Cohesive energy discrepancy drives the fabrication of multimetallic atomically dispersed materials for hydrogen evolution reaction.

Atomically dispersed single atom (SA) and atomic cluster (AC) metallic materials attract tremendous attentions in various fields. Expanding monometallic SA and AC to multimetallic SA/AC composites opens vast scientific and technological potentials yet exponentially increasing the synthesis difficulty. Here, we present a general energy-selective-clustering methodology to build the largest reported library of carbon supported bi-/multi-metallic SA/AC materials. The discrepancy in cohesive energy results into selective metal clustering thereby driving the symbiosis of multimetallic SA or/and AC. The library includes 23 bimetallic SA/AC composites, and expanded compositional space of 17 trimetallic, quinary-metallic, septenary-metallic SA/AC composites. We chose bimetallic M1SAM2AC to demonstrate the electrocatalysis utility. Unique decoupled active sites and inter-site synergy lead to 8/47 mV overpotential at 10 mA cm-2 for alkaline/acidic hydrogen evolution and over 1000 h durability in water electrolyzer. Moreover, delicate modulations towards composition and configuration yield high-performance catalysts for multiple electrocatalysis systems. Our work broadens the family of atomically dispersed materials from monometallic to multimetallic and provides a platform to explore the complex composition induced unconventional effects.

Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy.

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot's judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system's complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value.

Comparison of the correlation of creatinine- and cystatin C-Based estimated GFR and their differences with new-onset heart failure in a community-based population with type 2 diabetes.

AIMS: This study aimed to investigate the impact of different estimated glomerular filtration rate (eGFR) values like cystatin C-based eGFR (eGFRcys), creatinine-based eGFR (eGFRcr), and their difference (eGFRdiff; eGFRcys -eGFRcr), on the incidence of heart failure (HF) in patients with type 2 diabetes(T2D). METHODS: Being a prospective cohort study, it included 7,967 patients with T2D who underwent serum creatinine and cystatin C tests as part of the Kailuan Group's 6th annual health examination (2016). Subsequently, eGFRcys, eGFRcr, and eGFRdiff were calculated. Patients were categorized into three groups: negative (<-15 mL/min/1.73 m2), midrange (-15 to 15 mL/min/1.73 m2), and positive (> 15 mL/min/1.73 m2) eGFRdiff groups, respectively. Furthermore, the relationship between the various eGFR measurements and new-onset HF were studied using Cox proportional hazards regression, and the potential improvement in predictive capability was evaluated by adding these eGFR metrics to established HF risk models. RESULTS: Among 7967 participants with mean age of 60.51 years, there were 20.92% women and 79.08% men. At baseline, eGFRcys and eGFRcr values differed by more than 15 mL/min/1.73m2 in 41.3% of participants. During a median follow-up period of 3.76 years, there were 172 (2.16%) new HF cases and 517 (6.49%) all-cause deaths. The cumulative incidence of HF in the midrange, negative, and positive eGFRdiff groups was 1.74%, 4.10%, and 0.61%, respectively (p < 0.001). In multivariable adjusted models, participants in the negative eGFRdiff group had higher risk of HF compared with the midrange eGFRdiff group (HR, 2.15; 95% CI, 1.57-2.94). Conversely, participants in the positive eGFRdiff group had lower risk for HF (HR, 0.40; 95% CI, 0.17-0.93). And each 15 mL/min/ 1.73 m2 higher eGFRdiff was associated with 34% (HR, 0.66; 95% CI, 0.58 - 0.47)lower risk of incident HF. The predictive capacity for HF risk in diabetic individuals was enhanced by adding eGFRcys or eGFRdiff to established HF risk models, with eGFRcys showing more significant additional predictive value. CONCLUSION: These findings suggest that large differences between eGFRcys and eGFRcr were common in community-based population with T2D. Different eGFR metrics can independently predict HF incidence in patients with T2D. Additionally, metrics like eGFRcys and eGFRdiff provide significant predictive value for HF risks beyond traditional risk factors, with eGFRcys showing more pronounced benefits in such cases.

Diatomic Iron with a Pseudo-Phthalocyanine Coordination Environment for Highly Efficient Oxygen Reduction over 150,000 Cycles.

