Effect of Structural Changes at Various Length Scales in SiVOC Ceramic Nanocomposites on Electrocatalytic Performance for the Oxygen Reduction Reaction.

Polymer-derived processing of ceramics (PDC) is an efficient technique to prepare porous nanocomposites with precise control over their phase composition and in relation to the Si-based ceramic matrix containing free carbon. The microstructure of these nanocomposites can be fine-tuned at the molecular scale for obtaining necessary properties by tailoring the chemical configuration of the preceramic polymer. In the present work, vanadium-based nanocomposites were synthesized as oxygen reduction reaction (ORR) catalysts with the objective of elucidating the effect of microstructure changes on catalytic efficiency. For this purpose, a single-source precursor (SSP) was synthesized by crosslinking phenyl- and hydrido-substituted polysiloxane and vanadium acetylacetonate followed by pyrolysis at 1100 °C. The resulting solid was composed of sparsely distributed nanodomains of vanadium carbide (VC) crystals precipitated within an amorphous silicon oxycarbide (-Si-O-C-) matrix. High-temperature treatment of the pyrolyzed samples beyond 1300 °C induced the crystallization of ß-SiC as well as VC. Furthermore, Raman spectroscopy confirmed the segregation of sp2-hybridized, turbostratic free carbon. The samples exposed to 1300 °C revealed a specific surface area of 239 m2/g. The electrocatalytic activity of the sample heat-treated at 1300 °C showed the best performance with respect to the ORR performance with onset potential (Eo) and half-wave potential (E1/2) values of 0.81 and 0.72 V, respectively. In addition, improved kinetics with a Tafel slope of 57 mV/dec and enhanced current density in the diffusion-controlled region (Id) of 3.7 mA/cm2 were observed for this sample. The increase in Eo was attributed to the optimal interfacial characteristics between the VC and SiOC matrix with better embedment of VC with free carbon through V-C bonds. The higher E1/2 and faster kinetics are because of the higher electronic conductivity caused by the free carbon effectively connecting metallic VC crystallites. Besides, the higher specific surface area of this sample enhanced Id.

Sub PPM Detection of NO2 Using Strontium Doped Bismuth Ferrite Nanostructures.

The present work investigates the NO2 sensing properties of acceptor-doped ferrite perovskite nanostructures. The Sr-doped BiFeO3 nanostructures were synthesized by a salt precursor-based modified pechini method and characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The synthesized materials were drop coated to fabricate chemoresistive gas sensors, delivering a maximum sensitivity of 5.2 towards 2 ppm NO2 at 260 °C. The recorded values of response and recovery time are 95 s and 280 s, respectively. The sensor based on Bi0.8Sr0.2FeO3-δ (BSFO) that was operated was shown to have a LOD (limit of detection) as low as 200 ppb. The sensor proved to be promising for repeatability and selectivity measurements, indicating that the Sr doping Bismuth ferrite could be a potentially competitive material for sensing applications. A relevant gas-sensing mechanism is also proposed based on the surface adsorption and reaction behavior of the material.

Combating Li metal deposits in all-solid-state battery via the piezoelectric and ferroelectric effects.

All-solid-state Li-metal batteries (ASSLBs) are highly desirable, due to their inherent safety and high energy density; however, the irregular and uncontrolled growth of Li filaments is detrimental to interfacial stability and safety. Herein, we report on the incorporation of piezo-/ferroelectric BaTiO3 (BTO) nanofibers into solid electrolytes and determination of electric-field distribution due to BTO inclusion that effectively regulates the nucleation and growth of Li dendrites. Theoretical simulations predict that the piezoelectric effect of BTO embedded in solid electrolyte reduces the driving force of dendrite growth at high curvatures, while its ferroelectricity reduces the overpotential, which helps to regularize Li deposition and Li+ flux. Polarization reversal of soft solid electrolytes was identified, confirming a regular deposition and morphology alteration of Li. As expected, the ASSLBs operating with LiFePO4/Li and poly(ethylene oxide) (PEO)/garnet solid electrolyte containing 10% BTO additive showed a steady and long cycle life with a reversible capacity of 103.2 mAh g-1 over 500 cycles at 1 C. Furthermore, the comparable cyclability and flexibility of the scalable pouch cells prepared and the successful validation in the sulfide electrolytes, demonstrating its universal and promising application for the integration of Li metal anodes in solid-state batteries.

