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As one of the most promising energy storage systems, conventional lithium-ion batteries based on the organic electrolyte have posed challenges to the safety, fabrication, and environmental friendliness. By virtue of the high safety and ionic conductivity of water, aqueous lithium-ion battery (ALIB) has emerged as a potential alternative. Whereas, the narrow electrochemical stability window (ESW) of water severely restricts the performance of ALIB. In recent years, with the introduction of water-in-salt electrolyte (21 mol LiTFSI in 1 kg H2O), the ESW of aqueous electrolyte was expanded to ~3 V, which significantly improved the voltage and energy density of ALIBs. Nevertheless, in view of such high salt concentration, water-in-salt electrolyte will dramatically increase the cost of ALIBs. Hence, due to their lower cost and abundant resource, aqueous sodium-ion batteries and zinc-based batteries show great potential. Herein, the latest advances of water-in-salt electrolyte in aqueous rechargeable batteries are briefly reviewed. Some challenges and prospects of water-in-salt electrolyte are also discussed to broaden the horizons for future development.

The rational design and controllable synthesis of excellent bifunctional oxygen reduction and evolution reaction (ORR/OER) electrocatalysts are of vital importance for zinc–air battery applications. Herein, we report nitrogen/sulfur-codoped carbon nanosheets (NSC) decorated with dense Co9S8/Co1−xS heterostructures (Co9S8/Co1−xS@NSC), prepared by a one-pot salt template-assisted pyrolysis procedure. The population of the Co9S8/Co1−xS heterostructure can be effectively controlled by tuning the precursor components. Salt templates constructed hierarchical porosity for the wide-open carbon nanosheets, which maximized the N functional groups to anchor highly dispersive Co9S8/Co1−xS species. Experiments and theoretical simulations revealed notable electronic interactions within Co9S8/Co1−xS interfaces, which can effectively optimize the adsorption/desorption behaviors of intermediates in ORR/OER, thus promoting the bifunctional electrocatalytic performance. The half-wave potential for the ORR of 0.86 V and the OER electrocatalytic potential of 1.52 V at 10 mA cm−2 were obtained. Benefiting from the strong coupling effect between the Co9S8/Co1−xS species and the carbon substrate, superior durability was obtained for 2000 ORR/OER cycles. The practical zinc–air battery based on the Co9S8/Co1−xS@NSC cathode manifested a high open-circuit voltage, small voltage gap and robust reversibility. Our study revealed the great potential of the bi-elemental (Co and S) heterostructure in enhancing the ORR/OER activity, which suggests a logical extension to other electrocatalysis systems.

This study investigates the existence of quarterly accrual anomalies in Taiwan's stock market. Our empirical results indicate that the arbitrage portfolios formed by the accounting accruals of the fourth‐quarter financial reports could result in abnormal returns, and thus, inferred that quarterly accrual anomalies exist. We further examined the causes of the accrual anomalies and determined that the manager conduct earnings management behavior during the fourth quarter. Managers execute discretionary accruals for management earnings forecast to meet previously issued financial forecasts, which leads to the fourth‐quarter accrual anomalies. The portfolios formed by using discretionary accruals and management earnings forecasts produce arbitrage portfolios with optimal return.

Short-term electricity load forecasting has become increasingly important due to the privatization and deregulation in the energy market. This study proposes a probabilistic learning method to predict hour-ahead and day-ahead load demand. Unlike methods in previous studies, the proposed method integrates wavelet transform and feature selection as key preprocessing steps. Features are divided into current state related features and historical information related features. Current state related features are forecasted by the regression model before being added into the load prediction model. The entire learning and prediction process is based on the relevance vector machine (RVM) that utilizes load data characteristics. A number of test cases are presented using benchmark datasets from the New York Independent System Operator (NYISO) and ISO New England. Based on the detailed empirical comparison, the proposed RVM-based integrated method outperforms classical time series approaches and state-of-the-art artificial intelligence methods on short-term load forecasting.

