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Updated: Jan 15, 2026

Development and Functionalization of Electrolyte-Gated Graphene Field-Effect Transistor for Biomarker Detection

Development and Functionalization of Electrolyte-Gated Graphene Field-Effect Transistor for Biomarker Detection

Published on: February 1, 2022

Bridging Nanostructure Growth and Gas-Sensing Kinetics in Metal-Functionalized Graphene Using Machine-Learned

Akram Ibrahim¹, Ahmed M Hafez^2,3, Mahmooda Sultana⁴

¹Department of Physics, University of Maryland Baltimore County, Baltimore, Maryland 21250, United States.

ACS Applied Materials & Interfaces

|January 14, 2026

Summary

This summary is machine-generated.

Machine learning potentials enable molecular dynamics simulations for platinum-functionalized graphene gas sensors. This approach links synthesis to performance, revealing how nanostructure morphology impacts hydrogen detection limits and kinetics.

Keywords:

Pt nanostructure growth adsorption/desorption kinetics graphene functionalization hydrogen sensing machine-learned interatomic potentials reactive molecular dynamics

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Area of Science:

Materials Science
Nanotechnology
Computational Chemistry

Background:

Graphene functionalized with catalytic transition metals shows promise for chemiresistive gas sensing.
Understanding the atomistic links between synthesis, morphology, and gas-surface reaction kinetics is crucial but challenging.

Purpose of the Study:

To develop and apply an equivariant machine-learning interatomic potential for high-fidelity molecular dynamics simulations.
To investigate the synthesis, morphology, and hydrogen (H2) gas sensing mechanism of platinum (Pt)-functionalized graphene.

Main Methods:

Developed an equivariant machine-learning interatomic potential with DFT accuracy.
Performed molecular dynamics (MD) simulations for Pt nanostructure growth on graphene and H2 sensing kinetics.
Validated simulations with Transmission Electron Microscopy (TEM) and Raman spectroscopy.

Main Results:

Pt deposition leads to polycrystalline nanoclusters with predominantly noncovalent graphene interactions, causing moderate strain and doping.
H2 dissociative chemisorption and recombinative desorption occur primarily on Pt nanoclusters.
H adsorption on Pt weakens interfacial binding, creating an indirect electronic sensing pathway; intermediate metal loading optimizes detection limits.

Conclusions:

The machine-learned MD framework accurately models nanostructure growth and elucidates the gas-sensing mechanism.
Established a multiscale predictive pipeline correlating synthesis conditions, nanostructure morphology, and gas sensor performance metrics like detection limit and kinetics.