Electrochemical Knocking-Down of Zn Metal Clusters into Single Atoms.

Single Atoms Catalysts (SACs) have emerged as a class of highly promising heterogeneous catalysts, where the traditional bottom-up synthesis approaches often encounter considerable challenges in relation to aggregation issues and poor stability. Consequently, achieving densely dispersed atomic species in a reliable and efficient manner remains a key focus in the field. Herein, we report a new facile electrochemical knock-down strategy for the formation of SACs, whereby the metal Zn clusters are transformed into single atoms. While a defect-rich substrate plays a pivotal role in capturing and stabilizing isolated Zn atoms, the feasibility of this novel strategy is demonstrated through a comprehensive investigation, combining experimental and theoretical studies. Furthermore, when studied in exploring for potential applications, the material prepared shows a remarkable improvement of 58.21% for the Li+ storage and delivers a capacity over 300 Wh kg-1 after 500 cycles upon the transformation of Zn clusters into single atoms.

Hypoxia inducible factor-1α regulates microglial innate immune memory and the pathology of Parkinson's disease.

Neuroinflammation is one of the core pathological features of Parkinson's disease (PD). Innate immune cells play a crucial role in the progression of PD. Microglia, the major innate immune cells in the brain, exhibit innate immune memory effects and are recognized as key regulators of neuroinflammatory responses. Persistent modifications of microglia provoked by the first stimuli are pivotal for innate immune memory, resulting in an enhanced or suppressed immune response to second stimuli, which is known as innate immune training and innate immune tolerance, respectively. In this study, LPS was used to establish in vitro and in vivo models of innate immune memory. Microglia-specific Hif-1α knockout mice were further employed to elucidate the regulatory role of HIF-1α in innate immune memory and MPTP-induced PD pathology. Our results showed that different paradigms of LPS could induce innate immune training or tolerance in the nigrostriatal pathway of mice. We found that innate immune tolerance lasting for one month protected the dopaminergic system in PD mice, whereas the effect of innate immune training was limited. Deficiency of HIF-1α in microglia impeded the formation of innate immune memory and exerted protective effects in MPTP-intoxicated mice by suppressing neuroinflammation. Therefore, HIF-1α is essential for microglial innate immune memory and can promote neuroinflammation associated with PD.

Pyroptosis-mediator GSDMD promotes Parkinson's disease pathology via microglial activation and dopaminergic neuronal death.

GSDMD-mediated pyroptosis occurs in the nigrostriatal pathway in Parkinson's disease animals, yet the role of GSDMD in neuroinflammation and death of dopaminergic neurons in Parkinson's disease remains elusive. Here, our in vivo and in vitro studies demonstrated that GSDMD, as a pyroptosis executor, contributed to glial reaction and death of dopaminergic neurons across different Parkinson's disease models. The ablation of the Gsdmd attenuated Parkinson's disease damage by reducing dopaminergic neuronal death, microglial activation, and detrimental transformation. Disulfiram, an inhibitor blocking GSDMD pore formation, efficiently curtailed pyroptosis, thereby lessening the pathology of Parkinson's disease. Additionally, a modification in GSDMD was identified in the blood of Parkinson's disease patients in contrast to healthy subjects. Therefore, the detected alteration in GSDMD within the blood of Parkinson's disease patients and the protective impact of disulfiram could be promising for the diagnostic and therapeutic approaches against Parkinson's disease.

Nanosheet-structured ZnCo-LDH microsphere as active material for rechargeable zinc batteries.

