ABSTRACT
In this study, a room-temperature ammonia gas sensor using a ZnO and reduced graphene oxide (rGO) composite is developed. The sensor fabrication involved the innovative application of reverse offset and electrostatic spray deposition (ESD) techniques to create a ZnO/rGO sensing platform. The structural and chemical characteristics of the resulting material were comprehensively analyzed using XRD, FT-IR, FESEM, EDS, and XPS, and rGO reduction was achieved via UV-ozone treatment. Electrical properties were assessed through I-V curves, demonstrating enhanced conductivity due to UV-ozone treatment and improved charge mobility from the formation of a ZnO-rGO heterojunction. Exposure to ammonia gas resulted in increased sensor responsiveness, with longer UV-ozone treatment durations yielding superior sensitivity. Furthermore, response and recovery times were measured, with the 10 min UV-ozone-treated sensor displaying optimal responsiveness. Performance evaluation revealed linear responsiveness to ammonia concentration with a high R2 value. The sensor also exhibited exceptional selectivity for ammonia compared to acetone and CO gases, making it a promising candidate for ammonia gas detection. This study shows the outstanding performance and potential applications of the ZnO/rGO-based ammonia gas sensor, promising significant contributions to the field of gas detection.
ABSTRACT
The performance, stability, and lifespan of lithium-ion batteries are influenced by variations in the flow of lithium ions with temperature. In electric vehicles, coolants are generally used to maintain the optimal temperature of the battery, leading to an increasing demand for temperature and humidity sensors that can prevent leakage and short circuits. In this study, humidity and temperature sensors were fabricated on a pouch film of a pouch-type battery. IDE electrodes were screen-printed on the pouch film and humidity- and temperature-sensing materials were printed using a dispenser process. Changes in the capacitance of the printed Ag-CNF film were used for humidity sensing, while changes in the resistance of the printed PEDOT:PSS film were used for temperature sensing. The two sensors were integrated into a single electrode for performance evaluation. The integrated sensor exhibited a response of ΔR ≈ 0.14 to temperature variations from 20 °C to 100 °C with 20% RH humidity as a reference, and a response of ΔC ≈ 2.8 to relative humidity changes from 20% RH to 80% RH at 20 °C. The fabricated integrated sensor is expected to contribute to efficient temperature and humidity monitoring applications in various pouch-type lithium-ion batteries.
ABSTRACT
A mnemonic-opto-synaptic transistor (MOST) that has triple functions is demonstrated for an in-sensor vision system. It memorizes a photoresponsivity that corresponds to a synaptic weight as a memory cell, senses light as a photodetector, and performs weight updates as a synapse for machine vision with an artificial neural network (ANN). Herein the memory function added to a previous photodetecting device combined with a photodetector and a synapse provides a technical breakthrough for realizing in-sensor processing that is able to perform image sensing and signal processing in a sensor. A charge trap layer (CTL) was intercalated to gate dielectrics of a vertical pillar-shaped transistor for the memory function. Weight memorized in the CTL makes photoresponsivity tunable for real-time multiplication of the image with a memorized photoresponsivity matrix. Therefore, these multi-faceted features can allow in-sensor processing without external memory for the in-sensor vision system. In particular, the in-sensor vision system can enhance speed and energy efficiency compared to a conventional vision system due to the simultaneous preprocessing of massive data at sensor nodes prior to ANN nodes. Recognition of a simple pattern was demonstrated with full sets of the fabricated MOSTs. Furthermore, recognition of complex hand-written digits in the MNIST database was also demonstrated with software simulations.
ABSTRACT
The application of smartphones to medical devices has been gaining attention in addressing accessibility and cost issues in healthcare, and the detection of medically relevant compounds has been demonstrated using customized smartphone hardware and/or software. Metabolomics, a newly rising omics field, has also spawned many medical applications but requires highly sophisticated and expensive equipment. Here, we describe a portable smartphone platform, built with readily available and affordable materials, that can perform all of the critical aspects of metabolomics. Excluding the smartphone itself, the total materials for the platform were obtained at less than US $20. For spectral data acquisition, the system utilized visible light (400-700 nm) and a built-in camera. All of the data processing, statistical analysis, and final-visualization components necessary for decision making were implemented in the smartphone platform. The platform is generally applicable as long as the analytes absorb visible light. We provide a proof-of-concept example wherein the metabolomics platform was applied to the assessment of cisplatin-induced kidney toxicity in a rat model, correctly predicting 7 out of 8 test samples.
