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Chronic wounds experiencing infections with multidrug-resistant bacteria can be fatal, and in severe cases can lead to sepsis. Antimicrobial peptides are widely used in the field of wound care for their broad-spectrum antibacterial properties and good anti-drug resistance. We prepared bacterial cell membrane chromatography (BCMC) by extracting cell membranes of bacteria using SiO2microspheres as stationary phase. A library of antimicrobial peptides was synthesized in solid phase and screened by BCMC to identify the antimicrobial peptide LKAHR (later named LS5), which is characterized by biosafety, broad-spectrum antibacterial activity, and drug resistance, and a gelatin-based antimicrobial hydrogel (LS5-gel) was prepared to be better applied to wounds. LS5-gel was found to have good in vivobactericidal properties as well as the ability to promote wound healing in a wound healing model. In the sepsis model, LS5 was found to have a significant inhibitory effect on sepsis infection. It is important for the selection of next-generation antimicrobial drugs and the treatment of chronic wound healing.

Massive legal documents have promoted the application of legal intelligence. Among them, Legal Judgment Prediction (LJP) has emerged as a critical task, garnering significant attention. LJP aims to predict judgment results for multiple subtasks, including charges, law articles, and terms of penalty. Existing studies primarily focus on utilizing the entire factual description to produce judgment results, overlooking the practical judicial scenario where judges consider various crime circumstances to decide verdicts and sentencing. To this end, in this article, we propose a circumstance-aware LJP framework (i.e., NeurJudge) by exploring the circumstances of crime. Specifically, NeurJudge first separates the factual description into different circumstances with the predicted results of intermediate subtasks and then employs them to yield results of other subtasks. Besides, as confusing verdicts may degrade the performance of LJP, we further develop a variant of NeurJudge (NeurJudge+) that incorporates the semantics of labels (charges and law articles) into facts to yield more expressive and distinguishable fact representations. Finally, to provide explanations for LJP, we extend NeurJudge to an explainable LJP framework E-NeurJudge with a cooperative teacher-student system. The teacher system is NeurJudge which exploits legal particularities well but lacks explanation capability. The student system is a rationalization method that provides explainability but fails to utilize legal particularities. To combine the advantages of the above methods, we use a transferring function to transfer legal particularities from the teacher to the student, making a trade-off between yielding LJP results and rendering them explainable. Extensive experimental results on real-world datasets validate the effectiveness of our proposed frameworks.

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Aqueous zinc–iodine batteries (ZIBs) based on the reversible conversion between various iodine species have garnered global attention due to their advantages of fast redox kinetics, good reversibility, and multielectron conversion feasibility. Although significant progress has been achieved in ZIBs with the two-electron I–/I2pathway (2eZIBs), their relatively low energy density has hindered practical application. Recently, ZIBs with four-electron I–/I2/I+electrochemistry (4eZIBs) have shown a significant improvement in energy density. Nonetheless, the practical use of 4eZIBs is challenged by poor redox reversibility due to polyiodide shuttling during I–/I2conversion and I+hydrolysis during I2/I+conversion. In this Review, we thoroughly summarize the fundamental understanding of two ZIBs, including reaction mechanisms, limitations, and improvement strategies. Importantly, we provide an intuitive evaluation on the energy density of ZIBs to assess their practical potential and highlight the critical impacts of the Zn utilization rate. Finally, we emphasize the cost issues associated with iodine electrodes and propose potential closed-loop recycling routes for sustainable energy storage with ZIBs. These findings aim to motivate the practical application of advanced ZIBs and promote sustainable global energy storage.

To broaden the accessibility of cell and gene therapies, it is essential to develop and optimize nonviral, cell type-preferential gene carriers such as lipid nanoparticles (LNPs). While high-throughput screening (HTS) approaches have proven effective in accelerating LNP discovery, they are often costly, labor-intensive, and do not consistently yield actionable design rules that direct screening efforts toward the most relevant chemical and formulation parameters. In this study, we employed a machine learning (ML) workflow, utilizing well-curated plasmid DNA LNP transfection data sets across six cell types, to extract compositional and chemical insights from HTS studies. Our approach achieved prediction errors averaging between 5 and 10%, depending on the cell type. By applying SHapley Additive exPlanations to our ML models, we uncovered key composition–function relationships that govern cell type-preferential LNP transfection efficiency. Notably, we identified consistent LNP composition parameters that enhance in vitrotransfection efficiency across diverse cell types, including a helper lipid molar percentage of charged lipids between 9 and 50% and the inclusion of cationic/zwitterionic helper lipids. Additionally, several parameters were found to modulate cell type-preferentiality, such as the total molar percentage of ionizable and helper lipids, N/P ratio, PEGylated lipid molar percentage of uncharged lipids, and hydrophobicity of the helper lipid. This study leverages HTS of compositionally diverse LNP libraries combined with ML analysis to elucidate the interactions between lipid components in LNP formulations, providing insights that contribute to the design of LNP compositions tailored for cell type-preferential transfection.

DC microgrids with battery energy storage systems are being widely implemented for integrating renewable energy. The convergence performance of the battery controller is an important index in the evaluation of the microgrids performance. However, the convergence time of existing finite-time control, fixed-time control, and predefined-time control cannot be preset explicitly. Moreover, existing state-of-charge (SoC)-equalization-based accelerating control algorithms will make the batteries suffer excessive voltage and current. To deal with these problems, this article presents a distributed prescribed-time control scheme embedded with a prescribed-time dynamic average consensus (DAC) algorithm for both discharging mode and charging mode. In discharging mode, the droop coefficient is designed such that all batteries keep the same relative SoC variation rate. With the proposed secondary control input, theoretical analysis shows that the voltage regulation and accurate current sharing can be obtained within any physically allowable user-preassigned time, which is independent of any other control parameters and initial states. SoC balancing is also achieved within the preassigned time. All batteries can keep the same relative SoC variation rate, which is more reasonable than simple SoC equalization. In charging mode, an SoC-based virtual resistance and a prescribed-time virtual voltage compensation control are proposed, with which the current sharing and the same relative SoC variation rate of each battery is achieved within the preassigned time. Simulation studies are conducted to demonstrate the effectiveness of the proposed control scheme

Lithium-rich layered oxide cathode materials have the advantages of a high voltage and a high specific capacity. Their commercial applications have however been impeded by some disadvantages such as low initial coulombic efficiency and low cycle life. To overcome these issues, rare earth ion-doped lithium-rich layered oxide cathode materials are investigated in this work. The irreversible release of O2- in Li2MnO3 is suppressed by rare earth ions doping, which enhanced the initial coulombic efficiency of the materials. Meanwhile, the rare-earth ion radius used for doping is larger than the Mn4+ radius, which enlarges the (003)-crystalline plane spacing, resulting in a significant enhancement of the rate performance of the material.

