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Review

Multidomain Molecular Sensor Devices, Systems, and Algorithms for Improved Physiological Monitoring

1
College of Letters and Science, University of California, Berkeley, CA 94720, USA
2
Department of Science, Mathematics and Technology, and The AI Mega Centre, Singapore University of Technology and Design, Singapore 487372, Singapore
3
Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, UK
*
Authors to whom correspondence should be addressed.
Micromachines 2025, 16(8), 900; https://doi.org/10.3390/mi16080900 (registering DOI)
Submission received: 12 June 2025 / Revised: 27 July 2025 / Accepted: 30 July 2025 / Published: 31 July 2025

Abstract

Molecular sensor systems, e.g., implantables and wearables, provide extensive health-related monitoring. Glucose sensor systems have historically prevailed in wearable bioanalysis applications due to their continuous and reliable glucose monitoring, a feat not yet accomplished for other biomarkers. However, the advancement of reagentless detection methodologies may facilitate the creation of molecular sensor systems for multiple analytes. Improving the sensitivity and selectivity of molecular sensor systems is also crucial for biomarker detection under intricate physiological circumstances. The term multidomain molecular sensor systems is utilized to refer, in general, to both biological and chemical sensor systems. This review examines methodologies for enhancing signal amplification, improving selectivity, and facilitating reagentless detection in multidomain molecular sensor devices. The review also analyzes the fundamental components of multidomain molecular sensor systems, including substrate materials, bodily fluids, power, and decision-making units. The review article further investigates how extensive data gathered from multidomain molecular sensor systems, in conjunction with current data processing algorithms, facilitate biomarker detection for precision medicine.
Keywords: biomarkers; molecular sensors; machine learning; healthcare physiological monitoring; precision medicine biomarkers; molecular sensors; machine learning; healthcare physiological monitoring; precision medicine

Share and Cite

MDPI and ACS Style

Soriano, L.D.; Go, S.-X.; Li, L.; Bajalovic, N.; Loke, D.K. Multidomain Molecular Sensor Devices, Systems, and Algorithms for Improved Physiological Monitoring. Micromachines 2025, 16, 900. https://doi.org/10.3390/mi16080900

AMA Style

Soriano LD, Go S-X, Li L, Bajalovic N, Loke DK. Multidomain Molecular Sensor Devices, Systems, and Algorithms for Improved Physiological Monitoring. Micromachines. 2025; 16(8):900. https://doi.org/10.3390/mi16080900

Chicago/Turabian Style

Soriano, Lianna D., Shao-Xiang Go, Lunna Li, Natasa Bajalovic, and Desmond K. Loke. 2025. "Multidomain Molecular Sensor Devices, Systems, and Algorithms for Improved Physiological Monitoring" Micromachines 16, no. 8: 900. https://doi.org/10.3390/mi16080900

APA Style

Soriano, L. D., Go, S.-X., Li, L., Bajalovic, N., & Loke, D. K. (2025). Multidomain Molecular Sensor Devices, Systems, and Algorithms for Improved Physiological Monitoring. Micromachines, 16(8), 900. https://doi.org/10.3390/mi16080900

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