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Nitric oxide (NO), a potential therapeutic antiaging molecule, modulates various physiological and cellular processes. However, alterations in endogenous NO levels brought on by aging impact multiple organ systems and heighten susceptibility to age-related skin diseases. This correlation underscores the importance of investigating NO-based antiaging interventions. Nonthermal plasma-generated NO is a promising avenue for cosmetic and regenerative medicine due to its capacity to stimulate cellular growth. Herein, we examine the potential of plasma-generated nitric oxide water (NOW) as a bioactive agent in human dermal fibroblasts, emphasizing gene expression patterns linked to extracellular matrix (ECM) breakdown and cellular senescence. The findings of our study indicate that administering NOW at lower dosages enhances cell migration and proliferation. Moreover, the genetic signatures associated with ECM synthesis, antioxidant defense, and antisenescence pathways have been analyzed in NOW-exposed cells. Notably, the downregulation of ECM-degrading enzyme transcripts─collagenase, elastase, and hyaluronidase─suggests NOW’s potential in mitigating the intrinsic skin aging phenomena, emphasizing the promise of NO-based interventions in advancing antiaging strategies within regenerative medicine.

Domain wall (DW) devices have emerged as promising candidates for ultrafast and low power spintronic computing systems. However, the hardware implementation of these systems requires device models that are capable of describing all the underlying physical phenomena more accurately. This article presents a robust compact model that efficiently predicts the motion of DW under the influence of current induced spin Hall effect (SHE) and incorporates the effects of Dzyaloshinskii-Moriya interaction (DMI), longitudinal magnetic field, and thermal fluctuations to determine the position of the DW. The proposed model achieves a velocity of ~400 m/s that matches well with the micromagnetic simulations with an error of less than ±5%. Furthermore, the proposed model is also demonstrated to capture the behavior of leaky-integrate-fire (LIF) neuron and synapse. To enhance the robustness of the model, process variations have been incorporated to enable the analysis and optimization of DW-magnetic tunnel junction (DW-MTJ) based neuron and synapse. Finally, an all-spin neural network (NN) is implemented on MNIST dataset for image classification. The proposed DW-MTJ based NN achieves a training accuracy of 92.4% and it shows better performance in terms of area, energy, and latency when compared to NNs based other technologies such as spin transfer torque (STT)-MTJ, spin orbit torque (SOT)-MTJ, resistive random access memory (RRAM), phase change RAM (PCRAM), and ferroelectric-RAM (FeRAM).

AbstractMedicinal plants are used from prehistoric time to cure various life-threatening bacterial diseases. Acorus calamusis an important medicinal plant widely used to cure gastrointestinal, respiratory, kidney and liver disorders. The objective of the current research was to investigate the interaction of major phytoconstituents of Acorus calamuswith bacterial (6VJE) and fungal (1EA1) protein targets. Protein-ligand interactions were estimated using the AutoDock software, drug likeness was predicted by using the molinspiration server and toxicity was predicted with the swissADME and protox II servers. MD simulation of phytocompounds with the best profiles was done on the GROMACS software for 100 ns. Molecular docking results showed among all the selected major phytoconstituents, that β-cadinene showed best binding interaction in complex with bacterial (6VJE) and fungal (1EA1) protein targets with binding energy −7.66 ± 0.1 and −7.73 ± 0.15 kcal mol−1, respectively. Drug likeness and toxicity predictions showed that β-cadinene follows all rules of drug likeness and toxicity. MD simulation study revealed that β-cadinene fit in binding pocket of bacterial and fungal targets and found to be stable throughout the duration of the simulation. Based on the observations from this in-silico study it is being proposed that β-cadinene, a major phytocompound of Acorus calamus,can be considered for the treatment of bacterial and fungal infections since the study shows that it might be one of the compounds that contributes majorly to the plant’s biological activity. This study needs in vitroand in vivovalidation.Communicated by Ramaswamy H. Sarma

Coaxial through-glass via (coaxial-TGV), one of the promising 3-D integration solutions, can offer a reliable and high-performance signal transmission through a glass substrate at high frequencies. This article utilizes electromagnetic theory to derive analytical closed-form formulas for calculating the per-unit-length resistance and inductance of a coaxial-TGV configuration filled with the isolation dielectric benzocyclobutene (BCB). An accurate wavelet-based numerical multiresolution time-domain (MRTD) technique is developed to analyze the crosstalk effects in coupled coaxial-TGVs. This work focuses on determining the propagation delays, peak crosstalk voltage, and timings in CMOS-gate-driven coupled coaxial-TGVs for both in-phase and out-phase scenarios. The proposed MRTD method’s performance evaluation is carried out through recursive simulations using SPICE. The analysis reveals that the MRTD scheme exhibits higher accuracy compared to the conventional finite-difference time-domain (FDTD) scheme when compared with SPICE, with a maximum error of less than 1%. The application of the proposed MRTD technique can be expanded to encompass the transient analysis of ${N}$ -coupled coaxial-TGVs. This extension proves valuable in assessing signal integrity, electromagnetic interference (EMI), and electromagnetic compatibility (EMC) concerns related to coaxial-TGVs.

