Semiconductor-enriched
single-walled carbon nanotubes (s-SWCNTs)
have potential for application as a chemiresistor for the detection
of breath compounds, including tetrahydrocannabinol (THC), the main
psychoactive compound found in the marijuana plant. Herein we show
that chemiresistor devices fabricated from s-SWCNT ink using dielectrophoresis
can be incorporated into a hand-held breathalyzer with sensitivity
toward THC generated from a bubbler containing analytical standard
in ethanol and a heated sample evaporator that releases compounds
from steel wool. The steel wool was used to capture THC from exhaled
marijuana smoke. The generation of the THC from the bubbler and heated
breath sample chamber was confirmed using ultraviolet–visible
absorption spectroscopy and mass spectrometry, respectively. Enhanced
selectivity toward THC over more volatile breath components such as
CO2, water, ethanol, methanol, and acetone was achieved
by delaying the sensor reading to allow for the desorption of these
compounds from the chemiresistor surface. Additionally, machine learning
algorithms were utilized to improve the selective detection of THC
with better accuracy at increasing quantities of THC delivered to
the chemiresistor.
Detection of malignant cells in tissue is a difficult hurdle in medical diagnostics and screening. Carbon nanotubes are extremely sensitive to their local environments, and nanotube-based field-effect transistors (NTFETs) provide a plethora of information regarding the mechanism of interaction with target analytes. Herein, we use a series of functionalized metal nanoparticle-decorated NTFET devices forming an array with multiple nonselective sensor units as the electronic "tongue", sensing all five basic tastes. By extraction of selected NTFET characteristics and using linear discriminant analysis, we have successfully detected and discriminated between malignant and nonmalignant tissues and cells. We also studied the sensing mechanism and what NTFET characteristics are responsible for the variation of response between cell types, allowing for the design of future studies such as detection of malignant cells in a biopsy or the effects of malignant cells on healthy tissue.
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.
Non-invasive detection and quantification of the stress hormone cortisol not only provides an assessment of stress level but also enables close monitoring of mental and physical health. Here, we report two types of field-effect transistors (FETs) based on semiconducting single-walled carbon nanotubes (sc-SWCNTs) as selective cortisol sensors. In one FET device configuration, cortisol antibody is directly attached to sc-SWCNTs. In the other, gold nanoparticles (Au NPs) are used as linkers between the antibody and the sc-SWCNTs to enhance the device conductance. We fabricated and characterized both device configurations to investigate how the nanomaterial interface to cortisol antibody influences the biosensor performance. We tested the sensors in artificial sweat and compared these two types of sensors in terms of limit of detection and sensitivity, and the results indicate that direct binding between antibody and sc-SWCNTs yields better biosensor characteristics.
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