The development of efficient and low-cost flexible metal electrodes is significant for flexible rechargeable zinc-air batteries (ZABs). Herein, we reported a new type of flexible metal (zinc and nickel) electrode fabricated

Forward osmosis (FO) has attracted intensive attention in liquid separation because it is less energy-consuming as compared to the pressure-driven separation processes, but the lack of a high-perfo...

In this paper, the tests under biaxial compression were conducted on five kinds of recycled aggregate concrete (RAC) by stress ratios (0:−1, −0.1:−1, −0.25:−1, −0.5:−1, −0.75:−1, and −1:−1) at the strain rates of 10–5/s, 10–4/s, 10–3/s, and 10–2/s. This study was accomplished in the servo-hydraulic multi-axial testing system, which was carried at Dalian University of Technology. The experimental phenomena of RAC under biaxial compression are described, and the experimental results of biaxial compressive strength of RAC are analyzed. The effects of strain rate, stress ratio, and substitution percentages of recycled coarse aggregate (RCA) on the biaxial compressive strength of RAC were studied. During the experiments, the failure modes of concrete have not been changed because of the increased impact of the RCA on RAC. The strength of RAC is improved with the increase in strain rates. With the increase in stress ratios, the biaxial compressive strength increases at first. However, the strength then decreases. Based on the strength theory of common concrete and analysis of experimental data, this paper built a new dynamic strength criterion. The biaxial compressive strength criterion describes the characteristics of structural RAC at different stress ratios and strain rates. This strength criterion can provide theoretical foundation for future concrete multi-axis dynamic tests and the design specification with the considerations of multi-axis dynamic strength and nonlinear analysis. The feature of the model is that it can take into account the influence of strain rates.

The Li-CO

Single-cell whole-genome haplotyping allows simultaneous detection of haplotypes associated with monogenic diseases, chromosome copy-numbering and subsequently, has revealed mosaicism in embryos and embryonic stem cells. Methods, such as karyomapping and haplarithmisis, were deployed as a generic and genome-wide approach for preimplantation genetic testing (PGT) and are replacing traditional PGT methods. While current methods primarily rely on single-nucleotide polymorphism (SNP) array, we envision sequencing-based methods to become more accessible and cost-efficient. Here, we developed a novel sequencing-based methodology to haplotype and copy-number profile single cells. Following DNA amplification, genomic size and complexity is reduced through restriction enzyme digestion and DNA is genotyped through sequencing. This single-cell genotyping-by-sequencing (scGBS) is the input for haplarithmisis, an algorithm we previously developed for SNP array-based single-cell haplotyping. We established technical parameters and developed an analysis pipeline enabling accurate concurrent haplotyping and copy-number profiling of single cells. We demonstrate its value in human blastomere and trophectoderm samples as application for PGT for monogenic disorders. Furthermore, we demonstrate the method to work in other species through analyzing blastomeres of bovine embryos. Our scGBS method opens up the path for single-cell haplotyping of any species with diploid genomes and could make its way into the clinic as a PGT application. ispartof: NUCLEIC ACIDS RESEARCH vol:50 issue:11 ispartof: location:England status: published

With the continuous development of the Internet, social media based on short text has become popular. However, the sparsity and shortness of essays will restrict the accuracy of text classification. Therefore, based on the Bert model, we capture the mental feature of reviewers and apply them for short text classification to improve its classification accuracy. Specifically, we construct a model text at the language level and fine tune the model to better embed mental features. To verify the accuracy of this method, we compare a variety of machine learning methods, such as support vector machine, convolution neural networks, and recurrent neural networks. The results show the following: (1) Through feature comparison, it is found that mental features can significantly improve the accuracy of short text classification. (2) Combining mental features and text as input vectors can provide more classification accuracy than separating them as two independent vectors. (3) Through model comparison, it can be found that Bert model can integrate mental features and short text. Bert can better capture mental features to improve the accuracy of classification results. This will help to promote the development of short text classification.

Additional file 2: Supplementary Table S1. Oligos used in the study.