An electromagnetic suspension (EMS) vehicle achieves stable suspension through active feedback control of electromagnetic force. However, the vehicle’s suspension stability can be affected by various external disturbances. To improve the stability and robustness of the EMS vehicle’s suspension system, an active disturbance rejection control algorithm which is an adaptive parameter optimized by a backpropagation neural network (BP_ADRC) is proposed. Firstly, the dynamic model of the EMS vehicle’s suspension system is derived. Secondly, a nonlinear tracking differentiator and a nonlinear extended state observer are established. On this basis, a nonlinear state error feedback control law is designed, and the ADRC algorithm of the suspension system is constructed. Subsequently, an adaptive optimization algorithm using a BP neural network is given, and it can adaptively adjust the gain parameters of the nonlinear state error feedback control law according to the system changes. Finally, the effectiveness of the proposed BP_ ADRC algorithm is verified through simulations and experimental methods. The results indicate that the BP_ADRC algorithm exhibits good control performance under various disturbances, thereby effectively improving the stability of the EMS vehicle’s suspension system.

•E. faeciumaccounted for increasing ratios of enterococcal infections in Taiwan.•Vancomycin resistance in E. faeciumincreased from 3% in 2004 to 56.9% in 2018.•Transfer of Tn1546variants between E. faeciumisolates contributed to this increase.•The predominant clones of vancomycin-resistant E. faeciumbelong to ST17 and ST78.•Spread of ST17 and ST78 also contributed to the increased vancomycin resistance.

A crystal plasticity finite element model was developed for the drawing deformation of pure copper micro wire, based on rate-dependent crystal plasticity theory. The impact of wire diameter compression ratio on the micro-mechanical deformation behavior during the wire drawing process was investigated. Results indicate that the internal deformation and slip of the drawn wire are unevenly distributed, forming distinct slip and non-slip zones. Additionally, horizontal strain concentration bands develop within the drawn wire. As the wire diameter compression ratio increases, the strength of the slip systems and the extent of slip zones inside the deformation zone also increase. However, the fluctuating stress state, induced by contact pressure and frictional stress, results in a rough and uneven wire surface and diminishes the stability of the drawing process.

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is a benchmark hole-transporting (p-type) polymer that finds applications in diverse electronic devices. Most of its success is due to its facile synthesis in water, exceptional processability from aqueous solutions, and outstanding electrical performance in ambient. Applications in fields like (opto-)electronics, bioelectronics, and energy harvesting/storage devices often necessitate the complementary use of both p-type and n-type (electron-transporting) materials. However, the availability of n-type materials amenable to water-based polymerization and processing remains limited. Herein, we present a novel synthesis method enabling direct polymerization in water, yielding a highly conductive, water-processable n-type conjugated polymer, namely, poly[(2,2′-(2,5-dihydroxy-1,4-phenylene)diacetic acid)-stat-3,7-dihydrobenzo[1,2-b:4,5-b′]difuran-2,6-dione] (PDADF), with remarkable electrical conductivity as high as 66 S cm–1, ranking among the highest for n-type polymers processed using green solvents. The new n-type polymer PDADF also exhibits outstanding stability, maintaining 90% of its initial conductivity after 146 days of storage in air. Our synthetic approach, along with the novel polymer it yields, promises significant advancements for the sustainable development of organic electronic materials and devices.

Chemical doping is an important approach to manipulating charge-carrier concentration and transport in organic semiconductors (OSCs)1–3and ultimately enhances device performance4–7. However, conventional doping strategies often rely on the use of highly reactive (strong) dopants8–10, which are consumed during the doping process. Achieving efficient doping with weak and/or widely accessible dopants under mild conditions remains a considerable challenge. Here, we report a previously undescribed concept for the photocatalytic doping of OSCs that uses air as a weak oxidant (p-dopant) and operates at room temperature. This is a general approach that can be applied to various OSCs and photocatalysts, yielding electrical conductivities that exceed 3,000 S cm–1. We also demonstrate the successful photocatalytic reduction (n-doping) and simultaneous p-doping and n-doping of OSCs in which the organic salt used to maintain charge neutrality is the only chemical consumed. Our photocatalytic doping method offers great potential for advancing OSC doping and developing next-generation organic electronic devices.

The discovery of efficient and stable electrocatalysts for oxygen evolution reaction (OER) in acid is vital for the commercialization of the proton-exchange membrane water electrolyzer. In this work, we demonstrate that short-range Ru atom arrays with near-ideal Ru–Ru interatomic distances and a unique Ru–O hybridization state can trigger direct O*–O* radical coupling to form an intermediate O*–O*-Ru configuration during acidic OER without generating OOH* species. Further, the Ru atom arrays suppress the participation of lattice oxygen in the OER and the dissolution of active Ru. Benefiting from these advantages, the as-designed Ru array-Co3O4electrocatalyst breaks the activity/stability trade-off that plagues RuO2-based electrocatalysts, delivering an excellent OER overpotential of only 160 mV at 10 mA cm–2in 0.5 M H2SO4and outstanding durability during 1500 h operation, representing one of the best acid-stable OER electrocatalysts reported to date. 18O-labeled operando spectroscopic measurements together with theoretical investigations revealed that the short-range Ru atom arrays switched on an oxide path mechanism (OPM) during the OER. Our work not only guides the design of improved acidic OER catalysts but also encourages the pursuit of short-range metal atom array-based electrocatalysts for other electrocatalytic reactions.

