Notes

Spliceosome-regulated RSRP1-dependent NF-κB activation promotes your glioblastoma mesenchymal phenotype.
A flexible fabric gas sensor for the detection of sub-ppm-level NH3is reported in this paper. The reduced graphene oxide (rGO)-polyaniline (PANI) nanocomposite was successfully coated on cotton thread via anin situpolymerization technique. The morphology, microstructure and composition were analyzed by field-emission scanning electron microscope, x-ray diffraction, Fourier transform infrared spectroscopy and Raman spectroscopy. Furthermore, we have studied the responses of the rGO-PANI nanocomposite-based flexible sensors for the detection of NH3varying from 1-100 ppm, operated at 22 °C. At the optimized concentration of rGO, the response of these sensors increased by 4-5 times in comparison with the pristine rGO and PANI. These flexible sensors exhibited fast response, remarkable long-term stability, good selectivity and a low detection limit. The sensing mechanism for the high sensing performance has been thoroughly discussed and it is mainly due to the distinctive 1D fiber structure, the formation of a p-p heterojunction between the rGO nanosheets and PANI. The rGO-PANI composite-based fabric sensor with low power consumption is a potential flexible electronic device for the detection of NH3.Gas sensor technology is widely utilized in various areas ranging from home security, environment and air pollution, to industrial production. It also hold great promise in non-invasive exhaled breath detection and an essential device in future internet of things. The past decade has witnessed giant advance in both fundamental research and industrial development of gas sensors, yet current efforts are being explored to achieve better selectivity, higher sensitivity and lower power consumption. The sensing layer in gas sensors have attracted dominant attention in the past research. In addition to the conventional metal oxide semiconductors, emerging nanocomposites and graphene-like two-dimensional materials also have drawn considerable research interest. This inspires us to organize this comprehensive 2020 gas sensing materials roadmap to discuss the current status, state-of-the-art progress, and present and future challenges in various materials that is potentially useful for gas sensors.With the incorporation of carbon nanotubes (CNTs), CNT/polypropylene (PP) nanocomposites are found to possess enhanced mechanical properties, but the reinforcing effect is reduced at large added CNT weight percentages due to CNT aggregation. Optimizing the properties of a nanocomposite requires a fundamental understanding of the effects of CNT dispersion on the nanocomposite. In this work, coarse-grained molecular models of CNT/PP nanocomposites are constructed, which consist of randomly dispersed or aggregated CNT bundles. Our simulation results reveal that with randomly dispersed CNT bundles, the nanocomposite shows properties that continuously improve with increasing CNT contents due to the effective CNT/PP interface and the reinforcing effect of CNTs. By comparison, the nanocomposite with aggregated CNT clusters exhibits a decline in yield strength at CNT contents over 3 wt%, which results from a reduced CNT load-carrying capacity due to the formation of structural voids in the interfacial region. This study achieves anin situobservation of the structural void evolution of loaded nanocomposites, provides valuable insights into the effects of CNT dispersion on the mechanics of CNT/PP nanocomposites, and paves the way for optimizing the design of nanocomposites with superior mechanical properties by designing the CNT dispersion in the structure.The elaborate design and synthesis of low-cost, efficient and stable electrocatalysts for the oxygen evolution reaction (OER), which may alleviate the current energy shortage and environment pollution, is still a great challenge. Herein, metal phosphonate precursors with controllable morphologies were synthesizedin situon the surface of nickel foam with different solvents, and could be easily converted into carbon- and nitrogen-doped cobalt phosphate through a calcination method. The OER catalytic performance of the final products was studied in detail. The results showed that the nanowire shaped samples of CoPiNF-800 synthesized with deionized water under hydrothermal conditions had the strongest electrochemical performance. They exhibited extraordinary catalytic activity with a very low overpotential of 222 mV at 100 mA cm-2, the smallest impedance and excellent electrochemical stability. These results not only demonstrate the possibility of preparing low-cost OER catalysts based on transition metal phosphate, but also aid our understanding of the controllable synthesis process of different morphologies.Inorganic scintillators are widely used for fast timing applications in high-energy physics (HEP) experiments, time-of-flight positron emission tomography and time tagging of soft and hard x-ray photons at advanced light sources. As the best coincidence time resolution (CTR) achievable is proportional to the square root of the scintillation decay time it is worth studying fast cross-luminescence, for example in BaF2which has an intrinsic yield of about 1400 photons/MeV. However, emission bands in BaF2are located in the deep-UV at 195 nm and 220 nm, which sets severe constraints on photodetector selection. Recent developments in dark matter and neutrinoless double beta decay searches have led to silicon photomultipliers (SiPMs) with photon detection efficiencies of 20%-25% at wavelengths of 200 nm. We tested state-of-the-art devices from Fondazione Bruno Kessler and measured a best CTR of 51 ± 5 ps full width at half maximum when coupling 2 mm × 2 mm × 3 mm BaF2crystals excited by 511 keV electron-positron annihilation gammas. Using these vacuum ultraviolet SiPMs we recorded the scintillation kinetics of samples from Epic Crystal under 511 keV excitation, confirming a fast decay time of 855 ps with 12.2% relative light yield