Ivermectin for treatment of COVID-19: A systematic review and meta-analysis.

The effect of ivermectin (IVM) in treating coronavirus disease 2019 (COVID-19) is still controversial, yet the drug has been widely used in the world. The aim of this review was to systematically evaluate the clinical outcomes of IVM in patients with COVID-19. From inception to June 22, 2023, the PubMed, EMBASE, Web of Science (WOS), and scopus databases were searched for relevant observational studies on the risk of RA in migraineurs. We searched PubMed/Medline, EMBASE, the Cochrane Library, Web of Science, medRxiv, and bioRxiv to collect all relevant publications from inception to June 22, 2023. Primary outcomes were all-cause mortality rate, mechanical ventilation (MV) requirement, PCR negative conversion, and adverse events (AEs). Revman 5.4 was used to assess the risk of bias (RoB) and quality of evidence. Thirty-three RCTs (n = 10,489) were included. No significant difference in all-cause mortality rates or PCR negative conversion between IVM and controls. There were significant differences in MV requirement (RR 0.67, 95% CI 0.47-0.96) and AEs (RR 0.87, 95% CI 0.80-0.95) between the two groups. Ivermectin could reduce the risk of MV requirement and AEs in patients with COVID-19, without increasing other risks. In the absence of a better alternative, clinicians could use it with caution.

Ultra-Sensitive and Stable Multiplexed Biosensors Array in Fully Printed and Integrated Platforms for Reliable Perspiration Analysis.

Electrochemical biosensors have emerged as one of the promising tools for tracking human body physiological dynamics via non-invasive perspiration analysis. However, it remains a key challenge to integrate multiplexed sensors in a highly controllable and reproducible manner to achieve long-term reliable biosensing, especially on flexible platforms. Herein, a fully inkjet printed and integrated multiplexed biosensing patch with remarkably high stability and sensitivity is reported for the first time. These desirable characteristics are enabled by the unique interpenetrating interface design and precise control over active materials mass loading, owing to the optimized ink formulations and droplet-assisted printing processes. The sensors deliver sensitivities of 313.28 µA mm-1 cm-2 for glucose and 0.87 µA mm-1 cm-2 for alcohol sensing with minimal drift over 30 h, which are among the best in the literature. The integrated patch can be used for reliable and wireless diet monitoring or medical intervention via epidermal analysis and would inspire the advances of wearable devices for intelligent healthcare applications.

Adaptive Design of Alloys for CO2 Activation and Methanation via Reinforcement Learning Monte Carlo Tree Search Algorithm.

Data-driven machine learning (ML) has earned remarkable achievements in accelerating materials design, while it heavily relies on high-quality data acquisition. In this work, we develop an adaptive design framework for searching for optimal materials starting from zero data and with as few DFT calculations as possible. This framework integrates automatic density functional theory (DFT) calculations with an improved Monte Carlo tree search via reinforcement learning algorithm (MCTS-PG). As a successful example, we apply it to rapidly identify the desired alloy catalysts for CO2 activation and methanation within 200 MCTS-PG steps. To this end, seven alloy surfaces with high theoretical activity and selectivity for CO2 methanation are screened out and further validated by comprehensive free energy calculations. Our adaptive design framework enables the fast computational exploration of materials with desired properties via minimal DFT calculations.

Distilling universal activity descriptors for perovskite catalysts from multiple data sources via multi-task symbolic regression.

Developing activity descriptors via data-driven machine learning (ML) methods can speed up the design of highly active and low-cost electrocatalysts. Despite the fact that a large amount of activity data for electrocatalysts is stored in the literature, data from different publications are not comparable due to different experimental or computational conditions. In this work, an interpretable ML method, multi-task symbolic regression, was adopted to learn from data in multiple experiments. A universal activity descriptor to evaluate the oxygen evolution reaction (OER) performance of oxide perovskites free of calculations or experiments was constructed and reached high accuracy and generalization ability. Utilizing this descriptor with Bayesian-optimized parameters, a series of compelling double perovskites with excellent OER activity were predicted and further evaluated using first-principles calculations. Finally, the two ML-predicted nickel-based perovskites with the best OER activity were successfully synthesized and characterized experimentally. This work opens a new way to extend machine-learning material design by utilizing multiple data sources.