The sensing of different liquids is investigated with micro-ring resonator (MRR) sensor by changing refractive index (RI) of chemical solutions. Aniline, carbon disulphide, chlorobenzene, chloroform, ethanol, glycerin, and methanol are detected using the proposed MRR. The developed structures are simulated using finite difference-time domain (FDTD) by using DFT analyzer of optical field. The normalized amplitude vs. wavelength is measured by changing RI of inner ring waveguide that obtain liquid sensor with high sensitivity.

The antiferromagnetic (AFM) skyrmions are topologically protected nanoscale spin configurations that have the potential for applications in data processing, logic, and neuromorphic computing. Owing to their resistivity to stray fields, negligible demagnetization energy, and zero net topological charge, they are the prominent spin textures for spintronic devices. However, the analysis of creation, stabilization, and manipulation of AFM skyrmions for various applications is a complex task that involves solving complex equations and simulations. To resolve this issue, a novel approach is proposed based on training the machine learning (ML) neural network model. The training dataset is extracted through micromagnetic simulations by varying perpendicular magnetic anisotropy (PMA), Dzyaloshinskii–Moriya interactions (DMIs), saturation magnetization, and exchange constant at different temperatures on a shape configured nanotrack. The ML approach has shown promising results and holds the potential to significantly accelerate the development of skyrmion-based devices. It assists with identifying the optimal parameters for AFM skyrmion formation under different conditions. This neural network model achieves a 97.91% accuracy and completes inference in just 39.15 milliseconds (ms), in contrast to micromagnetic simulations that require over 200 hours to process more than 16 000 samples.

Antiferromagnetic (AFM) skyrmion technology holds great promise for the development of the next-generation spintronics-based neuromorphic computing due to its unique features. They offer negligible stray fields, resilience to external magnetic fields, ultrafast dynamics, and no skyrmion Hall effect (SkHE) due to zero net topological charge. These advantages make AFM skyrmions a more viable alternative for future spintronic applications compared to ferromagnetic (FM) skyrmions. In this study, a neuron device based on AFM skyrmion is proposed, which demonstrates the integrate-fire (IF) functionality by utilizing a previously proposed AFM skyrmion-based diode device and the interskyrmion repulsion effect on the nanotrack. The threshold of the device, which is determined by the maximum number of skyrmions pinned near the device window, can be adjusted by manipulating the applied current density. The device generates an output signal when the primary skyrmion overcomes the device window and reaches the detection region. By increasing the threshold, the device’s operating current density can be reduced, leading to improved energy efficiency. It is observed that the threshold current density is 4.6 GAm $^{-{2}}$ , 2.3 GAm $^{-{2}}$ , and 1.5 GAm $^{-{2}}$ for one, two, and three skyrmions, respectively, on a nanotrack. The minimum overall energy dissipation is estimated at 6.285 fJ. The proposed neuron device offers a 68% reduction in the energy dissipation when the number of skyrmions on a nanotrack is increased from 1 to 3, thus indicating the potential for developing energy-efficient devices in AFM spintronics for neuromorphic computing.

The ever-increasing demand for non-volatile memories with high density, low power consumption, and resistance to radiation has led to the emergence of Double Data Rate Magnetic Random Access Memory (DDR-MRAM) as a promising technology. However, the reliability of DDR-MRAM in harsh radiation environments remains a challenge. This paper presents a novel radiation-hardened pre-discharge sense amplifier specifically designed for DDR-MRAM application. The paper investigates the effects of radiation on the unhardened pre-discharge sense amplifier (PDSA) circuit and proposes solutions at both the device and circuit levels. At the circuit level, two distinct circuits have been proposed: one involves the duplication technique (PDSA-D) and the other employs a feedback circuitry (RT-PDSA) to harden sensitive transistors in the PDSA. The proposed radiation tolerant PDSA (RT-PDSA) circuit demonstrates significant improvements in terms of single event upset (SEU) as well as double node upset (DNU) mitigation by considering minimum sensitive nodes layout area separation concept and enhanced compared to unhardened PDSA circuit. The overall performance of the proposed circuit has been analyzed in terms of the figure of merit (FOM), which includes critical charge tolerability, the number of sensitive nodes, recovery time, and area. The proposed RT-PDSA circuit has $11.95\times $ and $8.7\times $ higher FOM compared to the hardened PDSA at the device level, and PDSA-D circuit, respectively. Moreover, the proposed circuit exhibits robustness against process variations, making it a promising solution for enhancing DDR-MRAM’s reliability in radiation-intensive environments.

In this article, artificial neural network (ANN) metamodels have been developed for the fast statistical signal integrity analysis of multiwalled carbon nanotube and multilayer graphene nanoribbon interconnect networks. These ANN metamodels, referred to as primary ANNs, are trained using transfer learning strategies where the initial guess of the weights and bias terms are learned from a pretrained secondary ANN. The secondary ANN is trained using data extracted from a cheap and approximate equivalent single conductor model of the interconnects. Starting with this informed initial guess, the weights and bias terms of the primary ANN can be optimized using a very small dataset generated from the rigorous but massive multiconductor circuit model of the interconnects. This makes the primary ANN significantly more efficient to train than existing metamodels. Two distinct transfer learning strategies based on the full and partial transfer of knowledge between the primary and secondary ANNs have been developed in this work.