A View Independent Classification Framework for Yoga Postures.

Yoga is a globally acclaimed and widely recommended practice for a healthy living. Maintaining correct posture while performing a Yogasana is of utmost importance. In this work, we employ transfer learning from human pose estimation models for extracting 136 key-points spread all over the body to train a random forest classifier which is used for estimation of the Yogasanas. The results are evaluated on an in-house collected extensive yoga video database of 51 subjects recorded from four different camera angles. We use a three step scheme for evaluating the generalizability of a Yoga classifier by testing it on (1) unseen frames, (2) unseen subjects, and (3) unseen camera angles. We argue that for most of the applications, validation accuracies on unseen subjects and unseen camera angles would be most important. We empirically analyze over three public datasets, the advantage of transfer learning and the possibilities of target leakage. We further demonstrate that the classification accuracies critically depend on the cross validation method employed and can often be misleading. To promote further research, we have made key-points dataset and code publicly available. Supplementary Information: The online version contains supplementary material available at 10.1007/s42979-022-01376-7.

Machine learning based reanalysis of clinical scores for distinguishing between ischemic and hemorrhagic stroke in low resource setting.

BACKGROUND: Identifying ischemic or hemorrhagic strokes clinically may help in situations where neuroimaging is unavailable to provide primary-care prior to referring to stroke-ready facility. Stroke classification-based solely on clinical scores faces two unresolved issues. One pertains to overestimation of score performance, while other is biased performance due to class-imbalance inherent in stroke datasets. After correcting the issues using Machine Learning theory, we quantitatively compared existing scores to study the capabilities of clinical attributes for stroke classification. METHODS: We systematically searched PubMed, ERIC, ScienceDirect, and IEEE-Xplore from 2001 to 2021 for studies that validated the Siriraj, Guys Hospital/Allen, Greek, and Besson scores for stroke classification. From included studies we extracted the reported cross-tabulation to identify and correct the above listed issues for an accurate comparative analysis of the performance of clinical scores. RESULTS: A total of 21 studies were included. Comparative analysis demonstrates Siriraj Score outperforms others. For Siriraj Score the reported sensitivity range (Ischemic Stroke-diagnosis) 43-97% (Median = 78% [IQR 65-88%]) is significantly higher than our calculated range 40-90% (Median = 70% [IQR 57-73%]), also the reported sensitivity range (Hemorrhagic Stroke-diagnosis) 50-95% (Median = 71% [IQR 64-82%]) is higher than our calculated range 34-86% (Median = 59% [IQR 50-79%]) which indicates overestimation of performance by the included studies. Guys Hospital/Allen and Greek Scores show similar trends. Recommended weighted-accuracy metric provides better estimate of the performance. CONCLUSION: We demonstrate that clinical attributes have a potential for stroke classification, however the performance of all scores varies across demographics, indicating the need to fine-tune scores for different demographics. To improve this variability, we suggest creating global data pool with statistically significant attributes. Machine Learning classifiers trained over such dataset may perform better and generalise at scale.

Tantalum Oxide as an Efficient Alternative Electron Transporting Layer for Perovskite Solar Cells.

Electron transporting layers facilitating electron extraction and suppressing hole recombination at the cathode are crucial components in any thin-film solar cell geometry, including that of metal-halide perovskite solar cells. Amorphous tantalum oxide (Ta2O5) deposited by spin coating was explored as an electron transport material for perovskite solar cells, achieving power conversion efficiency (PCE) up to ~14%. Ultraviolet photoelectron spectroscopy (UPS) measurements revealed that the extraction of photogenerated electrons is facilitated due to proper alignment of bandgap energies. Steady-state photoluminescence spectroscopy (PL) verified efficient charge transport from perovskite absorber film to thin Ta2O5 layer. Our findings suggest that tantalum oxide as an n-type semiconductor with a calculated carrier density of ~7 × 1018/cm3 in amorphous Ta2O5 films, is a potentially competitive candidate for an electron transport material in perovskite solar cells.

Microstructural, electrical and biological activity in [Formula: see text] ceramic composites designed for tissue engineering applications.

