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Abstract The pursuit of advanced brain‐inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (Sb2S3) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin‐film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of Sb2S3 thin films deposited via the vapor transport deposition technique. Experimental results indicate that the Ag/Sb2S3/Pt device possesses properties suitable for memory applications, including low operational voltages, robust endurance, and reliable switching behavior. Further, the reproducibility and stability of these properties across different device batches validate the reliability of these devices for practical implementation. Moreover, Sb2S3‐based memristors exhibit artificial neuroplasticity with prolonged stability, promising considerable advancements in neuromorphic computing. Leveraging the photosensitivity of Sb2S3 enables the Ag/Sb2S3/Pt device to exhibit significant low operating potential and conductivity modulation under optical stimulation for memory applications. This research highlights the potential applications of Sb2S3 in future memory devices and optoelectronics and in shaping electronics with versatility.

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.

Computational efficiency is significantly enhanced using artificial neural network-based computing. A two-terminal memristive device is a powerful electronic device that can mimic the behavior of a biological synapse in addition to storing information and performing logic operations. This work focuses on the fabrication of a memristive device that utilizes a resistive switching layer composed of polyvinyl alcohol infused with ZnO nanoparticles. By incorporating ZnO nanoparticles into the polymer film, the fabricated memristive devices exhibit functionalities that closely resemble those of biological synapses, including short-term and long-term plasticity, paired-pulse facilitation, and spike time-dependent plasticity. These findings establish the ZnO nanoparticle-polymer nanocomposite as a highly promising material for future neuromorphic systems.

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.

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.