Atomically dispersed Fe-N-C catalysts emerged as promising alternatives to commercial Pt/C for the oxygen reduction reaction. However, the majority of Fe-N-C catalysts showed unsatisfactory activity and durability due to their inferior O-O bond-breaking capability and rapid Fe demetallization. Herein, we create a pseudo-phthalocyanine environment coordinated diatomic iron (Fe2-pPc) catalyst by grafting the core domain of iron phthalocyanine (Fe-Nα-Cα-Nß) onto defective carbon. In situ characterizations and theoretical calculation confirm that Fe2-pPc follows the fast-kinetic dissociative pathway, whereby Fe2-pPc triggers bridge-mode oxygen adsorption and catalyzes direct O-O radical cleavage. Compared to traditional Fe-N-C and FePc-based catalysts exhibiting superoxo-like oxygen adsorption and an *OOH-involved pathway, Fe2-pPc delivers a superior half-wave potential of 0.92 V. Furthermore, the ultrastrong Nα-Cα bonds in the pPc environment endow the diatomic iron active center with high tolerance for reaction-induced geometric stress, leading to significantly promoted resistance to demetallization. Upon an unprecedented harsh accelerated degradation test of 150,000 cycles, Fe2-pPc experiences negligible Fe loss and an extremely small activity decay of 17 mV, being the most robust candidate among previously reported Fe-N-C catalysts. Zinc-air batteries employing Fe2-pPc exhibit a power density of 255 mW cm-2 and excellent operation stability beyond 440 h. This work brings new insights into the design of atomically precise metallic catalysts.

Accelerating the Phase Formation Kinetics of Alluaudite Sodium Iron Sulfate Cathodes via Ultrafast Thermal Shock.

Alluaudite sodium iron sulfate (NFS) exhibits great potential for use in sodium-ion battery cathodes due to its elevated operating potential and abundant element reserves. However, conventional solid-state methods demonstrate a low heating/cooling rate and sluggish reaction kinetics, requiring a long thermal treatment to effectively fabricate NFS cathodes. Herein, we propose a thermal shock (TS) strategy to synthesize alluaudite sodium iron sulfate cathodes using either hydrous or anhydrous raw materials. The analysis of the phase formation process reveals that TS treatment can significantly facilitate the removal of crystal water and decomposition of the intermediate phase Na2Fe(SO4)2 in the hydrous precursor. In the case of the anhydrous precursor, the kinetics of the combination reaction between Na2SO4 and FeSO4 can be also accelerated by TS treatment. Consequently, pure NFS phase formation can be completed after a substantially shorter time of post-sintering, thereby saving significant time and energy. The TS-treated NFS cathode derived from hydrous precursor exhibits higher retention after 200 cycles at 1C and better rate capability than the counterpart prepared by conventional long-term tube furnace sintering, demonstrating the great potential of this novel strategy.

Optimizing potassium polysulfides for high performance potassium-sulfur batteries.

Potassium-sulfur batteries attract tremendous attention as high-energy and low-cost energy storage system, but achieving high utilization and long-term cycling of sulfur remains challenging. Here we show a strategy of optimizing potassium polysulfides for building high-performance potassium-sulfur batteries. We design the composite of tungsten single atom and tungsten carbide possessing potassium polysulfide migration/conversion bi-functionality by theoretical screening. We create two ligand environments for tungsten in the metal-organic framework, which respectively transmute into tungsten single atom and tungsten carbide nanocrystals during pyrolysis. Tungsten carbide provide catalytic sites for potassium polysulfides conversion, while tungsten single atoms facilitate sulfides migration thereby significantly alleviating the insulating sulfides accumulation and the associated catalytic poisoning. Resultantly, highly efficient potassium-sulfur electrochemistry is achieved under high-rate and long-cycling conditions. The batteries deliver 89.8% sulfur utilization (1504 mAh g-1), superior rate capability (1059 mAh g-1 at 1675 mA g-1) and long lifespan of 200 cycles at 25 °C. These advances enlighten direction for future KSBs development.

Review of Carbon Support Coordination Environments for Single Metal Atom Electrocatalysts (SACS).