Temperature-Modulated Selective Detection of Part-per-Trillion NO2 Using Platinum Nanocluster Sensitized 3D Metal Oxide Nanotube Arrays.

Semiconductor chemiresistive gas sensors play critical roles in a smart and sustainable city where a safe and healthy environment is the foundation. However, the poor limits of detection and selectivity are the two bottleneck issues limiting their broad applications. Herein, a unique sensor design with a 3D tin oxide (SnO2 ) nanotube array as the sensing layer and platinum (Pt) nanocluster decoration as the catalytic layer, is demonstrated. The Pt/SnO2 sensor significantly enhances the sensitivity and selectivity of NO2 detection by strengthening the adsorption energy and lowering the activation energy toward NO2 . It not only leads to ultrahigh sensitivity to NO2 with a record limit of detection of 107 parts per trillion, but also enables selective NO2 sensing while suppressing the responses to interfering gases. Furthermore, a wireless sensor system integrated with sensors, a microcontroller, and a Bluetooth unit is developed for the practical indoor and on-road NO2 detection applications. The rational design of the sensors and their successful demonstration pave the way for future real-time gas monitoring in smart home and smart city applications.

Inverse design with deep generative models: next step in materials discovery.

Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery.

Microheater Integrated Nanotube Array Gas Sensor for Parts-Per-Trillion Level Gas Detection and Single Sensor-Based Gas Discrimination.

Real-time monitoring of health threatening gases for chemical safety and human health protection requires detection and discrimination of trace gases with proper gas sensors. In many applications, costly, bulky, and power-hungry devices, normally employing optical gas sensors and electrochemical gas sensors, are used for this purpose. Using a single miniature low-power semiconductor gas sensor to achieve this goal is hardly possible, mostly due to its selectivity issue. Herein, we report a dual-mode microheater integrated nanotube array gas sensor (MINA sensor). The MINA sensor can detect hydrogen, acetone, toluene, and formaldehyde with the lowest measured limits of detection (LODs) as 40 parts-per-trillion (ppt) and the theoretical LODs of ∼7 ppt, under the continuous heating (CH) mode, owing to the nanotubular architecture with large sensing area and excellent surface catalytic activity. Intriguingly, unlike the conventional electronic noses that use arrays of gas sensors for gas discrimination, we discovered that when driven by the pulse heating (PH) mode, a single MINA sensor possesses discrimination capability of multiple gases through a transient feature extraction method. These above features of our MINA sensors make them highly attractive for distributed low-power sensor networks and battery-powered mobile sensing systems for chemical/environmental safety and healthcare applications.

Impairment of µ-calpain activation by rhTNFR:Fc reduces severe burn-induced membrane disruption in the heart.

Stress cardiomyopathy is a major clinical complication after severe burn. Multiple upstream initiators have been identified; however, the downstream targets are not fully understood. This study assessed the role of the plasma membrane in this process and its relationship with the protease µ-calpain and tumor necrosis factor-alpha (TNF-α). Here, third-degree burn injury of approximately 40% of the total body surface area was established in rats. Plasma levels of LDH and cTnI and cardiac cell apoptosis increased at 0.5 h post burn, reached a peak at 6 h, and gradually declined at 24 h. This effect correlated well with not only the disruption of cytoskeletal proteins, including dystrophin and ankyrin-B, but also with the activation of µ-calpain, as indicated by the cleaved fragments of α-spectrin and membrane recruitment of the catalytic subunit CAPN1. More importantly, these alterations were diminished by blocking calpain activity with MDL28170. Burn injury markedly increased the cellular uptake of Evans blue, indicating membrane integrity disruption, and this effect was also reversed by MDL28170. Compared with those in the control group, cardiac cells in the burn plasma-treated group were more prone to damage, as indicated by a marked decrease in cell viability and increases in LDH release and apoptosis. Of note, these alterations were mitigated by CAPN1 siRNA. Moreover, after neutralizing TNF-α with rhTNFR:Fc, calpain activity was blocked, and heart function was improved. In conclusion, we identified µ-calpain as a trigger for severe burn-induced membrane disruption in the heart and provided evidence for the application of rhTNFR:Fc to inhibit calpain for cardioprotection.

