the Monte Carlo method for different hypothetical numbers of titanium dioxide (TiO2) nanoparticles - Table 7 : Estimated percentages of empty and occupied particles using the Monte Carlo method for actual numbers of titanium dioxide (TiO2) nanoparticles - Table 8 : Area occupied on the fabric surface by single, double and multiple titanium dioxide (TiO2) nanopigments - Table 9 : L*, a*, b* and ∆E values for samples with different numbers of titanium dioxide (TiO2) nanopigments - Table 10 : CIELab and ∆E values and the root mean square error (RMSE) for reflectance predictions using the Monte Carlo and partitive mixing method - Table 11 : CIELab and ∆E values and the root mean square error (RMSE) for reflectance predictions using the Monte Carlo and Mie method - Table 12 : T-stat and P values for the experimental and Monte Carlo simulation results DOI : https://doi.org/10.1111/cote.12632 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12632 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=39203 [article]

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