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Nanoconstructs of gold nanoparticles (AuNPs) conjugated with SARS-CoV-2 specific antisense oligonucleotides (ASO) have been utilized to develop sensitive electrochemical nucleic acid biosensor for the detection of SARS-CoV-2 RNA. AuNPs were prepared through a one-pot synthesis method by utilizing Poly-L-Lysine (PLL) biopolymer and as synthesised AuNP were characterized by various analytical techniques such as UV–Vis spectroscopy, X-ray Diffraction (XRD) analysis, Fourier Transform Infra-Red spectroscopy (FT-IR), zeta potential, and Transmission Electron Microscopy (TEM). Poly-L-Lysine functionalized AuNPs (PLL-AuNPs) nanoconstructs platform was employed for immobilization of SARS-CoV-2 specific antisense oligonucleotides (ASO-conjugated PLL-AuNPs) via electrostatic interactions. The PLL-AuNPs were drop casted on glassy carbon electrode (GCE) following immobilization of ASO for fabrication of electrochemical biosensor. The ASO-conjugated PLL-AuNPs nanoconstructs were characterized by cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) techniques. The responsiveness of ASO-conjugated PLL-AuNPs nanoconstructs in presence SARS-CoV-2 RNA was monitored using the DPV, SWV and EIS technique, where methylene blue was employed as an electrochemical indicator for DNA-RNA hybridization detection. The biosensor exhibits a detection range for SARS-CoV-2 RNA infection ranging from 0 to 100 nM, with a limit of detection at 30.2 nM. The electrode, modified with ASO-conjugated PLL-AuNPs, was employed for the detection of SARS-CoV-2 RNA from clinical samples collected from COVID-19-positive individuals.

Metal oxide resistive memory is a potential device that can substantially influence the current roadmap for nonvolatile memory and neuromorphic computing. However, common amorphous oxide-based resistive random-access memory suffers from high forming voltages that complicate circuit design and abrupt SET behavior incompatible with analog weight updates. To overcome such limitations, wurtzite ZnO nanorods were synthesized on a fluorine-doped tin oxide (FTO) substrate and a bipolar resistive memory with the Ag/w-ZnO/FTO stacking sequence was fabricated. The hexagonal NR morphology of w-ZnO with controlled vertical growth and nanochannel formation between the NRs were produced by in situ crystalline growth. This morphology enabled a forming-free switching and an analog switching effect that emulated neuromorphic functionalities such as potentiation–depression and complex spike-time dependent plasticity-based Hebbian learning rules. Importantly, the device exhibited nonabrupt switching behavior suitable for analog weight updates in neuromorphic computing in contrast to conventional resistive memory.

Abstract In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type features as compared to a linear model and artificial neural network (ANN). Moreover, the DT algorithm predicts the performance of categorical-type features very well. The feature importance score was calculated for each continuous and categorical feature by the gradient boosting (GB) algorithm. Finally, the suggested ML guidelines were employed to fabricate the copper oxide-based RS device and demonstrated its non-volatile memory properties. The results of ML algorithms and experimental devices are in good agreement with each other, suggesting the importance of ML techniques for understanding and optimizing memory devices.

In the Kesterite family, the Cu2ZnSn(S,Se)4 (CZTSSe) thin-film solar cells (TFSCs) have demonstrated the highest device efficiency with non-stoichiometric cation composition ratios. These composition ratios have a strong influence on the structural, optical, and electrical properties of the CZTSSe absorber layer. So, in this work, a machine learning (ML) approach is employed to evaluate effect composition ratio on the device parameters of CZTSSe TFSCs. In particular, the bi-metallic ratios like Cu/Sn, Zn/Sn, Cu/Zn, and overall Cu/(Zn+Sn) cation composition ratio are investigated. To achieve this, different machine learning algorithms, such as decision trees (DTs) and classification and regression trees (CARTs), are used. In addition, the output performance parameters of CZTSSe TFSCs are predicted by both continuous and categorical approaches. Artificial neural networks (ANN) and XGBoost (XGB) algorithms are employed for the continuous approach. On the other hand, support vector machine and k-nearest neighbor’s algorithms are also used for the categorical approach. Through the analysis, it is observed that the DT and CART algorithms provided a critical composition range well suited for the fabrication of highly efficient CZTSSe TFSCs, while the XGB and ANN showed better prediction accuracy among the tested algorithms. The present work offers valuable guidance towards the integration of the ML approach with experimental studies in the field of TFSCs.

Resistive-switching-based memory devices meet most of the requirements for use in next-generation information and communication technology applications, including standalone memory devices, neuromorphic hardware, and embedded sensing devices with on-chip storage, due to their low cost, excellent memory retention, compatibility with 3D integration, in-memory computing capabilities, and ease of fabrication. Electrochemical synthesis is the most widespread technique for the fabrication of state-of-the-art memory devices. The present review article summarizes the electrochemical approaches that have been proposed for the fabrication of switching, memristor, and memristive devices for memory storage, neuromorphic computing, and sensing applications, highlighting their various advantages and performance metrics. We also present the challenges and future research directions for this field in the concluding section.