Wireless Self-Powered High-Performance Integrated Nanostructured-Gas-Sensor Network for Future Smart Homes.

The accelerated evolution of communication platforms including Internet of Things (IoT) and the fifth generation (5G) wireless communication network makes it possible to build intelligent gas sensor networks for real-time monitoring chemical safety and personal health. However, this application scenario requires a challenging combination of characteristics of gas sensors including small formfactor, low cost, ultralow power consumption, superior sensitivity, and high intelligence. Herein, self-powered integrated nanostructured-gas-sensor (SINGOR) systems and a wirelessly connected SINGOR network are demonstrated here. The room-temperature operated SINGOR system can be self-driven by indoor light with a Si solar cell, and it features ultrahigh sensitivity to H2, formaldehyde, toluene, and acetone with the record low limits of detection (LOD) of 10, 2, 1, and 1 ppb, respectively. Each SINGOR consisting of an array of nanostructured sensors has the capability of gas pattern recognition and classification. Furthermore, multiple SINGOR systems are wirelessly connected as a sensor network, which has successfully demonstrated flammable gas leakage detection and alarm function. They can also achieve gas leakage localization with satisfactory precision when deployed in one single room. These successes promote the development of using nanostructured-gas-sensor network for wide range applications including smart home/building and future smart city.

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, µ/t, where µ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

Porous defective carbon nitride obtained by a universal method for photocatalytic hydrogen production from water splitting.

For the first time, herein this work, we have developed an effective and adaptable method to introduce defects onto the polymeric carbon nitride by simply grinding urea with urea nitrate which resulting new carbon nitride composite (UNU-C3N4) and melamine with urea nitrate which resulting new carbon nitride composite (UNM-C3N4). The UNU-C3N4 reveals high performance towards photocatalytic hydrogen production and as well as photocatalytic removal of contaminants. The results confirm that the defects enhanced the specific surface area, and improved performance of adsorbed oxygen which beneficial to generate more active radicals and more conducive sties to improve d the overall photocatalytic performance. The high N, H, and O content-enhanced electron polarization effects, by introducing the additional N, H, and O atoms into the g-C3N4 matrix, which will increase the charge transfer rate and charge separation efficiency. At the same time, the results of ESR also expression that the new type of as-prepared carbon nitride samples exhibit abundant of hydrogen radical (H) formation, which is also assist to improve the photocatalytic hydrogen production performance. As expected, the H2 evolution rate of UNU-C3N4(or UNM-C3N4) underneath simulated solar light irradiation is 9.93 times (13.76 times) than that of U-C3N4 (urea as raw material) (or M-C3N4 (melamine as raw material)). The high hydrogen evolution rates of UNU-C3N4 and UNM-C3N4 are 830.94 and 556.79 µmol g-1  h-1 under the visible-light irradiation, respectively. Meanwhile, the synthesized UNU-C3N4 and UNM-C3N4 material are demonstrated an efficient ability to degrade pollutants. In general, this work provides a viable way to introduce defects and hydrogen bands into the structure of carbon nitride.

Single-phase alkylammonium cesium lead iodide quasi-2D perovskites for color-tunable and spectrum-stable red LEDs.

While red is one of the primary colors for display applications, the investigation of visible red emitting perovskites, particularly 2D perovskites, is relatively limited. In this work, we demonstrate a single-phase Ruddlesden-Popper quasi-2D (C3H7NH3)2CsPb2I7 perovskite for red color LEDs. Through increasing the annealing temperature of (C3H7NH3)2CsPb2I7 perovskite thin films, we have successfully achieved tunable emission wavelengths from 654 to 691 nm. Equally important, for all the quasi-2D perovskite LEDs, once the annealing temperature is fixed, the emission spectrum is independent of bias voltages, which is very important for their use in lighting and displays. With the analysis of the crystallinity, morphology, and thermodynamic stability of the quasi-2D perovskite, we find that the obtained (C3H7NH3)2CsPb2I7 perovskite is a single-phase quasi-2D perovskite with only n = 2 phase. Besides, we found that the red shifting of emission wavelength is caused by the increase of perovskite crystal size while increasing the annealing temperature. Our results also show that the temperature-induced color tunability can be applied to a series of quasi-2D perovskites with different alkylammonium cations. Importantly, we find that short alkylammonium spacers offer better electrical properties for efficient current transport and high performance in LED applications. This work contributes to controlling the optoelectronic properties of quasi-2D perovskites via controlling their crystal growth as well as paves the way to realize practical lighting and display applications of perovskite LEDs.