This topical review focuses on the distinct role of carbon support coordination environment of single-atom catalysts (SACs) for electrocatalysis. The article begins with an overview of atomic coordination configurations in SACs, including a discussion of the advanced characterization techniques and simulation used for understanding the active sites. A summary of key electrocatalysis applications is then provided. These processes are oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), nitrogen reduction reaction (NRR), and carbon dioxide reduction reaction (CO2 RR). The review then shifts to modulation of the metal atom-carbon coordination environments, focusing on nitrogen and other non-metal coordination through modulation at the first coordination shell and modulation in the second and higher coordination shells. Representative case studies are provided, starting with the classic four-nitrogen-coordinated single metal atom (MN4 ) based SACs. Bimetallic coordination models including homo-paired and hetero-paired active sites are also discussed, being categorized as emerging approaches. The theme of the discussions is the correlation between synthesis methods for selective doping, the carbon structure-electron configuration changes associated with the doping, the analytical techniques used to ascertain these changes, and the resultant electrocatalysis performance. Critical unanswered questions as well as promising underexplored research directions are identified.

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries.

In this paper, an adaptive remaining useful life prediction model is proposed for electric vehicle lithium batteries. Capacity degradation of the electric car lithium batteries is modeled by the multi-fractal Weibull motion. The varying degree of long-range dependence and the 1/f characteristics in the frequency domain are also analyzed. The age and state-dependent degradation model is derived, with the associated adaptive drift and diffusion coefficients. The adaptive mechanism considers the quantitative relations between the drift and diffusion coefficients. The unit-to-unit variability is considered a random variable. To facilitate the application, the convergence of the RUL prediction model is proved. Replacement of the lithium battery in the electric car is recommended according to the remaining useful life prediction results. The effectiveness of the proposed model is shown in the case study.

Effects of Sevoflurane and Propofol on Neurological Recovery of Traumatic Brain Injury Patients in the Early Postoperative Stage: A Retrospective Cohort Study.

Objective To investigate the effects of propofol and sevoflurane on neurological recovery of traumatic brain injury (TBI) patients in the early postoperative stage.Methods We retrospectively analyzed the clinical data of TBI patients who underwent craniotomy or decompressive craniectomy. Generalized additive mixed model (GAMM) was used to analyze effects of propofol and sevoflurane on Glasgow Coma Scale (GCS) on postoperative days 1, 3, and 7. Multivariate regression analysis was used to analyze effects of the two anesthetics on Glasgow Outcome Scale (GOS) at discharge.Results A total of 340 TBI patients were enrolled in this study. There were 110 TBI patients who underwent craniotomy including 75 in the propofol group and 35 in the sevoflurane group, and 134 patients who underwent decompressive craniectomy including 63 in the propofol group and 71 in the sevoflurane group. It showed no significant difference in GCS at admission between the propofol and the sevoflurane groups among craniotomy patients (ß = 0.75, 95%CI: -0.55 to 2.05, P = 0.260). However, elevation in GCS from baseline was 1.73 points (95%CI: -2.81 to -0.66, P = 0.002) less in the sevoflurane group than that in the propofol group on postoperative day 1, 2.03 points (95%CI: -3.14 to -0.91, P < 0.001) less on day 3, and 1.31 points (95%CI: -2.43 to -0.19, P = 0.022) less on day 7. The risk of unfavorable GOS (GOS 1, 2, and 3) at discharge was higher in the sevoflurane group (OR = 4.93, 95%CI: 1.05 to 23.03, P = 0.043). No significant difference was observed among two-group decompressive craniectomy patients in GCS and GOS.Conclusions Compared to propofol, sevoflurane was associated with worse neurological recovery during the hospital stay in TBI patients undergoing craniotomy. This difference was not detected in TBI patients undergoing decompressive craniectomy.

Synthesis, evaluation and mechanism exploration of 2-(N-(3-nitrophenyl)-N-phenylsulfonyl)aminoacetohydroxamic acids as novel urease inhibitors.

Thirteen 2-(N-(3-nitrophenyl)-N-phenylsulfonyl)aminoacetohydroxamic acids which were reported for the first time were designed and synthesized as novel urease inhibitors. Most of them showed higher potency than the positive control acetohydroxamic acid, with 2-(N-(3-nitrophenyl)-N-(4-bromophenylsulfonyl)aminoacetohydroxamic acid (d7) being the most active (IC50 = 0.13 ± 0.01 µM). Compound d7 reversibly inhibits urease with mixed mechanism showing excellent binding affinity to urease active site (KD = 0.34 nM, Ki=0.065 ± 0.003 µM andKi' = 1.20 ± 0.09 µM) and very low cytotoxicity against mammalian cells (cell viability of 91.4 % against HepG2 at 250 µg/mL). These positive results indicated that d7 may be used as the lead for further research to develop urease inhibitors with promising properties.

