Diving into Sweat: Advances, Challenges, and Future Directions in Wearable Sweat Sensing.


Journal

ACS nano
ISSN: 1936-086X
Titre abrégé: ACS Nano
Pays: United States
ID NLM: 101313589

Informations de publication

Date de publication:
26 Aug 2024
Historique:
medline: 26 8 2024
pubmed: 26 8 2024
entrez: 26 8 2024
Statut: aheadofprint

Résumé

Sweat analysis has advanced from diagnosing cystic fibrosis and testing for illicit drugs to noninvasive monitoring of health biomarkers. This article introduces the rapid development of wearable and flexible sweat sensors, highlighting key milestones and various sensing strategies for real-time monitoring of analytes. We discuss challenges such as developing high-performance nanomaterial-based biosensors, ensuring continuous sweat production and sampling, achieving high sweat/blood correlation, and biocompatibility. The potential of machine learning to enhance these sensors for personalized healthcare is presented, enabling real-time tracking and prediction of physiological changes and disease onset. Leveraging advancements in flexible electronics, nanomaterials, biosensing, and data analytics, wearable sweat biosensors promise to revolutionize disease management, prevention, and prediction, promoting healthier lifestyles and transforming medical practices globally.

Identifiants

pubmed: 39185844
doi: 10.1021/acsnano.4c10344
doi:

Types de publication

News

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Andre Childs (A)

Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.

Beatriz Mayol (B)

Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.

José A Lasalde-Ramírez (JA)

Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California 91125, United States.

Yu Song (Y)

Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, China.

Juliane R Sempionatto (JR)

Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States.

Wei Gao (W)

Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, California 91125, United States.

Classifications MeSH