In recent 3-D integrated circuits, tapered-shaped bumps (T-bump) have provided an appealing solution over the conventional cylindrical (C-bump) structures due to their reduced parasitic and area requirement. At high frequencies, the nonuniform current distribution and electromagnetic (EM) field produce the eddy effect that primarily affects the through silicon via (TSV) bump performance in the 3-D packaging system. In this regard, this article presents the first realistic and holistic method to analyze the impact of eddy currents on the T- and C-bump. Furthermore, a novel resistance–inductance–conductance–capacitance model is proposed to incorporate the effects of bump, intermetal dielectric, and eddy currents. An analytical solution for the C- and T-bump is proposed using the principles of the partial inductance, Maxwell equations, and differential scalar potential. In order to benchmark, the quantitative values of TSV-bump parasitics are validated against the experimental and structural EM simulations. Encouragingly, good agreement with an average deviation of only 3.1% has been observed. Using a driver via load at 7-nm technology, the crosstalk-induced delay, peak noise voltage, power dissipation, insertion (S21), and reflection (S11) losses are analyzed with and without considering the eddy impact. The performance parameters are further validated with the analytical finite-difference time-domain approach. It is worth emphasizing that the T-bump significantly reduces power dissipation, crosstalk, peak noise, insertion, and reflection losses when compared with the C-bump counterparts. The overall percentage deviation while incorporating the eddy effect is approximately 11.78%, 10.18%, 17.17%, 26.70%, and 45.08% for the crosstalk, peak noise, power dissipation, insertion, and reflection losses, respectively.

ABSTRACT Experimental study on the effect of mechanical behavior of quaternary binders, prepared using various supplementary cementitious materials was performed. Fly ash (FA), Ground Granulated Blast furnace Slag (GGBS), Metakaolin [...]

Conventional memories face several issues such as poor reliability, high static power, and charge sharing caused by radiation effects as a result of process technology downscaling. Spintronic memory is a viable contender for addressing these challenges. However, radiation induced charges can still affect hybrid spintronic/CMOS peripheral circuitry. Particularly, the charge sharing effect is more significant, causing multiple node upsets due to a single event. The number of sensitive nodes is an essential performance metric for evaluating single event multiple node upset (SEMNU). The smaller the number of sensitive nodes, the less is the charge sharing between them, thereby lowering the possibility of SEMNU. This brief proposes a novel highly radiation tolerant with lowest sensitive nodes (HRLS) read circuit for differential spin orbit torque magnetic random access memory (DSOT-MRAM). The obtained results show that the proposed HRLS circuit is extremely resilient to SEUs independent of its polarity. It can tolerate 8.06X, 10.41X, 9.8X, and 3.49X more critical charges compared to the 13T-RH, 11T-RHBD, 11T-HRC, and CMOS cross-coupled transistor radiation hardened circuits, respectively with a penalty of some area overhead. Moreover, the proposed circuit has the lowest number of sensitive nodes resulting in 6X, 6X, 3X, 3X, and 3X improvement in the charge sharing effects compared to the previously reported 13T-RH, 11T-RHBD, 11T-HRC, CMOS cross-coupled transistor, and 13T radiation hardened circuits, respectively. The proposed radiation-hardened DSOT-MRAM has also an improvement in the write energy thanks to the usage of DSOT-MTJ.

Spiking neural networks (SNNs) have gained a significant interest in recent years due to their biological system-like processing. However, the hardware implementation of spiking neurons, synapses, and related algorithms by CMOS technology is limited by area and power constraints. In this work, an approach for spin-orbit-torque magnetic random access memory (SOT-MRAM)-based hardware accelerator for SNNs is presented. The accelerator for the neuromorphic core consists of crossbar arrays of SOT-MRAM devices interfaced with spiking neurons and peripheral circuits. The proposed design is compared with various other nonvolatile memory devices, including phase-change memory (PCM), resistive random access memory (RRAM), and spin-transfer torque MRAM (STT-MRAM). SOT-MRAM provides subnanosecond switching with low energy consumption and high throughput. The benefits of the proposed design for a large-scale neuromorphic accelerator are explored using a complete device-circuit-algorithm framework for a standard MNIST image classification. The results show that SOT-MRAM-based neuromorphic core achieves $6.4\times $ , $70.32\times $ , $20.25\times $ , and $4.83\times $ higher throughput per unit Watt as compared to SRAM, PCM, RRAM, and STT-MRAM-based designs, respectively.

ABSTRACT The aim of this research work is to produce quaternary cement binders and mortars with combination of ordinary Portland cement (OPC) and supplementary cementitious materials (SCMs), such as, fly [...]

In this article, a modified knowledge-based artificial neural network (KBANN) metamodel is developed for the efficient uncertainty quantification of on-chip multiwalled carbon nanotube (MWCNT) interconnects. The proposed KBANN metamodel utilizes the notion of control variates to enable much faster training than what is possible with standard KBANNs. Importantly, techniques to calculate the optimal value of the control variates in an a priori manner without augmenting the training dataset have been developed in this article. Furthermore, techniques to exploit the control variates depending on whether one or multiple low-fidelity models of the MWCNT interconnects are available have also been developed in this article. The benefits of the proposed KBANN metamodel using control variates over standard KBANN metamodels have been validated using multiple MWCNT interconnect examples spanning multiple technology nodes.