This paper presents an organic memristive device based on the soft and quasi-liquid trypsin biomaterial. Accordingly, trypsin is isolated from the bovine pancreas and deposited on a conducting fluorine-doped tin oxide (FTO) substrate. The current–voltage (I–V) measurements of the trypsin/FTO device show a hysteresis loop at multiple frequencies and voltages. The results demonstrate that the current and resistance of the device can be modulated by altering the frequency and magnitude of the applied signal, suggesting the feasibility of this material for brain-inspired computing devices. The hysteresis area of the device increases in proportion to the frequency of the signal, signifying the inverse-memristive phenomenon. The time-domain flux, time-domain charge, and charge-flux properties are calculated from the experimental I–V data to demonstrate the memristive nature of the trypsin hydrogel. Interestingly, the trypsin hydrogel memristive device mimics bio-synaptic properties such as potentiation–depression and four complex spike-time-dependent plasticity learning rules. Electrochemical studies are performed to understand the electrochemical kinetics of the device. A possible switching mechanism is illustrated to show the memristive effect of the trypsin hydrogel. The results of the present investigation suggest that trypsin can be a possible biomaterial to develop an electronic synaptic device for neuromorphic computing applications.

The massively parallel computing capabilities of the human brain can be mimicked with the help of neuromorphic computing approach and this can be achieved by developing the electronic synaptic device. In the present work, we have synthesized gallium-doped ZnO thin films using a cost-effective hydrothermal method and characterized the thin films using field-emission scanning electron microscopy and energy-dispersive X-ray spectroscopy. Furthermore, gallium-doped ZnO memristive devices were developed using standard procedure and electrically characterized for the neuromorphic application. In particular, resistive switching and synaptic properties of gallium-doped ZnO thin films were investigated. The bipolar resistive switching with an analog memory like behavior was observed in the developed memristive devices. In the present case, good synaptic properties, endurance, and retention characteristics were observed for 0.5% Ga doped memristive device. Our results suggested that the synaptic weight, potentiation-depression, and symmetric Hebbian learning can be tuned with properly engineering the ZnO memristive device with appropriate gallium doping. The detailed analysis of I-V results suggested that resistive switching is occurred due to Ohmic and Schottky conduction mechanisms. Keywords: Memristive device, Resistive switching, Gallium doped ZnO, Electronic synapse, Potentiation and depression, Hebbian learning

In this work, we have developed a versatile three-in-one single beam visible colorimeter for undergraduate laboratory courses. The battery and mains electricity supply (utility power), wired and wireless data logging, and 3D-printed components are three important highlights of the developed visible colorimeter. The system is based on a low-cost ARDUINO UNO microcontroller and can measure the absorbance of the liquid samples in the visible region of the electromagnetic spectrum. For on-field measurements, the present colorimeter can work on a small 9 V battery. Therefore, the present portable colorimeter can be used for different field studies. In addition to this, the system can also work on a mains electricity supply for benchtop measurements. The measurement results can be displayed on an OLED display or can be recorded using an open-source data acquisition tool on personal computers/laptops. In addition to this, measured data can be transmitted using a Bluetooth module to Android phones. For this, an open-source Android application was developed using the MIT App Inventor web application. For rapid prototyping of the system, all components of the visible colorimeter are designed and developed using 3D-printing technology. The functioning of the colorimeter was tested by measuring the absorbance of aqueous KMnO[subscript 4] and K[subscript 2]Cr[subscript 2]O[subscript 7] samples, and experimental results were compared with the commercially available single beam visible colorimeter. The measurement difference between the present colorimeter and commercial spectrophotometer was less than 4%, suggesting the satisfactory performance of the colorimeter. Therefore, students can use the present system to understand some basic concepts of the colorimeter.

As leading sensors of ambient conditions, temperature sensors act as important sensing devices. They are essential in a large variety of products. A variety of temperature sensors is available on the market to meet specific application needs. The most common include thermocouples, resistive temperature detectors (RTD), thermistors, and silicon-based sensors. But of all of these temperature sensors, the thermistor is the only one that holds the esteemed position owing to its high sensitivity, accuracy and low cost. Temperature measurement systems utilizing thermistors are less expensive to produce because few additional components are required, especially in the present era of embedded systems. With the recent advances in manufacturing techniques, the drawbacks of conventional thermistors, like aging, interchangeability, unreliability etc., are almost eliminated. Booming demand for DVD players and portable communication appliances have pushed up the popularity of NTC thermistors. This article reviews the latest trends in thermistor manufacturing and the associated applications. [ABSTRACT FROM AUTHOR]

This article describes a novel indexing mechanism to improve the productivity of the paper cutting machines. The mechanism is based on dual light sources and an optical detector. [ABSTRACT FROM AUTHOR]