Dépouillements

Next generation antimicrobial waterborne polyurethane / Emma G. Wrigglesworth in COATINGS TECH, Vol. 21, N° 3 (05-06/2024)  

[article]  inCOATINGS TECH > Vol. 21, N° 3 (05-06/2024) . - p. 51-54 Titre : Next generation antimicrobial waterborne polyurethane Type de document : texte imprimé Auteurs : Emma G. Wrigglesworth, Auteur ; Eldon W. Tate, Auteur Année de publication : 2024 Article en page(s) : p. 51-54 Note générale : Bibliogr. Langues : Américain (ame) Catégories : Antifongiques Antimicrobiens Argent Caractérisation Lixiviation Polyuréthanes Revêtements en phase aqueuse Revêtements en phase aqueuse -- Additifs Revêtements organiques Tests d'efficacité Tests de toxicité Index. décimale : 667.9 Revêtements et enduits Résumé : Microbial contamination on surfaces is a problematic and potentially deadly issue, particularly in high-risk settings such as healthcare, aged care, and food and beverage. In this research, we present an antimicrobial waterborne polyurethane additive that will be of interest to the coatings community due to its high compatibility with a variety of coating systems, which results in a range of highly diverse and useful applications. This next-generation antimicrobial uses a nanocomposite approach to create a silver-based active ingredient that is non-toxic and truly non-leaching. The presented technology produces strong binding between silver and the polymer backbone. This prevents leaching while retaining high activity with proven results against a range of pathogens. Note de contenu : - EXPERIMENTAL - CHARACTERIZATION - INDEPENDENT TESTING : Antimicrobial testing - Leach testing - Toxicology testing - Preservative testing - RESULTS AND DISCUSSION : Silver particle characterization - COMPOSITE PROPERTIES : Antimicrobial activity - Leach testing - Toxicology tests - SCALE UP & APPLICATIONS - Table 1 : Test details for antifungal trials - Table 2 : Test details for toxicology tests - Table 3 : Results of antimicrobial testing conducted on the silver-polyurethane composite - Table 4 : Results of toxicology testing conducted on silver-polyurethane composite - Table 5 : Results of preservative testing En ligne : https://drive.google.com/file/d/1lp_ItsRohgSZrGpN2GeAcMhPSktZiBp-/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=41091 [article]

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Additives to prevent coating defects caused by film dewetting / Jim Reader in COATINGS TECH, Vol. 21, N° 3 (05-06/2024)  

[article]  inCOATINGS TECH > Vol. 21, N° 3 (05-06/2024) . - p. 56-62 Titre : Additives to prevent coating defects caused by film dewetting Type de document : texte imprimé Auteurs : Jim Reader, Auteur Année de publication : 2024 Article en page(s) : p. 56-62 Note générale : Bibliogr. Langues : Américain (ame) Catégories : Agents mouillants Démouillage (chimie des surfaces) Revêtements -- Additifs Revêtements -- Défauts Index. décimale : 667.9 Revêtements et enduits Résumé : This paper will compare the advantages and disadvantages of different additive chemistries used to prevent defects caused by dewetting. Many surface defects, such as fisheyes, edge pull, and retraction are caused when the liquid film dewets after application. Application of the coating by brush, roller, or spray may effectively force wetting and spread the film across the substrate, but defects may form soon after application. There is competition between the hydrodynamic inertia of the applied film and the interfacial tension forces that can cause the coating to dewet or retract. Additives can be used to prevent these defects by reducing the interfacial forces that drive retraction. However, with many different additives to choose from, that may also cause unwanted side effects, the formulator can find additive selection difficult. This paper will compare the advantages and disadvantages of different additive chemistries used to prevent defects caused by dewetting. Note de contenu : - Additives to prevent dewetting defects in coatings - Table 1 : Comparison of wetting agents : Fundamental properties in water - Table 2 : Comparison of wetting agents properties En ligne : https://drive.google.com/file/d/1CG37ZiUAO2-8T79ZHDfaBNX4u-EEIvW8/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=41092 [article]

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Using AI to rapidly develop new and improved high-performance coatings in COATINGS TECH, Vol. 21, N° 3 (05-06/2024)  

[article]  inCOATINGS TECH > Vol. 21, N° 3 (05-06/2024) . - p. 45-49 Titre : Using AI to rapidly develop new and improved high-performance coatings Type de document : texte imprimé Année de publication : 2024 Article en page(s) : p. 45-49 Langues : Américain (ame) Catégories : Analyse multivariée Formulation (Génie chimique) Intelligence artificielle Modélisation prédictive Recherche industrielle Revêtements organiques Simulation par ordinateur Index. décimale : 667.9 Revêtements et enduits Résumé : In the era of big data and digitalization, applying AI in product development is becoming essential for manufactures to stay competitive in their industry. Gone are the days where a limited group of domain experts plan and execute design of experiments (DOE) until a set of target properties are achieved. R&D teams across many industries are transforming their workflows by embracing AI initiatives to intelligently glean information from their historical, experimental, and product databases. AI models built on past data can be used to reduce experimentation through simulation and achieve optimal properties much faster using numerical optimization. In this article, a high-performance coating dataset will be used along with ProSensus’ FormuSense software. The article will examine the typical steps to effectively utilize R&D datasets including : 1. Data preparation to ensure that the dataset is appropriate for model building. Common pitfalls such as missing data, insufficient variation, and data anomalies are investigated and resolved. 2. Model building using multivariate analysis and its intuitive plots that help subject matter experts interpret their dataset highlighting key correlations and trade-offs. 3. Simulation using the model to predict the outcome of potential experiments without running the actual physical experiment. 4. Constrained numerical optimization to calculate the ideal formulation and processing conditions required to achieve a targeted quality or set of qualities. Note de contenu : - Predictive modeling : PLS for product development - Constrained optimization - Coatings case study : Assemble dataset - Mixture properties - Build & interpret PLS model - Simulate new experiments - Predicted quality - Model validity - Cost - Opitmmization - Table 1 : Model validity metrics and formulation cost - Table 2 : Ingredients used per class En ligne : https://drive.google.com/file/d/12cX85i3IxmOIIQ0uBD58rEdYqkTmiOkV/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=41093 [article]

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