The sensitivity of triboelectric nanogenerators (TENGs) to the surface charge density highlights the significance of triboelectric materials and their modifications. Efforts have been directed toward developing effective strategies for increasing the surface charge density, expanding the potential applications of TENGs. This study proposes the use of irradiation technology for grafting to modify the electron-donating capability of poly(ether sulfone) (PES), thereby affording a dual benefit of enhancing the surface charge density and inducing a shift in the position of PES from negative to positive within the triboelectric series. The TENG based on grafted PES has resulted in a significant 3-fold increase in surface charge density compared to that of pristine PES, reaching 263 µC m–2. The surface charge density can be further increased to 502 µC m–2through charge pumping. Notably, irradiation technology presents advantages over chemical grafting methods, particularly in terms of sustainability and environmental friendliness. This innovative approach shows great potential in advancing the domain of TENGs.

To compare the effects of Ivor-Lewis esophagectomy and McKeown esophagectomy on perioperative anxiety and depression in patients with esophageal cancer. Sixty-three patients with stage I-III middle and lower esophageal carcinoma from June 2021 to December 2022 were randomly divided into observation group (n = 32) treated with laparoscopic Ivor-Lewis esophagectomy and control group (n = 31) treated with laparoscopic McKeown esophagectomy. Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) were measured on the second day of admission and the fifth day after surgery to assess the presence of depression and anxiety. The preoperative and postoperative clinical data of both groups were compared, and multivariate analysis was used to identify risk factors associated with depression and anxiety in patients with esophageal cancer. There was no significant difference in SDS and SAS standard scores between the observation group and the control group (P > 0.05). The postoperative SDS and SAS scores in the control group were significantly higher than those before and after operation in the observation group (P < 0.01). According to univariate analysis, patients with TNM stage III, tumor diameter greater than 3 cm, postoperative complications, radical McKeown esophagectomy, and C-reactive protein levels above 10 mg/L had a higher incidence of depression and anxiety (P < 0.05). Multivariate logistic analysis showed that TNM stage III (depression: OR 1.683, 95 CI 1.429–1.861; Anxiety: OR 1.739, 95 CI 1.516–1.902), postoperative complications (depression: OR 2.345, 95 CI 1.435–3.891; Anxiety: OR 1.872, 95 CI 1.372–3.471), surgical approach (depression: OR 1.609, 95 CI 1.502–3.193; Anxiety: OR 1.658, 95 CI 1.469–2.059), and C-reactive protein (depression: OR 2.260, 95 CI 1.157–4.059; Anxiety: OR 0.373, 95 CI 0.253–0.976) were all independent factors for depression and anxiety in patients after esophageal cancer surgery (P < 0.05). The Ivor-Lewis esophagectomy has the advantages of fewer complications and low inflammatory response, which can help alleviate anxiety and depression and improve patients’ quality of life and prognosis.

•The correlation between the phase and facet of Indium oxides with their activity of CO2electroreduction has been established.•The cubic phase is more active for CO2electroreduction than the hexagonal phase, and cubic (111) surface shows the highest intrinsic activity.•The cubic In2O3nanosheets (C-In2O3-NS) with predominately exposed (111) surface has shown a significantly enhanced activity and formate selectivity than other In2O3catalyststs with different phases or surfaces.

Atomic thick two-dimensional (2D) materials with exciting physical, chemical, and electronic properties are gaining increasing attention in next-generation science and technology, showing great promise in catalysis and energy science. However, the precise design and synthesis of efficient catalytic systems based on such materials still face many difficulties, especially in how to control the preparation of structurally determined, highly active, atomic-scale distribution of material systems. Here, we report that a highly active zerovalent osmium single-atom-layer with a thickness of single atom size has been successfully and controllably self-organized on the surface of 2D graphdiyne (GDY) material. Detailed characterizations showed that the incomplete charge transfer effect between the Os atoms and GDY not only stabilized the catalytic system but also improved the intrinsic activity, making the Gibbs free energy reach the best and resulting in remarkable performance with a small overpotential of 49 mV at 500 mA cm–2, large specific j0of 18.6 mA cm–2, and turnover frequency of 3.89 H2s–1at 50 mV. In addition, the formation of sp-C–Os bonds guarantees the high long-term stability of 800 h at a large current density of 500 mA cm–2in alkaline simulated seawater.

Human amniotic membranes (hAMs) are widely used as wound management biomaterials, especially as grafts for corneal reconstruction due to the structure of the extracellular matrix and excellent biological properties. However, their fragile nature and rapid degradation rate hinder widespread clinical use. In this work, we engineered a novel self-powered electronic dress (E-dress), combining the beneficial properties of an amniotic membrane and a flexible electrical electrode to enhance wound healing. The E-dress displayed a sustained discharge capacity, leading to increased epidermal growth factor (EGF) release from amniotic mesenchymal interstitial stem cells. Live/dead staining, CCK-8, and scratch-wound-closure assays were performed in vitro. Compared with amniotic membrane treatment alone, the E-dress promoted cell proliferation and migration of mouse fibroblast cells and lower cytotoxicity. In a mouse full-skin defect model, the E-dress achieved significantly accelerated wound closure. Histological analysis revealed that E-dress treatment promoted epithelialization and neovascularization in mouse skin. The E-dress exhibited a desirable flexibility that aligned with tissue organization and displayed maximum bioactivity within a short period to overcome rapid degradation, implying great potential for clinical applications.

ABSTRACT To account for the improvement of mechanical properties for recycled crumb rubber concrete (RCRC) under uniaxial compression and flexural toughness test in detail, three groups of RCRC specimen covering [...]

ABSTRACT This paper presents an experimental study on shaft lining concrete specimens under different confining and pore pressure difference. The initial permeability, peak strength, relationship between the axial strain, volumetric [...]