Additional file 1: Supplementary Figure 1. The expression and function of hsa_circ_0003258 in PCa. (A). The heatmap showing some of the differentially expressed circRNAs between normal and prostate cancer patient plasma samples. (B). The representative images of FISH analysis of hsa_circ_0003258 in normal and PCa tissues. (C). The protein coding potential of hsa_circ_0003258. (D). The melting curve of hsa_circ_0003258 amplified product using the divergent primers. (E-H). Transwell assay detecting the migratory capacity of PCa cells after transfecting with siRNAs or vector plasmid. (I-J). Colony formation assays and CCK8 assays were utilized to evaluate the proliferation of PCa cells after knockdown of hsa_circ_0003258 in DU145 and C4���2 cells. ns: Not Significant. Supplementary Figure 2. Hsa_circ_0003258/ miR-653-5p/Arhgap5 pathway in C4-2. (A). The heatmap of differentially expressed mRNAs in DU145/sh-nc and DU145/sh- hsa_circ_0003258 cells. Each sample was mixed in three replicates. (B). Transwell assay detecting the migratory capacity of PCa cells after SCH772984 treatment for 48 h in DU145 Lv- hsa_circ_0003258 cells. (C). The potential binding miRNAs predicted by CircInteractome database. (D). Lysates from C4���2 cells was subjected to biotinylated-hsa_circ_0003258 pull-down assay and the expression levels of hsa_circ_0003258 were measured by qRT-PCR. (E). The expression levels of the top three candidate miRNAs predicted by Circinteractome database quantified by qRT-PCR after biotinylated-hsa_circ_00032583 pull-down assay in C4���2 cells. (F). The relative expression of hsa_circ_0003258 detected by qRT-PCR after the transfection of miR-653-5p mimic or inhibitor. (G-H). The qRT-PCR and Western blot showing the ARHGAP5 mRNA and protein level change of the predicated targets after silencing or overexpression of hsa_circ_0003258. (I-J). The qRT-PCR and Western blot showing the ARHGAP5 mRNA and protein level change of the predicated targets after silencing or overexpression of miR-653-5p. ns: Not Significant; * P

The rapid consumption of fossil fuels has caused increasingly climatic issues and energy crisis, which leads to the urgent demand for developing sustainable and clean energies. Electrocatalysts play a key role in the development of electrochemical energy conversion and storage devices. Especially, developing efficient and cost-effective catalysts is important for the large-scale application of these devices. Among various electrocatalyst candidates, earth abundant transition metal compound (TMC)-based electrocatalysts are being widely and rapidly studied owing to their high electrocatalytic performances. This paper reviews the recent and representative advances in efficient TMC-based electrocatalysts (i.e., oxides, sulfides, selenides, phosphides, carbides and nitrides) for energy electrocatalytic reactions, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). Different compounds with different applications are summarized and the relative mechanisms are also discussed. The strategies for developing earth-abundant and low-cost TMC-based electrocatalysts are introduced. In the end, the current challenges and future perspectives in the development of TMC research are briefly discussed. This review also provides the latest advance and outlines the frontiers in TMC-based electrocatalysts, which should provide inspirations for the further development of low-cost and high-efficiency catalysts for sustainable clean energy technologies.

Data-driven methods for machinery health monitoring and prediction, such as machine learning and statistical pattern recognition techniques, normally requires high quality (less noised), frequently-sampled and large volume time-series data to estimate proper models between system inputs and outputs. However, most of the industrial time-series are highly noisy and only few types of sensory data are available, which challenges the estimation accuracy. This paper proposes a data-driven spectral decomposition framework (denoted as Koopman-CBM) for the machinery health monitoring and prediction problem. Specifically, considering noisy industrial signals in the form of one dimensional time-series, we use the higher-order dynamic mode decomposition (DMD) embeds time-lagged snapshots to increase the spatial complexity of low-dimensional time series and use the total-least-square algorithm to compensate the effect of measurement noise, thereby, extracting accurate dynamical features. The obtained de-noised model characteristics (i.e., eigenvalues, eigenfunctions, Koopman operator) is effective in predicting the system health stages. In parallel, using the Koopman operator as the linear predictor associated with the nonlinear dynamics, we can then perform remaining useful life (RUL) predictions with high accuracy. The experimental validation of the proposed framework is carried out on a rolling bearing datasets for degradation health-stage inspection and RUL prediction. Results show that– compared with other mainstream methods– our approach is capable of identifying the critical degradation stages and achieving higher RUL prediction accuracies.