Porosity Engineering towards Nitrogen-Rich Carbon Host Enables Ultrahigh Capacity Sulfur Cathode for Room Temperature Potassium-Sulfur Batteries.

Potassium-sulfur batteries (KSBs) are regarded as a promising large-scale energy storage technology, owing to the high theoretical specific capacity and intrinsically low cost. However, the commercialization of KSBs is hampered by the low sulfur utilization and notorious shuttle effect. Herein, we employ a porosity engineering strategy to design nitrogen-rich carbon foam as an efficient sulfur host. The tremendous micropores magnify the chemical interaction between sulfur species and the polar nitrogen functionalities decorated carbon surface, which significantly improve the sulfur utilization and conversion. Meanwhile, the abundant mesopores provide ample spaces, accommodating the large volume changes of sulfur upon reversible potassation. Resultantly, the constructed sulfur cathode delivers an ultrahigh initial reversible capacity of 1470 mAh g-1 (87.76% of theoretical capacity) and a superior rate capacity of 560 mAh g-1 at 2 C. Reaching the K2S phase in potassiation is the essential reason for obtaining the ultrahigh capacity. Nonetheless, systematic kinetics analyses demonstrate that the K2S involved depotassiation deteriorates the charge kinetics. The density functional theory (DFT) calculation revealed that the nitrogen-rich micropore surface facilitated the sulfur reduction for K2S but created a higher energy barrier for the K2S decomposition, which explained the discrepancy in kinetics modification effect produced by the porosity engineering.

Generalized Cauchy process based on heavy-tailed distribution and grey relational analysis for reliability predicting of distribution systems.

Failure interruption often causes large blackouts in power grids, severely impacting critical functions. Because of the randomness of power failure, it is difficult to predict the leading causes of failure. ASAI, an essential indicator of power-supply reliability, can be measured from the outage time series. The series is non-stationary stochastic, which causes some difficulty in analyzing power-supply reliability. Considering that the time series has long-range dependence (LRD) and self-similarity, this paper proposes the generalized Cauchy (GC) process for the prediction. The case study shows that the proposed model can predict reliability with a max absolute percentage error of 8.28%. Grey relational analysis (GRA) has proved to be an effective method for the degree of correlation between different indicators. Therefore, we propose the method, which combines both GC and GRA to obtain the correlation coefficients between different factors and ASAI and to get the main factors based on this coefficient. The case study illustrates the feasibility of this approach, which power enterprises can employ to predict power-supply reliability and its influencing factors and help them identify weaknesses in the grid to inform employees to take protective measures in advance.

Boosting Energy Efficiency and Stability of Li-CO2 Batteries via Synergy between Ru Atom Clusters and Single-Atom Ru-N4 sites in the Electrocatalyst Cathode.

The Li-CO2 battery is a novel strategy for CO2 capture and energy-storage applications. However, the sluggish CO2 reduction and evolution reactions cause large overpotential and poor cycling performance. Herein, a new catalyst containing well-defined ruthenium (Ru) atomic clusters (RuAC ) and single-atom Ru-N4 (RuSA ) composite sites on carbon nanobox substrate (RuAC+SA @NCB) (NCB = nitrogen-doped carbon nanobox) is fabricated by utilizing the different complexation effects between the Ru cation and the amine group (NH2 ) on carbon quantum dots or nitrogen moieties on NCB. Systematic experimental and theoretical investigations demonstrate the vital role of electronic synergy between RuAC and Ru-N4 in improving the electrocatalytic activity toward the CO2 evolution reaction (CO2 ER) and CO2 reduction reaction (CO2 RR). The electronic properties of the Ru-N4 sites are essentially modulated by the adjacent RuAC species, which optimizes the interactions with key reaction intermediates thereby reducing the energy barriers in the rate-determining steps of the CO2 RR and CO2 ER. Remarkably, the RuAC+SA @NCB-based cell displays unprecedented overpotentials as low as 1.65 and 1.86 V at ultrahigh rates of 1 and 2 A g-1 , and twofold cycling lifespan than the baselines. The findings provide a novel strategy to construct catalysts with composite active sites comprising multiple atom assemblies for high-performance metal-CO2 batteries.