This article investigates the finite-time synchronization (FTS) and $H_{\infty }$ synchronization for two types of coupled neural networks (CNNs), that is, the cases with multistate couplings and with multiderivative couplings. By designing appropriate state feedback controllers and parameter adjustment strategies, some FTS and finite-time $H_{\infty }$ synchronization criteria for CNNs with multistate couplings are derived. In addition, we further consider the FTS and finite-time $H_{\infty }$ synchronization problems for CNNs with multiderivative couplings by utilizing state feedback control approach and selecting suitable parameter adjustment schemes. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed criteria.

Operation of rechargeable batteries at ultralow temperature is a significant practical problem because of poor kinetics of the electrode. Here, we report for the first time stabilized multiphase conversions for fast kinetics and long-term durability in ultralow-temperature, organic-sodium batteries. We establish that disodium rhodizonate organic electrode in conjunction with single-layer graphene oxide obviates consumption of organic radical intermediates, and demonstrate as a result that the newly designed organic electrode exhibits excellent electrochemical performance of a highly significant capacity of 130 mAh g–1at −50 °C. We evidence that the full-cell configuration coupled with Prussian blue analogues exhibits exceptional cycling stability of >7000 cycles at −40 °C while maintaining a discharge capacity of 101 mAh g–1at a high current density 300 mA g–1. We show this is among the best reported ultralow-temperature performance for nonaqueous batteries, and importantly, the pouch cell exhibits a continuous power supply despite conditions of −50 °C. This work sheds light on the distinct energy storage characteristics of organic electrode and opens up new avenues for the development of reliable and sustainable ultralow-temperature batteries.

Polycystic ovary syndrome (PCOS) is a complex disorder. Genome-wide association studies (GWAS) have identified several genes associated with this condition, including DENND1A. DENND1Aencodes a clathrin-binding protein that functions as a guanine nucleotide exchange factor involved in vesicular transport. However, the specific role of DENND1Ain reproductive hormone abnormalities and follicle development disorders in PCOS remain poorly understood. In this study, we investigated DENND1Aexpression in ovarian granulosa cells (GCs) from PCOS patients and its correlation with hormones. Our results revealed an upregulation of DENND1Aexpression in GCs from PCOS cases, which was positively correlated with testosterone levels. To further explore the functional implications of DENND1A, we generated a transgenic mouse model overexpressing Dennd1a(TG mice). These TG mice exhibited subfertility, irregular estrous cycles, and increased testosterone production following PMSG stimulation. Additionally, the TG mice displayed diminished responsiveness to FSH, characterized by smaller ovary size, less well-developed follicles, and abnormal expressions of FSH-priming genes. Mechanistically, we found that Dennd1aoverexpression disrupted the intracellular trafficking of follicle stimulating hormone receptor (FSHR), promoting its internalization and inhibiting recycling. These findings shed light on the reproductive role of DENND1Aand uncover the underlying mechanisms, thereby contributing valuable insights into the pathogenesis of PCOS and providing potential avenues for drug design in PCOS treatment.

Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. Recently, convolutional neural networks (CNNs) have achieved significant advantages in general object detection. With the development of Transformer, the scale of SIRST models is constantly increasing. Due to the limited training samples, performance has not been improved accordingly. The quality, quantity, and diversity of the infrared dataset are critical to the detection of small targets. To highlight this issue, we propose a negative sample augmentation method in this article. Specifically, a negative augmentation approach is proposed to generate massive negatives for self-supervised learning. First, we perform a sequential noise modeling technology to generate realistic infrared data. Second, we fuse the extracted noise with the original data to facilitate diversity and fidelity in the generated data. At last, we proposed a negative augmentation strategy to enrich diversity as well as maintain semantic invariance. The proposed algorithm produces a synthetic SIRST-5K dataset, which contains massive pseudodata and corresponding labels. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed. Compared with other state-of-the-art (SOTA) methods, our method achieves outstanding performance in terms of probability of detection ( $P_{d}$ ), false-alarm rate ( $F_{a}$ ), and intersection over union (IoU). The code is available at: https://github.com/luy0222/SIRST-5K.

Organic semiconductor (OSC) gas sensors with good mechanical flexibility have received considerable attention as commercial and wearable devices. However, due to poor resistance to moisture and low conductivity, the improvement in the sensing capability of individual OSCs is limited. Reported here is a promising pathway to construct a series of conjugated organic polymers (COPs) with well-defined pyrimidine (Py-COP) or boron β-diketone (BF-COP) units. Unlike traditional metal- or carbon-based hybrid materials, the developed COPs can provide abundant absorption sites for gaseous analytes. As a result, the as-prepared BF-COPresults in an excellent sensing response of over 1500 (Ra/Rg) toward 40 ppm of NH3at room temperature, which is the highest value among those of pristine COPs as n-type sensing materials. Notably, they can maintain their initial sensing responses for two months and 90% relative humidity resistance. Combining the results of in situFourier transform infrared spectroscopy and theoretical calculations, the β-diketone skeleton is found to activate the surface electronic environment, verifying that the electron-deficient B ← O groups are adsorption centers. The B/N-heterocyclic decoration effectively modulates the redox properties and electronic interactions, as well as perturbs charge transfer in typical π-conjugated COPs. These results offer insight into developing highly efficient OSC gas sensors, which potentially have broadened sensing applications in the areas of organoboron chemistry.

