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Single crystal Ni-rich cathode material with enhanced cyclability and reversibility is achieved via surface modification with high valence metal oxides.

Emerging lipid-modifying agents show potential but lack evidence for the management of uric acid and gout. We aimed to explore the causal effects of lipid traits, lipid-modifying drugs on uric acid levels and risk of gout. Two-sample MR analyses were performed to investigate the associations of genetically predicted lipid traits (LDL-C, HDL-C and TG) and lipid-modifying drug targets (PCSK9, HMGCR, NPC1L1, CETP, ABCG5/G8, APOB, LDLR, LPL, ANGPTL3, and APOC3) with uric acid levels and gout risk. Validation analyses were performed using the independent cohort of the UK Biobank. Summary-data-based MR was further conducted to estimate the associations of the expression of drug target genes with the outcomes. Genetically predicted lower HDL-C and higher TG were significantly associated with elevated uric acid levels (β(95% CI): -0.11 [-0.18, -0.04], p= 0.001 for HDL-C; 0.18 [0.09, 0.27], p< 0.001 for TG) and increased risk of gout (OR (95% CI): 0.83 [0.71, 0.97], p= 0.017 for HDL-C; 1.54 [1.25, 1.91], p< 0.001 for TG). Notably, LPL activation among lipid-modifying drug targets demonstrated significant associations with both reduced uric acid levels (β[95% CI]: -0.13 [-0.16, -0.10], p< 0.001) and decreased risk of gout (OR 95% CI: 0.84 [0.76, 0.93], p= 0.001). These findings were corroborated in the UK Biobank dataset. Furthermore, the expression of LPL was significantly associated with lower uric acid levels (β[95% CI]: -0.03 [-0.04, -0.01], p= 0.002). Our results suggest that LPL activation, which reduces TG levels, holds promise as a candidate drug for the treatment and prevention of hyperuricemia and gout.

Moiré superlattices, such as those formed from transition metal dichalcogenide heterostructures, have emerged as an exciting platform for exploring quantum many-body physics. They have the potential to serve as solid-state analogues to ultracold gases for quantum simulations. A key open question is the coherence and dynamics of the quantum phases arising from photoexcited moiré excitons, particularly amid dissipation. Here we use transient photoluminescence and ultrafast reflectance microscopy to image non-equilibrium exciton phase transitions. Counterintuitively, experimental results and theoretical simulations indicate that strong long-range dipolar repulsion freezes the motion of the Mott insulator phase for over 70 ns. In mixed electron–exciton lattices, reduced dipolar interactions lead to diminished freezing dynamics. These findings challenge the prevailing notion that repulsion disperses particles, whereas attraction binds them. The observed phenomenon of frozen dynamics due to strong repulsive interactions is characteristic of highly coherent systems, a feature previously realized exclusively in ultracold gases.

Conflicts of interest among stakeholders in government data openness significantly impact the development and utilization of government data, necessitating establishing targeted collaborative mechanisms. This study, leveraging the perspective of a multi-stakeholder value co-creation network, constructs a tripartite game model to deeply analyze the dynamic interactions and evolutionary stability among local governments, data developers, and consumers. MATLAB simulations are employed to explore the effects of key parameters on system evolution. The results indicate that the initial strengths of intentions among the three parties substantially influence the final stable equilibrium state, with interactions occurring among their respective strategies. To establish a sustainable collaboration mechanism, local governments should construct a fair benefit distribution system to incentivize the opening of raw data effectively; data developers should enhance their data risk management capabilities to ensure data security; and active supervision by consumers effectively supports the enthusiasm for government data openness.

We briefly summarize the present progress and highlight the perspective regarding high-performance all-perovskite tandems focusing on the following aspects: low-bandgap perovskite bottom subcells, wide-bandgap perovskite top subcells, and interconnecting layers.

Transdermal insulin delivery in a painless, convenient, and on-demand way remains a long-standing challenge. A variety of smart microneedles (MNs) fabricated by glucose-responsive phenylboronic acid hydrogels have been previously developed to provide painless and autonomous insulin release in response to a glucose level change. However, like the majority of MNs, their transdermal delivery efficiency was still relatively low compared to that with subcutaneous injection. Herein, we report an iontophoresis (ITP)-integrated smart MNs delivery platform with enhanced transdermal delivery efficiency and delivery depth. Carbon nanotubes (CNTs) were induced in the boronate-containing hydrogel to develop a semi-interpenetrating network hydrogel with enhanced stiffness and conductivity. Remarkably, ITP not only facilitated efficient and deeper transdermal delivery of insulin via electroosmosis and electrophoresis but also well-maintained glucose responsiveness. This ITP-combined smart MNs delivery platform, which could provide on-demand insulin delivery in a painless, convenient, and safe way, is promising to achieve persistent glycemic control. Furthermore, transdermal delivery of payloads with a wide size range was achieved by this delivery platform and thus shed light on the development of an efficient transdermal delivery platform with deep skin penetration in a minimally invasive way.