The escalating global prevalence of diabetes necessitates the development of advanced diagnostic methodologies that are both minimally invasive and devoid of discomfort. This study introduces a wireless battery-free passive microneedle patch with CMC-pHEA GelMA-GOx (CMC: sodium carboxymethyl cellulose; pHEA: polyethylene glycol diacrylate, 2-hydroxyethyl acrylate; GelMA: gelatin methacryloyl; GOx: glucose oxidase) light-cured swellable hydrogel for minimally-invasive glucose detection. The core of this technology combines microneedle with Radio-Frequency (RF) coupling detection, achieving extraction capacity; the microneedle lies in the use of composite hydrogel composed of a freeze-dried CMC-pHEA nanogel and GelMA-GOx, specifically fabricated to be selectively glucose-responsive through cross-linking with glucose oxidase. Upon exposure to interstitial fluid, the hydrogel microneedles swell in proportion to the glucose concentration, enabling glucose binding and facilitating the detection process. A flexible antenna, which is integral to the microneedle substrate as a passive sensing component, detects changes in the swelling of the hydrogel; these are reflected in the RF parameters (frequency, magnitude) variations, which are directly correlated with glucose levels. Furthermore, the results are near field wirelessly detected through a readout coil, simplifying sensing patch configurations. This method not only integrates the glucose monitoring process by combining extraction and detection into a single step but also enhances patient comfort. The platform’s sensitivity, specificity, and minimal invasiveness make it a promising tool for diabetes management and continuous glucose monitoring. By demonstrating the successful application of RF technology in wireless sensing applications, this study paves the way for advanced, user-friendly glucose monitoring solutions.

The article describes the development of a hetro-core optical fiber sensor structure based on localized surface plasmon resonance (LSPR) for the detection of cardiac troponin I (cTnI) solution. This was accomplished by fabricating a single-mode fiber - multimode fiber - single-mode fiber (SMS) structure. Then, fiber structure is immobilized with gold nanoparticles (AuNPs) and cerium oxide nanoparticles (CeO2-NPs) to improve its sensing capabilities. An UV-Vis spectrophotometer and a high-resolution transmission electron microscope (HR-TEM) are used to determine the morphology of synthesized nanoparticles. Scanning electron microscopy (SEM) is used to examine the state of immobilized NPs on the surface of sensing region. The developed sensor probe has a linear range of 0 to 1000 ng/mL cTnI, a sensitivity of 3 pm/(ng/mL), and a limit of detection (LoD) of 108.15 ng/mL. In real time, the proposed sensor will be used in a practice to detect acute myocardial infarction (AMI).

In the present work, a highly sensitive prism-based surface plasmon resonance (SPR) sensor for the detection of formalin using multilayer black phosphorous (BP) is proposed. Formalin, which is widely used as a preservative, can cause a variety of diseases, making its detection crucial. The transfer matrix method is utilized to compare and analyze the proposed structures of the SPR-based prism sensor in the Kretschmann configuration. The effect of prism materials (BAK3, BK7, SF10, SF11, and 2S2G), metals (gold, silver, copper, and aluminum), and 2-D materials (graphene, MoS2, WS2, and BP) on the performance of the sensor for formalin detection has been analyzed and discussed. The analysis of the impact of employing multilayer 2-D materials is carried out. Optimization of the thickness of the 2-D material is performed to achieve a structure that gives better sensitivity. The sensitivity achieved for structures with multilayer graphene, MoS2, and WS2 are 172.82°/RIU, 195.97°/RIU, and 218.04°/RIU, respectively. A highly sensitive structure for formalin detection is proposed with BK7, silver, and nine layers of BP. A sensitivity of 289.4565°/RIU and a detection accuracy (DA) of 0.3125/° are obtained. The limit of detection of the proposed sensor for different concentrations of formalin lies in the range of 3.45– $3.56~ \mu $ RIU.

Multi-level cell (MLC) is an attractive method to increase the memory storage density and reduce the cost per bit. Write disturb rate (WDR) and large writing step counts are the main challenge to implement MLCs. In this article, a three-bit spin-orbit torque magnetic random-access memory (SOT-MRAM)-based MLC structure termed as triple level cell (TLC) is proposed. The majority of the bits in TLC require two steps of writing for storage, and the cell exhibits WDR less than $10^{-8}$ . The performance evaluation of the proposed structure is done on the SPICE framework utilizing a Verilog-A model for the structure. The proposed TLC device is 96% and 92% more energy efficient than spin transfer torque (STT)-based TLC and STT-/SOT-based TLC structures, respectively. The worst case write latency of the proposed TLC is 2 ns that shows 88% improvement compared to the recently published STT-/SOT-based TLC-MRAM. The variability analysis performed using Monte Carlo simulations shows sufficient margins between various writing currents employed for switching the stacked magnetic tunnel junctions (MTJs) that signify the reliable switching of the different bits in the TLC.

