Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors

Bian, Long; Sorescu, Dan C.; Chen, Lucy; White, David L.; Burkert, Seth C.; Khalifa, Yassin; Zhang, Zhenwei; Sejdic, Ervin; Star, Alexander

doi:10.1021/acsami.8b15785

Machine-Learning Identification of the Sensing Descriptors Relevant in Molecular Interactions with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors

Journal Article · Fri Dec 14 00:00:00 EST 2018 · ACS Applied Materials and Interfaces

DOI:https://doi.org/10.1021/acsami.8b15785· OSTI ID:1532660

Bian, Long ^[1]; Sorescu, Dan C. ^[1]; Chen, Lucy ^[1]; White, David L. ^[1]; Burkert, Seth C. ^[1]; Khalifa, Yassin ^[1]; Zhang, Zhenwei ^[1]; Sejdic, Ervin ^[1]; ^[1]

Univ. of Pittsburgh, PA (United States)

We report that carbon nanotube-based field-effect transistors (NTFETs) are ideal sensor devices as they provide rich information regarding carbon nanotube interactions with target analytes and have potential for miniaturization in diverse applications in medical, safety, environmental, and energy sectors. Herein, we investigate chemical detection with cross-sensitive NTFETs sensor arrays comprised of metal nanoparticle-decorated single-walled carbon nanotubes (SWCNTs). By combining analysis of NTFET device characteristics with supervised machine-learning algorithms, we have successfully discriminated among five selected purine compounds, adenine, guanine, xanthine, uric acid, and caffeine. Interactions of purine compounds with metal nanoparticle-decorated SWCNTs were corroborated by density functional theory calculations. Furthermore, by testing a variety of prepared as well as commercial solutions with and without caffeine, our approach accurately discerns the presence of caffeine in 95% of the samples with 48 features using a linear discriminant analysis and in 93.4% of the samples with only 11 features when using a support vector machine analysis. Lastly, we also performed recursive feature elimination and identified three NTFET parameters, transconductance, threshold voltage, and minimum conductance, as the most crucial features to analyte prediction accuracy.

View Accepted Manuscript (DOE)

Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE)

OSTI ID:: 1532660

Report Number(s):: NETL-PUB-21938

Journal Information:: ACS Applied Materials and Interfaces, Journal Name: ACS Applied Materials and Interfaces Journal Issue: 1 Vol. 11; ISSN 1944-8244

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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