Nanoelectronic Discrimination of Nonmalignant and Malignant Cells Using Nanotube Field-Effect Transistors

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors

The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid-related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug-testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)-decorated single-walled carbon nanotube (SWCNT)-based field-effect transistors (FETs) are utilized for machine learning-assisted identification of fentanyl from codeine, hydrocodone, and morphine. The unique sensing performance of fentanyl led to use machine learning approaches for accurate identification of fentanyl. Employing linear discriminant analysis (LDA) with a leave-one-out cross-validation approach, a validation accuracy of 91.2% is achieved. Meanwhile, density functional theory (DFT) calculations reveal the factors that contributed to the enhanced sensitivity of the Au-SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au-SWCNT FET sensor as specific receptors, expanding the linear range of the sensor in the lower concentration range, and enabling ultrasensitive detection of fentanyl with a limit of detection at 10.8 fg mL-1.

Size-Based Norfentanyl Detection with SWCNT@UiO-MOF Composites

Single-walled carbon nanotube (SWCNT)@metal-organic framework (MOF) field-effect transistor (FET) sensors generate a signal through analytes restricting ion diffusion around the SWCNT surface. Four composites made up of SWCNTs and UiO-66, UiO-66-NH2, UiO-67, and UiO-67-CH3 were synthesized to explore the detection of norfentanyl (NF) using SWCNT@MOF FET sensors with different pore sizes. Liquid-gated FET devices of SWCNT@UiO-67 showed the highest sensing response toward NF, whereas SWCNT@UiO-66 and SWCNT@UiO-66-NH2 devices showed no sensitivity improvement compared to bare SWCNT. Comparing SWCNT@UiO-67 and SWCNT@UiO-67-CH3 indicated that the sensing response is modulated by not only the size-matching between NF and MOF channel but also NF diffusion within the MOF channel. Additionally, other drug metabolites, including norhydrocodone (NH), benzoylecgonine (BZ), and normorphine (NM) were tested with the SWCNT@UiO-67 sensor. The sensor was not responding toward NH and or BZ but a similar sensing result toward NM because NM has a similar size to NF. The SWCNT@MOF FET sensor can avoid interference from bigger molecules but sensor arrays with different pore sizes and chemistries are needed to improve the specificity.

Sensors Based on the Carbon Nanotube Field-Effect Transistors for Chemical and Biological Analyses

Nano biochemical sensors play an important role in detecting the biomarkers related to human diseases, and carbon nanotubes (CNTs) have become an important factor in promoting the vigorous development of this field due to their special structure and excellent electronic properties. This paper focuses on applying carbon nanotube field-effect transistor (CNT-FET) biochemical sensors to detect biomarkers. Firstly, the preparation method, physical and electronic properties and functional modification of CNTs are introduced. Then, the configuration and sensing mechanism of CNT-FETs are introduced. Finally, the latest progress in detecting nucleic acids, proteins, cells, gases and ions based on CNT-FET sensors is summarized.

Bacterial Vaginosis Monitoring with Carbon Nanotube Field-Effect Transistors

The ability to rapidly and reliably screen for bacterial vaginosis (BV) during pregnancy is of great significance for maternal health and pregnancy outcomes. In this proof-of-concept study, we demonstrated the potential of carbon nanotube field-effect transistors (NTFET) in the rapid diagnostics of BV with the sensing of BV-related factors such as pH and biogenic amines. The fabricated sensors showed good linearity to pH changes with a linear correlation coefficient of 0.99. The pH sensing performance was stable after more than one month of sensor storage. In addition, the sensor was able to classify BV-related biogenic amine-negative/positive samples with machine learning, utilizing different test strategies and algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), and principal component analysis (PCA). The biogenic amine sample status could be well classified using a soft-margin SVM model with a validation accuracy of 87.5%. The accuracy could be further improved using a gold gate electrode for measurement, with accuracy higher than 90% in both LDA and SVM models. We also explored the sensing mechanisms and found that the change in NTFET off current was crucial for classification. The fabricated sensors successfully detect BV-related factors, demonstrating the competitive advantage of NTFET for point-of-care diagnostics of BV.