In the present theoretical investigations, we have reported the potential of magnesium oxide nanoribbons (MgONRs) for spintronic applications by deploying density functional theory (DFT) in correlation with nonequilibrium Green’s function (NEGF). All the considered MgONRs are thermodynamically stable according to binding energy ( ${E}_{b}$ ) results. Pristine MgONR (HH-MgO-HH) is ferromagnetic (FM) metal while the selective edge hydrogenated MgONRs with one edge partially bare (HH-MgO-HB) and one edge partially hydrogenated (HB-MgO-BB) are half-metals in FM and anti-FM (AFM) ground states (GSs), respectively. Furthermore, the HH-MgO-HB and HB-MgO-BB are investigated for their transport characteristics. Current magnitude in HH-MgO-HB is negligibly small and hence HB-MgO-BB is further investigated for spintronic applications. Spin-based rectification characteristics are reported in AP spin orientation with a rectification ratio (RR) of the order of $10^{{6}}$ for spin-up currents. A very high spin filtering efficiency (SFE) is noticed in HB-MgO-BB with near 100% efficiency. Moreover, giant magnetoresistance (GMR) is noticed in HB-MgO-BB with computed value of $5.52\times 10^{{11}}$ and $1.00\times 10^{{10}}$ for spin-down and spin-up currents, respectively. Obtained findings suggest the MgONRs are promising candidates for future spintronics technology.

Over the past few decades, the field of organic electronics has depicted proliferated growth, due to the advantageous characteristics of organic semiconductors, such as tunability through synthetic chemistry, simplicity in processing, cost-effectiveness, and low-voltage operation, to cite a few. Organic electrochemical transistors (OECTs) have recently emerged as a highly promising technology in the area of biosensing and flexible electronics. OECT-based biosensors are capable of sensing brain activities, tissues, monitoring cells, hormones, DNAs, and glucose. Sensitivity, selectivity, and detection limit are the key parameters adopted for measuring the performance of OECT-based biosensors. This article highlights the advancements and exciting prospects of OECTs for future biosensing applications, such as cell-based biosensing, chemical sensing, DNA/ribonucleic acid (RNA) sensing, glucose sensing, immune sensing, ion sensing, and pH sensing. OECT-based biosensors outperform other conventional biosensors because of their excellent biocompatibility, high transconductance, and mixed electronic–ionic conductivity. At present, OECTs are fabricated and characterized in millimeter and micrometer dimensions, and miniaturizing their dimensions to nanoscale is the key challenge for utilizing them in the field of nanobioelectronics, nanomedicine, and nanobiosensing.

Objective: The present article aimed to enhance the therapeutic efficacy of carboplatin (CP) using the formulation of chitosan-poly (lactic glycolic acid) nanoparticles (CS-PLGA NPs). Methods: Nanoparticles were synthesized by an ionic gelation method and were characterized for their morphology, particle size, and surface potential measurements by TEM and zeta sizer. This study was highlighted for the evaluation of drug entrapment, loading and in vitro drug release capabilities of the prepared nanoparticles by spectrophotometric analysis. The stability study was also conducted after 3 months for their particle size, zeta potential, drug loading and encapsulation efficiencies. Further, ovarian cancer cell line PEO1 was used to evaluate the toxicity and efficacy of nano-formulation by MTT assay. Additionally, the study was evaluated for apoptosis using flow cytometric analysis. Results: The CS-PLGA-CP NPs were uniform and spherical in shape. The particle size and zeta potential of CS-PLGA-CP NPs were measured to be 156 ±6.8 nm and +52 ±2.4 mV, respectively. High encapsulation (87.4 ± 4.5%) and controlled retention capacities confirmed the efficiency of the prepared nanoparticles in a time and dose-dependent manner. The cytotoxicity assay results also showed that CS-PLGA-CP NPs have a high efficiency on PEO1 cells compared to the free drug. The flow cytometric result showed 64.25% of the PEO1 cells were apoptotic, and 8.42% were necrotic when treated with CS-PLGA-CP NPs. Conclusion: Chitosan-PLGA combinational polymeric nanoparticles were not only steady but also non-toxic. Our experiments revealed that the chitosan-PLGA nanoparticles could be used as a challenging vehicle candidate for drug delivery for the therapeutic treatment of ovarian cancer.

The electrothermal performance of a junctionless nanowire [JL-nanowire (NW)] gate-all-around (GAA) transistors under self-heating effect (SHE) is examined for sub-5 nm technology nodes. These devices are generally fabricated on silicon-on-insulator (SOI) wafers, in which the thin active device layer confines silicon thermal conductivity ( $\kappa {)}$ and provokes SHE in the device. In this work, we proposed and upgraded the early reported compact thermal conductivity model (C-TCM) by considering the phosphorus doping effect for SHE analysis with any combination of doping, such as phosphorus/boron and arsenic/boron doping, which is validated with reported experimental data. The C-TCM is also compared with the existing Sentaurus TCAD Connelly thermal conductivity model (CN-TCM). The CN-TCM is only valid for undoped conditions at room temperature. This work shows that C-TCM can be used to simulate the realistic thermal behavior of the device with ambient temperature ( ${T}_{A}{)}$ variation. With C-TCM, the nonuniform lattice temperature ( ${T}_{L}{)}$ and heat generation were observed along the channel length due to the non-uniformity $\kappa $ of the device. The influence of SHE on the device performance in terms of electrical characteristics and hot-carrier injection (HCI) is also observed along with ${T}_{A}$ variation. SHE rises the peak of ${T}_{L}{,}$ causing $\sim $ 10% ON-current and $\sim $ 0.7% mobility degradation. The SHE implication on HCI is also examined, and it was found that gate leakage current ( ${I}_{\text {G}}{)}$ is enhanced by a factor of $\sim $ 55, highlighting the importance of thermal study for next-generation high-performance devices that do not compromise reliability.