Efficient catalysts for hydrogen generation from seawater are essential for advancing clean energy technologies. In this study, we present a straightforward method for producing Pt nanoparticles enclosed within metallic 1T-phase MoS2nanosheets on graphite paper as a promising catalyst for the hydrogen evolution reaction (HER). The resulting 14.3 wt % Pt-MoS2nanosheets demonstrate an ultralow onset potential of 65.6 mV vs the reversible hydrogen electrode (RHE) and a minimal Tafel slope of 64 mV/dec with remarkable stability and durability in simulated seawater, offering comparable catalytic performance to the 40 wt % Pt/C commercial catalyst at a lower cost. This exceptional hydrogen production is attributed to the robust reducing ability of 1T-phase MoS2and the confinement of Pt nanoparticles within the MoS2interlayers and nanosheets. Our findings highlight the significance of this approach in developing practical and sustainable electrocatalysts for seawater splitting. This research represents a crucial step toward a greener and more sustainable future, leveraging innovative catalyst design strategies for clean energy production.

AbstractDanzhi-xiaoyao-San (DZXYS), a Traditional Chinese Medicine, plays an essential role in the clinical treatment of depression, but its mechanisms in humans remain unclear. To investigate its pharmacological effects and mechanisms as an add-on therapy for depression, we conducted a double-blind, placebo-controlled trial with depressed patients receiving selective serotonin reuptake inhibitors (SSRIs). Serum and fecal samples were collected for metabolomic and microbiome analysis using UHPLC-QTRAP-MS/MS and 16S rRNA gene sequencing technologies, respectively. Depression symptoms were assessed using the 24-item Hamilton Depression Scale. We employed network pharmacology, metabolomics, and molecular docking to identify potential targets associated with DZXYS. We also examined the correlation between gut microbes and metabolites to understand how DZXYS affects the microbiota-gut-brain axis. The results showed that DZXYS combined with SSRIs was more effective than SSRIs alone in improving depression. We identified 39 differential metabolites associated with DZXYS treatment and found seven upregulated metabolic pathways. The active ingredients quercetin and luteolin were docked to targets (AVPR2, EGFR, F2, and CDK6) associated with the enriched pathways ‘pancreatic cancer’ and ‘phospholipase D signaling pathway’, which included the metabolite lysophosphatidic acid [LPA(0:0/16:0)]. Additionally, we identified 32 differential gut microbiota species related to DZXYS treatment, with Bacteroides coprophilusand Ruminococcus gnavusshowing negative correlations with specific metabolites such as L-2-aminobutyric acid and LPA(0:0/16:0). Our findings indicate that DZXYS's antidepressant mechanisms involve multiple targets, pathways, and the regulation of LPA and the microbiota-gut-brain axis. These insights from our systems pharmacology analysis contribute to a better understanding of DZXYS's potential pharmacological mechanisms in depression treatment.Communicated by Ramaswamy H. Sarma

Hot extruded aluminum alloy profiles are widely used in the preparation of automotive bodies and key components. In this study, the influence of extrusion parameters on the metal flow behaviors and energy consumption of a 6063 aluminum alloy profile with complex cross-section were investigated by numerical simulation and mathematical modeling. An extrusion experiment was conducted to study the microstructure and mechanical properties of the profile. The simulation results show that the standard deviation of metal flow velocity (representing the forming quality) was decreased as container temperature decreases, and the extrusion speed, billet temperature and die temperature increase. Through analyzing the energy transfer behavior during hot extrusion, a mathematical model of energy consumption was established. It was found that preparing high-quality profiles usually requires high energy consumption. Taking metal flow behavior and energy consumption into account simultaneously, reasonable extrusion process parameters were obtained, and an extruded profile without defects was prepared. By monitoring the driving motor current of the extruder, the energy consumption model was verified. The extruded profile has homogenous recrystallized grains with strong Cube and Goss textures, and yield strength distributed in the range of 75.93–89.03 MPa.

Soft capacitive sensors present numerous appealing characteristics, including simple structure, low power consumption, and fast response. However, they often suffer from low sensitivity and a limited linear sensing range. Herein, a concept is presented to enhance the sensitivity and linearity of supercapacitive pressure sensors by functionally grading the heights of macrodomes constructed from a highly elastic and ionic conductive elastomer made of poly(vinyl alcohol) and phosphoric acid (PVA/H3PO4). The resultant supercapacitive sensors exhibit a high sensitivity (423.42 kPa–1), wide linear sensing range (0–400 kPa), ultralow limit of detection (0.48 Pa), and high durability (stable signal outputs up to 5000 cycles of loading/unloading). Additionally, the sensors can maintain consistent sensing performance within a temperature range of 25–40 °C. The potential of the sensor in health monitoring is demonstrated through ultrahigh-resolution weight measurement, pulse detection, and respiration monitoring.

Mastitis is a common mammalian disease occurring in the mammary tissue and poses a major threat to agriculture and the dairy industry. Hordenine (HOR), a phenylethylamine alkaloid naturally extracted from malt, has various pharmacological effects, but its role in mastitis is unknown. The aim of this study was to investigate the role of HOR and its underlying mechanism in a lipopolysaccharide (LPS)-induced inflammatory response model of mouse mammary epithelial cells (EpH4-Ev) and mouse mastitis model. The experimental results showed that HOR attenuated LPS-induced mammary tissue damage (from 3.75 ± 0.25 to 1.75 ± 0.25) and restored the integrity of the blood–milk barrier. Further mechanistic studies revealed that HOR inhibited LPS-induced overactivation of the TLR4-MAPK/NF-κB signaling pathway and activated the AMPK/Nrf2/HO-1 signaling pathway. Additionally, HOR altered the composition of the intestinal microbiota in mice, ultimately reducing the extent of inflammatory injury (from 3.33 ± 0.33 to 0.67 ± 0.33) and upregulating the expression of tight junction proteins (ZO-1, occludin, and claudin-3). The findings of this study provide a theoretical basis in the rational use of HOR for the prevention and treatment of mastitis and the maintenance of mammalian mammary gland health.