Toward Theoretical Capacity and Superhigh Power Density for Potassium-Selenium Batteries via Facilitating Reversible Potassiation Kinetics.

Potassium-selenium (K-Se) batteries attract tremendous attention because of the two-electron transfer of the selenium cathode. Nonetheless, practical K-Se cells normally display selenium underutilization and unsatisfactory rate capability. Herein, we employ a synergistic spatial confinement and architecture engineering strategy to establish selenium cathodes for probing the effect of K+ diffusion kinetics on K-Se battery performance and improving the charge transfer efficiency at ultrahigh rates. By impregnating selenium into hollow and solid carbon spheres with similar diameters and porous structures, the obtained parallel Se/C composites possess nearly identical selenium loadings, molecular structures, and heterogeneous interfaces but enormously different paths for K+ diffusion. Remarkably, as the solid-state K+ diffusion distance is significantly reduced, the K-Se cell achieves 96% of 2e- transfer capacity (647.1 mA h g-1). Reversible capacities of 283.5 and 224.1 mA h g-1 are obtained at 7.5 and 15C, respectively, corresponding to an unprecedented high power density of 8777.8 W kg-1. Quantitative kinetic analysis demonstrated a twofold higher capacitive charge storage contribution and a 1 order of magnitude higher K+ diffusion coefficient due to the short K+ diffusion path. By combining the determination of potassiation products by ex situ characterization and density functional theory (DFT) calculations, it is identified that the kinetic factor is decisive for K-Se battery performances.

Identification, potency evaluation, and mechanism clarification of α-glucosidase inhibitors from tender leaves of Lithocarpus polystachyus Rehd.

Lithocarpus polystachyus Rehd. known as Sweet Tea in China has attracted lots of interest for its good hypoglycemic effect and the potential as a hypoglycemic agent. Based on affinity separation-UPLC-Q-TOF-MS/MS, 54 potential α-glucosidase inhibitiors were identified and 44 were structurally determined. Out of them, 41 were identified for the first time from this plant including flavonoids, fatty acids, triterpenes, alkaloids, and coumarins. Enzyme assays revealed that flavonoids exhibited higher inhibitory activity against α-glucosidase than others with astilbin (IC50 = 6.14 µg·mL-1), morin (IC50 = 8.46 µg·mL-1), and naringenin (IC50 = 10.03 µg·mL-1) showing 2- to 4-fold higher potency than the positive control acarbose. They were proved as reversible inhibitors with mixed inhibition mechanism. Ki (Ki') values and molecular dockings strongly supported the potency order of astilbin, morin and naringenin that showed in the enzyme assays.

Synthesis and Biological Evaluation of Dithiobisacetamides as Novel Urease Inhibitors.

Thirty-eight disulfides containing N-arylacetamide were designed and synthesized in an effort to develop novel urease inhibitors. Biological evaluation revealed that some of the synthetic compounds exhibited strong inhibitory potency against both cell-free urease and urease in intact cell with low cytotoxicity to mammalian cells even at concentration up to 250 µM. Of note, 2,2'-dithiobis(N-(2-fluorophenyl)acetamide) (d7), 2,2'-dithiobis(N-(3,5-difluorophenyl)acetamide) (d24), and 2,2'-dithiobis(N-(3-fluorophenyl)acetamide) (d8) were here identified as the most active inhibitors with IC50 of 0.074, 0.44, and 0.81 µM, showing 32- to 355-fold higher potency than the positive control acetohydroxamic acid. These disulfides were confirmed to bind urease without covalent modification of the cysteine residue and to inhibit urease reversibly with a mixed inhibition mechanism. They also showed very good anti-Helicobacter pylori activities with d8 showing a comparable potency to the clinical used drug amoxicillin. The impressive in vitro biological profile indicated their immense potential as therapeutic agents to tackle H. pylori caused infections.

Metabolism and difference iterative forecasting model based on long-range dependent and grey for gearbox reliability.