A Carbon Nanotube Sensor Array for the Label-Free Discrimination of Live and Dead Cells with Machine Learning

Developing robust cell recognition strategies is important in biochemical research, but the lack of well-defined target molecules creates a bottleneck in some applications. In this paper, a carbon nanotube sensor array was constructed for the label-free discrimination of live and dead mammalian cells. Three types of carbon nanotube field-effect transistors were fabricated, and different features were extracted from the transfer characteristic curves for model training with linear discriminant analysis (LDA) and support-vector machines (SVM). Live and dead cells were accurately classified in more than 90% of samples in each sensor group using LDA as the algorithm. The recursive feature elimination with cross-validation (RFECV) method was applied to handle the overfitting and optimize the model, and cells could be successfully classified with as few as four features and a higher validation accuracy (up to 97.9%) after model optimization. The RFECV method also revealed the crucial features in the classification, indicating the participation of different sensing mechanisms in the classification. Finally, the optimized LDA model was applied for the prediction of unknown samples with an accuracy of 87.5-93.8%, indicating that live and dead cell samples could be well-recognized with the constructed model.

Development of pattern recognition based on nanosheet-DNA probes and an extendable DNA library

Pattern recognition, also called "array sensing," is a recognition strategy with a wide and expandable analysis range, based on high-throughput analysis data. In this work, we constructed a sensor array for the identification of targets including bacterial pathogens and proteins by using FAM-labeled DNA probes and 2D nanosheet materials. We designed an ordered and extendible DNA library for the collection of recognition probes. Unlike traditional DNA probes with random and massive sequences, our DNA library was constructed following a 5-digit binary number (00000-11111, 0 = CCC, and 1 = TTT), and especially, 8 special symmetry sequences were chosen from the library. Two different nanosheet materials were used as the quencher. When targets were added, the interaction between DNA and the nanosheets was competitively affected, and as a result, the fluorescence signal changed accordingly. Finally, by using our fluorescent sensor array, 17 bacteria and 8 proteins were precisely recognized. We believe that our work has provided a simple and valuable strategy for the improvement of the recognition range and discrimination precision for the development of pattern recognition.

Portable Analytical Techniques for Monitoring Volatile Organic Chemicals in Biomanufacturing Processes: Recent Advances and Limitations

It is essential to develop effective analytical techniques for accurate and continuous monitoring of various biomanufacturing processes, such as the production of monoclonal antibodies and vaccines, through sensitive and quantitative detection of characteristic aqueous or gaseous metabolites and other analytes in the cell culture media. A comprehensive summary toward the use of mainstream techniques for bioprocess monitoring is critically reviewed here, which illustrates the instrumental and procedural advances and limitations of several major analytical tools in biomanufacturing applications. Despite those drawbacks present in modern detection systems such as mass spectrometry, gas chromatography or chemical/biological sensors, a considerable number of useful solutions and inspirations such as electronic or optoelectronic noses can be offered to greatly overcome the restrictions and facilitate the development of advanced analytical techniques that can target a more diverse range of key nutritious components, products or potential contaminants in different biomanufacturing processes.

Ion-Selective Carbon Nanotube Field-Effect Transistors for Monitoring Drug Effects on Nicotinic Acetylcholine Receptor Activation in Live Cells

We developed ion-selective field-effect transistor (FET) sensors with floating electrodes for the monitoring of the potassium ion release by the stimulation of nicotinic acetylcholine receptors (nAChRs) on PC12 cells. Here, ion-selective valinomycin-polyvinyl chloride (PVC) membranes were coated on the floating electrode-based carbon nanotube (CNT) FETs to build the sensors. The sensors could selectively measure potassium ions with a minimum detection limit of 1 nM. We utilized the sensor for the real-time monitoring of the potassium ion released from a live cell stimulated by nicotine. Notably, this method also allowed us to quantitatively monitor the cell responses by agonists and antagonists of nAChRs. These results suggest that our ion-selective CNT-FET sensor has potential uses in biological and medical researches such as the monitoring of ion-channel activity and the screening of drugs.

