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Optimized triplex nanomaterial all-around gate tunnel field-effect transistors for high-precision biomolecule detection

  • Preethi Srinivasan ORCID logo EMAIL logo , Vijayalakshmi Nanjappan and Sony Varghese
Published/Copyright: November 18, 2025

Abstract

In this present work, a biosensor built upon the triplex nanomaterial all-around gate tunnel field-effect transistor (TFET) is proposed concerning biomolecule detection. This design leverages an advanced all-around gate structure, enhancing sensitivity and selectivity in biosensing applications. Field-effect transistor (FET) biosensors offer portability and rapid, high-throughput screening capabilities, which make them particularly suitable for detecting biomarkers. The proposed 3MAG TFET biosensor includes precisely engineered nanocavities over a 1 nm gate oxide, designed to capture and detect target biomolecules efficiently. To investigate its performance, a two-dimensional simulation is conducted using technology computer-aided design (TCAD) software, enabling thorough parameter exploration. The triplex material composition contributes to low power consumption and high device sensitivity, making the biosensor a promising candidate for various biomolecular detection applications. Analytical insights were derived and validated through Sentaurus TCAD simulation results. The proposed device exhibits distinct electrical responses to biomolecules of different dielectric constants and charge states, such as DNA and proteins. Selective detection can be realized through receptor functionalization of the nanocavity surface.


Corresponding author: Preethi Srinivasan, Centre of Excellence for Additive Manufacturing, Sathyabama Institute of Science and Technology, Chennai, India, E-mail:

Acknowledgement

The authors are grateful to the MAKERSPACE, Centre of Excellence for Additive Manufacturing, Sathyabama Institute of Science and Technology, Chennai for the support extended to carry out this research work.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-06-26
Accepted: 2025-10-31
Published Online: 2025-11-18

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 20.11.2025 from https://www.degruyterbrill.com/document/doi/10.1515/zpch-2025-0092/pdf
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