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Updated: Jan 9, 2026

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Machine Learning-Augmented Graphene Transistor Biosensing: Quantitative Platform Validation and Immunotesting of

Sofia Albesa1,2, Ezequiel Giménez1,2, Jose M Piccinini2

  • 1Institute of Theoretical and Applied Physical Chemistry Research (INIFTA), Department of Chemistry, Faculty of Exact Sciences, National University of La Plata (UNLP), CONICET. Street 64 and 113, La Plata 1900, Buenos Aires, Argentina.

ACS Sensors
|December 3, 2025
PubMed
Summary

Machine learning (ML) integrated with graphene field-effect transistors (GFETs) overcomes sensor variability for accurate, calibration-free analysis. This advancement improves diagnostic accuracy for conditions like cystic fibrosis and Hepatitis E virus infection.

Keywords:
Hepatitis Egraphene-based biosensingimmunotestingmachine learning algorithmnanobody funcionalization

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Area of Science:

  • Materials Science
  • Biotechnology
  • Data Science

Background:

  • Graphene-based chips exhibit sensor variability from manufacturing defects and contamination, hindering reliable healthcare diagnostics.
  • Existing analytical methods often require calibration and struggle with diverse analytes.

Purpose of the Study:

  • To develop a machine learning (ML) model integrated with graphene field-effect transistors (GFETs) for quantitative, calibration-free sensing.
  • To enhance the analytical reliability and diagnostic capabilities of graphene-based sensors.

Main Methods:

  • Utilized Random Forest Regression and field-effect metrics to build an ML model for GFETs.
  • Validated the ML-augmented platform using pH sensing as a reference.
  • Applied ML-integrated GFETs for chloride detection and Hepatitis E virus (HEV) antigen detection using llama nanobodies.

Main Results:

  • Achieved a significant improvement in accuracy (93% to 97%) and reduced coefficient of variability (14% to 3%) for pH sensing.
  • Demonstrated enhanced immunoassay sensitivity-specificity from 89-69% to 100-100% for HEV antigen detection.
  • Enabled quantitative prediction of HEV antigen concentration in capillary blood samples without pretreatment.

Conclusions:

  • ML integration with GFETs provides a robust solution for calibration-free, quantitative sensing.
  • The developed platform shows high potential for accurate clinical diagnostics, including cystic fibrosis and viral infections.
  • This approach enhances GFET performance, enabling sensitive and specific detection of biomarkers in complex samples.