We report zinc cobalt-layered double hydroxides (ZnCo-LDH) as the active cathode materials for the development of high-performance Zn-ZnCo batteries. Electrochemical investigations show the battery's capacity increases linearly with increasing the ZnCo-LDH loading (up to 60 mg cm-2). The resulting Zn-ZnCo battery exhibits excellent rate performance and cycle stability, retaining 86% of its capacity even after 5000 cycles of testing. By incorporating ZnCo-LDH with a Pt/C-coated gas diffusion layer to form an integrated multifunctional air-cathode, we demonstrate a hybrid Zn battery, which combines the merits of Zn-ZnCo and Zn-air batteries to show a characteristic two-stage charge-discharge voltage profile. The current work demonstrates the linear relationship between the battery capacity and the active material loading. The results also highlight that a greater battery capacity requires further increasing of loading though very challenging.

Anchoring Metal-Nitrogen Sites on Porous Carbon Polyhedra with Highly Accessible Multichannels for Efficient Oxygen Reduction.

Transition metal-nitrogen-carbon complexes, featuring single metal atoms embedded in a nitrogen-doped carbon matrix, emerge as promising alternatives to traditional platinum-based catalysts, offering cost-effectiveness, abundance, and enhanced catalytic performance. This work introduces a novel method for the etching and doping of zeolitic imidazolate frameworks (ZIFs) with transition metals, creating a uniform distribution of secondary metal centers on ZIF surfaces. By disrupting the crystalline symmetry of ZIFs through synthetic defect engineering, we gain access to their entire internal volume, creating multichannel pathways. The absorption of metal ions is theoretically simulated, demonstrating their thermodynamically spontaneous nature. The selective removal of defect channels under Lewis acidic conditions, induced by metal ion alcoholysis/hydrolysis, facilitates the introduction of metal atoms into ZIF cavities. The resulting single-atom catalyst, after pyrolysis, features a three-dimensional (3D) multichannel structure, high surface area, and uniformly dispersed metal atoms within the N-doped carbon matrix, establishing it as an exceptional catalyst for the oxygen reduction reaction (ORR). Our findings highlight the potential of using metal etching in defect-engineered metal-organic frameworks (MOFs) for single-atom catalyst preparation, paving the way for the next generation of high-performance, cost-effective ORR catalysts in sustainable energy systems.

Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling.

Battery health assessment and recuperation play crucial roles in the utilization of second-life Li-ion batteries. However, due to ambiguous aging mechanisms, it is challenging to estimate battery health and devise an effective strategy for cell rejuvenation. This paper presents aging and reconditioning experiments of 62 commercial lithium iron phosphate cells, which allow us to use machine learning models to predict cycle life and identify important indicators of recoverable capacity. An average test error of 16.84% ± 1.87% (mean absolute percentage error) for cycle life prediction is achieved by gradient boosting regressor. Some of the recoverable lost capacity is found to be attributed to the non-uniformity in electrodes. An experimentally validated equivalent circuit model is built to demonstrate how such non-uniformity can be accumulated, and how it can give rise to recoverable capacity loss. Furthermore, Shapley additive explanations (SHAP) analysis also reveals that battery operation history significantly affects the capacity recovery.

Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.

Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.

Engineering FeCo Dual Sites on Tube-on-Plate Hollow Structure for Efficient Oxygen Electroreduction.

Atomically dispersed single-atom catalysts are intriguing catalysts in the field of electrocatalysis for nearly 100% exploitation of metal atoms. However, they are still far from practical usage due to the scaling relationship limit and metal loading limit. Generation of a diatomic complex would offer superior catalytic performance through the cooperation of two neighboring atoms as active sites. Herein, Fe/Co dual atomic sites embedded in a tube-on-plate hollow structure are designed and fabricated for an efficient electrochemical oxygen reduction reaction (ORR). The unique structure composed of ultrathin nanotube building blocks dramatically maximizes the surface area for copious active site exposure. Thanks to the synergetic interaction between Fe/Co pairs, the obtained FeCo/NC exhibits outstanding ORR activity and stability in alkaline media. Furthermore, density functional theory calculations have revealed that the remarkable activity is attributed to the electron-deficient Fe sites in FeCoN6. This work may pave the way for the innovative design of highly dispersed dual-site catalysts for broader applications in the realm of electrochemical catalysis.