ArticleMicrosystems & nanoengineering2023
Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions.
Article in Microsystems & nanoengineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Graphene-Based Biosensors: Enabling the Next Generation of Diagnostic Technologies-A Review.Biosensors · 2025Review
- Hybrid Series of Carbon-Vacancy Electrodes for Multi Chemical Vapors Diagnosis Using a Residual Multi-Task Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Identification of Gas Mixture Components with Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data.ACS sensors · 2025Article
- Recent advances in graphene-based electroanalytical devices for healthcare applications.Nanoscale · 2024Review
- AI-Driven Sensing Technology: Review.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
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Abstract
This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for "breath chemovapor fingerprinting" for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.
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