Convolutional neural network (CNN) is one of the most popular deep learning algorithms for artificial intelligence applications. However, due to its data-intensive workloads, the training of deep CNN is constrained by large memory requirements. Previously, spin transfer torque (STT)-based multilevel cells (MLCs) have been used to implement CNN accelerators to address this issue. However, due to multistep write and read operations, energy, latency, and reliability are critical constraints for larger and deeper training models. This article presents a hybrid STT and differential spin Hall effect (DSHE)-MLC-based data-aware memory design to achieve single-step write and read operations. Depending on the data type, the two-bit data are stored in either STT-MLC or DSHE-MLC memory blocks. The proposed scheme improves the training energy and latency in VGG-16, VGG-19, AlexNet, and LeNet CNN architectures by nearly 50% when compared with conventional STT-MLC-based architectures. Moreover, it also allows for the resolution of device-level write and read reliability issues.

The ongoing pandemic caused by the novel coronavirus, SARS-CoV-2, is influencing global health. Moreover, there is a major threat of future coronaviruses affecting the entire world in a similar, or even more dreadful, manner. Therefore, effective and biocompatible therapeutic options against coronaviruses are urgently needed. To address this challenge, medical specialists require a well-informed and safe approach to treating human coronaviruses (HCoVs). Herein, an environmental friendly approach for viral inactivation, based on plasma technology, was considered. A microwave plasma system was employed for the generation of the high amount of gaseous nitric oxide to prepare nitric oxide enriched plasma-activated water (NO-PAW), the effects of which on coronaviruses, have not been reported to date. To determine these effects, alpha-HCoV-229E was used in an experimental model. We found that NO-PAW treatment effectively inhibited coronavirus infection in host lung cells, visualized by evaluating the cytopathic effect and expression level of spike proteins. Interestingly, NO-PAW showed minimal toxicity towards lung host cells, suggesting its potential for therapeutic application. Moreover, this new approach resulted in viral inactivation and greatly improved the gene levels involved in host antiviral responses. Together, our findings provide evidence of an initiation point for further progress toward the clinical development of antiviral treatments, including such coronaviruses.

In this article, a thermal conductivity ( $\kappa $ ) model is proposed (i.e., dependent on the temperature, thickness, and doping concentration) for investigating the thermal behavior of silicon-on-insulator (SOI)-based devices. The proposed model is less complex in calculating the thermal conductivity ( $\kappa $ ) compared to the existing model because the analysis of the existing model is complicated due to multiple equations. The proposed model is also easier to implement in the TCAD simulator than the existing model, which is currently being used in the Sentaurus TCAD electrothermal module to study the thermal behavior of the devices. The thermal conductivity ( $\kappa $ ) predictions are analyzed using the proposed model, which agrees with the reported experimental data and existing complex analytical models. The thermal behavior of junctionless nanowire (JL-NW) FET is investigated using the sub-5-nm technology node-based physical parameters. It is observed that the thermal behavior depends on the temperature, thickness, and doping concentrations of the SOI devices. The proposed model provides an insightful and accurate analysis of the behavior of the SOI-based nanodimension transistors.

As energy efficiency is one of the prominent issues in the today’s era. Reversible computing may serve as a critical step toward this paramount issue. Optical reversible computing is one of the tactics to serve this aspiration. In this paper, an optical reversible hybrid adder-subtractor device delineated with the help of electro-optic effect inside lithium-niobate based Mach-Zehnder interferometer (MZI) for WDM applications. The proposed device in this work is combinational circuits and can be used as an adder as well as subtractor in advance applications. The perception is established with the help of simulation results of beam propagation method (BPM) and mathematically proven with MATLAB simulation results.

Emerging pharmaceutical micropollutant: ampicillin-degradation in water using atmospheric pressure plasma jets (APPJs) is examined, and its environmental impact is evaluated. To this end, comprehensive plasma characterization techniques, including electrical and optical analyses, are used, to supplement existing research that primarily focused on degradation outcomes. The results of these techniques provide deeper insights into the degradation process. Additionally, the solution properties are assessed by examining the treated liquids and quantifying the corresponding hydrogen peroxide and nitrite concentrations, clarifying the role of reactive species in the degradation process. High-performance liquid chromatography analysis provides essential insights into degradation kinetics and rate constants. A comprehensive framework is developed for understanding the degradation of ampicillin by APPJs, combining plasma diagnostics, solution characterization, chemical assays, degradation kinetics, and toxicity analysis. Our study, grounded in these multidisciplinary approaches, contributes to both the fundamental understanding of plasma-based degradation and optimization of plasma plume length. Our findings highlight the potential of plasma jet technologies in addressing pharmaceutical pollution and promoting sustainable wastewater management practices. This research can advance efforts to enhance the efficiency of pharmaceutical waste treatment, ultimately safeguarding the health and well-being of aquatic ecosystems and human populations.

The Continuous miniaturization of electronics set stringent requirements of corrosion resistance as imposed by high-density packaging. The present work elucidates electrochemical corrosion behaviours of two conventional lead-free solder alloys, i.e., Low-Ag (Sn–1.0Ag–0.5Cu, SAC105) and High-Ag (Sn–3.8Ag–0.7Cu, SAC387) solder alloys. The Electrochemical Impedance Spectroscopy and Potentiodynamic polarization performed in naturally aerated 0.5 M NaCl solution for 1 h, 24 h, 168 h, 336 h, 504 h and 672 h suggest that SAC387 solder offers higher corrosion resistance than SAC105 solder. The SAC105 solder is sparsely cover with fine-fibrous corrosion product, while platelet-like and fibrous mass entirely covers SAC387 solder. Microstructural and elemental characterization of corrosion product revealed the presence of tin oxide, Cu and Ag containing products as several pit, cracks and pore-like structures.