The article investigates electrically active ceramic composite of [Formula: see text] (HAP) and [Formula: see text] (BST) for biomedical applications. The study is a systematic blend of the materials science aspect of composites with a special focus on the dielectric and biological properties and their relationships. The article emphasized primarily extracting the dielectric constant ([Formula: see text] of the specimens (that lay in the range of 3-65) and related them to microstructural properties like the grain size and at.% of BST. A broad outlook on the importance of [Formula: see text] in determining the suitability of bioceramics for clinical applications is presented. Bioactivity analysis of the specimens led to probing the surface charges (that were negative), and it was found crucial to the growth of dense apatite layers. Furthermore, the cytocompatibility of the specimens displayed cell viability above 100% for Day 1, which increased substantially for Day 3. To reveal other biological properties of the composites, protein adsorption studies using bovine serum albumin (BSA) and fetal bovine serum (FBS) was carried out. Electrostatic interactions govern the adsorption, and the mathematical dependence on surface charges is linear. The protein adsorption is also linearly correlated with the [Formula: see text], intrinsic to the biomaterials. We delve deeper into protein-biomaterials interactions by considering the evolution of the secondary structure of BSA adsorbed into the specimens. Based on the investigations, 20 at.% HAP-80 at.% BST (20H-80B) was established as a suitable composite comprising the desired features of HAP and BST. Such explorations of electrical and biological properties are interesting for modulating the behavior of bioceramic composites. The results project the suitability of 20H-80B for designing electrically active smart scaffolds for the proposed biomedical applications and are expected to incite further clinical trials.

Ultrahigh-sensitive mixed-potential ammonia sensor using dual-functional NiWO4 electrocatalyst for exhaust environment monitoring.

The exhaust monitoring for in-situ quantification of gas pollutants has always been a challenge due to the harsh thermo-chemical environments, for which the solid-electrolyte based gas sensors appear as a realistic solution. In this work, an ultrahigh-sensitive mixed-potential ammonia sensor was developed using a new dual-functional NiWO4 electrocatalyst, synthesized through a low-temperature molten-salt synthesis route. The electrode morphology and diffusion lengths were tuned for optimum performance. The sensor operated at 550 ℃ displayed response of -100 mV to 80 ppm NH3, with response/recovery times of 28/68 s and a record-high sensitivity of 90 mV/decade. Besides, it displayed excellent selectivity and trace-level NH3 detection ability upto 400 ppb. While examining the sensing mechanism, the sensor exhibited an NH3 concentration-dependent transformation of rate-determining kinetics from charge-transfer limited Butler-Volmer type to diffusional mass-transport limited reaction kinetics. Moreover, the remarkable long-term stability with negligible response degradation (< 4%) confirms the suitability of the sensor for exhaust environment monitoring.

Structural and Magnetic Properties of Ni1+x MnSb Bulk Heusler Composite Materials.

Variation in structural and magnetic properties with changing valence electron count (VEC) has been studied well in the family of Heusler compounds, while such changes in VEC resulting in half-Heusler (HH) and full-Heusler (FH) composites have not been reported to observe their effect on the magnetic properties. Herein, we have synthesized the composite of HH and FH phases in Ni1+x MnSb (x = 0.0, 0.3, and 0.6) via changing VEC from 22 to 28 in order to investigate the structural and magnetic properties. Interestingly, a transition from half-metallic ferromagnetic to normal ferromagnetic was revealed in Ni1+x MnSb (x = 0.0, 0.3, and 0.6) materials with increasing VEC. The structural investigations of these materials were performed using a X-ray diffraction technique and analyzed by Rietveld Refinement software for all the samples. Rietveld analysis reveals the presence of a significant amount of the NiSb paramagnetic impurity phase in the HH NiMnSb system while in the case of Ni1+x MnSb (x = 0.3 and 0.6), no such impurity phase was observed. Only FH and HH phases in Ni1+x MnSb (x = 0.3 and 0.6) samples were noticed. The magnetic measurement performed on samples employing a vibrating sample magnetometer reveals the ferromagnetic ordering in all samples. A weak hysteresis loop with saturated magnetic moments ∼2.99 and 2.98 µB at room temperature was observed for NiMnSb and Ni1.3MnSb, respectively, while a strong hysteresis loop with lower magnetic moment of 0.88 µB was observed in the Ni1.6MnSb composite. Furthermore, the observed magnetic moments for the composite Ni1.3MnSb have been explained on the basis of the Slater-Pauling rule in relation to VEC.