Interest in the use of proteolysis-targeting chimeras (PROTACs) in cancer therapy has increased in recent years. Targeting bromodomain and extra terminal domain (BET) proteins, especially bromodomain-containing protein 4 (BRD4), has shown inhibitory effects on basal-like breast cancer (BLBC). However, the bioavailability of BRD4 PROTACs is restricted by their non-selective biodegradability and low tumor-targeting ability. We demonstrated that 6b (BRD4 PROTAC) suppresses BLBC cell growth by targeting BRD4, but not BRD2 and BRD3, for cereblon (CRBN)-mediated ubiquitination and proteasomal degradation. Compound 6b also inhibited expression of Krüppel-like factor 5 (KLF5) transcription factor, a key oncoprotein in BLBC, controlled by BRD4-mediated super-enhancers. Moreover, 6b inhibited HCC1806 tumor growth in a xenograft mouse model. The combination of 6b and KLF5 inhibitors showed additive effects on BLBC. These results suggest that BRD4-specific PROTAC can effectively inhibit BLBC by downregulating KLF5, and that 6b has potential as a novel therapeutic drug for BLBC.

A strategy of incorporating transparent inorganic semiconductors is proposed for high-performance semitransparent organic photovoltaics. Visible-transparent p-type inorganic semiconductor copper(I) thiocyanate (CuSCN) and a near-infrared acceptor BTP-eC9 can form a heterojunction with exciton dissociation ability.

The high-density Fe-6.5 wt% Si soft magnetic alloy samples were prepared using selective laser melting (SLM) technology. Annealing treatments with different temperatures were employed to promote grain growth. The microstructure, texture and magnetic hysteresis loops were characterized, aiming to investigate the relationship between microstructure and magnetic properties. The as-printed Fe-6.5 wt% Si alloy had weak texture and low density of ordered phases, and was featured by coarse grains in the top-view section and columnar grains in the side-view section. After annealing at 800 °C–1000 °C, the textures were slightly weakened, while the grain growth was not significant. Increasing the annealing temperature to 1100 °C led to abnormal grain growth behaviors. The grains of the as-printed Fe-6.5 wt% Si alloy showed randomly abnormal growth behaviors rather than oriented growth, which may be related to the low stored energy and initial size advantage before annealing. After annealed at 1100 °C for 1 h, the abnormal grain growth and the formation of large Goss ({110}<001>) and Cube ({100}<001>) grains resulted in microstructure coarsening and texture optimization. Thus, the corresponding ring-shaped sample exhibited excellent magnetic performance. The magnetic induction B8is 1.21 T, the maximum relative permeability is 14.71 × 103and the core loss P10/50is 11.69 W/kg.

By post-treatment of 1.77-eV wide-bandgap perovskite with a multifunctional 4-aminotetrahydrothiopyran hydrochloride, the ion migration and resulting phase segregation are effectively suppressed, which contributes to the efficiency of 19.32% and improved stability.

Tiny machine learning (TinyML) envisions executing a deep neural network (DNN)-based inference on an edge device for improving battery life, latency, security, and privacy. Toward this vision, recent microcontroller units (MCUs) integrate in-memory computing (IMC) hardware to leverage its high energy efficiency and throughput in vector–matrix multiplication (VMM). However, those existing works require large IMC hardware, severely increasing the area overhead. In addition, most existing works use analog–mixed-signal (AMS) IMC hardware, exhibiting limited robustness over process, voltage, and temperature (PVT) variations. Finally, none can support a practical software development framework such as TensorFlow Lite for Microcontrollers (TFLite-micro). Due to these limitations, those MCUs did not present the performance for the standard benchmark MLPerf-Tiny, which makes it difficult to evaluate them against the state-of-the-art neural (not necessarily IMC-based) MCUs. In this article, we design a new IMC-based MCU, titled iMCU, for TinyML to address those challenges. In the design process, we: 1) define the optimal set of acceleration targets and 2) devise an area-efficient computation flow that requires the least amount of IMC hardware yet still provides a significant acceleration. In addition, we develop: 1) state-of-the-art digital IMC macros and 2) create the accelerator based on the macros, which can support the proposed computation flow in a fully pipelined manner. Combining those innovations, we prototyped the iMCU in a 28-nm CMOS. The measurement results show that the iMCU significantly outperforms the prior IMC-based MCUs in compute density, energy efficiency, and SRAM density (total SRAM size/total SRAM area). It also achieves a compact footprint of 2.73 mm2.

Ni-rich layered ternary cathodes are promising candidates thanks to their low toxic Co-content and high energy density (∼800 Wh/kg). However, a critical challenge in developing Ni-rich cathodes is to improve cyclic stability, especially under high voltage (>4.3 V), which directly affects the performance and lifespan of the battery. In this study, niobium-doped strontium titanate (Nb-STO) is successfully synthesized via a facile solvothermal method and used as a surface modification layer onto the LiNi0.8Co0.1Mn0.1O2(NCM811) cathode. The results exhibited that the Nb-STO modification significantly improved the cycling stability of the cathode material even under high-voltage (4.5 V) operational conditions. In particular, the best sample in our work could provide a high discharge capacity of ∼190 mAh/g after 100 cycles under 1 C with capacity retention over 84% in the voltage range of 3.0–4.5 V, superior to the pristine NCM811 (∼61%) and pure STO modified STO-811-600 (∼76%) samples under the same conditions. The improved electrochemical performance and stability of NCM811 under high voltage should be attributed to not only preventing the dissolution of the transition metals, further reducing the electrolyte’s degradation by the end of charge, but also alleviating the internal resistance growth from uncontrollable cathode–electrolyte interface (CEI) evolution. These findings suggest that the as-synthesized STO with an optimized Nb-doping ratio could be a promising candidate for stabilizing Ni-rich cathode materials to facilitate the widespread commercialization of Ni-rich cathodes in modern LIBs.

