Machine-Learning
Identification of the Sensing Descriptors
Relevant in Molecular Interactions
with Metal Nanoparticle-Decorated Nanotube Field-Effect Transistors
posted on 2018-12-14, 00:00authored byLong Bian, Dan C. Sorescu, Lucy Chen, David L. White, Seth C. Burkert, Yassin Khalifa, Zhenwei Zhang, Ervin Sejdic, Alexander Star
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. 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.