Conversational semantic role labeling (CSRL) is believed to be a crucial step toward dialogue understanding. By incorporating the CSRL information into the conversational models, previous work (Xu et al., 2021) has confirmed the usefulness of CSRL to downstream conversation-based tasks, including multi-turn dialogue rewriting and multi-turn dialogue response generation. However, (Xu et al., 2021) found that the quality of the extracted CSRL structures would consequently affect the performance of downstream dialogue tasks while the performance of existing CSRL models is still unsatisfactory. There are two major problems in existing CSRL models to handle predicate-aware and conversational structural information. First, they ignore the fact that explicitly correlating the predicate and the context utterances could help the model better identify the arguments. Secondly, these models do not encode some vital conversational structural information, such as the speaker information which is necessary for modeling inter-speaker dependency. In this paper, we model the conversational structure-aware features based on three components: 1) the predicate-aware module which aims to capture rich correlations between the predicate and utterances; 2) a speaker-aware graph network which explicitly encodes the speaker-dependent information; 3) a novel structure-aware dialogue modeling method for the model warm-up. Experimental results on benchmark datasets show that our model significantly outperforms the baselines. We also examine the efficiency of our model and its effectiveness in low-resource scenarios. We find that our model can achieve better performance with less training time and training data than the existing models. In addition, further improvements are observed when applying the CSRL information extracted by our model into downstream dialogue tasks, which consistently indicates the superiority of our model.

The field emission phenomenon is surface sensitive, and the tape pretreatment method has been commonly used to improve the disordered surface morphology of cathodes to boost the field emission performance. However, there are few reports on detailed surface investigations of tape pretreatment. In this study, we initially investigate the stickiness variation of the tape and the most suitable preprocessing times for improved field emission performance and then utilize multiwalled carbon nanotubes (MWCNTs)/silicon carbide (SiC) composites as cathodes to thoroughly investigate the compound ratio to reduce the serious shielding effect and to maintain a proper field emission density. A nonmonotonic relationship between the turn-on field and cathode composite ratio is found. In addition, the luminance and luminance uniformity for the field emission devices of the MWCNTs/SiC composites prove the enhanced field emission properties of the MWCNTs/SiC composites with proper tape pretreatment times, demonstrating potential applications in vacuum electronic devices.

Capacitively-Coupled Instrumentation Amplifiers (CCIAs) are widely used as AC-coupled, low-noise amplifiers in many multi-channel, area-intensive sensor interface applications. However, the need for a trade-off between the front-end gain and CCIA area, imposed by the input capacitance, restricts the miniaturization of such sensors. We propose a Complementary Transimpedance Boosting (CTB) technique to relieve this challenging gain versus area trade-off. The proposed CTB applies to both the DC and AC paths of a CCIA. CTB also reduces AC gain degradation, suppressing low-side gain peaking of the frequency response without significantly increasing input-referred noise. The Gain-Noise-Area Efficiency (GNA) and Gain-Power-Area Efficiency (GPA) factors are also introduced for holistic comparisons with other front-end amplifiers regarding noise, gain, and power consumption. CTB-based CCIAs (CTB-CCIA) are fabricated in $0.18~\mu $ m CMOS, with the highest single-stage AC gain of 289 V/V achieved. Occupying only 0.011 mm2, a conventional CCIA with the same area will have $14.4\times $ less gain while requiring at least two stages. The CTB-CCIA has a competitive NEF of 4.92 within a signal bandwidth of 0.21 Hz – 8.7 kHz, suitable for multi-channel applications.

Different from traditional cavity-based fiber lasers, well-defined resonant cavities are replaced by random feedback mechanisms for lasing generation in random fiber lasers (RFLs). Ever since the first demonstration in 2007, RFLs with unique advantages, such as structural simplicity, high efficiency, good wavelength flexibility, and high stability have attracted plenty of attention. Due to their superior properties, RFLs have been widely used to improve system performances of optical fiber communication (OFC) and optical fiber sensing (OFS). In this article, we conducted an overview of the applications of RFLs in OFC and OFS. We initially describe the basic operation principles and the typical properties of the RFLs. Then, the scenarios of the RFLs in the OFC systems are discussed, including random fiber lasing amplification (RFLA) techniques for extending signal transmission distance in long-haul OFC and OFS systems, static and dynamic point sensing based on RFLs, and RFL-based novel light sources for OFC and OFS applications.

In order to investigate the damage process caused by the seismic pseudo-static load, quasi-distributed fiber bragg grating (FBG) sensors were placed in CRTS III ballastless track specimens. Low-cyclic reversed load experiments were then carried out. We studied the strain conditions at various points along the ballastless track under cyclic loading as well as the damage modes, strain curves, residual strains, and cross-sectional stresses of the self-compacting concrete layer and foundation slab. The damage process may be broken down into three stages: First, fractures show up at the base plate anchors; second, the shear reinforcement deforms; and third, the concrete layer is lifted until it is crushed. The CRTS III ballastless slab's maximum transverse tensile strain is greater than its maximum longitudinal tensile strain. Premature cracking of the self-compacting concrete layer will result from greater strain at the edge and corner of the groove and from a lack of reinforcing. It is tough to monitor the deformation of the groove area at simultaneously, which is not possible with more conventional monitoring methods like strain gauge sensors. Quasi-distributed FBG sensing can effectively observe the detailed strain development of the groove and its surrounding areas.

Presently, a variety of soft sensing technologies are available, but there is still an opportunity to research on flexible sensors that can withstand high loads. Here, we report a highly stretchable, wide measuring range, and easy-to-fabricate liquid metal sensor capable of detecting force and strain modes. The key enabling design feature of the proposed sensor is the spiral winding combination of two different types of stretchable liquid metal microfluidic fibers. Pressing or elongating the fibers changes the geometry and, thus, the electrical resistance in a predictable way. The proposed sensor can offer a simple mechanism to measure force up to about 400 N and strain up to about 200% under limited laboratory conditions. The sensor signal is repeatable in both cases. The ability of our sensor in wide measuring range in actual application is verified via detecting the loads of one human body and driving car wheel. We further show a proof of concept of the soft sensor by a soft robotic actuator, enabling the identification of bending angles and object surface features.