• C f /(CrZrHfNbTa)C–SiC high-entropy ceramic matrix composites with excellent load-bearing, electromagnetic shielding and ablation resistance were designed and reported for the first time. • Single-phase (CrZrHfNbTa)C was successfully synthesized at 1700 ℃ with a small amount of Si as an additive, which is the lowest temperature for the synthesis of (CrZrHfNbTa)C via solid-phase route reported in literature. • C f /(CrZrHfNbTa)C–SiC composites were fabricated by a combined processing of SIL and PIP. • The total electromagnetic interference shielding efficiency of the composites are as high as 88.2 dB and 90 dB respectively in X-band and Ku-band. In this work, C f /(CrZrHfNbTa)C–SiC high-entropy ceramic matrix composites with good load-bearing, electromagnetic shielding and ablation resistance were designed and reported for the first time. The composites were fabricated by an efficient combined processing of slurry infiltration lamination (SIL) and precursor infiltration and pyrolysis (PIP). Density and porosity of the as-fabricated composites are 2.72 g/cm3 and 12.44 vol.%, respectively, and the flexural strength is 185 ± 13 MPa. Due to the carbon fiber reinforcement with high conductivity and strong reflection, and high-entropy (CrZrHfNbTa)C ceramic matrix with strong absorbability, the total Electromagnetic interference shielding efficiency (SE T) of the composites with a thickness of 3 mm are as high as 88.2 dB and 90 dB respectively in X-band and Ku-band. This means that higher than 99.999999 % electromagnetic shielding is achieved at 8–18 GHz, showing excellent electromagnetic shielding performance. The C f /(CrZrHfNbTa)C–SiC composites also present excellent ablation resistance, with the linear and mass ablation rates of 0.9 µm/s and 1.82 mg/s after ablation at the heat flux of 5 MW/m2 for 300 s (∼2450 °C). This work opens a new insight for the synergistic design of structural and functional integrated materials with load-bearing, electromagnetic shielding and ablation resistance, etc. [Display omitted] [ABSTRACT FROM AUTHOR]

The purpose of this study was to identify ovalbumin-derived immunomodulatory peptides by in vitrocell experiments, de novo sequencing, and molecular docking. Ovalbumin hydrolysates were prepared by two enzymes (alkaline protease and papain) individually, sequentially, or simultaneously, respectively. The simultaneous enzymatic hydrolysate (OVAH) had a high degree of hydrolysis (38.12 ± 0.48%) and exhibited immune-enhancing and anti-inflammatory activities. A total of 160 peptides were identified by LC–MS/MS in OVAH. Three novel peptides NVMEERKIK, ADQARELINS, and WEKAFKDE bound to TLR4-MD2 through hydrogen bonds and hydrophobic interactions with high binding affinity and binding energies of -181.40, -178.03, and -168.12 kcal/mol, respectively. These three peptides were synthesized and validated for two-way immunomodulatory activity. NVMEERKIK exhibiting the strongest immunomodulatory activity, increased NO and TNF-a levels by 128.69 and 38.01%, respectively, in normal RAW264.7 cells and reduced NO and TNF-a levels by 27.31 and 39.13%, respectively, in lipopolysaccharide-induced inflammatory RAW264.7 cells. Overall, this study first revealed that ovalbumin could be used as an immunomodulatory source for controlling inflammatory factor secretion.

Since the 1960s, researchers have worked to advance water flooding technology to address challenges like high viscosity, low fluidity, and depleting reservoirs, aiming to prevent oil fields from becoming unproductive. The integration of artificial intelligence (AI), computer vision, and advanced algorithms like BP neural networks has recently revolutionized this field. These technological advancements have upgraded water injection methodologies, overcoming past limitations and enabling real-time monitoring and dynamic control of water injection into different oil layers. This 'intelligent layering' ensures optimized water management, enhancing overall recovery rates. This overview highlights the progression of water injection techniques, critiques traditional methods' shortcomings, and delves into the cutting-edge applications of AI-driven intelligent layering systems. It serves as a valuable guide for oil industry stakeholders, equipment manufacturers, and research institutions seeking to refine water injection practices and boost hydrocarbon extraction efficiency.

We here demonstrate that amine-functionalized hydrophobic cross-linked polymer microsphere-supported palladium nanoparticles (Pd/NFHCPs) are an efficient catalyst for the selective hydrogenation of citral to citronellal. The R3-N groups in NFHCPs offer abundant anchoring sites for the dispersion and stabilization of Pd nanoparticles. In addition, the surface of NFHCPs is lipophilic and hydrophobic, which is beneficial for mass transformation and diffusion. These characteristics enable the catalyst to display high activity and stability for reducing citral to citronellal under alkaline conditions. The effects of solvents, different auxiliaries, and wettability on the catalytic performance were systematically investigated, paving the way for its potential industrial applications.