Summary Background Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis. Methods We used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level. The ECGs were collected from three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and annotated by cardiologists. We used these datasets to develop a convolutional neural network approach to generate diagnoses of arrythmias. We collected a test dataset of ECGs from a new group of patients not included in the training dataset. The test dataset was annotated by consensus of a committee of board-certified, actively practicing cardiologists. To evaluate the performance of the model we assessed the F1 score and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying sensitivity and specificity. To validate our results, findings for the test dataset were compared with diagnoses made by 53 ECG physicians working in cardiology departments who had a wide range of experience in ECG interpretation (range 0 to >12 years). An external public validation dataset of 962 ECGs from other hospitals was used to study generalisability of the diagnostic model. Findings Our training and validation dataset comprised 180 112 ECGs from 70 692 patients, collected between Jan 1, 2012, and Apr 30, 2019. The test dataset comprised 828 ECGs corresponding to 828 new patients, recorded between Sept 11, 2012, and Aug 30, 2019. At the multilabel level, our deep learning approach to diagnosing heart abnormalities resulted in an exact match in 658 (80%) of 828 ECGs, exceeding the mean performance of physicians (552 [67%] for physicians with 0–6 years of experience; 571 [69%] for physicians with 7–12 years of experience; 621 [75%] for physicians with more than 12 years of experience). Our model had an overall mean F1 score of 0·887 compared with 0·789 for physicians with 0–6 years of experience, 0·815 for physicians with 7–12 years of experience, and 0·831 for physicians with more than 12 years of experience. The model had a mean AUC ROC score of 0·983 (95% CI 0·980–0·986), sensitivity of 0·867 (0·849–0·885) and specificity of 0·995 (0·994–0·996). Promising F1 scores were also obtained from the external public database using our proposed model without any model modifications (mean F1 scores of 0·845 in multilabel and 0·852 in single-label ECGs). Interpretation Our model is more accurate than physicians working in cardiology departments at distinguishing a range of distinct arrhythmias in single-label and multilabel ECGs, laying a promising foundation for computational decision-support systems in clinical applications. Funding National Natural Science Foundation of China and Hubei Science and Technology Project.

Cytogenetic abnormalities are powerful prognostic factors in multiple myeloma (MM) and are routinely analyzed by FISH on bone marrow (BM) plasma cells (PC). Although considered the gold standard, FISH experiments can be laborious and expensive. Therefore, array-CGH (aCGH) has been introduced as an alternative approach for detecting copy number aberrations (CNA), reducing the number of FISH experiments per case and yielding genome-wide information. Currently, next generation sequencing (NGS) technologies offer new perspectives for the diagnostic workup of malignant disorders. In this study, we examined ultra-low depth whole genome sequencing (LDS) as a valid alternative for aCGH for the detection of CNA in BM PC in MM. To this end, BM aspirates obtained in a diagnostic setting from 20 MM cases were analyzed. CD138+ cell-sorted samples were subjected to FISH analysis. DNA was extracted for subsequent aCGH and LDS analysis. CNA were detected by aCGH and LDS in all but one case. Importantly, all CNA identified by parallel first generation aCGH analysis were also detected by LDS, along with six additional CNA in five cases. One of these additional aberrations was in a region of prognostic importance in MM and was confirmed using FISH. However, risk stratification in these particular cases was unaffected. Thus, a perfectly concordant prognostication between array-CGH and LDS was observed. This validates LDS as a novel and cost-efficient tool for the detection of CNA in MM. ispartof: GENES CHROMOSOMES & CANCER vol:59 issue:8 pages:465-471 ispartof: location:United States status: published