AbstractInfection due to the Chikungunya virus (CHIKV) has taken the life of lots of people; and researchers are working to find the vaccine or promisng drug candidates against this viral infection. In this work, the authors have designed one component reaction based on the thia-/oxa-azolidineone and created a library of 2000 molecules based on the product obtained. Further, the compounds were screened through the docking using iGemdock against the non-structural protein 2 (nsp2) of CHIKV. Molecular docking gives the binding energy (BE) or energy for the formation of the complex between the designed compound and nsp2 of CHIKV; and CMPD222 gave the lowest energy. This is based on the energy obtained from van der Waal’s interaction, hydrogen bonding and electrostatic instructions. Further, molecular dynamics simulations (MDS) of nsp2 of CHIKV with and without screened compound (222) were performed to validate the docking results and the change in free energy for the formation of the complex is −10.8327 kcal/mol. To explore the potential of CMPD222, the MDS of the CMPD222-nsp2 of CHIKV were performed at different temperatures (325, 350, 375 and 400 K) to understand the inhibition of the protease. MM-GBSA calculations were performed to determined change in entropy, change in enthalpy and change in free energy to understand the inhibition. Maximum inhibition of nsp2 of CHIKV with CMPD222 is observed at 375 K with a change in free energy of −19.3754 kcal/mol.Communicated by Ramaswamy H. Sarma

The basic logic gates play a key role in performing the mathematical computation. The plasmonics has the uniqueness of confining surface plasmons beyond the diffraction limit. Plasmonic-based Mach-Zehnder interferometer with an extinction ratio of 26 dB is proposed to design the structure of all-optical XOR and XNOR logic gate. A theoretical analysis of proposed gate is carried out using finite-difference-time-domain method and MATLAB simulation results.

Nonthermal biocompatible plasma (NBP) sources operating in atmospheric pressure environments and their characteristics can be used for plasma bioscience, medicine, and hygiene applications, especially for COVID-19 and citizen. This review surveyed the various NBP sources, including a plasma jet, micro-DBD (dielectric barrier discharge) and nanosecond discharged plasma. The electron temperatures and the plasma densities, which are produced using dielectric barrier discharged electrode systems, can be characterized as 0.7 ~ 1.8 eV and (3–5) × 1014–15cm−3, respectively. Herein, we introduce a general schematic view of the plasma ultraviolet photolysis of water molecules for reactive oxygen and nitrogen species (RONS) generation inside biological cells or living tissues, which would be synergistically important with RONS diffusive propagation into cells or tissues. Of the RONS, the hydroxyl radical [OH] and hydrogen peroxide H2O2species would mainly result in apoptotic cell death with other RONS in plasma bioscience and medicines. The diseased biological protein, cancer, and mutated cells could be treated by using a NBP or plasma activated water (PAW) resulting in their apoptosis for a new paradigm of plasma medicine.

Spintronic-based integrated circuits have been extensively explored as viable contenders for space use since magnetic tunnel junctions (MTJs) are intrinsically immune to radiation effects. On the other hand, their complementary metal–oxide semiconductor (CMOS) peripheral circuitry is still susceptible to radiation-induced single-event upset (SEU) and multinode upset (MNU) caused by charge sharing. It results in localized ionization and flips the data state of memory cells or other logic circuits. To ensure a fault-free operation, this letter proposes a novel radiation-hardened (RH) CMOS peripheral circuitry for a magnetic full adder (MFA) using spin-orbit torque MTJs. The circuit can recover from SEUs as well as MNUs regardless of the accumulated charge. Moreover, the read time and read energy of the circuit are improved by 17.6% and 64%, respectively, when compared to the previously reported RH MFA.

The ascorbic acid (AA) is a biomarker that can be used to detect the symptoms of severe disorders such as scurvy, Parkinson’s, Alzheimer’s, and cardiovascular diseases. In this work, a simple and effective sensor model is developed to diagnose the presence of AA samples. To develop the sensor, a tapered single-mode optical fiber has been used with the well-known phenomenon of localized surface plasmon resonance (LSPR). For LSPR, the tapered region is immobilized with synthesized gold nanoparticles (AuNPs) and zinc oxide nanoparticles (ZnO-NPs) whose absorbance peak wavelengths appear at 519nm and 370nm, respectively. On the basis of nanoparticles (NPs) configurations, two different biosensor probes are developed. In the first one, the sensing region is immobilized with AuNPs and named Probe I. In the second probe, the immobilized layer of AuNPs is further coated with a layer of ZnO-NPs, and a resultant probe is termed as Probe II. The characterizations of synthesized AuNPs and developed fiber probes are done by the ultraviolet-visible (UV-vis) spectrophotometer, high-resolution transmission electron microscope (HR-TEM), atomic force microscopy (AFM), and scanning electron microscope (SEM). To enhance the selectivity, a sensing region of probes is functionalized with ascorbate oxidase enzyme that oxidizes the AA in the presence of oxygen. The response of developed sensor probes is authenticated by sensing the samples of AA in the range from 500 nM to 1 mM, which covers the range of AA found in human bodies, i.e., 40µM–120µM. The performance analysis of the developed sensor probes has been done in terms of their stability, reproducibility, reusability, and selectivity. To observe the stability of AA, a pH-test has also been done that results in a better solubility of AA molecules in phosphate-buffered saline (PBS) solution.