Cu, N co-doped carbon nanosheets encapsulating ultrasmall Cu nanoparticles efficiently catalyze the selective oxidation of 1,2-propanediol to lactic acid through an optical pathway of 1,2-propanediol → hydroxylacetone → lactaldehyde → lactic acid.

ABSTRACT Overlay systems have been extensively used to extend the service life of concrete bridge decks. There is, however, a lack of systematic studies on nondestructive evaluation (NDE) of concrete [...]

Addressing the repair of large-scale bone defects has become a hot research topic within the field of orthopedics. This study assessed the feasibility and effectiveness of using porous tantalum scaffolds to treat such defects. These scaffolds, manufactured using the selective laser melting (SLM) technology, possessed biomechanical properties compatible with natural bone tissue. To enhance the osteogenesis bioactivity of these porous Ta scaffolds, we applied calcium phosphate (CaP) and magnesium-doped calcium phosphate (Mg-CaP) coatings to the surface of SLM Ta scaffolds through a hydrothermal method. These degradable coatings released calcium and magnesium ions, demonstrating osteogenic bioactivity. Experimental results indicated that the Mg-CaP group exhibited biocompatibility comparable to that of the Ta group in vivo and in vitro. In terms of osteogenesis, both the CaP group and the Mg-CaP group showed improved outcomes compared to the control group, with the Mg-CaP group demonstrating superior performance. Therefore, both CaP and magnesium-CaP coatings can significantly enhance the osseointegration of three-dimensional-printed porous Ta, thereby increasing the surface bioactivity. Overall, the present study introduces an innovative approach for the biofunctionalization of SLM porous Ta, aiming to enhance its suitability as a bone implant material.

Inverted perovskite light-emitting diodes (PeLEDs) based on quantum dots (QDs) are some of the most promising candidates for next-generation lighting and display applications. Due to the strong fluorescence quenching caused by zinc oxide, high performance in such inverted devices remains challenging. Here, we report an efficient inverted green CsPbBr3QDs LED using an emitting buffer layer. Ultrathin CsPbBr3QD emitters act as the buffer layer to reduce the interface luminescence quenching reaction at the ZnO/upper emitting layer interface, increasing the probability of exciton recombination within the emissive layer and regulating the charge transport, leading to effective carrier recombination. The resulting device exhibits an external quantum efficiency of 13.1%, enhanced by about 4.7 times compared with that without a buffer layer device. This work provides a path to fabricating high-performance inverted PeLEDs.

Developing efficient electrocatalysts for water electrolysis is critical for sustainable hydrogen energy development. For enhancing the catalytic performance of metal catalysts, dual doping has attracted enormous interest for its high effectiveness and facile realization. Dual doping is effective for tuning the electronic properties, enhancing the electrical conductivity, populating active sites, and improving the stability of metal catalysts. In this review, recent developments in cation–cation, cation–anion, and anion–anion dual‐doped catalysts for water splitting are comprehensively summarized and discussed. An emphasis is put on illustrating how dual doping regulates the external and internal properties and boosts the catalytic performance of catalysts. Additionally, perspectives are pointed out to guide future research on engineering high‐performance heteroatom‐doped electrocatalysts. In this review, recent achievements in the design of dual‐doped metal catalysts for water electrolysis applications are thoroughly analyzed. The effects of dual doping on metal catalysts' properties are first discussed. Then, the development of cation–cation, cation–anion, and anion–anion dual‐doped catalysts for water splitting is summarized. Future research opportunities in the design of high‐performance catalysts are presented.

Exploration of non-corrosive chemical activation agents that are capable of transforming sustainable biomass into nanocarbon with large specific surface area and suitable heteroatomic content for electrochemical energy storage applications is highly desired. Herein, potassium peroxodisulfate (K2S2O8) has been proposed as the effective activation agent for the synthesis of N, S co-doped carbon nanosheets (N, S-CNSs) from renewable onions. Different from the other activation agents, it was found that the K2S2O8can act as the physical activating agent and chemical activating agent simultaneously owing to its temperature-dependent decomposition behavior. Consequently, the N, S-CNSs with large specific surface area of 2110.6 m2g-1, optimal oxygen, nitrogen, and sulfur-containing functional groups were rationally emerged. The assembled Zn-ion hybrid supercapacitors with the as-prepared N, S-CNSs delivered a maximum specific capacity of 141.5 mAh g-1, robust cycling stability with 98.3% capacity retention after 20,000 cycles, and large specific energy up to 113.4 Wh k g-1. As explicitly discussed with the relevant characterizations, the study deeply explored the adaptability of the chemical activating agent in general synthesis of porous carbon-based materials from sustainable biomass, and great insight for designing high-performance electrochemical energy storage devices.