Graphical Abstract:

Graphdiyne(GDY) based atom catalysts(ACs) show extraordinary electrocatalytic activities towards hydrogen evolution reaction(HER) and water oxidation reaction(OER), which have attracted wide attention of scientists. However, a precise understanding on the activities trends and origins of ACs is still under exploration. We investigate the electrocatalytic performance of a series of ACs in this work. Density functional theory(DFT) calculations reveal that the half reactions of OER and HER are site-dependent on ACs, which are preferentially proceeded on the metal site and the nearby bonded carbon site, respectively. Besides, the d-band center of metal atoms is the most important descriptor, which can perfectly correlate the high activities of ACs towards the two half reactions. Especially for HER, the deeper d-band position of metal atoms corresponds to much stronger d-px(py) orbital coupling in ACs, which activate more obvious charge transfer between the metal site and the nearby bonded carbon site, resulting in the HER activity enhancement of ACs. These results are of great significance in guiding the precise synthesis of highly efficient bifunctional metal atom catalysts supported by GDY in future.

Designing appropriate nitrogen (N) application rates and planting densities is crucial for ensuring high-yield and high-quality rice production. The effects of N fertilizer and planting density on the soil microbial community composition and abundance and on root N uptake mechanisms were investigated. In this study, high-nitrogen low-density (HNLD), medium-nitrogen medium-density (MNMD) and low-nitrogen high-density (LNHD) experiments were conducted via the bucket planting method, and plant physiology, dry matter mass and N uptake, root enzyme activities and rhizosphere soil microbial communities were examined via metagenomic sequencing and isotope tracing. The HNLD treatment exhibited the highest N absorption and utilization capacity. Compared to those in the other treatments, the δ15N values and abundance increased by 52.96–63.45% during the maturation stage and by 34.48–48.36% during the heading stage. Compared with those of the other treatments, the Ndff (15N derived from fertilizer) increased by 59.33–60.92% during the maturation stage and by 40.24–47.56% during the heading stage. Actinobacteria, Acidobacteria, Chloroflexi, and Proteobacteriawere confirmed to be the dominant bacterial groups among the treatments. Based on these findings, a correlation-oriented model and dual-factor correlation network were constructed, focusing on the variations in root nitrite reductase (NIR) activity, rhizosphere soil sucrase (S-SC) and soil catalase (S-CAT) activities, and Actinobacteriaand Proteobacteriacommunities. Overall, this study provides theoretical support for determining N application rates and planting densities in practical rice production, with the ultimate goal of enhancing root N uptake efficiency.

In this article, we demonstrate great potential of lightly-erbium(Er3+)-doped ZrF4 fiber in efficient power scaling at ∼2.8 μm, when excited at ∼1.7 μm. Core-pumped by a random Raman fiber laser at 1.69 μm, the 1.5 mol.% Er3+-doped ZrF4 fiber in a F-P cavity has experimentally yielded an output power of >6 W at 2.783 μm with a slope efficiency of up to 58.4% (with respect to the absorbed pump), close to its Stokes limit of 60.7% (1.69/2.783). To our knowledge, this is the highest efficiency of Er3+-doped ZrF4 fiber lasers in the mid-infrared. Further power scaling is only limited by the current pump power. Based on the validated numerical model, the dominant role of the excited state absorption (ESA) process (i.e., $^4{\mathrm{I}}_{13/2}{ \to }^4{\mathrm{I}}_{9/2}$) in exciting efficient ∼2.8 μm laser has been highlighted, and the resonant ground state absorption process (i.e., $^4{\mathrm{I}}_{15/2}{ \to }^4{\mathrm{I}}_{13/2}$) is also essential. The simulation results indicate that the pump wavelength closer to the ESA peak can yield a higher slope efficiency. In addition, heat load analyses via numerical modeling reveals that the $^4{\mathrm{I}}_{9/2}{ \to }^4{\mathrm{I}}_{11/2}$ multiphonon relaxation is the main heat source of the system. By taking fiber optical damage limit into account simultaneously, a high-power lightly-Er3+-doped ZrF4 fiber laser arrangement has been proposed, which theoretically enables 100-W-class output under a slope efficiency of >50% and a relatively low heat load.

The intricate correlation between lattice geometry, topological behavior and charge degrees of freedom plays a key role in determining the physical and chemical properties of a quantum-magnetic system. Herein, we investigate the introduction of the unusual oxidation state as an alternative pathway to modulate the magnetic ground state in the well-known S= 1 Haldane system nickelate Y2BaNiO5(YBNO). YBNO is topologically reduced to incorporate d9-Ni+(S= 1/2) in the one-dimensional Haldane chain system. The random distribution of Ni+for the first time results in the emergence of a one-dimensional ferromagnetic phase with a transition temperature far above room temperature. Theoretical calculations reveal that the antiferromagnetic interplay can evolve into ferromagnetic interactions with the presence of oxygen vacancies, which promotes the formation of ferromagnetic order within one-dimensional nickel chains. The unusual electronic instabilities in the nickel-based Haldane system may offer new possibilities towards unconventional physical and chemical properties from quantum interactions.

Velocity and reflectivity models are fundamental outcomes obtained from seismic exploration, enabling the identification of subsurface structures and physical properties. Two primary methods for obtaining these high-resolution results are two-way wave equation-based full waveform inversion (FWI) and least-squares reverse time migration (LSRTM). Despite sharing a similar least-squares inversion framework, FWI and LSRTM present challenges in effectively combining them to generate both velocity and reflectivity models simultaneously due to their different modeling engines and dependencies on data components. In this study, we propose a novel joint migration inversion (JMI) approach by incorporating a two-way full-wavefield modeling engine parameterized by the subsurface velocity and reflectivity. By providing adjoint velocity and reflectivity sensitive kernels, this JMI can produce high-quality velocity and reflectivity models concurrently. The method developed in this study has the potential to directly process seismic data containing full-wavefield information, reducing data preprocessing complexity and avoiding potential damage to valid signals during the processing. Through a synthetic data test and a benchmark test, we demonstrate the effectiveness of our JMI approach. Moreover, when compared with conventional approaches for velocity building and imaging, our method yields velocity and reflectivity models with higher resolution and improved consistency.

