Selective Identification of Hazardous Gases Using Flexible, Room-Temperature Operable Sensor Array Based on Reduced Graphene Oxide and Metal Oxide Nanoparticles via Machine Learning.

Selective detection and monitoring of hazardous gases with similar properties are highly desirable to ensure human safety. The development of flexible and room-temperature (RT) operable chemiresistive gas sensors provides an excellent opportunity to create wearable devices for detecting hazardous gases surrounding us. However, chemiresistive gas sensors typically suffer from poor selectivity and zero-cross selectivity toward similar types of gases. Herein, a flexible, RT operable chemiresistive gas sensors array is designed, featuring reduced graphene oxide (rGO) and rGO decorated with zinc oxide (ZnO), titanium dioxide (TiO2), and tin dioxide (SnO2) nanoparticles (NPs) on a flexible polyimide (PI) substrate. The sensor array consists of four different sensing layers capable of the selective identification of various hazardous gases such as NO2, NO, and SO2 using machine learning (ML). The gas sensor array exhibits a stable response even when mechanically deformed or exposed to high humidity (up to 60%). Each gas sensor, due to the different metal oxide NPs, shows unique responses in terms of sensitivity, responsiveness, response time, and recovery time to different gases. Consequently, the sensor array generates distinct response patterns that effectively differentiate between the target gases. By leveraging these distinctive recovery patterns and employing a data fusion approach in ML, specific concentrations of target gases can be distinguished. Using ML with fused array sensing data, the training and test accuracies achieved were 98.20 and 97.70%, respectively. This innovative combination of sensor arrays and ML offers significant potential for selective gas detection in environmental monitoring and personal safety applications.

Utilizing a High-Performance Piezoelectric Nanocomposite as a Self-Activating Component in Piezotronic Artificial Mechanoreceptors.

Challenges such as poor dispersion and insufficient polarization of BaTiO3 (BTO) nanoparticles (NPs) within poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)) composites have hindered their piezoelectricity, limiting their uses in pressure sensors, nanogenerators, and artificial sensory synapses. Here, we introduce a high-performance piezoelectric nanocomposite material consisting of P(VDF-TrFE)/modified-BTO (mBTO) NPs for use as a self-activating component in a piezotronic artificial mechanoreceptor. To generate high-performance piezoelectric nanocomposite materials, the surface of BTO is hydroxylated, followed by the covalent attachment of (3-aminopropyl)triethoxysilane to improve the dispersibility of mBTO NPs within the P(VDF-TrFE) matrix. We also aim to enhance the crystallization degree of P(VDF-TrFE), the efficiency characteristics of mBTO, and the poling efficiency, even when incorporating small amounts of mBTO NPs. The piezoelectric potential mechanically induced from the P(VDF-TrFE)/mBTO NPs nanocomposite was three times greater than that from P(VDF-TrFE) and twice as high as that from the P(VDF-TrFE)/BTO NPs nanocomposite. The piezoelectric potential generated by mechanical stimuli on the piezoelectric nanocomposite was utilized to activate the synaptic ionogel-gated field-effect transistor for the development of self-powered piezotronics artificial mechanoreceptors on a polyimide substrate. The device successfully emulated fast-adapting (FA) functions found in biological FA mechanoreceptors. This approach has great potential for applications to future intelligent tactile perception technology.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception.

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Metal-enhanced fluorescence biosensor integrated in capillary flow-driven microfluidic cartridge for highly sensitive immunoassays.

Point-of-care testing (POCT) for low-concentration protein biomarkers remains challenging due to limitations in biosensor sensitivity and platform integration. This study addresses this gap by presenting a novel approach that integrates a metal-enhanced fluorescence (MEF) biosensor within a capillary flow-driven microfluidic cartridge (CFMC) for the ultrasensitive detection of the Parkinson's disease biomarker, aminoacyl-tRNA synthetase complex interacting multi-functional protein 2 (AIMP-2). Crucial point to this approach is the orientation-controlled immobilization of capture antibody on a nanodimple-structured MEF substrate within the CFMC. This strategy significantly enhances fluorescence signals without quenching, enabling accurate quantification of low-concentration AIMP-2 using a simple digital fluorescence microscope with a light-emitting diode excitation source and a digital camera. The resulting platform exhibits exceptional sensitivity, achieving a limit of detection in the pg/mL range for AIMP-2 in human serum. Additionally, the CFMC design incorporates a capillary-driven passive sample transport mechanism, eliminating the need for external pumps and further simplifying the detection process. Overall, this work demonstrates the successful integration of MEF biosensing with capillary microfluidics for point-of-care applications.

Co2+/PMS based sulfate-radical treatment for effective mineralization of spent ion exchange resin.

Sulfate radical advance oxidation processes (SR-AOPs) have attracted a greater attention as a suitable alternative of the hydroxyl radical based advance oxidation process (HR-AOPs). In this study, for the first time we report liquid phase mineralization of nuclear grade cationic IRN-77 resin in Co2+/peroxymonosulfate (PMS) based SR-AOPs. After the dissolution of cationic IRN-77 resin, 30 volatile and 15 semi-volatile organic compounds were analyzed/detected using non-targeted GC-MS analysis. The optimal reaction parameters for the highest chemical oxygen demand (COD) removal (%) of IRN-77 resin were determined, and the initial pH, PMS dosage, and reaction temperature were found to be the most influential parameters for the resin degradation. We successfully achieved ∼90% COD removal (1000 mg/L; 1000 ppm) of dissolved spent resin for SR-AOPs by optimizing the reaction parameters as initial pH = 9, Co2+ = 4 mM (catalyst), PMS = 60 mM (as oxidant) at 60 °C temperature for 60 min reaction. The electron spin resonance spectroscopy (ESR) spectra confirmed the presence of SO4∙- and OH∙ as main reactive species in the Co2+/PMS resin system. In addition, Fourier transform infrared spectroscopy (FT-IR) analyses were used for structural characterization of solid and liquid phase resin samples. We believe that this work will offer a robust approach for the effective treatment of spent resin generated from nuclear industry.