Direct formate fuel cells have gained traction due to their eco-friendly credentials and inherent safety. However, their potential is hampered by the kinetic challenges of the formate oxidation reaction (FOR) on Pd-based catalysts, chiefly due to the unfavorable adsorption of hydrogen species (Had). These species clog the active sites, hindering efficient catalysis. Here, we introduce a straightforward strategy to remedy this bottleneck by incorporating Pd with Cu to expedite the removal of Pd–Hadin alkaline media. Notably, Cu plays a pivotal role in bolstering the concentration of hydroxyl adsorbates (OHad) on the surface of catalyst. These OHadspecies can react with Had, effectively unblocking the active sites for FOR. The as-synthesized catalyst of PdCu/C exhibits a superior FOR performance, boasting a remarkable mass activity of 3.62 A mg–1. Through CO-stripping voltammetry, we discern that the presence of Cu in Pd markedly speeds up the formation of adsorbed hydroxyl species (OHad) at diminished potentials. This, in turn, aids the oxidative removal of Pd–Had, leveraging a synergistic mechanism during FOR. Density functional theory computations further reveal intensified interactions between adsorbed oxygen species and intermediates, underscoring that the Cu–Pd interface exhibits greater oxyphilicity compared to pristine Pd. In this study, we present both experimental and theoretical corroborations, unequivocally highlighting that the integrated copper species markedly amplify the generation of OHad, ensuring efficient removal of Had. This work paves the way, shedding light on the strategic design of high-performing FOR catalysts.

Recently, the use of carbon fiber-reinforced plastic (CFRP) has been picking up in aerospace and automotive industries. However, machining of CFRP produces fine cutting chips, which disperse in the machining environment and can be hazardous to workers and machine tools. Therefore, a cutting-chip disposal technology is required to address this problem. This study investigated the chip dispersal behavior during the drilling of CFRP to identify the factors that affect the dispersal of cutting chips. The focus of this study was the airflow field generated by drill rotation. Therefore, a simulation analysis for around the drill edge was used. The results of the drill edge simulation and chip discharge behavior during CFRP drilling were validated experimentally. The results suggested that the airflow field around the drill edge caused by drill rotation did not significantly affected chip dispersal.

Macular corneal dystrophy (MCD) is a progressive, bilateral stromal dystrophic disease that arises from mutations in carbohydrate sulfotransferase 6 (CHST6). Corneal transplantation is the ultimate therapeutic solution for MCD patients. Unfortunately, postoperative recurrence remains a significant challenge. We conducted a retrospective review of a clinical cohort comprising 102 MCD patients with 124 eyes that underwent either penetrating keratoplasty (PKP) or deep anterior lamellar keratoplasty (DALK). Our results revealed that the recurrence rate was nearly three times higher in the DALK group (39.13%, 9/23 eyes) compared with the PKP group (10.89%, 11/101 eyes), suggesting that surgical replacement of the corneal endothelium for treating MCD is advisable to prevent postoperative recurrence. Our experimental data confirmed the robust mRNA and protein expression of CHST6 in human corneal endothelium and the rodent homolog CHST5 in mouse endothelium. Selective knockdown of wild-type Chst5in mouse corneal endothelium (ACsiChst5), but not in the corneal stroma, induced experimental MCD with similar extracellular matrix synthesis impairments and corneal thinning as observed in MCD patients. Mice carrying Chst5point mutation also recapitulated clinical phenotypes of MCD, along with corneal endothelial abnormalities. Intracameral injection of wild-type Chst5rescued the corneal impairments in ACsiChst5mice and retarded the disease progression in Chst5mutant mice. Overall, our study provides new mechanistic insights and therapeutic approaches for MCD treatment by high-lighting the role of corneal endothelium in MCD development.

Electrocatalytic coupling of CO and N2to synthesize urea under ambient conditions is considered a promising strategy to replace traditional industrial technology. It is crucial to find efficient electrocatalysts that can adsorb and activate N2and promote the C–N coupling reaction. Herein, a new two-dimensional porous carbon nitride material with multiactive sites is designed, in which boron and transition metal are embedded. Through a series of screening, B2Cr2, B2Mn2, and B2Os2are predicted to be potential electrocatalysts for urea synthesis. Mechanistic studies are performed on bidentate metal–metal and metal–boron sites, and both NCON and CO mechanisms are explored. The electronic structure analysis shows that there is a strong N2chemical adsorption within the bidentate site and that the N≡N bond is significantly activated. A new mechanism where free CO is inserted for C–N coupling within the two-dimensional porous structure is proposed.

Biomedical image segmentation plays an important role in Diabetic Retinopathy (DR)-related biomarker detection. DR is an ocular disease that affects the retina in people with diabetes and could lead to visual impairment if management measures are not taken in a timely manner. In DR screening programs, the presence and severity of DR are identified and classified based on various microvascular lesions detected by qualified ophthalmic screeners. Such a detection process is time-consuming and error-prone, given the small size of the microvascular lesions and the volume of images, especially with the increasing prevalence of diabetes. Automated image processing using deep learning methods is recognized as a promising approach to support diabetic retinopathy screening. In this article, we propose a novel compound scaling encoder-decoder network architecture to improve the accuracy and running efficiency of microvascular lesion segmentation. In the encoder phase, we develop a lightweight encoder to speed up the training process, where the encoder network is scaled up in depth, width, and resolution dimensions. In the decoder phase, an attention mechanism is introduced to yield higher accuracy. Specifically, we employ Concurrent Spatial and Channel Squeeze and Channel Excitation (scSE) blocks to fully utilise both spatial and channel-wise information. Additionally, a compound loss function is incorporated with transfer learning to handle the problem of imbalanced data and further improve performance. To assess performance, our method is evaluated on two large-scale lesion segmentation datasets: DDR and FGADR datasets. Experimental results demonstrate the superiority of our method compared to other competent methods.