In India, agriculture monitoring is severely hampered by frequent cloud cover. Crop health assessment from the early growing stage onwards is vital for accurate and timely yield prediction. In this study, time series of Sentinel-1A SAR images over central India have been processed to quantify kharif crops’ growth rate. Sentinel-1 and Sentinel-2 images acquired on the same day have been used to compare the radar backscattered energy at the VH channel with normalized difference vegetation index (NDVI). A good coefficient of determination (R2= 0.824) was found between backscatter and NDVI. It attests that the NDVI can be used in combination with SAR backscatter during the kharif season. In addition, k-means clustering classification of Sentinel-1 images indicated that the total area covered by paddy, soybean, and other crops were 103,506.86 ha, 85,390.93 ha, and 71,667.02 ha, respectively. The classification result has been validated with ground information, which has indicated an overall accuracy of 83.47%. The work indicated that radar signals’ temporal behavior is sensitive to the health status of the crops from sowing to harvesting stages. Sentinel-1 SAR images can be used to analyze kharif crops during the whole phenological cycle. The approach may serve as a solution for assessing both the health and spatial distribution of kharif crops.

Magnetic skyrmions are particle-like, nanometer-sized topological spin textures observed in several magnetic materials. They have emerged as an alternative to conventional spintronic memories and domain walls (DWs) and offer high storage density, more robust stability, low critical currents, and increased scalability. Recent advances have set the stage for their use in quantum computing, logic circuits, and neuromorphic computing. With the aid of electrical methods, it is possible to precisely create, manipulate, and destroy skyrmions in device-compatible materials. However, the maximum speed achievable by magnetic skyrmions and the reliable detection of data have been restricted by the skyrmion Hall effect (SkHE). Other issues include a low read margin and a lack of proper skyrmion motion control in nanowires. Most of these can be addressed by exploiting novel materials, such as antiferromagnets; employing specialized fabrication techniques; tuning driving current profiles; and circuit-level engineering. In this article, theoretical and experimental breakthroughs and challenges relevant to magnetic skyrmions and their applications in data storage, logic computing, and neuromorphic computing are highlighted.

The article explores how combining virtual reality and fieldwork yields insights into the mind of the apple fly, and how the brain translates information and patterns. Topics include apple fly's survival depends on locating and identifying objects in its environment; and process requires an interdisciplinary confluence of natural history and technology with digital cloud in understanding the brain's process of pattern recognition with virtual reality setup as a controlled environment.

This article reviews the recent developments and challenges in spintronic based spiking neural networks (SNNs). The present CPUs and GPUs are powerful tools that are capable of computing complex artificial intelligence (AI) tasks, yet they face several challenges. The systems utilizing machine learning techniques require extensive computations including vector-matrix multiplication. The traditional von Neumann architecture is inefficient in the hardware implementation of such systems. Neuromorphic architectures are computationally more efficient that ushers the era of high-performance hardware for AI applications. This also caters to the need of hardware solutions for biologically inspired in-memory computing functionality using nonvolatile memory (NVM). These biologically inspired networks have attracted a great attention due to their inherent computational efficiency in performing pattern/speech recognition and image classification tasks. During the last decade, SNNs have emerged as one of the suitable solutions for imitating the biological brain. Information is temporarily encoded in SNNs and the spikes serve as a mode of communication between the neurons. Various NVM devices such as spin-based memories, resistive randomaccess memory (RRAM), phase change memory (PCM), and conductive-bridging RAM (CBRAM) have been explored for the hardware implementation of NNs. Spin-based memory devices demonstrate much more energy efficiency while providing superior information processing capabilities than other NVMs. These devices also provide high endurance, scalability, CMOS compatibility, and ultralow power consumption characteristics. The spiking functionality in neurons can be easily mapped to various spin devices with synaptic learning, therefore, paving the path for future AI hardware solutions.

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ABSTRACTThis paper discourses the variations in methods of fabrication and modeling of Through-Silicon Vias (TSVs) in chronological order. Three-dimensional (3D) integration is an emerging technology that forms multi-functioning high-performance ICs by vertically stacking of disparate technologies and components altogether. TSV has the potential to become an essential component in integration and packaging of 3D ICs. However, the limited information about TSV technologies brings the challenge in selecting the appropriate filler material of TSVs. This review paper overviews the copper (Cu)- and carbon nanotube (CNT)-based TSVs and their pros and cons to be established as suitable filler material for 3D TSVs. The equivalent electrical models of both Cu and CNT-based TSVs are discussed addressing their advantages and disadvantage to reason out the most promising method for the modeling of TSVs.

•Navigation is challenging in natural environments.•Insects have evolved sophisticated navigation strategies with small nervous systems.•Insect navigation is a model for developing strategies for artificial systems.•Virtual reality (VR) is an excellent tool to study insect ethology.•Natural history and ecology are essential for understanding real world navigation.