Designing Bimetallic Sensors for Acetone Biomarker Detection

Area of Science:

• Computational chemistry
• Materials science
• Nanotechnology

Background:

• Volatile organic compounds (VOCs) in exhaled breath offer a non-invasive method for lung disease diagnosis.
• Current diagnostic methods require improvement in speed and accuracy.
• Developing sensitive and selective biosensors is crucial for early disease detection.

Purpose of the Study:

• To computationally design and investigate novel bimetallic biosensors for detecting acetone, a lung cancer biomarker.
• To evaluate the performance of bimetallic sensors based on Fe2N5P/C60 fullerene.
• To explore the synergistic effects of dual doping on sensor sensitivity and selectivity.

Main Methods:

• Density Functional Theory (DFT) calculations were employed to study gas molecule adsorption.
• Investigated adsorption of acetone and interfering molecules (N2, CO2, H2O) on Fe2N5P/C60 and bimetallic analogues (FeCoN5P/C60, FeNiN5P/C60, FeCuN5P/C60, FeZnN5P/C60).
• Analyzed adsorption energies, work functions, and recovery times to assess sensor performance.

Main Results:

• The FeCuN5P/C60 bimetallic system exhibited a superior synergistic effect for acetone detection.
• Acetone adsorption energy increased on FeCuN5P/C60 in the presence of interfering molecules.
• All designed sensors showed sensitivity to acetone, indicated by work function changes.
• Fe2N5P-based biosensors demonstrated relatively fast recovery times.

Conclusions:

• Bimetallic fullerene-based biosensors, particularly FeCuN5P/C60, show significant promise for sensitive and selective detection of lung cancer biomarkers.
• Computational design using DFT is effective for developing advanced gas sensors.
• These findings pave the way for rapid, non-invasive breath analysis for lung disease diagnosis.