This article examines for the first time an integrated structure of smart injection molding systems (IMS) based on Industry 4.0 technologies and provides a system-level solution for manufacturing smart products. The fully automated smart IMS structure allows manufacturers to produce thermoplastic products directly from raw materials without requiring any human labor. Following this, we focus on the control problem associated with the auxiliary robot manipulators that support the smart IMS. A cooperative distributed predictive control (DPC) algorithm with one-tap memory is proposed to achieve optimal closed-loop performance for multiple robot manipulators simultaneously performing their respective tasks. Using one-tap memory in smart IMS, we optimize the local performance index, which includes penalized terms diverging from it, and the global cooperation effort with limited memory acceleration to reach manifold consensus, without requiring manipulators to exchange information iteratively at each step. The cooperative DPC algorithm is also applied to five robot manipulators in the smart IMS, which require them to work cooperatively to achieve rhythmic and synchronized movements. Industrial experiment results demonstrate the feasibility of smart IMS combined with the cooperative DPC. Moreover, the cooperative DPC method outperforms the other two predictive control methods based on three metrics of the smart IMS.

Depth-Image-based Rendering (DIBR) oriented view synthesis is a crucial technique of virtual view synthesis in 3D video. It generates virtual viewpoint images using a few (usually two) reference viewpoints with DIBR based 3D warping. Geometric warping error, often overlooked by existing methods, is inevitably encountered in the 3D warping process due to the integer pixel representationx of images. To reduce the geometric warping error (GWE) in virtual view synthesis, a GWE aware spatial-temporal enhancement framework is proposed in this letter to improve the DIBR oriented view synthesis. In the spatial domain, a GWE aware view warping and blending enhancement module is developed to adaptively improve the warped views and merging the warped views from different reference viewpoints by considering and utilizing the preserved GWE. In the temporal domain, a refinement and temporal enhancement module is designed for refining and enhancing the blended view with temporal information. Experiments conducted on diverse datasets demonstrate the effectiveness of addressing the geometric warping error and using the temporal information, while further ablation study confirms the effectiveness of each proposed module.

The coordination of traffic flow among regions is necessary for a large-scale road traffic network to avoid local congestions and improve the overall traffic efficiency. In this paper, by incorporating the random characteristic of traffic flow, we formulate the problem of perimeter traffic flow control for a multi-region traffic network as a Markov decision process with adaptive state definition. Based on stochastic macroscopic fundamental diagrams (MFD) of regions, a state transition probability model is proposed to describe the state changes of the multi-region traffic network under different perimeter control policies. With the stochastic MFD-based state transition probabilities rather than counting from the historical data, a policy iteration algorithm with perturbation analysis is introduced to get the optimal perimeter control policy in real-time without the requirement of online or offline learning. The proposed method is compared with the classic perimeter control methods by simulation, which indicates its effectiveness in mitigating the congestion and improving the network throughput, as well as the promising implementation prospect.

Organic solar cells have attracted academic and industrial interests due to the advantages like lightweight, flexibility and roll-to-roll fabrication. Nowadays, 18% power conversion efficiency has been achieved in the state-of-the-art organic solar cells. The recent rapid progress in organic solar cells relies on the continuously emerging new materials and device fabrication technologies, and the deep understanding on film morphology, molecular packing and device physics. Donor and acceptor materials are the key materials for organic solar cells since they determine the device performance. The past 25 years have witnessed an odyssey in developing high-performance donors and acceptors. In this review, we focus on those star materials and milestone work, and introduce the molecular structure evolution of key materials. These key materials include homopolymer donors, D-A copolymer donors, A-D-A small molecular donors, fullerene acceptors and nonfullerene acceptors. At last, we outlook the challenges and very important directions in key materials development.

This article presents multistep accumulation capacitor coupling static random-access memory (MACC-SRAM), capacitor-based in-memory computing (IMC) SRAM macro for 4-b deep convolutional neural network (DNN) inference. The macro can simultaneously activate all its 128 $\times $ 128 custom 9T1C bitcells to perform the vector-matrix multiplication (VMM). MACC-SRAM also integrates 128 stepwise-charging and discharging input drivers (SCD-IDRs) to efficiently convert the digital codes of the input activations into analog voltages in a 2-b serial fashion. As a result, it can save up to 66% of the capacitor-driving energy. Also, the macro adopts an adder-first architecture to reduce the analog-to-digital (A/D) conversion overhead for the analog-mixed-signal (AMS) computation. The partial sums of the four adjacent rows, representing different bit positions in the 4-b weights, are first accumulated with an analog switched-capacitor adder and then converted to digital codes by a 6-bit successive approximation register (SAR) analog-to-digital converter (ADC). Compared with the ADC-first architecture, where partial sums of each row are first converted to digital codes and then accumulated in the digital domain, the adder-first architecture can save 60.7% of the area and 66.7% of the energy consumption of the A/D conversion. Moreover, the co-optimization of the DNN model by increasing the sparsity further reduces 39.4% of the capacitor-driving energy with a neglectable 0.4% DNN accuracy loss. We prototyped the macro in 28 nm technology, and the measurement shows an energy efficiency of 163 tera-operations per second per watt (TOPS/W) and a throughput of 211 giga-operations per second (GOPS) for a 4-b/4-b DNN model under 0.9 V supply, among the highest of the recent IMC SRAMs.

