High-capacity SiOx/graphite (SiO/G) anodes offer great potential for advancing lithium-ion battery technology; however, their practical application is limited by low initial coulombic efficiency (ICE) and rapid capacity decay. These challenges primarily arise from unstable phase transitions and the formation of the solid electrolyte interphase (SEI). Prelithiation strategies that aimed at compensating lithium loss have emerged as an effective solution, showing significant advancements in both anode and cathode research. Nevertheless, the interfacial evolution and mechanisms underlying performance enhancement remain unclear. In this work, we demonstrate roll-to-roll contact prelithiation of SiO/G anodes using an ultrathin lithium film, resulting in improved ICE, cycling stability, and rate capability. The contact prelithiation mechanism of silicon-based anodes was investigated via a combination of in situ and ex situ characterizations alongside electrochemical analyses. These studies reveal that the formation of SEI contains multiple lithium silicate phases during the first cycle of prelithiation. This SEI exhibits enhanced conductivity and stability, which contribute to improved cycling performance and rate capability of the prelithiated anode. The prelithiated silicon-carbon composite anode achieved an ICE of 96% in 5.4 Ah pouch cell tests and demonstrated excellent capacity retention of 74% after 500 cycles. This study not only elucidates the critical role of interfacial evolution in SiOx/graphite anodes but also proposes a rational strategy based on phase-phase interface synergistic design for developing durable, high-performance silicon-based anodes suitable for next-generation lithium-ion batteries. (sic)(sic)(sic)SiOx/(sic)(sic) ( SiO/G ) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) , (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) , (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)SiO/G(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) , (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)/(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) , (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) , (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) ( SEI ) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic) , (sic)(sic)(sic)(sic)SiO/G(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)SEI.(sic)SEI(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) , (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)5.4 Ah(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)96%(sic)(sic)(sic)(sic)(sic)(sic) , (sic)500(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)74%(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)SiOx/(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) , (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).

PurposeIt is necessary but difficult to accurately predict the water levels in front of sluice gates of an open channel water transfer project due to the complex interactions among hydraulic structures. The existing methods have certain shortcomings. For example, although one-dimensional hydrodynamic simulation is technically feasible, little is known about hydrodynamic models for prediction. Another example is that, neural networks can hardly predict the information of nonmonitoring sections. To these problems, this paper presents a novel GRA-NARX and hydrodynamic coupled prediction model (H-GRA-NARX-HPM) that is based on the GRA-NARX (gray relation analysis-nonlinear auto-regressive exogenous) neural network with automatic hyperparameter calibration.Design/methodology/approachFirstly, the GRA is used to determine the correlations of influencing factors and find the optimal influencing factors. Secondly, the selected factors are taken as the input variables of the NARX neural network. Finally, the GRA-NARX neural network with automatic hyperparameter calibration (H-GRA-NARX model) provides accurate 24-h water level prediction to be used as the boundary condition of the hydrodynamic model, and then the H-GRA-NARX-HPM is constructed.FindingsThe section from the inlet sluice gate of Tang River aqueduct to the outlet sluice gate of Zhang River inverted siphon in the Middle Route of the South-to-North Water Transfer Project, China, is taken as the study area. The water levels before the outlet sluice gate of Anyang River inverted siphon on February 22, 2018 and February 26, 2018, are predicted by the H-GRA-NARX-HPM and then compared with those of the prediction models (GRA-BP-HPM, GRA-NARX-HPM) that use GRA-BP(gray relation analysis-back-propagation) neural network and GRA-NARX neural network prediction information as boundary conditions. The results show that the H-GRA-NARX-HPM has the highest accuracy with MAE values of 0.0028 m and 0.0141 m and MSE values of 1.636 x 10-5 and 2.658 x 10-4 on February 22 and February 26, respectively. In order to verify the universality and applicability of the model, the section from the inlet sluice gate of Ming River aqueduct to the outlet sluice gate of Qili River inverted siphon is taken as another study area. The water levels before the outlet sluice gate of Nansha River inverted siphon on March 18, 2018 and March 19, 2018, are predicted by the H-GRA-NARX-HPM and then also compared with GRA-BP-HPM and GRA-NARX-HPM. The results show that the H-GRA-NARX-HPM has the highest accuracy as well.Originality/valueThe main contribution of this paper is to propose a novel GRA-NARX and hydrodynamic coupled prediction model (H-GRA-NARX-HPM) which can overcome the main limitations of the individual modelling approaches.