Thin-felt ZSM-11/stainless-steel(SS)-fiber composites are successfully prepared via a combined washcoating and steam-assisted crystallization (SAC) method. Pre-aging of synthesis sol at a lower temperature of 80 °C can facilitate the zeolite nucleation and subsequent SAC growth at 180 °C. Such synthesis approach realizes the flexible tuning of acidity via adjusting SiO2/Al2O3 molar ratio in a wide range, including but not limited from 105 to 425. The as-synthesized ZSM-11/SS-fiber composites are employed as catalysts for the methanol-to-propylene reaction, and the promising thin-felt catalyst with SiO2/Al2O3 molar ratio of 200 shows remarkable stability improvement in comparison with its powdered counterpart, due to enhanced zeolite utilization efficiency and heat/mass transfer by the microfibrous-structured design.

Primary biliary cholangitis (PBC) is an autoimmune liver disease featured with bile duct injury, ductopenia and proliferation, periportal inflammation and fibrosis. Clinical manifestations of PBC vary from almost no symptoms to different degrees of pruritus plus symptoms of liver dysfunction. This review intends to update our understanding in the mechanisms of hepatic fibrogenesis under chronic biliary injury. Underlying mechanisms are proposed for a better understanding and more effective therapeutic targets. With genetic predisposition, lesions from bile ducts cause damage to biliary epithelial cells (BECs); and simultaneous autoimmunity reinforces a vicious cycle of BEC damage, inflammatory infiltration, BEC proliferation and fibrogenic responses. Therapeutic efficacy of immunosuppressive agents has been unsatisfactory. As a major variable in PBC progression, fibrosis has been paid a great deal of attention but has not been responsive to various treatments.

Energy and environmental issues received widespread attentions due to the fast growth of world population and rapid development of social economy. As a transition metal dichalcogenide, tungsten disulfide (WS2) nanomaterials make important research progress in the field of energy conversion and storage. In view of the versatile and rich microstructure of these materials, the modification and controllable synthesis of WS2 nanomaterials also inspire a research interest. This review mainly focuses on WS2-based nanomaterials in the application of energy conversion and storage as well as discusses some basic characteristics and modification strategies of them. Finally, the research progress of WS2-based nanomaterials is reviewed and some prospects for future research directions are proposed. This review is expected to be beneficial to the future study of WS2 nanomaterials used in the field of energy conversion and storage.

Aqueous battery systems feature high safety, but they usually suffer from low voltage and low energy density, restricting their applications in large-scale storage. Here, we propose an electrolyte-decoupling strategy to maximize the full potential of Zn–MnO2 batteries by simultaneously enabling the optimal redox chemistry of both the Zn and MnO2 electrodes. The decoupled Zn–MnO2 battery exhibits an open-circuit voltage of 2.83 V (in contrast to the typical voltage of 1.5 V in conventional Zn–MnO2 batteries), as well as cyclability with only 2% capacity fading after deep cycling for 200 h. Benefiting from the full utilization of MnO2, the Zn–MnO2 battery is also able to maintain approximately 100% of its capacity at various discharge current densities. We also demonstrate the feasibility of integrating the Zn–MnO2 battery with a wind and photovoltaic hybrid power generating system. This electrolyte-decoupling strategy is shown to be applicable for other high-performance zinc-based aqueous batteries such as Zn–Cu and Zn–Ag batteries. Low energy density and limited cyclability are preventing the commercialization of aqueous Zn–MnO2 batteries. Here, the authors combine the merits of operating Zn anodes in alkaline conditions and MnO2 cathodes in acidic conditions, via an electrolyte-decoupling strategy, to realize high-performance batteries.

Developing superior electrocatalysts for formic acid oxidation (FAO) is the most crucial step in commercializing direct formic acid fuel cells. Herein, we electrodeposited palladium membranes with periodically ordered mesoporosity obtained by asymmetrically replicating the bicontinuous cubic phase structure of a lyotropic liquid-crystal template. The Pd membrane with the largest periodicity and highest degree of order delivered up to 90.5 m2 g-1 of electrochemical active surface area and 3.34 A mg-1 electrocatalysis capability towards FAO, 3.8 and 7.8 times the values of the commercial Pd/C catalyst, respectively. By controlling the temperature and potential of the electrodeposition procedure, the periodicity area and order degree of the mesoporosity are highly tunable. These Pd membranes gave prototype formic acid fueled cells with 4.3 and 2.4 times the maximum current and power density of the commercial Pd/C catalyst.