The authentication technology of mobile device users has been studied for decades. To balance security, privacy, and usability, motion sensors-based user authentication methods are widely investigated in recent years. However, existing studies meet the problems such as scarcity of training samples, underutilization of data, poor de-noising ability, insufficient transferability, privacy leakage, and low accuracy. To overcome these difficulties, we propose a system, called TrapCog, with the following capabilities: 1) In the phase of data collection, TrapCog can eliminate man-made noise (mislabeling) through differential training based on down-sampling. 2) In the model training stage, the siamese neural network with Long Short-Term Memory (LSTM) as the sub-network is used to achieve sufficient coverage of sample patterns and the transferability of the model. 3) In the phase of real-world authentication, the privacy of the user is tremendously protected through end-side model deployment and local authentication. Experimental results on a dataset composed of 1,513 users with real-world noise show that TrapCog has high accuracy and strong transferability, which is much better than state-of-the-art studies.

In this work, a gallium–nickel alloy target with a weight percentage ratio of 7:3 was successfully prepared on Cu substrate in a chloride system at room temperature. A novel organic-solvent-free separation procedure based on two columns for extracting 68Ge from irradiated Ga–Ni alloy target with high radionuclidic purity (99.99 %) was developed. By this separation procedure, the recovery of 68Ge is 95 ± 5 % and the yield of 68Ge via the nuclear reaction natGa(p,xn)68Ge was measured to be 0.27 ± 0.02 MBq/μA h.

In the context of a sudden contraction plug conduit, the near-wall area experiences a significant shearing effect of water flow, however, the extent to which this shearing effect occurs in bubble-water flow and the related variation mechanisms of air bubble size and number remain unclear. This study employs a model test method to investigate the diffusion process of bubble-water flow in a sudden contraction plug conduit. The size and number of bubbles, as well as their distribution along the shearing section under varying initial air volume conditions, are studied in detail using a high-speed image acquisition system. The experimental findings reveal a self-similar relationship between the number and size of bubbles and their cross-sectional distribution over time. The bubble number and size vary in three stages, i.e., quasi-suspension, shearing, and shearing completion stages. The direction perpendicular to the conduit exhibits peak values in bubble number distribution over the three stages, with peak value location varying with the near-wall area. As time progresses, the peak value increases, and a larger initial air volume corresponds to a smaller distance of the peak value location from the wall. The size of air bubbles near the wall is consistent with the minimum diameter of air bubbles in shear flow and is hardly affected by the initial air volume. These results aid in comprehending the change law of two-phase water and air flow under a strong shearing effect in the plug conduit, and provide useful insights for hydraulic design in fluid engineering.

Monolayer WSe2exhibits a fascinating optical dark exciton ground state, which holds significant promise for advancing single-photon source and quantum information processing. In this study, we systematically explored the temperature- and strain-dependent optical properties of monolayer WSe2using a three-dimensional silicon-based nanowrinkle structure as a template. We observed emission from a variety of excitonic states, including bright exciton (X0), bright trion (XT), dark exciton (XD), and dark trion (XDT), all originating from the same sample. Notably, we can selectively control and enhance their emissions. At a temperature of 153 K, the dominant emission source was the bright exciton, but as the mechanical compressive strain decreased to less than −0.015% at 105 K, dark excitons began to dominate the emission. Furthermore, when the mechanical strain was tensile and exceeded 0.03%, the intensities and energies of bright exciton (X0), bright trion (XT), dark exciton (XD), and dark trion (XDT) remained stable, indicating that all the excitons are drawn toward regions with the highest local strain due to a funneling effect. Additionally, we found that the expansion coefficient and Debye temperature of monolayer WSe2were critically influenced by the strain distribution. Specifically, the expansion coefficient tripled as the strain increased from a compressive −0.05% to a tensile 0.05% strain, and the Debye temperature increased from 20 to 600 K. Considering the compatibility of silicon-based substrates and monolayer transition metal dichalcogenides with semiconductor and photonic circuits, our approach holds promise for achieving site-controlled quantum emission of specific excitons and creating high-density quantum emitters through strain engineering.

Severe dry eye (SDE) can cause grievous damage to the ocular surface and result in vision impairment and even blindness. To investigate the fate of limbal stem cells in SDE and the underlying mechanism, the current study established an SDE rat model by removing the extraorbital and infraorbital lacrimal glands and maintaining them in a low-humidity environment. One month after the surgery, aqueous tear secretion was reduced dramatically, blood vessels invaded into the central cornea, and inflammatory cells infiltrated into the limbal stroma. The expressions of keratin 12 and paired box gene 6 were down-regulated dramatically, while those of keratin 10, small proline-rich protein 1b, and mucin 5AC were up-regulated in the corneal epithelium of the SDE rats. Cell proliferation in the limbal epithelium was up-regulated, while the stem/progenitor marker adenosine 5′-triphosphate-binding cassette member 2 and the limbal epithelial colony-forming efficiency were decreased in the SDE condition. Furthermore, the p38 mitogen-activated protein kinase signaling pathway was activated in the limbal corneal epithelium of SDE rats. The abnormal differentiation and stemness loss in the corneal epithelium could be reversed upon treatment with a p38 inhibitor in a SDE in vivomodel and in vitrohyperosmolar corneal epithelial culture conditions. These data suggest that SDE can lead to limbal stem cell dysfunction, and p38 mitogen-activated protein kinase signaling pathway activation plays an essential role in this process.

We report the colloidal synthesis of Bi-doped Cs2AgxNa1–xInCl6double perovskite nanoplatelets (NPLs) exhibiting a near-unity photoluminescence quantum yield (PLQY), a record emission efficiency for nanoscale lead-free metal halides. A combination of optical spectroscopies revealed that nonradiative decay processes in the NPL were suppressed, indicating a well-passivated surface. By comparison, nanocubes with the same composition and surface ligands as the NPLs had a PLQY of only 40%. According to our calculations, the type of trap states arising from the presence of surface defects depends on their specific location: defects located on the facets of nanocubes generate only shallow traps, while those at the edges result in deep traps. In NPLs, due to their extended basal facets, most of the surface defects are facet defects. This so-called facet-defect tolerant behavior of double perovskites explains the more efficient optical emission of NPLs compared to that of nanocubes.