The practical application of aqueous Zn-ion batteries in grid-scale energy storage is constrained by dendrite formation and side reactions at Zn anodes. Herein, we report an in situ constructed three-dimensional bicontinuous Zn powder-based anode in bulk phase via spark plasma sintering technology. The CuZn5 phase alloy provides abundant nucleation sites to prevent Zn aggregation, while its superior lattice compatibility with Zn promotes flatter deposition morphology and inhibits dendrite formation. The symmetric cell with the three-dimensional bicontinuous-phase anode maintained long-term cycling over 1000 h with a voltage hysteresis of 48 mV. The full cell with MnO2 cathode delivered a reversible capacity of 180 mAh g−1, achieving long-term cycling for 500 cycles at 1 A g−1. Moreover, the Zn-iodine cell delivered stable cycling over 3500 cycles with 100% capacity retention. This work provides new ideas for designing Zn anode materials and offers valuable insights into various metal anode systems.

The limited-memory BFGS (L-BFGS) Hessian update scheme is the critical kernel in many quasi-Newton optimization algorithms. The most common approach to implementing L-BFGS uses 2m sequential rank-1 updates as part of solving a linear system when there are m history steps. The performance of this approach suffers when the latency of synchronization is significant, and its poor temporal locality increases the memory traffic when vectors do not fit in cache. The compact dense representation of L-BFGS results in an approach that has minimal synchronization latency and better temporal locality, but it requires an additional pass over the basis vectors and an additional basis that must be recomputed when the B_0 matrix changes as in variable-metric methods. In the Portable Extensible Toolkit for Scientific Computation and the Toolkit for Advanced Optimization (PETSc/TAO), we have implemented an intermediate dense formulation of BFGS that retains most of the good characteristics of both the recursive and compact dense approaches. We report single-node performance tests of these implementations on the U.S. Department of Energy's Polaris and Frontier machines, testing both GPU-based and CPU-based computations.

Developing high-energy-density Li-metal batteries (LMBs) with >= 500 Wh kg-1 and >= 1,000 Wh L-1 using an ultrathin anode (<50 mu m) is crucial for all-electric vehicles. However, ultrathin anodes suffer from poor reversibility due to spatially heterogeneous Li nucleation and temporally irreversible dendritic growth, limiting battery life and rate performance. Herein, we propose a spatiotemporally coupled lithiotropic framework as a Li-metal anode design paradigm that overcomes both spatial heterogeneity and temporal irreversibility. This framework spatially reduces critical Li nucleation energy and improves nucleation rate. It also temporally optimizes Li adsorption and diffusion at interphases and body phases, thereby homogenizing the interfacial electric field and full-stage Li+ fluxes. This optimization maintains stable full-cell cycling for over 1.7 years, also enabling LMBs with high gravimetric and volumetric energy densities exceeding 500 Wh kg-1 and 1,200 Wh L-1, as well as a long cycle life of over 150 cycles at 2 C.

While large language models have significantly accelerated scientific code generation, comprehensively evaluating the generated code remains a major challenge. Traditional benchmarks reduce evaluation to test-case matching, an approach insufficient for library code in HPC where solver selection, API conventions, memory management, and performance are just as critical as functional correctness. To address this gap, we introduce petscagent-bench, an agentic framework built on an agents-evaluating-agents paradigm. Instead of relying on static scripts, petscagent-bench deploys a tool-augmented evaluator agent that compiles, executes, and measures code produced by a separate model-under-test agent, orchestrating a 14-evaluator pipeline across five scoring categories: correctness, performance, code quality, algorithmic appropriateness, and library-specific conventions. Because the agents communicate through standardized protocols (A2A and MCP), the framework enables black-box evaluation of any coding agent without requiring access to its source code. We demonstrate the framework on a benchmark suite of realistic problems using the PETSc library for HPC. Our empirical analysis of frontier models reveals that while current models generate readable, well-structured code, they consistently struggle with library-specific conventions that traditional pass/fail metrics completely miss.

The Portable Extensible Toolkit for Scientific Computation (PETSc) library provides scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization via the Toolkit for Advanced Optimization (TAO). PETSc is used in dozens of scientific fields and is an important building block for many simulation codes. During the U.S. Department of Energy's Exascale Computing Project, the PETSc team has made substantial efforts to enable efficient utilization of the massive fine-grain parallelism present within exascale compute nodes and to enable performance portability across exascale architectures. We recap some of the challenges that designers of numerical libraries face in such an endeavor, and then discuss the many developments we have made, which include the addition of new GPU backends, features supporting efficient on-device matrix assembly, better support for asynchronicity and GPU kernel concurrency, and new communication infrastructure. We evaluate the performance of these developments on some pre-exascale systems as well as the early exascale systems Frontier and Aurora, using compute kernel, communication layer, solver, and mini-application benchmark studies, and then close with a few observations drawn from our experiences on the tension between portable performance and other goals of numerical libraries.