and 805 ns with 84.0% abundance, together with a smaller rise time of 4 ps beyond the resolution of our setup. The total intrinsic light yield was determined to be 8500 photons/MeV. We also revealed a faster component with 136 ps decay time and 3.7% light yield contribution, which is extremely interesting for the fastest timing applications. Timing characteristics and CTR results on BaF2samples from different producers and with different dopants (yttrium, cadmium and lanthanum) are given, and clearly show that the the slow 800 ns emission can be effectively suppressed. Such results ultimately pave the way for high-rate ultrafast timing applications in medical diagnosis, range monitoring in proton or heavy ion therapy and HEP.Elaborating the sensitization effects of different noble metals on In2O3has great significance in providing an optimum method to improve ethanol sensing performance. In this study, long-range ordered mesoporous In2O3has been fabricated through replicating the structure of SBA-15. STO-609 Different noble metals (Au, Ag, Pt and Pd) with the same doping amount (1 at%) have been introduced by anin situdoping routine. The results of the gas sensing investigation indicate that the gas responses towards ethanol can be obviously increased by doping different noble metals. In particular, the best sensing performance towards ethanol detection can be achieved through Pd doping, and the sensors based on Pd-doped In2O3not only possess the highest response (39.0-100 ppm ethanol) but also have the shortest response and recovery times at the optimal operating temperature of 250 °C. The sensing mechanism of noble metal doped materials can be attributed to the synergetic effect combining 'catalysis' and 'electronic and chemical sensitization' of noble metals. In particular, the chemical state of the noble metal also has a great influence on the gas sensing mechanism. A detailed explanation of the enhancement of gas sensing performance through noble metal doping is presented in the gas sensing mechanism part of the manuscript.SnO2is considered as one of the high specific capacity anode materials for Lithium-ion batteries. However, the low electrical conductivity of SnO2limits its applications. This manuscript reports a simple and efficient approach for the synthesis of Sb-doped SnO2nanowires (NWs) core and carbon shell structure which effectively enhances the electrical conductivity and electrochemical performance of SnO2nanostructures. Sb doping was performed during the vapor-liquid-solid synthesis of SnO2NWs in a horizontal furnace. Subsequently, carbon nanolayer was coated on the NWs using the DC Plasma Enhanced Chemical Vapor Deposition approach. The carbon-coated shell improves the Solid-Electrolyte Interphase stability and alleviates the volume expansion of the anode electrode during charging and discharging. The Sb-doped SnO2core carbon shell anode showed the superior specific capacity of 585 mAhg-1after 100 cycles at the current density of 100 mA g-1, compared to the pure SnO2NWs electrode. The cycle stability evaluation revealed that the discharge capacity of pure SnO2NWs and Sb doped SnO2NWs electrodes were dropped to 52 and 152 mAh g-1after100th cycles. The process of Sb doping and carbon nano shielding of SnO2nanostructures is proposed for noticeable improvement of the anode performance for SnO2based materials.Objective.Implanted devices providing real-time neural activity classification and control are increasingly used to treat neurological disorders, such as epilepsy and Parkinson's disease. Classification performance is critical to identifying brain states appropriate for the therapeutic action (e.g. neural stimulation). However, advanced algorithms that have shown promise in offline studies, in particular deep learning (DL) methods, have not been deployed on resource-restrained neural implants. Here, we designed and optimized three DL models or edge deployment and evaluated their inference performance in a case study of seizure detection.Approach.A deep neural network (DNN), a convolutional neural network (CNN), and a long short-term memory (LSTM) network were designed and trained with TensorFlow to classify ictal, preictal, and interictal phases from the CHB-MIT scalp EEG database. A sliding window based weighted majority voting algorithm was developed to detect seizure events based on each DL model's classifmentations that had no time or computational resource limitations. Generic microcontrollers can provide the required memory and computational resources, while model designs can be migrated to application-specific integrated circuits for further optimization and power saving. The results suggest that edge DL inference is a feasible option for future neural implants to improve classification performance and therapeutic outcomes.To overcome multi-drug resistance in microbes, highly efficient antimicrobial substances are required that have a controllable antibacterial effect and are biocompatible. In the present study, an efficient phototherapeutic antibacterial agent, human serum albumin (HSA)/reduced graphene oxide (rGO)/Cladophora glomeratabionanocomposite was synthesized by the incorporation of rGO nanoparticles with HSA, forming protein-rGO, and decorated with a natural freshwater seaweedCladophora glomerata. The prepared HSA/rGO/Cladophora glomeratabionanocomposite was characterized by spectroscopic (UV-vis, FTIR, XRD and Raman) and microscopic (TEM and SEM) techniques. The as-synthesized bionanocomposite showed that sunlight/NIR irradiation stimulated ROS-generating dual-phototherapic effects against antibiotic-resistant bacteria. The bionanocomposite exerted strong antibacterial effects (above 96 %) against amoxicillin-resistantP. aeruginosaandS. aureus, in contrast to single-model-phototherapy. The bionanocomposite not only generated abundant ROS for killing bacteria, but also expressed a fluorescence image for bacterial tracking under sunlight/NIR irradiation.
Read More: https://www.selleckchem.com/products/sto-609.html