Biology subjects

Weber, I. C.

Publications and source records attributed to Weber, I. C..

3 recordsLinked to original sources

bioRxiv2026

Machine Learning-Driven Multiplexed Biomarker Detection with Polymer-Enhanced Electrochemical Sensors

Biomarkers in sweat and saliva offer a promising avenue for non-invasive health monitoring. Electrochemical sensors have the potential to measure such biomarkers simultaneously. However, they are limited in discriminating individual biomarkers in mixtures, as redox potentials often overlap, resulting in current signatures that cannot be deconvoluted. This study focuses on differentiating biomarkers using orthogonal sensing materials combined with machine learning. We introduce a flexible electrochemical sensor array comprising carbon flower electrodes modified with poly(vinylidene fluoride) (PVDF) or poly(4-vinylpyridine) (P4VP) for the detection of estradiol (E2), ascorbic acid (AA), serotonin (5-HT), and melatonin (Mel). The two polymers act by altering the redox potential and current response of each biomarker, thereby enhancing signal diversity and enabling peak separation. Using multi-output regression models on 450 single and mixture measurements, the array accurately predicts concentrations (R2 = 0.95) over a wide dynamic range spanning nanomolar to micromolar levels. Polymer-resolved analysis reveals that PVDF-modifications enhance E2 and Mel detection, while P4VP-modifications improve AA and 5-HT quantification, highlighting the benefit of complementary orthogonal sensing electrodes. This finding is further supported by feature attribution analysis, which shows that the machine learning model relies on polymer-specific electrochemical signatures, directly linking improved performance to distinct polymer-analyte interactions. Overall, these results demonstrate that combining polymer-modified orthogonal electrodes with machine learning enables accurate, multiplexed sensing in complex mixtures, advancing selective detection strategies for future sensor platforms.

bioengineering↗

bioRxiv2026

Tuning selectivity of electrochemical sensors with polymer coatings

Electrochemical sensors are promising for health monitoring due to their high repeatability and sensitivity, particularly when nanostructured. Yet, their translation into real applications is hindered by limited selectivity in the absence of specific binding receptors: many biomarkers exhibit similar oxidation potentials, producing overlapping voltammetric signals that impede molecular discrimination. Here, we demonstrate that the oxidation potential of several small molecule biomarkers can be controlled through polymeric coatings, specifically poly(4-vinylpyridine), deposited onto glassy carbon electrodes. The polymer coating alters diffusion and adsorption characteristics, which ultimately lead to oxidation potential shifts of ascorbic acid and serotonin, enabling their separation of otherwise overlapping signals. These findings are supported by Chronocoulometry and Fourier-transform infrared spectroscopy analysis that reveal changes in diffusion coefficient, adsorbed charge, and hydrogen bonding that are likely responsible for the altered sensor performance. These findings can be expanded to further polymers and biomarkers, including estradiol and melatonin. Finally, we demonstrate that the same selectivity trends persist on nanostructured, stretchable carbon-flower electrodes, where the high surface area further enhances sensitivity. Collectively, these findings reveal polymer-controlled peak-potential tuning as a powerful and broadly applicable route toward highly selective electrochemical sensors, enabling molecular discrimination in complex mixtures and opening new avenues for sensor-array-based detection.

bioengineering↗

bioRxiv2025

Sensitive Hormone and Neurotransmitter Detection with Carbon Flower Electrodes

Carbon-based electrochemical sensors have attracted substantial attention for continuous health monitoring due to their high surface area, wide potential window, capability for repeated measurements, and compatibility with soft wearable electronics. However, they face challenges when detecting target biomarker concentrations in the low nanomolar range (sensitivity) and differentiating them in a mixture (selectivity), limiting their applicability in real scenarios. Herein, we present sensitive and selective carbon flower sensors, fabricated via a facile and patternable spray-coating process on soft, stretchable substrates. The carbon flowers--obtained through the synthesis of polyacrylonitrile and subsequent heat treatments--exhibit unique hierarchical morphologies, high surface area, and excellent conductivity, ideal for mass transport and electrochemical detection. We demonstrate the detection of estradiol, serotonin, melatonin, dopamine, uric acid, and ascorbic acid with detection limits as low as sub-nanomolar. The carbon flower sensors exhibit good repeatability across 100 cycles and over several weeks, robustness to pH and salt variations, and excellent performance in complex bio-fluids such as artificial saliva. In mixtures containing up to four analytes, they differentiate individual molecules, demonstrating the selectivity of the carbon flowers. This combination of sensitivity, selectivity, and mechanical compatibility makes carbon flower sensors well-suited for biomolecular sensing in soft, skin-conformable wearable electronic patches.

bioengineering↗