Due to high safety and low-cost, aqueous Zn-ion batteries (ZIBs) are expected to be a promising next-generation energy storage technology. Exploiting high capacity and stable cathode materials are extremely important for the development of aqueous ZIBs. However, commercial V2O5 despite its high theoretical specific capacity, suffers from structural instability during charging and discharging, and strong electrostatic forces seriously limit the diffusion of Zn2+. Herein, a novel dynamic transport mechanism of Zn2+ is designed by intercalating organic cations (C8H15N2 +) from ionic liquids into the interlayers of V2O5. The electrostatic interaction between the C8H15N2+ and the V-O layers enhance the structure stability of the layers and increase the interlayer spacing. Meanwhile, the C8H15N2+ between the V-O layers reduce the diffusion energy barrier of Zn2+ and enable the rapid dynamically transport of Zn2+. The diffusion coefficient is enhanced by an order of magnitude. The optimized V2O5 cathode exhibits a high capacity of 268 mA h/g at 100 mA/g and excellent cycling stability (over 500 cycles at 200 mA/g with nearly 100 % coulombic efficiency). The home-made pouch cells deliver a high energy density of 310 W h kg-1. This interesting idea opens up a new research direction for high-energy secondary batteries.

Generative AI, especially through large language models (LLMs), is transforming how technical knowledge can be accessed, reused, and extended. PETSc, a widely used numerical library for high-performance scientific computing, has accumulated a rich but fragmented knowledge base over its three decades of development, spanning source code, documentation, mailing lists, GitLab issues, Discord conversations, technical papers, and more. Much of this knowledge remains informal and inaccessible to users and new developers. To activate and utilize this knowledge base more effectively, the PETSc team has begun building an LLM-powered system that combines PETSc content with custom LLM tools -- including retrieval-augmented generation (RAG), reranking algorithms, and chatbots -- to assist users, support developers, and propose updates to formal documentation. This paper presents initial experiences designing and evaluating these tools, focusing on system architecture, using RAG and reranking for PETSc-specific information, evaluation methodologies for various LLMs and embedding models, and user interface design. Leveraging the Argonne Leadership Computing Facility resources, we analyze how LLM responses can enhance the development and use of numerical software, with an initial focus on scalable Krylov solvers. Our goal is to establish an extensible framework for knowledge-centered AI in scientific software, enabling scalable support, enriched documentation, and enhanced workflows for research and development. We conclude by outlining directions for expanding this system into a robust, evolving platform that advances software ecosystems to accelerate scientific discovery.

BACKGROUND:Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment.METHODS:Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine.RESULTS:One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders.CONCLUSIONS:Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.

The Portable Extensible Toolkit for Scientific Computation (PETSc) library provides scalable solvers for nonlinear time-dependent differential and algebraic equations and for numerical optimization via the Toolkit for Advanced Optimization (TAO). PETSc is used in dozens of scientific fields and is an important building block for many simulation codes. During the U.S. Department of Energy's Exascale Computing Project, the PETSc team has made substantial efforts to enable efficient utilization of the massive fine-grain parallelism present within exascale compute nodes and to enable performance portability across exascale architectures. We recap some of the challenges that designers of numerical libraries face in such an endeavor, and then discuss the many developments we have made, which include the addition of new GPU backends, features supporting efficient on-device matrix assembly, better support for asynchronicity and GPU kernel concurrency, and new communication infrastructure. We evaluate the performance of these developments on some pre-exascale systems as well the early exascale systems Frontier and Aurora, using compute kernel, communication layer, solver, and mini-application benchmark studies, and then close with a few observations drawn from our experiences on the tension between portable performance and other goals of numerical libraries.

