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In recent years, on-chip interconnects have been considered as one of the most challenging areas in ultra-large scale integration. In ultra-small feature size, the interconnect delay becomes more pronounced than the gate delay. The continuous scaling of interconnects introduces significant parasitic effects. The resistivity of interconnects increases because of the grain boundary scattering and side wall scattering of electrons. An increased Joule heating and the low current carrying capability of interconnects in a nano-scale dimension make it unreliable for future technology. The devices resistivity and reliability have become more and more serious problems for choosing the best interconnect materials, like Cu, W, and others. Because of its remarkable electrical and its other properties, graphene becomes a reliable candidate for next-generation interconnects. Graphene is the lowest resistivity material with a high current density, large mean free path, and high electron mobility. For practical implementation, narrow width graphene sheet or graphene nanoribbon (GNR) is the most suitable interconnect material. However, the geometric structure changes the electrical property of GNR to a small extent compared to the ideal behavior of graphene film. In the current article, the structural and electrical properties of single and multilayer GNRs are discussed in detail. Also, the fabrication techniques are discussed so as to pattern the graphene nanoribbons for interconnect application and measurement. A circuit modeling of the resistive-inductive-capacitive distributed network for multilayer GNR interconnects is incorporated in the article, and the corresponding simulated results are compared with the measured data. The performance of GNR interconnects is discussed from the view of the resistivity, resistive-capacitive delay, energy delay product, crosstalk effect, stability analysis, and so on. The performance of GNR interconnects is well compared with the conventional interconnects, like Cu, and other futuristic potential materials, like carbon nanotube and doped GNRs, for different technology nodes of the International Technology Roadmap for Semiconductors (ITRS).

Conducting polymers (CPs) have garnered significant interest in being used as an active material in gas sensors mainly because of their structural flexibility, ease of synthesis, and enhanced performance at room temperature. The p-type CPs and their composites are mostly studied in gas sensing, which, unfortunately, exhibit limitations in terms of selectivity, stability, and sensitivity toward reducing gases. This study focuses on one of the widely studied n-type polymers, BBL(benzimidazobenzophenanthroline), as an active material for the detection of two reducing gases, namely, hydrogen sulfide (H S) and ammonia (NH ), theoretically. Through molecular dynamics (MD) simulation and density functional theory (DFT) approach, we understand the adsorption behavior and selectivity of H S and NH in the BBL film. The DFT calculated adsorption energy of the preferential site at the top of a stack for H S and NH are – 0.22 eV and – 0.33 eV, respectively, and at the sides of a stack for H S and NH are – 0.42 eV and – 0.47 eV, respectively. MD simulations show that adsorption takes place in the free voids within the thin films, and the overall structure of the polymer film remained almost unaltered upon gas adsorption without any apparent swelling or significant morphological changes in the film. Our results show that BBL displays remarkable adsorption along with a higher magnitude of charge transfer for ammonia over hydrogen sulfide gas and other common gases present in the air. Moreover, both H S and NH gas adsorption happen without compromising the size of the stacked crystallites within the polymer film, which indicates, upon detection of reducing gases, the generated free electrons via the redox reactions between the gas molecules and polymer, will be able to be smoothly transported through the stack network present in the film. The detailed theoretical insights obtained from this study indicate the suitability of the n-type conducting polymer, BBL, for detecting reducing gases, NH and H S.

Phagosome-lysosome fusion in innate immune cells like macrophages and neutrophils marshal an essential role in eliminating intracellular microorganisms. In microbe-challenged macrophages, phagosome-lysosome fusion occurs 4 to 6 h after the phagocytic uptake of the microbe. However, live pathogenic mycobacteria hinder the transfer of phagosomes to lysosomes, up to 20 h post-phagocytic uptake. This period is required to evade pro-inflammatory response and upregulate the acid-stress tolerant proteins. The exact sequence of events through which mycobacteria retards phagolysosome formation remains an enigma. The macrophage coat protein Coronin1(Cor1) is recruited and retained by mycobacteria on the phagosome membrane to retard its maturation by hindering the access of phagosome maturation factors. Mycobacteria-infected macrophages exhibit an increased cAMP level, and based on receptor stimulus, Cor1 expressing cells show a higher level of cAMP than non-Cor1 expressing cells. Here we have shown that infection of bone marrow-derived macrophages with H37Rv causes a Cor1 dependent rise of intracellular cAMP levels at the vicinity of the phagosomes. This increased cAMP fuels cytoskeletal protein Cofilin1 to depolymerize F-actin around the mycobacteria-containing phagosome. Owing to reduced F-actin levels, the movement of the phagosome toward the lysosomes is hindered, thus contributing to the retarded phagosome maturation process. Additionally, Cor1 mediated upregulation of Cofilin1 also contributes to the prevention of phagosomal acidification, which further aids in the retardation of phagosome maturation. Overall, our study provides first-hand information on Cor1 mediated retardation of phagosome maturation, which can be utilized in developing novel peptidomimetics as part of host-directed therapeutics against tuberculosis.

Accurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep convolutional neural network-based framework for the detection of COVID-19 status from chest X-ray and CT scan imaging data acquired from three benchmark imagery datasets. VGG-19, ResNet-50 and Inception-V3 models are employed in this research study to perform image classification. A variety of evaluation metrics including kappa statistic, Root-Mean-Square Error (RMSE), accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Recall, precision, and F-measure are used to ensure adequate performance of the proposed framework. Our findings indicate that the Inception-V3 model has the best performance in terms of COVID-19 status detection.

The graphene field-effect transistor (GFET) has the severe drawback of the device turn off owing to the zero band gap of graphene that results in a limited on/off current ratio at room temperature. In this report, we propose a hybrid channel of few graphene oxide (GO) implanted with undoped p -type anatase TiO 2 nanoparticles (~16 nm) to achieve a depletion type (normally-on) FET with very high on/off ratio at room temperature (300 K). The percentage of GO and TiO 2 is optimized based on the performance of FET where 99 vol% GO (0.2 wt%) having 1 vol% TiO 2 (0.14 M) exhibited on/off current ratio of 2.8 × 10 3 ( I ON at V GS =0 V and I OFF at V GS =1.2 V), the acceptable transconductance of 0.286 µS and high transport gap of 54.2 meV at room temperature. The remarkable performance improvement in GO/ p -TiO 2 hybrid FET is achieved by two distinct effects, i.e., (i) the hole accumulation in GO channel due to interfacial charge transfer between GO and p -TiO 2 and (ii) the formation of high potential barrier in GO/ p -TiO 2 /GO junctions near the Dirac point voltage.

The present study concerns the synthesis of highly ordered graphene oxide (GO) loaded TiO2 nanotubes array by the electrochemical anodization route. Structural and morphological characterizations of pure and GO loaded TiO2 nanotubes array were carried out by x-ray diffraction spectroscopy, energy dispersive spectroscopy and field emission scanning electron microscopy study. Optical characterizations were performed with Raman spectroscopy and photoluminescence study to explore the composition of both the TiO2 nanotubes array. A conductometric solid state vapor sensing device having sandwich-type structure (Au/TiO2 nanotubes/Ti) was fabricated by using both the pure and GO-loaded TiO2 nanotube array and tested towards reducing vapor like methanol. The response was double in case of GO loaded TiO2 nanotube array (40%) as compared to pure TiO2 nanotube array (20%) based sensor at room temperature (300 K). The overall study confirmed that electrical properties of the TiO2 nanotubes array were improved due to the GO incorporation while morphological parameters were intact.

In this work, we estimate extreme sea surface temperature (SST) hotspots, that is, high threshold exceedance regions, for the Red Sea, a vital region of high biodiversity. We analyze high-resolution satellite-derived SST data comprising daily measurements at 16,703 grid cells across the Red Sea over the period 1985–2015. We propose a semiparametric Bayesian spatial mixed-effects linear model with a flexible mean structure to capture spatiallyvarying trend and seasonality, while the residual spatial variability is modeled through a Dirichlet process mixture (DPM) of low-rank spatial Student's 𝑡 processes (LTPs). By specifying cluster-specific parameters for each LTP mixture component, the bulk of the SST residuals influence tail inference and hotspot estimation only moderately. Our proposed model has a nonstationary mean, covariance, and tail dependence, and posterior inference can be drawn efficiently through Gibbs sampling. In our application, we show that the proposed method outperforms some natural parametric and semiparametric alternatives. Moreover, we show how hotspots can be identified, and we estimate extreme SST hotspots for the whole Red Sea, projected until the year 2100, based on the Representative Concentration Pathways 4.5 and 8.5. The estimated 95% credible region, for joint high threshold exceedances include large areas covering major endangered coral reefs in the southern Red Sea.

A one-dimensional TiO2 nanorod-based thin film transistor (TFT) for butanone sensing is presented here. A low-cost hydrothermal process was used to deposit TiO2 nanorods on a Si/SiO2 substrate. X-ray diffraction, field-emission scanning electron microscopy (FESEM), photoluminescence spectroscopy, and Raman spectroscopy were used to examine the structural, morphological, and optical features of the nanostructure. Formation of aligned nanorods as the carrier transport channels in FET structure were confirmed through FESEM. Electrical characterization revealed the threshold voltage (VT), mobility (µ), transconductance (gm), Ion/Ioff ratio, and sub-threshold swing (SS) as 0.77 V, 12.2 cm2/V-s, 5.37 mS, 0.6 × 104, and 64.63 mV/dec, respectively. Sensor study exhibited an increase in the drain current (Id) and shifts in the threshold voltage (VT) upon exposure to different concentrations of butanone with respect to those in air. These two effects were correlated with the de-trapping of charge carriers at surface sites in association with resistance variation in the sensing layer. However, the room- temperature response towards butanone (82%) at Vgs of 3 V were found to be 16 times greater than the response under a no biasing (5%) condition. The repeatability of the as-fabricated and aged TFT indicates the potentiality of the device over conventional device structures even in harsh environments.