Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/117083
DC FieldValueLanguage
dc.contributorDepartment of Mechanical Engineeringen_US
dc.creatorWang, Qen_US
dc.creatorLi, Pen_US
dc.creatorYuan, Qen_US
dc.creatorZhang, Wen_US
dc.creatorMa, Men_US
dc.creatorLuo, Gen_US
dc.creatorLang, Yen_US
dc.creatorZhou, Len_US
dc.creatorSu, Zen_US
dc.date.accessioned2026-02-02T06:40:10Z-
dc.date.available2026-02-02T06:40:10Z-
dc.identifier.urihttp://hdl.handle.net/10397/117083-
dc.language.isoenen_US
dc.publisherAmerican Chemical Societyen_US
dc.subjectAerosol jet printingen_US
dc.subjectGraphene-based acoustic sensoren_US
dc.subjectPiezoresistive sensoren_US
dc.subjectSpeech recognitionen_US
dc.subjectWearable sensoren_US
dc.titleAn aerosol jet-printed wearable graphene/cellulose nanocrystal acoustic sensor for speech recognitionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage8521en_US
dc.identifier.epage8530en_US
dc.identifier.volume10en_US
dc.identifier.issue11en_US
dc.identifier.doi10.1021/acssensors.5c02157en_US
dcterms.abstractWearable acoustic sensors offer a promising solution for effective communication for individuals with speech impairments by calibrating throat vibrations and converting them to synthesized speech. We developed a new type of piezoresistive acoustic sensor that is additively manufactured with polyurethane (PU) film-encapsulated graphene/cellulose nanocrystals (CNCs) via aerosol jet printing. Featuring a high degree of biocompatibility and flexibility, the sensor is capable of accurately measuring varying sound pressure levels (SPLs). Results have demonstrated that the acoustic sensitivity of the sensor can be regulated by changing the graphene concentration, and the sensor with the 20 wt. % of graphene concentration manifests the high sensitivity of 9.7 × 10–6 dB–1, a broad working range from 30 to 90 dB, and a minimum resolution for SPL change of 10 dB, along with a linear correlation between SPL and the resistance change measured by the sensor. When adhered to the throat of a study participant as a wearable device, the sensor faithfully captures subtle vocal characteristics including timbre and rhythm. Integrated with a support vector machine (SVM)-based machine learning algorithm, the device achieves a high accuracy of 95.9% when used to recognize digits (0–9), assisting people with speech difficulties to communicate in a digital manner.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationACS sensors, 28 Nov. 2025, v. 10, no. 11, p. 8521-8530en_US
dcterms.isPartOfACS sensorsen_US
dcterms.issued2025-11-28-
dc.identifier.scopus2-s2.0-105023401745-
dc.identifier.pmid41129616-
dc.identifier.eissn2379-3694en_US
dc.description.validate202602 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.SubFormIDG000830/2026-01-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe work described in this paper was supported by a grant from the NSFC/RGC Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the National Natural Science Foundation of China (Project No. N_PolyU597/24). The research was also supported by the Research Grants Council of Hong Kong via General Research Funds (Nos. 15214323 and 15200922), and the Innovation and Technology Commission of Hong Kong via an Innovation and Technology Fund (ITF) project (ITS/005/24SC). This work also is funded by the Basic Research Special Project (Shenzhen Natural Science Foundation, Grant No. JCYJ20220818100405012) and Guangdong Pearl River Talent Program (Grant No.: 2021JC020054).en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2026-10-23en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
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