Moiré superlattices have emerged as a new platform for studying strongly correlated quantum phenomena, but these systems have been largely limited to van der Waals layer two-dimensional materials. Here we introduce moiré superlattices leveraging ultrathin, ligand-free halide perovskites, facilitated by ionic interactions. Square moiré superlattices with varying periodic lengths are clearly visualized through high-resolution transmission electron microscopy. Twist-angle-dependent transient photoluminescence microscopy and electrical characterizations indicate the emergence of localized bright excitons and trapped charge carriers near a twist angle of ~10°. The localized excitons are accompanied by enhanced exciton emission, attributed to an increased oscillator strength by a theoretically predicted flat band. This research showcases the promise of two-dimensional perovskites as unique room-temperature moiré materials.

Batch processes are typically nonlinear systems with constraints. Model predictive control (MPC) and iterative learning control (ILC) are effective methods for controlling batch processes. By combining batch-wise ILC and time-wise MPC, this article proposes a multirate control scheme for constrained nonlinear systems. Two-dimensional (2-D) framework is used to combine historical batch data with current measurements. The ILC part uses run-to-run control with previous iteration data, and the MPC part uses real-time control with current sampled measurements. Real-time feedback-based MPC in the time axis and run-to-run ILC in the batch axis are combined to optimize the current inputs based on previous batch input-output data and real-time system measurements. Rather than achieving control objectives in a single batch, our design allows multiple batches to be executed successively. To establish the stability of the combined scheme, rigorous theoretical analysis is presented next. The combined scheme with improved performance is then validated through two illustrative numerical examples.

Soapwort (Saponaria officinalis) is a flowering plant from the Caryophyllaceae family with a long history of human use as a traditional source of soap. Its detergent properties are because of the production of polar compounds (saponins), of which the oleanane-based triterpenoid saponins, saponariosides A and B, are the major components. Soapwort saponins have anticancer properties and are also of interest as endosomal escape enhancers for targeted tumor therapies. Intriguingly, these saponins share common structural features with the vaccine adjuvant QS-21 and, thus, represent a potential alternative supply of saponin adjuvant precursors. Here, we sequence the S. officinalisgenome and, through genome mining and combinatorial expression, identify 14 enzymes that complete the biosynthetic pathway to saponarioside B. These enzymes include a noncanonical cytosolic GH1 (glycoside hydrolase family 1) transglycosidase required for the addition of d-quinovose. Our results open avenues for accessing and engineering natural and new-to-nature pharmaceuticals, drug delivery agents and potential immunostimulants.

The generation and polarization multiplexing of vortex waves are of great significance to vortex wave communication systems and are worthy of further exploration and research. However, available multiple-polarization vortex beam generators suffer from a high profile. In this communication, we propose a new multiple-polarization vortex wave generator based on a folded transmitarray antenna (FTA) with high-gain and low-profile characteristics. The proposed vortex beam generator can generate a high-gain l = +1 and l = −1 dual-beam vortex wave operating at 19 GHz. Left-hand circular polarization (LHCP), right-hand circular polarization (RHCP), and linear polarization (LP) vortex waves can be generated by only changing the polarization of the LP feeding antenna. The fabricated vortex beam generator exhibits high gain (the maximum gains are 20.05, 20.03, and 20.63 dBi for RHCP, LHCP, and LP, respectively). The proposed vortex beam generator not only reduces the profile by 1/2 compared to the traditional reflection array and transmission array generators, but also has the advantages of high-gain, multiple vortex beams, and easy integration. Our work opens a door for the design of high-gain and low-profile multiple-polarization multibeam vortex wave generator.

A novel dual mode slow-wave patch resonators (SWPRs) are proposed based on capacitive-load patches arrays (CLPAs) with short circuit posts in this brief. Owing to CLPAs and cross slots, size of patch can be reduced significantly. Using SWPR’ s TM100 and TM010, two types of bandpass filters (BPFs) with size-reduced and highly-selective are realized. Moreover, different cascading methods of dual-mode SWPRs for high-pole filters’ design by stepped impedance folded coupling line (SIFCL) are analyzed and compared to maintain high selectivity. For the demonstration, two four-pole quasi-elliptic BPFs are designed, fabricated, and measured to verify the performance and design method of the proposed dual-mode SWPR filters. Compared with traditional patch filters, size of the proposed filters can be reduced by 55% and 42%.

Recent SRAM-based in-memory computing (IMC) hardware demonstrates high energy efficiency and throughput for matrix–vector multiplication (MVM), the dominant kernel for deep neural networks (DNNs). Earlier IMC macros have employed analog-mixed-signal (AMS) arithmetic hardware. However, those so-called AIMCs suffer from process, voltage, and temperature (PVT) variations. Digital IMC (DIMC) macros, on the other hand, exhibit better robustness against PVT variations, but they tend to require more silicon area. This article proposes novel DIMC hardware featuring approximate arithmetic (DIMCA) to improve area efficiency without hurting compute density (CD). We also propose an approximation-aware training model and a customized number format to compensate for the accuracy degradation caused by the approximation hardware. We prototyped the test chip in 28-nm CMOS. It contains two versions: the DIMCA with single-approximate hardware (DIMCA1) and DIMCA with double-approximate hardware (DIMCA2). The measurement results show that DIMCA1 supports a 4 b-activation and 1 b-weight (4 b/1 b) CNN model, achieving 327 kb/mm2, 458–990 TOPS/W (normalized to 1 b/1 b), 8.27–392 TOPS/mm2 (normalized to 1 b/1 b), and 90.41% accuracy for CIFAR-10. DIMCA2 supports a 1 b/1 b CNN model, achieving 485 kb/mm2, 932–2219 TOPS/W, 14.4–607 TOPS/mm2, and 86.96% accuracy for CIFAR-10.