Grid-level large-scale electrical energy storage (GLEES) is an essential approach for balancing the supply–demand of electricity generation, distribution, and usage. Compared with conventional energy storage methods, battery technologies are desirable energy storage devices for GLEES due to their easy modularization, rapid response, flexible installation, and short construction cycles. In general, battery energy storage technologies are expected to meet the requirements of GLEES such as peak shaving and load leveling, voltage and frequency regulation, and emergency response, which are highlighted in this perspective. Furthermore, several types of battery technologies, including lead–acid, nickel–cadmium, nickel–metal hydride, sodium–sulfur, lithium-ion, and flow batteries, are discussed in detail for the application of GLEES. Moreover, some possible developing directions to facilitate efforts in this area are presented to establish a perspective on battery technology, provide a road map for guiding future studies, and promote the commercial application of batteries for GLEES.

Objective To investigate the effect of the monocytes/macrophages on acute lung injury in rats with severe heatstroke, by modulating the expression of triggering receptor expressed on myeloid cells-1 (TREM-1). Methods Forty rats were randomized evenly into the control group (Con group), heatstroke group (HS group), the low dose inhibitor group (LD group) and the high dose inhibitor group (HD group). Before heatstroke induction, the rats of LD and HD groups were administrated with a 50 mg/kg and 100 mg/kg bolus of LP-17, respectively. All rats were exposed to an environment with temperature of (40±2) ℃ and humidity of 65%±5% for 60 minutes to induce heatstroke. Enzyme-linked immunosorbent assay (ELISA) was utilized to quantify the concentration of tumor necrosis factor-α (TNF-α), interleukin (IL)-6 and IL-1β in the peripheral blood and pulmonary tissue. The expression of TREM-1 on peripheral monocytes was identified by flow cytometry. Moreover, the histological phenotypes were evaluated after HE stain and the expression of inducible nitric oxide synthase (iNOS) was analyzed by immunohistochemistry in pulmonary tissues. Furthermore, Western blotting was used to detect the protein level of TREM-1 and monocyte chemoattractant protein-1 (MCP-1). Results Compared to HS rat, in rats pretreated with LP-17, the levels of TNF-α, IL-6 and IL-1β in peripheral blood (P

Renewable arenes and aromatic alcohols can be derived from lignocellulose by biorefineries, which has been considered as a sustainable alternative to replace petrochemical feedstocks in the synthesis of monobenzylation products, key industrial intermediates, via benzylation reactions. Zeolites with micropores are the most widely used catalysts in the benzylation of arenes, however, their performance suffers from diffusion limitations in converting large arenes. In this work, mesoporous and microporous H–Beta zeolites were prepared and applied in the systematic study of benzylation of arenes (benzene, toluene, p-xylene and mesitylene) with benzyl alcohol (BA). The porous structure of these zeolites has been confirmed by XRD, BET and TEM techniques. The catalytically active Bronsted acid sites (BAS) were determined by quantitative 1H magic-angle spinning (MAS) nuclear magnetic resonance (NMR) experiments. The benzylation studies have shown that introducing mesopores into H–Beta zeolites can significantly increase the diffusion/access of arenes to surface sites, particularly for bulky arenes (e.g. mesitylene), while micropores are mainly selective for the conversion of small arenes (e.g. benzene). Increasing the nucleophilicity of arenes with more alkyl groups can enhance their catalytic performance in mesopores, however, the increase hinders their conversion in micropores because of the shape selectivity due to their increasing molecular size. Compared to mesopores, micropores promote the conversion of small arenes (e.g. benzene), which can be additionally enhanced by a high Bronsted acidity. Therefore, introducing a suitable porosity balanced with acidity are keys in the tailoring of the catalytic performance of H-Beta zeolites for target benzylation reactions.