Universal DNA detection realized by peptide based carbon nanotube biosensors

Although DNA recognition has been achieved using numerous biosensors with various sensing probes, the utilization of bio-interaction between DNA and biomolecules has seldom been reported in universal DNA detection. Peptides as natural molecules have the unique ability to bind to universal DNA and excellent selectivity for DNA after being functionalized with specific groups. In this work, we report a peptide based carbon nanotube (CNT) thin-film-transistor (TFT) biosensor, which can achieve sensitive sequence-independent DNA detection. In the presence of DNA, a significant increase of ΔIon could be observed within 5 minutes, which was considered to be due to the electrostatic adsorption between the DNA and peptide of opposite zeta potential. With the gradual increase of the concentration of DNA, the ΔIon signals agree with the Hill-Langmuir model (R 2 = 0.98), indicating a negatively cooperative interaction between the peptide and DNA (the Hill coefficient n < 1). Compared with the former reported universal DNA bio-detector and NanoDrop (a spectrometer from Thermo Scientific™), this unique peptide based CNT-DNA sensor demonstrated a broader sensing range from nearly 1.6 × 10-4 to 5 μmol L-1 and a much lower detection limit of approximately 0.88 μg L-1. For the quantification of cDNA from T47D cancer cells, this unique peptide based CNT sensor could achieve efficient cDNA detection. To the best of our knowledge, this is the first report on the utilization of a peptide as a sensing element in the design of CNT based DNA biosensors, which enables highly efficient universal DNA detection.

Chemiresistive Sensor Array and Machine Learning Classification of Food

Successful identification of complex odors by sensor arrays remains a challenging problem. Herein, we report robust, category-specific multiclass-time series classification using an array of 20 carbon nanotube-based chemical sensors. We differentiate between samples of cheese, liquor, and edible oil based on their odor. In a two-stage machine-learning approach, we first obtain an optimal subset of sensors specific to each category and then validate this subset using an independent and expanded data set. We determined the optimal selectors via independent selector classification accuracy, as well as a combinatorial scan of all 4845 possible four selector combinations. We performed sample classification using two models-a k-nearest neighbors model and a random forest model trained on extracted features. This protocol led to high classification accuracy in the independent test sets for five cheese and five liquor samples (accuracies of 91% and 78%, respectively) and only a slightly lower (73%) accuracy on a five edible oil data set.

Tetrahydrocannabinol Detection Using Semiconductor-Enriched Single-Walled Carbon Nanotube Chemiresistors

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.

Probing Ca2+-induced conformational change of calmodulin with gold nanoparticle-decorated single-walled carbon nanotube field-effect transistors

Nanomaterials are ideal for electrochemical biosensors, with their nanoscale dimensions enabling the sensitive probing of biomolecular interactions. In this study, we compare field-effect transistors (FET) comprised of unsorted (un-) and semiconducting-enriched (sc-) single-walled carbon nanotubes (SWCNTs). un-SWCNTs have both metallic and semiconducting SWCNTs in the ensemble, while sc-SWCNTs have a >99.9% purity of semiconducting nanotubes. Both SWCNT FET devices were decorated with gold nanoparticles (AuNPs) and were then employed in investigating the Ca²⁺-induced conformational change of calmodulin (CaM) - a vital process in calcium signal transduction in the human body. Different biosensing behavior was observed from FET characteristics of the two types of SWCNTs, with sc-SWCNT FET devices displaying better sensing performance with a dynamic range from 10^-15 M to 10^-13 M Ca²⁺, and a lower limit of detection at 10^-15 M Ca²⁺.

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

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.

Carbon Nanotube Chemical Sensors

Carbon nanotubes (CNTs) promise to advance a number of real-world technologies. Of these applications, they are particularly attractive for uses in chemical sensors for environmental and health monitoring. However, chemical sensors based on CNTs are often lacking in selectivity, and the elucidation of their sensing mechanisms remains challenging. This review is a comprehensive description of the parameters that give rise to the sensing capabilities of CNT-based sensors and the application of CNT-based devices in chemical sensing. This review begins with the discussion of the sensing mechanisms in CNT-based devices, the chemical methods of CNT functionalization, architectures of sensors, performance parameters, and theoretical models used to describe CNT sensors. It then discusses the expansive applications of CNT-based sensors to multiple areas including environmental monitoring, food and agriculture applications, biological sensors, and national security. The discussion of each analyte focuses on the strategies used to impart selectivity and the molecular interactions between the selector and the analyte. Finally, the review concludes with a brief outlook over future developments in the field of chemical sensors and their prospects for commercialization.