Objectives: The up-regulation of proinflammatory cytokines in the hypothalamic paraventricular nucleus (PVN) is well demonstrated to be involved in the development of neurogenic hypertension, including stress-induced hypertension (SIH). IL-17A has been found to be increased in the PVN of several hypertensive animal models and has been shown to play a key role in the development of hypertension. Although IL-36γ was found to be expressed in spinal neurons, its role in hypertension remains elusive. Here, we investigated the co-expression of IL-17 receptor A (IL-17RA) and IL-36γ in the PVN cells of SIH rats. Methods: The electric foot shock combined with buzzer noise stressors were used to make hypertensive rat model. The immunochemical staining or immunofluorescence staining was used to reveal cells as requested. The Western blot was used to detect the related protein levels. Results and Conclusion: In the PVN of the SIH rats, the number of CD3 + CD4 + T cells was significantly increased by the immunochemical staining. Additionally, the protein levels of RORγt and IL-17A were significantly upregulated by Western blot, confirming the infiltration of CD4 + T cells and differentiation into Th17 cells in the PVN of SIH rats. Immunofluorescence staining revealed abundant expression of IL-17RA in PVN neurons, with relatively less expression in astrocytes or microglia. Furthermore, IL-36γ positive cells and protein expression of IL-36R were significantly increased. Notably, this study demonstrates for the first time that most IL-36γ cells were strongly colocalized with IL-17RA positive cells in the PVN of SIH rats, and the colocalized cells were significantly higher in SIH rats. This suggests that IL-17A secreted by infiltrated Th17 cells may stimulate PVN neurons to produce IL-36γ via IL-17RA, indicating that the combination of IL-17 and IL-36γ might produce strong pro-inflammatory effects in the PVN of SIH rats.

High-entropy oxides（HEOs） are a new class of single-phase structures with unique electronic and catalytic properties;therefore,it is worthwhile to explore their applications in electrocatalysis.In this study,a Pt/（FeCoNiCrAl） 3 O 4 nanohybrid using HEO as a support was developed as an efficient catalyst for the hydrogen evolution reaction（HER）.Pt/（FeCoNiCrAl） 3 O 4 exhibited high HER activity with a low overpotential of 22 mV at10 mA·cm -2 ,outperforming other binary,ternary,and quaternary supports.The HER activity of Pt/（FeCoNiCrAl） 3 O 4 was higher than that of a commercial Pt/C with a significantly lower Pt loading.The catalyst exhibited good activity and long-term stability（60 h） in an electrolytic water-splitting device.This good activity can be attributed to the fact that the introduction of Pt effectively facilitates electronic interactions between Pt and the HEO.In addition,the HEO substrate was more favorable for dispersing Pt particles,optimizing the electrochemical specific surface area,and significantly reducing the charge resistance of the HER.This study extends the application of HEOs in electrocatalysis and demonstrates the promising prospects of HEOs as supports for electrocatalysts.

Herein, a poly(N-isopropylacrylamide)-based conductive composite hydrogel system coenhanced by clay and polydopamine-modified MXene was synthesized to achieve strain and temperature sensibility simultaneously. The noncovalently cross-linked network via MXene, clay, and polymer chains endowed the synthesized hydrogel with excellent mechanical properties (tensile strength at a break of 117 kPa and elongation at a break of 1723%). This hydrogel also exhibits strong adhesion and good electrical conductivity (0.13 S/m). Regarding the sensing properties, its temperature sensitivity is 2.749 degrees C-1, while its strain detection limit is as low as 0.05%. Based on the unique characteristics of the prepared hydrogel, the as-assembled sensor can detect stress and temperature simultaneously, exhibiting great application potential in human physiological monitoring.

In this work, we provide a mathematical formulation for error propagation in flow trajectory prediction using data-driven turbulence closure modeling. Under the assumption that the predicted state of a large eddy simulation prediction must be close to that of a subsampled direct numerical simulation, we retrieve an upper bound for the prediction error when utilizing a data-driven closure model. We also demonstrate that this error is significantly affected by the time step size and the Jacobian which play a role in amplifying the initial one-step error made by using the closure. Our analysis also shows that the error propagates exponentially with rollout time and the upper bound of the system Jacobian which is itself influenced by the Jacobian of the closure formulation. These findings could enable the development of new regularization techniques for ML models based on the identified error-bound terms, improving their robustness and reducing error propagation.

OBJECTIVE:To evaluate the effects of Zuyangping (, ZYP) formula on wound healing in diabetic rats, as well as the molecular mechanisms involved. METHODS:The main compounds in ZYP formula were identified by the Liquid chromatography-tandem mass spectrometry. Sprague-Dawley rats, injected with streptozotocin (STZ) to establish diabetes model, then, formed a defective skin trauma in the back, and each group was treated with corresponding drugs once a day. Granulation was taken from each time node for histological analysis. The Western blotting was used to measured protein expression of advanced glycation end products receptor (RAGE) and hypoxia-inducible factor-1α (HIF-1α) axis-related proteins. The relative expression levels of inflammatory cytokines and growth factors were measured by the enzyme-linked immunosorbent assay method. RESULTS:The main ingredients were identified in the ZYP formula. Histological analysis showed that the ZYP formula could inhibit the expression of inflammation, promote angiogenesis and collagen deposition. In addition, the ZYP formula could regulate the expression of RAGE and HIF1-α axis-related proteins, thus promoting the wound healing in diabetic rats. CONCLUSION:The ZYP formula could accelerate wound healing in diabetic rats.

