SynCells: A 60 × 60 µm2 Electronic Platform with Remote Actuation for Sensing Applications in Constrained Environments.

Autonomous electronic microsystems smaller than the diameter of a human hair (<100 µm) are promising for sensing in confined spaces such as microfluidic channels or the human body. However, they are difficult to implement due to fabrication challenges and limited power budget. Here we present a 60 × 60 µm electronic microsystem platform, or SynCell, that overcomes these issues by leveraging the integration capabilities of two-dimensional material circuits and the low power consumption of passive germanium timers, memory-like chemical sensors, and magnetic pads. In a proof-of-concept experiment, we magnetically positioned SynCells in a microfluidic channel to detect putrescine. After we extracted them from the channel, we successfully read out the timer and sensor signal, the latter of which can be amplified by an onboard transistor circuit. The concepts developed here will be applicable to microsystems targeting a variety of applications from microfluidic sensing to biomedical research.

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.

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.

Ionic Liquid-Carbon Nanotube Sensor Arrays for Human Breath Related Volatile Organic Compounds.

High sensitivity, selectivity, and stability are key requirements for carbon nanotube (CNT)-based sensors to realize their full potential in applications ranging from chemical warfare agent detection to disease diagnostics. Herein we demonstrate the sensing of volatile organic compounds (VOCs) relevant to human diseases using an array of chemiresistive carbon nanotube (CNT)-based sensors functionalized with ionic liquids (ILs). The ILs are fluid at ambient temperature and were selected to produce a discriminating sensor array capable of the gas-phase detection of human disease-related VOCs. We find that sensor arrays consisting of imidazolium-based ILs with different substituents and counterions provide selective responses for known biomarkers of infectious diseases of the lungs. Specifically, the sensors discriminate the various volatile biomarkers for tuberculosis based on their polarity, solubility, and chemical affinities. In addition to selectivity, the sensors also show a high level of reversibility and promising long-term stability, which renders them to be suitable candidates for practical applications in breath analysis.

Translating Catalysis to Chemiresistive Sensing.

Activating molecules or functional groups with high chemoselectivity in complex environments is the central goal of transition-metal-based catalysis. Promoting strong interactions between a selected substrate and a catalytic system can also be used to create highly selective and customizable sensors, and these concepts are widely recognized for enzymatic processes. We demonstrate the successful translation of organometallic reactions to sensing capability. Specifically, we have developed single-walled carbon nanotube (SWCNT) chemiresistive sensors for the highly selective detection of acrylates using conditions for the aerobic oxidative Heck reaction. The sensors mirror the catalytic processes and selectively respond to electron-deficient alkenes by adapting a catalytic reaction system to modulate the doping levels in carbon nanotubes. The sensors readily detect acrylates at parts per million (ppm) levels in untreated air. The concepts presented here are generally applicable and can guide future sensor development based upon known catalytic processes.

Chemiresistive Graphene Sensors for Ammonia Detection.

The primary objective of this work is to demonstrate a novel sensor system as a convenient vehicle for scaled-up repeatability and the kinetic analysis of a pixelated testbed. This work presents a sensor system capable of measuring hundreds of functionalized graphene sensors in a rapid and convenient fashion. The sensor system makes use of a novel array architecture requiring only one sensor per pixel and no selector transistor. The sensor system is employed specifically for the evaluation of Co(tpfpp)ClO4 functionalization of graphene sensors for the detection of ammonia as an extension of previous work. Co(tpfpp)ClO4 treated graphene sensors were found to provide 4-fold increased ammonia sensitivity over pristine graphene sensors. Sensors were also found to exhibit excellent selectivity over interfering compounds such as water and common organic solvents. The ability to monitor a large sensor array with 160 pixels provides insights into performance variations and reproducibility-critical factors in the development of practical sensor systems. All sensors exhibit the same linearly related responses with variations in response exhibiting Gaussian distributions, a key finding for variation modeling and quality engineering purposes. The mean correlation coefficient between sensor responses was found to be 0.999 indicating highly consistent sensor responses and excellent reproducibility of Co(tpfpp)ClO4 functionalization. A detailed kinetic model is developed to describe sensor response profiles. The model consists of two adsorption mechanisms-one reversible and one irreversible-and is shown capable of fitting experimental data with a mean percent error of 0.01%.

Bio-Inspired Carbon Monoxide Sensors with Voltage-Activated Sensitivity.

Carbon monoxide (CO) outcompetes oxygen when binding to the iron center of hemeproteins, leading to a reduction in blood oxygen level and acute poisoning. Harvesting the strong specific interaction between CO and the iron porphyrin provides a highly selective and customizable sensor. We report the development of chemiresistive sensors with voltage-activated sensitivity for the detection of CO comprising iron porphyrin and functionalized single-walled carbon nanotubes (F-SWCNTs). Modulation of the gate voltage offers a predicted extra dimension for sensing. Specifically, the sensors show a significant increase in sensitivity toward CO when negative gate voltage is applied. The dosimetric sensors are selective to ppm levels of CO and functional in air. UV/Vis spectroscopy, differential pulse voltammetry, and density functional theory reveal that the in situ reduction of FeIII to FeII enhances the interaction between the F-SWCNTs and CO. Our results illustrate a new mode of sensors wherein redox active recognition units are voltage-activated to give enhanced and highly specific responses.