Matrix materials as functional enablers in metabolite sensing: from mechanistic demands to translational potential.
5 affiliations
- 1Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 3, 3010, Bern, Switzerland. [email protected].
- 2Graduate School for Cellular and Biomedical Sciences, University of Bern, Mittelstrasse 43, 3012, Bern, Switzerland.
- 3Diabetes Center Berne, Freiburgstrasse 3, 3010, Bern, Switzerland.
- 4Department of Digital Medicine, University of Bern, 3010, Bern, Switzerland.
- 5Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3, 3012, Bern, Switzerland.
Abstract
The performance of metabolite sensors is strongly shaped by the physicochemical properties of the matrix that governs analyte transport, interfacial stability, and signal coupling, yet the role of the matrix has largely been overlooked, often being viewed as a passive support rather than an active design parameter. This review analyses how matrix architecture controls sensing behaviour across polymeric, inorganic, carbon-based, and supramolecular material classes, examining mechanistic trade-offs between diffusion and binding, conductivity and biocompatibility, and structural stability and responsiveness in the context of electrochemical, optical, and electrical transduction modes. Particular emphasis is placed on operation in complex biological media, where protein adsorption, ionic fluctuations, mechanical mismatch, and dynamic metabolite flux impose constraints rarely captured in buffered laboratory systems. The comparative analysis reveals that no single matrix class universally optimizes sensitivity, selectivity, and stability; instead, sensor behaviour and performance-relevant characteristics emerge from alignment between the matrix architecture and the signal-generating mechanism. By framing matrix selection through these mechanistic principles, this review provides a foundation for rational matrix design toward continuous, multi-analyte, and clinically deployable metabolite sensors.
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BibTeX
@article{varga2026matrix,
title = {{Matrix materials as functional enablers in metabolite sensing: from mechanistic demands to translational potential.}},
author = {Martin Varga and Guglielmo Risi and Saiyyna Stepanova and Mahsa Nasehi and Celal Bahtiyar and Emmanouil Rousakis and Lilian Witthauer},
journal = {Journal of materials chemistry. B},
year = {2026},
month = oct,
doi = {10.1039/d6tb01338d},
url = {https://europepmc.org/article/MED/42974911},
note = {CC BY},
}
RIS
TY - JOUR
TI - Matrix materials as functional enablers in metabolite sensing: from mechanistic demands to translational potential.
AU - Martin Varga
AU - Guglielmo Risi
AU - Saiyyna Stepanova
AU - Mahsa Nasehi
AU - Celal Bahtiyar
AU - Emmanouil Rousakis
AU - Lilian Witthauer
PY - 2026
DA - 2026/10/30
JO - Journal of materials chemistry. B
DO - 10.1039/d6tb01338d
UR - https://europepmc.org/article/MED/42974911
AB - The performance of metabolite sensors is strongly shaped by the physicochemical properties of the matrix that governs analyte transport, interfacial stability, and signal coupling, yet the role of the matrix has largely been overlooked, often being viewed as a passive support rather than an active design parameter. This review analyses how matrix architecture controls sensing behaviour across polymeric, inorganic, carbon-based, and supramolecular material classes, examining mechanistic trade-offs between diffusion and binding, conductivity and biocompatibility, and structural stability and responsiveness in the context of electrochemical, optical, and electrical transduction modes. Particular emphasis is placed on operation in complex biological media, where protein adsorption, ionic fluctuations, mechanical mismatch, and dynamic metabolite flux impose constraints rarely captured in buffered laboratory systems. The comparative analysis reveals that no single matrix class universally optimizes sensitivity, selectivity, and stability; instead, sensor behaviour and performance-relevant characteristics emerge from alignment between the matrix architecture and the signal-generating mechanism. By framing matrix selection through these mechanistic principles, this review provides a foundation for rational matrix design toward continuous, multi-analyte, and clinically deployable metabolite sensors.
SN - 2050-750X
SN - 2050-7518
ER -