Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/111343
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dc.contributorSchool of Fashion and Textilesen_US
dc.creatorLee, Cen_US
dc.creatorTan, Jen_US
dc.creatorTan, JJen_US
dc.creatorTang, HTen_US
dc.creatorYu, WSen_US
dc.creatorLam, NYKen_US
dc.date.accessioned2025-02-19T06:48:20Z-
dc.date.available2025-02-19T06:48:20Z-
dc.identifier.issn1944-8244en_US
dc.identifier.urihttp://hdl.handle.net/10397/111343-
dc.language.isoenen_US
dc.publisherAmerican Chemical Societyen_US
dc.rights© 2025 American Chemical Societyen_US
dc.rightsThis document is the Accepted Manuscript version of a Published Work that appeared in final form in ACS Applied Materials & Interfaces, copyright © 2025 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acsami.4c19174.en_US
dc.subjectIntelligent textileen_US
dc.subjectHeating textileen_US
dc.subjectTemperature controlen_US
dc.subjectThermochromic textile and healthcareen_US
dc.titleIntelligent thermochromic heating e-textile for personalized temperature control in healthcareen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage5515en_US
dc.identifier.epage5526en_US
dc.identifier.volume17en_US
dc.identifier.issue3en_US
dc.identifier.doi10.1021/acsami.4c19174en_US
dcterms.abstractHeating electronic textiles (e-textiles) are widely used for thermal comfort and energy conservation, but prolonged heating raises concerns about heat-related illnesses, especially in the elderly. Despite advancements, achieving universal user satisfaction remains difficult due to diverse thermal needs. This paper introduces an intelligent thermochromic heating e-textile with an artificial intelligence (AI)-based temperature control system for optimized personal comfort and color indicators for elderly caregivers. The fabric integrates conductive yarn, temperature-induced discoloration yarn (TIDY), and polymeric optical fiber (POF) to visualize temperature changes, ensuring efficiency and comfort. Equipped with microcontrollers, ambient sensors, and Bluetooth connectivity, the system offers comprehensive intelligent heating solutions. An AI model, trained on data from 50 wearability test subjects, determines optimal heating temperatures (40–50 °C) with 5.083 mean squared error (MSE), showing a high correlation between predicted and actual comfort levels. This concept enhances thermal comfort and mitigates overheating risks, promising for wearable healthcare applications.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationACS applied materials and interfaces, 22 Jan. 2025, v. 17, no. 3, p. 5515-5526en_US
dcterms.isPartOfACS applied materials and interfacesen_US
dcterms.issued2025-01-22-
dc.identifier.eissn1944-8252en_US
dc.description.validate202502 bcrcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera3404-
dc.identifier.SubFormID50065-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextInnoHK Research Clusters, Hong Kong Special Administrative Regionen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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