Achieving High-Quality Sn-Pb Perovskite Films on Complementary Metal-Oxide-Semiconductor-Compatible Metal/Silicon Substrates for Efficient Imaging Array.

Although Sn-Pb perovskites sensing near-ultraviolet-visible-near-infrared light could be an attractive alternative to silicon in photodiodes and imaging, there have been no clear studies on such devices constructed on metal/silicon substrates, hindering their direct integration with complementary metal-oxide semiconductor (CMOS) and silicon electronics. Typically, high surface roughness and severe pinholes of Sn-rich binary perovskites make it difficult for them to fulfill the requirements of efficient photodiodes and imaging. These issues cause inherently high dark current and poor (dark and photo-) current uniformity. Herein, we propose and demonstrate the room-temperature crystallization in the Sn-rich binary perovskite system to effectively control film crystallization kinetics. With experimental and theoretical studies of the crystallization mechanism, we successfully tune the density and location of nanocrystals in precursor films to achieve compact nanocrystals, which coalesce into high-quality (smooth, dense, and pinhole-free) perovskites with intensified preferred orientation and decreased trap density. The high-quality perovskites reduce dark current and improve (dark and photo-) current uniformity of perovskite photodiodes on CMOS-compatible metal/silicon substrates. Meanwhile, self-powered devices achieve a high responsivity of 0.2 A/W at 940 nm, a large dynamic range of 100 dB, and a fast fall time of 2.27 µs, exceeding those of most silicon-based imaging sensors. Finally, a 6 × 6 pixel integrated photodiode array is successfully demonstrated to realize the imaging application. The work contributes to understanding the fundamentals of the crystallization of Sn-rich binary perovskites and advancing perovskite integration with Si-based electronics.

Fully Stretchable and Humidity-Resistant Quantum Dot Gas Sensors.

Stretchable gas sensors that accommodate the shape and motion characteristics of human body are indispensable to a wearable or attachable smart sensing system. However, these gas sensors usually have poor response and recovery kinetics when operated at room temperature, and especially suffer from humidity interference and mechanical robustness issues. Here, we demonstrate the first fully stretchable gas sensors which are operated at room temperature with enhanced stability against humidity. We created a crumpled quantum dot (QD) sensing layer on elastomeric substrate with flexible graphene as electrodes. Through the control over the prestrain of the flexible substrate, we achieved a 5.8 times improvement in NO2 response at room temperature with desirable stretchability even under 1000 stretch/relax cycles mechanism deformation. The uniformly wavy structural configuration of the crumpled QD gas-sensing layer enabled an improvement in the antihumidity interference. The sensor response shows a minor vibration of 15.9% at room temperature from relative humidity of 0 to 86.7% compared to that of the flat-film sensors with vibration of 84.2%. The successful assembly of QD solids into a crumpled gas-sensing layer enabled a body-attachable, mechanically robust, and humidity-resistant gas sensor, opening up a new pathway to room-temperature operable gas sensors which may be implemented in future smart sensing systems such as stretchable electronic nose and multipurpose electronic skin.

Highly sensitive response of solution-processed bismuth sulfide nanobelts for room-temperature nitrogen dioxide detection.