The reliability prediction of gearbox is a complex and challenging topic. The purpose of this research is to propose a hybrid difference iterative forecasting model to forecast reliability of the gearbox. On this score, a hybrid model based on the fractional Lévy stable motion (fLsm), the Grey Model (GM) and the metabolism method is proposed. To solve the problem of insensitivity to weak faults inside the gearbox, we use feature extraction method to reveal the gearbox degradation. Then, the least square theory is used to separate the degradation sequence in the gearbox into a deterministic term with monotonicity and a stochastic term with Long-Range Dependence (LRD). Next, the fLsm with LRD and non-Gaussian is used to forecast the stochastic term, the deterministic term is simulated by the GM, and the hybrid forecasting model is used to modify the prediction results. The metabolism method is used to update the degradation sequence and to forecast longer-term trend. Finally, a case demonstrated that superiority and generality of the hybrid forecasting model.

Fractional Lévy stable motion with LRD for RUL and reliability analysis of li-ion battery.

The Remaining Useful Life (RUL) is important for reliability analysis of li-ion battery. Reliability of li-ion battery decreases with shortened the RUL. The RUL of li-ion battery can be revealed by the capacity change. The future change of the capacity is related to the current and the historical states, namely, the capacity change of li-ion battery has Long-Range Dependence (LRD). This article describes a RUL prediction method based on fractional order Lévy stable motion (fLsm), which solves the LRD was not obvious caused by the excessive difference of the integer-order model. First, the LRD of the fLsm is revealed by stability index and integral kernel function with Hurst parameter. Then, the fLsm is used as a diffusion term, which reflects the stochastic and LRD of the RUL degradation, to establish a degradation prediction model. The iterative form of the prediction model is established through the incremental distribution of the fLsm. Finally, the RUL is predicted by the Monte Carlo simulation and degradation prediction model. The predictive performance of the fLsm degradation model is verified by battery data in different operating environments. The reliability of li-ion battery is analyzed by the RUL.

Recent Efforts in the Discovery of Urease Inhibitor Identifications.

Urease is an attractive drug target for designing anti-infective agents against pathogens such as Helicobacter pylori, Proteus mirabilis, and Ureaplasma urealyticum. In the past century, hundreds of medicinal chemists focused their efforts on explorations of urease inhibitors. Despite the FDA's approval of acetohydroxamic acid as a urease inhibitor for the treatment of struvite nephrolithiasis and the widespread use of N-(n-butyl)thiophosphoric triamide as a soil urease inhibitor as nitrogen fertilizer synergists in agriculture, urease inhibitors with high potency and safety are urgently needed. Exploration of novel urease inhibitors has therefore become a hot research topic recently. Herein, inhibitors identified worldwide from 2016 to 2021 have been reviewed. They structurally belong to more than 20 classes of compounds such as urea/thioure analogues, hydroxamic acids, sulfonamides, metal complexes, and triazoles. Some inhibitors showed excellent potency with IC50 values lower than 10 nM, having 10000-fold higher potency than the positive control thiourea.

Identifying Heteroatomic and Defective Sites in Carbon with Dual-Ion Adsorption Capability for High Energy and Power Zinc Ion Capacitor.

Aqueous zinc-based batteries (AZBs) attract tremendous attention due to the abundant and rechargeable zinc anode. Nonetheless, the requirement of high energy and power densities raises great challenge for the cathode development. Herein we construct an aqueous zinc ion capacitor possessing an unrivaled combination of high energy and power characteristics by employing a unique dual-ion adsorption mechanism in the cathode side. Through a templating/activating co-assisted carbonization procedure, a routine protein-rich biomass transforms into defect-rich carbon with immense surface area of 3657.5 m2 g-1 and electrochemically active heteroatom content of 8.0 at%. Comprehensive characterization and DFT calculations reveal that the obtained carbon cathode exhibits capacitive charge adsorptions toward both the cations and anions, which regularly occur at the specific sites of heteroatom moieties and lattice defects upon different depths of discharge/charge. The dual-ion adsorption mechanism endows the assembled cells with maximum capacity of 257 mAh g-1 and retention of 72 mAh g-1 at ultrahigh current density of 100 A g-1 (400 C), corresponding to the outstanding energy and power of 168 Wh kg-1 and 61,700 W kg-1. Furthermore, practical battery configurations of solid-state pouch and cable-type cells display excellent reliability in electrochemistry as flexible and knittable power sources.