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Updated: Jun 20, 2026

Development and Functionalization of Electrolyte-Gated Graphene Field-Effect Transistor for Biomarker Detection
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A Review of Biomass-Derived Nanomaterials as Functional Interfaces for Advanced Electrochemical Hormone Detection
Shweta J Malode1, Aashutosh Dube1, Abdullah N Alodhayb2,3
1Center for Energy and Environment, School of Advanced Sciences, KLE Technological University, Vidyanagar, Hubbali 580031, Karnataka, India.
None:
The study of high-performance sensing platforms is essential to achieve a high sensitivity for hormonal analytes, short response times, and cost-effectiveness. Nevertheless, standard analytical procedures, such as high-performance liquid chromatography, inductively coupled plasma mass spectrometry, and gas chromatography mass spectrometry (HPLC, ICP-MS, and GC-MS), are rather limited because their sensitivity is low at pM/nM concentrations, and the costs of these processes are too high. Moreover, modern sensor technology can be ill-equipped to selectively respond to intricate biological sample types, and there is no single standard used to quantify sensor signals. This review addresses these knowledge gaps by providing a comprehensive discussion on the role of biomass-derived materials as useful interfaces in electrochemical hormone sensing systems. To achieve improved results, we critically investigated the recent advances in the electrode modification process through biomass-based nanomaterials to promote electron transfer kinetics and interfacial engineering. The present article also discusses contemporary issues such as problems with stability, reproducibility, and selectivity in complex matrices. Furthermore, we highlighted some of the new approaches, such as the development of artificial intelligence, Internet of Things, and wearable point-of-care devices, to create the next generation hormone sensors. Overall, this current review is a comprehensive overview of biomass-derived materials, which have the prospect of enhancing surface modification at the advanced phase, AI integration, and the development of point-of-care diagnostics.
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