Dendrites and side reactions of Zn anodes severely restrict the application of aqueous Zn-based batteries for grid-scale energy storage. While surface/interface modification strategies have shown some progress in improving Zn anode reversibility, they still fall short in addressing the overall regulation and intrinsic mechanisms from the bulk-phase perspective. Herein, a bulk-phase composite Zn/CNTs anodes fabricated by a powder-metallurgy-based strategy is introduced. Benefiting from the regulation of grain boundary engineering on local electric conductivity, electric field distributions, and Zn atom absorption energy, the Zn/CNTs anodes effectively suppress dendrite growth and enhance corrosion resistance during Zn stripping/plating cycles. Symmetrical cells equipped with Zn/CNT4 anodes exhibit extended cycling stability with minimal voltage hysteresis (only 22 mV). Furthermore, the full cells incorporating Zn/CNT4 with commercial MnO2 demonstrate superior rate performance and specific capacitance retention after 500 h cycling. This breakthrough opens up new avenues for optimizing metallic anodes at the bulk phase level using powder metallurgy, enabling scalable manufacturing processes, and providing valuable insights for various metal anode systems. Distinctly different from the traditional surface/interface modification for the Zn anodes, this work provides a groundbreaking regulation perspective to achieve highly reversible zinc anode from bulk-phase grain boundary optimization. Benefiting from grain boundary regulation with highly conductive CNTs, the bulk-phase composite Zn/CNTs anodes exhibit substantial suppression of dendrite growth and enhanced corrosion resistance during Zn stripping/plating processes.image

Objectives Prolactinoma is a common intracranial tumor with a high incidence and serious harm to human health. At present, there is only one therapeutic drug in China, bromocriptine, and the Chinese herb Prunella vulgaris L. ( P. vulgaris)(PV) has shown certain anti-prolactinoma effects in the early stage. We expect to develop a candidate drug against prolactinoma. Materials and Methods First, the extracts of P. vulgaris L.(PVE)were extracted with water, and the cell proliferation test of rat pituitary tumor cells MMQ was checked by the Cell Counting Kit-8 (CCK-8) assay. Then, core targets and correlative pathways were selected by the “protein–protein interaction” (PPI) network and the “PV–Target–Prolactinoma” network. The core targets and main components simulate the binding by molecular docking. Finally, the PVE and MMQ cells were used to verify the results. Results Through the CCK-8 assay, the PVE inhibited the proliferation of MMQ cells. From the network pharmacology, the 21 targets, 9 signaling pathways, and 20 gene ontology (GO) projects were attained ( p < 0.05). As a result the estrogen receptor α (ESR1), RAC-alpha serine/threonine-protein kinase(AKT1), and mitogen-activated protein kinase 3(MAPK3)were the core targets of protein against prolactinomas, which was in line with western blotting analysis. Conclusion Our findings demonstrate that the PVE was verified against prolactinomas through the ESR1, MAPK3 targets, and the phosphoinositide 3-kinase/protein kinase B pathway.

Developing high‐performance anode current collectors with three‐dimensional structure and lithiophilic layers is of great importance to further advance the application of lithium metal batteries. However, relatively few research has focused on the transition of substrate and the intrinsic structure stability after electrodeposition of lithium on substrates, which leads to an incomplete understanding of the behavior of lithium deposition. Herein, a lithium metal anodes host with a highly stable and 3D structure has been effectively produced through in situ development of nanoflake arrays embedded with Co 3 O 4 obtained from ZIF on nickel foams (Co 3 O 4 ‐NF). And the actual lithium deposition sites and lithium deposition process on Co 3 O 4 ‐NF are elucidated via a combination of characterization techniques and electrochemical analytical methods. Consequently, the resulting Co 3 O 4 ‐NF@Li anodes could effectively inhibit lithium dendrite formation and mitigate volume expansion, demonstrating a significantly extended and consistent lifespan of 800 cycles (1600 h) at 1 mA cm −2 with low overpotential and insignificant voltage fluctuation for the process of lithium stripping and plating in symmetric cells. Herein, it is aimed to examine the transitions of metal oxides as a lithiophilic site for the lithium metal anode. It offers novel perspectives and approaches for the design of dendrite‐free lithium metal anodes.