This paper reports an epitaxial lift-off (ELO) method that uses water-soluble MgTe to obtain free-standing single-crystal CdTe/MgCdTe double-heterostructure (DH) thin films from samples grown monolithically on lattice-matched InSb substrates. Photoluminescence measurements on as-grown and free-standing films reveal that the optical quality of the DH survives ELO. This method potentially enables substantial increases in the conversion efficiencies of thin-film solar cells through enhancement of the photon-recycling effect. This process holds promise in multijunction PV applications that considers the integration of a 1.7-eV MgCdTe top cell and a crystalline Si bottom cell to form a tandem device with efficiency potentially higher than 30%. Additionally, the method can be applied to other families of devices, such as infrared photodetectors.

A better fundamental understanding of the plasma-catalyst interaction and the reaction mechanism is vital for optimizing the design of catalysts for ammonia synthesis by plasma-catalysis. In this work, we report on a hybrid plasma-enhanced catalytic process for the synthesis of ammonia directly from N2 and H2 over transition metal catalysts (M/Al2O3, M = Fe, Ni, Cu) at near room temperature (∼35 °C) and atmospheric pressure. Reactions were conducted in a specially designed coaxial dielectric barrier discharge (DBD) plasma reactor using water as a ground electrode, which could cool and maintain the reaction at near-room temperature. The transparency of the water electrode enabled operando optical diagnostics (intensified charge-coupled device (ICCD) imaging and optical emission spectroscopy) of the full plasma discharge area to be conducted without altering the operation of the reactor, as is often needed when using coaxial reactors with opaque ground electrodes. Compared to plasma synthesis of NH3 without a catalyst, plasma-catalysis significantly enhanced the NH3 synthesis rate and energy efficiency. The effect of different transition metal catalysts on the physical properties of the discharge is negligible, which suggests that the catalytic effects provided by the chemistry of the catalyst surface are dominant over the physical effects of the catalysts in the plasma-catalytic synthesis of ammonia. The highest NH3 synthesis rate of 471 μmol g-1 h-1 was achieved using Ni/Al2O3 as a catalyst with plasma, which is 100% higher than that obtained using plasma only. The presence of a transition metal (e.g., Ni) on the surface of Al2O3 provided a more uniform plasma discharge than Al2O3 or plasma only, and enhanced the mean electron energy. The mechanism of plasma-catalytic ammonia synthesis has been investigated through operando plasma diagnostics combined with comprehensive characterization of the catalysts using N2 physisorption measurements, X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), high-resolution transmission electron microscopy (HRTEM), NH3-temperature-programmed desorption (TPD), and N2-TPD. Four forms of adsorbed NH x (x = 0, 1, 2, and 3) species were detected on the surfaces of the spent catalysts using XPS. It was found that metal sites and weak acid sites could enhance the production of NH3 via formation of NH2 intermediates on the surface.

Considering the environmental problems caused by a large amount of cotton textile waste and its possible applications in flexible electrodes, it is very promising to reuse the cotton textile waste as an electrode material, reducing the cost of flexible electrodes and alleviating environmental problems. In this work, we present a rechargeable flexible Zn-air battery based on cotton textile waste, which employs Ni-metallized cotton textile waste (NMCTW) as a flexible substrate for Zn anodes and air cathodes. The transparent NiFe hydroxide thin film horizontally grown on the surface of the NMCTW substrate was synthesized in situ by the electrodeposition method, which exhibits excellent catalytic activity because of the high surface area of the two-dimensional (2D) thin film, large contact area between the thin film and substrate, and fast charge transport of the 2D thin-film structure. In view of the high catalytic performance of the NiFe hydroxide thin film, it was used as the catalytic material of the air cathode for the flexible Zn-air battery. The assembled Zn-air battery based on cotton textile waste demonstrated a good rate performance and outstanding charge and discharge cycling stability. The assembled Zn-air battery was applied to power the light-emitting diode, which exhibits exceptional flexibility and stable output power even under severe mechanical bending deformation, proving the feasibility for its application in flexible electronics.

Flowerlike Bi/BiOCl was prepared by one-pot hydrothermal method, where Bi(NO