Surface decorated spinel-oxide electrodes for mixed-potential ammonia sensor: Performance and DRT analysis.

The mixed-potential gas sensors appeared as the most promising sensing technology for the in-situ quantification of exhaust pollutants due to their simple configuration, low-cost, and thermochemical stability. Presently, high sensitivity and selectivity supplemented by long-term stability is the bottleneck challenge for these sensors to commercialize. Herein, highly sensitive and ammonia (NH3) selective mixed-potential gas sensors were developed using surface decorated CuFe2O4 (CFO)-MOX (M = Sn, Ni, Zn) composite sensing electrodes (SE). The CFO-NiO SE enriched of the surface oxygen vacancies produced a maximum response of -62 mV to 80 ppm NH3, supported by excellent sensitivity at 650 ℃. The comprehensive analysis of the response behavior and current-voltage (I-V) characteristics verified the sensing mechanism to be based upon the mixed-potential model conforming to the reaction-rate limited Butler-Volmer NH3 oxidation kinetics. Finally, the distribution of relaxation times (DRT) analysis of impedance spectra confirmed that the overall polarization resistance was invariable of the mass-transport processes and solely governed by the extent of interfacial redox reactions proceeding at the triple-phase boundaries (TPB). Moreover, the high sensitivity, selectivity, and exceptional stability over five months substantiate the suitability of the presented sensor as a potential candidate for in-situ ammonia quantifications in industrial and automotive applications.

Assessing the Utility of GeneXpert MTB/Rif Assay in a Tertiary Care Centre in Southern India with Established Microscopy and Liquid Culture Facilities.

BACKGROUND: To evaluate the diagnostic accuracy of the Xpert MTB/RIF assay for the detection of M. tuberculosis in pulmonary and extra pulmonary specimens and to compare it with conventional techniques. METHODS: A prospective study was conducted with the introduction of GeneXpert in a tertiary care hospital which relied on microscopy and culture for diagnosis of tuberculosis. All patients for whom geneXpert was ordered by the physician were included in the study. Samples which did not have accompanying microscopy or MGIT culture requests were excluded from the analysis of the results. Sensitivity and specificity of GeneXpert was calculated using liquid culture as the reference test. RESULTS: Xpert MTB/RIF assay was performed on 742 samples of which 116 were positive for Mycobacterium tuberculosis. Rifampicin resistance was seen in 6 samples. The pulmonary samples showed a positivity rate of 16.8% while 17.1% of the extrapulmonary samples were positive by GeneXpert. A comparative analysis of microscopy, liquid culture and GeneXpert could be done for 88 samples. Of the 88 geneXpert positive samples, 42 were positive by smear microscopy and MGIT culture while 46 showed discordant results. Of these, 18 samples were positive by geneXpert but showed no growth in MGIT culture. 15 of these patients had undergone anti-tuberculous treatment (ATT) within the past 12 months. The sensitivity of geneXpert was 89.7% and specificity was 95.1% when compared to liquid culture as a gold standard. Sensitivity for extrapulmonary samples was 85.7% and specificity was 98.05%. CONCLUSION: To conclude, though GeneXpert detects tuberculosis within the shortest possible time, it still suffers from intermediate level sensitivity, which makes culture facilities relevant even in settings that offer an Xpert/Rif assay.

ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis.

Current Zika virus (ZIKV) outbreaks that spread in several areas of Africa, Southeast Asia, and in pacific islands is declared as a global health emergency by World Health Organization (WHO). It causes Zika fever and illness ranging from severe autoimmune to neurological complications in humans. To facilitate research on this virus, we have developed an integrative multi-omics platform; ZikaVR (http://bioinfo.imtech.res.in/manojk/zikavr/), dedicated to the ZIKV genomic, proteomic and therapeutic knowledge. It comprises of whole genome sequences, their respective functional information regarding proteins, genes, and structural content. Additionally, it also delivers sophisticated analysis such as whole-genome alignments, conservation and variation, CpG islands, codon context, usage bias and phylogenetic inferences at whole genome and proteome level with user-friendly visual environment. Further, glycosylation sites and molecular diagnostic primers were also analyzed. Most importantly, we also proposed potential therapeutically imperative constituents namely vaccine epitopes, siRNAs, miRNAs, sgRNAs and repurposing drug candidates.