Low dimensional nanomaterials have emerged as candidates for gas sensors owing to their unique size-dependent properties. In this paper, Bi2S3 nanobelts were synthesized via a facile solvothermal process and spin-coated onto alumina substrates at room temperature. The conductometric devices can even sensitively response to the relatively low concentrations of NO2 at room temperature, and their sensing performance can be effectively enhanced by the ligand exchange treatment with inorganic salts. The Pb(NO3)2-treated device exhibited superior sensing performance of 58.8 under 5ppm NO2 at room-temperature, with the response and recovery time of 28 and 106s. The competitive adsorption of NO2 against O2 on Bi2S3 nanobelts, with the enhancement both in gas adsorption and charge transfer caused by the porous network of the very thin Bi2S3 nanobelts, can be a reasonable explanation for the improved performance at room temperature. Their sensitive room-temperature response behaviors combined with the excellent solution processability, made Bi2S3 nanobelts very attractive for the construction of low-cost gas sensors with lower power consumption.

Solution-Processed Gas Sensors Employing SnO2 Quantum Dot/MWCNT Nanocomposites.

Solution-processed SnO2 colloidal quantum dots (CQDs) have emerged as an important new class of gas-sensing materials due to their potential for low-cost and high-throughput fabrication. Here we employed the design strategy based on the synergetic effect from highly sensitive SnO2 CQDs and excellent conductive properties of multiwalled carbon nanotubes (MWCNTs) to overcome the transport barrier in CQD gas sensors. The attachment and coverage of SnO2 CQDs on the MWCNT surfaces were achieved by simply mixing the presynthesized SnO2 CQDs and MWCNTs at room temperature. Compared to the pristine SnO2 CQDs, the sensor based on SnO2 quantum dot/MWCNT nanocomposites exhibited a higher response upon exposure to H2S, and the response toward 50 ppm of H2S at 70 °C was 108 with the response and recovery time being 23 and 44 s. Because of the favorable energy band alignment, the MWCNTs can serve as the acceptor of the electrons that are injected from H2S into SnO2 quantum dots in addition to the charge transport highway to direct the electron flow to the electrode, thereby enhancing the sensor response. Our research results open an easy pathway for developing highly sensitive and low-cost gas sensors.

A simple and sensitive surface molecularly imprinted polymers based fluorescence sensor for detection of λ-Cyhalothrin.

In this study, surface molecularly imprinted YVO4:Eu(3+) nanoparticles with molecular recognitive optosensing activity were successfully prepared by precipitation polymerization using λ-Cyhalothrin (LC) as template molecules, methacrylic acid and ethylene glycol dimethacrylate as the polymerization precursors which could complex with template molecules, and the material has been characterized by SEM, TEM, FT-IR, XRD, TGA and so on. Meanwhile, the as-prepared core-shell structured nanocomposite (YVO4:Eu(3+)@MIPs), which was composed of lanthanide doped YVO4:Eu(3+) as fluorescent signal and surface molecular imprinted polymers as molecular selective recognition sites, could selectively and sensitively optosense the template molecules. After the experimental conditions were optimized, two linear relationship were obtained covering the concentration range of 2.0-10.0 µM and 10.0-90.0 µM, and the limit of detection (LOD) for LC was found to be 1.76 µM. Furthermore, a possible mechanism was put forward to explain the fluorescence quenching of YVO4:Eu(3+)@MIPs. More importantly, the obtained sensor was proven to be suitable for the detection of residues of LC in real examples. And the excellent performance of this sensor will facilitate future development of rapid and high-efficiency detection of LC.

Molecularly imprinted polymer microspheres for optical measurement of ultra trace nonfluorescent cyhalothrin in honey.

In this study, we first present a general protocol for making fluorescent molecularly imprinted polymer microspheres via precipitation polymerisation. We first prepared the fluorescent molecularly imprinted polymer microspheres upon copolymerisation of acrylamide with a small quantity of allyl fluorescein in the presence of cyhalothrin to form recognition sites without doping. The as-synthesised microspheres exhibited spherical shape, high fluorescence intensity and highly selective recognition. Under optical conditions, polymer microspheres were successfully applied to selectively and sensitively detect cyhalothrin, and a linear relationship could be obtained covering the lower concentration range of 0-1.0nM with a correlation coefficient of 0.9936 described by the Stern-Volmer equation. A lower limit of detection was found to be 0.004nM. The results of practical detection suggested that the developed method was satisfactory for determination of cyhalothrin in honey samples. This study therefore demonstrated the potential of molecularly imprinted polymers for detection of cyhalothrin in food.