The latest progress of Ru-based catalysts for acidic media is reviewed and specifically, feasible strategies for optimizing OER performance based on these mechanisms are provided.

Background: The pathological form of synaptic plasticity, ischemic long-term potentiation (iLTP), induced by oxygen and glucose deprivation (OGD), is implicated in the acute phase of stroke with the potentiation of N-methyl-D-aspartate receptor (NMDAR). While there has been widespread attention on the excitatory system, a recent study reported that γ-aminobutyric acid (GABA)ergic system is also involved in iLTP. Valproic acid (VPA), a histone deacetylase inhibitor, protects against ischemic damage. However, whether VPA regulates early phase plasticity in ischemic stroke remains unknown. The present study aims to investigate the potential role and mechanism of VPA in ischemic stroke. Methods: A brief exposure of OGD on the hippocampal slices and the induction of photothrombotic ischemia (PTI) were used as ex vivo and in vivo models of ischemic stroke, respectively. Results: Using extracellular recordings, iLTP was induced in the hippocampal Schaffer collateral pathway following OGD exposure. VPA treatment abolished hippocampal iLTP via GABAAreceptor enhancement and extracellular signal-regulated kinase (ERK) phosphorylation. Administration of VPA reduced brain infarct volume and motor dysfunction in mice with PTI. Moreover, VPA protected against ischemic injury by upregulating the GABAergic system and ERK phosphorylation, as well as by reducing of matrix metalloproteinase in a PTI-induced ischemic stroke model. Conclusions: Together, this study revealed the protection of VPA in ex vivo OGD-induced pathological form of neuroplasticity and in vivo PTI-induced brain damage and motor dysfunction through rescuing GABAergic deficiency and the pathological hallmarks of ischemia.

Maximum Power Point Tracking (MPPT) is the core technology for harvesting maximum power from Photovoltaic Power Generation Systems (PVGS). Global MPPT (GMPPT) algorithms have been broadly proposed in the past few years for PVGSs under Partial Shading (PS); however, most of these algorithms make the speed of MPPT slower and consequently lead to more tracking losses when there is no PS. Therefore, an efficient PS Detection (PSD) for PVGSs is proposed in the paper. The proposed PSD estimates the solar irradiance and temperature of a PVGS by the characteristic output function and measured voltages and currents. The occurrence of PS can then be detected by the estimated solar irradiance and temperature along with the proposed PS factor. After the PS condition of a PVGS was determined, the appropriate MPPTs can be chosen to track the MPP effectively and efficiently. This can accelerate MPPT performance, reduce tracking time, and improve power generation efficiency. Simulation results show that the detection accuracy of the proposed PSD can exceed 99% under different PS and temperature conditions. Experimental results demonstrate the validity and performance of the proposed PSD for PVGSs.

Wide input-output (IO) chip-to-chip interfaces, such as 3-D chip stacking [through-silicon via (TSV)], silicon interposer in high-bandwidth memory (HBM), and other 2.5-D chip-to-chip interface, handle a large amount of data in the server and artificial intelligence (AI) applications. With a large number of IOs, power consumption becomes a huge burden. This article presents a novel charge recycling (CR) logic with >20% power reduction under random data streaming. The presented generic CR technique is applicable to both TSV and transmission line (T-Line) link IOs. The CR logic is implemented on two silicon dies where the single-channel CR (CR1) uses a storage capacitor to recycle charge at each data transition and multi-channel CR (CR2/4/8) replenishes the charge between multiple channels during the opposite transitions. Fabricated in a 40-nm 1P8M standard CMOS, the TSV link (2.56 Gb/s) and the T-Line link (5.12 Gb/s) save energy up to 32.2% and 47%, respectively, under periodic data transmission and up to >20% under pseudo-random binary sequence (PRBS). The eye diagrams and the bit error rate (BER) show that signal integrity is maintained when compared with conventional data links.

This brief presents a hybrid boost DC-DC converter for driving piezoelectric actuators that overcomes the frequency limitation of traditional boost converters and the 3-level, double-step-down (DSD) topology when dealing with high conversion ratios (VCR). The proposed converter combines the benefits of conventional hybrid Fibonacci and Dickson switched-capacitor (SC) converters to achieve high VCR with fewer capacitors and lower-voltage-rating switches. Two inductors are used to reduce DC resistance (DCR) loss and achieve charge and current balance. The converter operates as a $14\times $ boost circuit with a duty ratio (D) as high as 0.5. With the SC stages reducing the voltage stress of inductors, only two small inductors are required to complete voltage conversion, reducing the inductor current ripple. This brief was fabricated in the 180nm 1P6M BCD process. It can deliver a maximum load current of 2mA to an output voltage of 40-70V with a standard battery input voltage of 2.5-5V. The measurement results show a 54.8% peak efficiency and 2.312mW/mm3 power density, which are higher than the commercial products and the prior arts.

The effect of alternating magnetic field (AMF) on the microstructure evolution and mechanical properties during tempering of M50 bearing steel have been investigated for evaluating the application prospect of magnetic field in steels. Microstructural observation and EDS analysis shows that during solidification process, M2C carbides rich in molybdenum are easily formed on the matrix of M50 bearing steel. Microstructure characterization results show that the application of AMF promotes the dissolution and refinement of carbides. At medium tempering temperature (300 °C, 380 °C), Vickers hardness increased with the application of AMF. Furthermore, due to the occurrence of secondary hardening, the Vickers hardness increases at high tempering temperature of 540 °C and the application of AMF has little effect on the hardness change. In addition, tensile fracture morphology observation shows that the morphology is mainly quasi-cleavage fracture. At medium tempering temperature, the presence of large carbides and transgranular cracks on the matrix results in the low tensile strength. For the high tempering temperature, the AMF could significantly reduce the carbide size and increase the tensile strength. This provides a new idea for improving the mechanical properties of M50 bearing steel by using AMF during tempering process.