Explore the vast range of titles available.

Seawater electrolysis driven by offshore renewable energy is a promising avenue for large-scale hydrogen production but faces challenges in designing robust anodes that suppress surface chlorine reactions and corrosion at high current densities. Here we report a strategy by selectively docking PW 12 -polyoxometalate (PW 12 -POM) onto Fe sites of CoFe hydroxide anode to modulate the electronic structure of adjacent Co active centers and regulate Cl⁻/OH⁻ adsorption for efficient alkaline seawater oxidation. Our CoFe-based anode achieves low overpotentials, high catalytic selectivity, and notable durability, with continuous operation at 1 A cm⁻² for over 1300 hours and at 2 A cm⁻² more than 600 hours. Theoretical calculations and ex situ/in situ analyses reveal that PW 12 -POM coordination at Fe sites stabilizes Fe, suppresses its leaching, modulates Co acidity, promotes OH⁻ adsorption, and protects metal sites from Cl⁻ corrosion. Seawater electrolysis for hydrogen production faces challenges in creating robust anodes that resist corrosion. Here, the authors report a PW 12 -polyoxometalate-modified CoFe hydroxide anode that achieves selective and durable seawater oxidation over 1300 hours at 1 A cm⁻².

Direct seawater electrolysis at ampere-level current densities, powered by coastal/offshore renewables, is an attractive avenue for sustainable hydrogen production but is undermined by chloride-induced anode degradation. Here we demonstrate the use of hexafluorophosphate (PF₆⁻) as an electrolyte additive to overcome this limitation, achieving prolonged operation for over 5,000 hours at 1 A cm −2 and 2300 hours at 2 A cm −2 using NiFe layered double hydroxide (LDH) as anode. Together with the experimental findings, PF₆⁻ can intercalate into LDH interlayers and adsorb onto the electrode surface under an applied electric field, blocking Cl⁻ and stabilizing Fe to prevent segregation. The constant-potential molecular dynamics simulations further reveal the accumulation of high surface concentrations of PF 6 − on the electrode surface that can effectively exclude Cl − , mitigating corrosion. Our work showcases synchronous interlayer and surface engineering by single non-oxygen anion species to enable Cl − rejection and marks a crucial step forward in seawater electrolysis. Seawater electrolysis at ampere-level current densities demands durable anodes for practical hydrogen production. Here, the authors report that a single non-oxygen anion enables dual modulation of surface and interlayer structures, stabilizing layered double hydroxide anodes for over 5,000 hours.

Accurately estimating the road adhesion coefficient is essential for ensuring vehicle driving safety. This study proposes a method that integrates a seven-degree-of-freedom vehicle model and the magic formula tire model with the eXtreme Gradient Boosting algorithm, leveraging Shapley Additive exPlanations values to determine feature importance. Based on this, a road adhesion coefficient estimation model is constructed, incorporating a Bayesian neural network and a Support vector regression to enhance interpretability. To address potential issues such as the model predicting road adhesion coefficients beyond the range present in the training data, a data fusion strategy is designed to improve generalization. Simulation results demonstrate that the fusion estimator outperforms individual models in terms of robustness, real-time performance, and prediction accuracy, especially under varying vehicle speeds and road conditions. Compared to a single estimator, the data fusion model reduced the mean square error by an average of 16.01% and 18.89%, respectively, while the root mean square error was reduced by an average of 11.33% and 16.04%, respectively. This approach provides valuable theoretical support to advance the development of vehicle active safety systems.

As one of the most common neurological disorders, epilepsy causes great physical and psychological damage to the patients. The long-term recurrent and unprovoked seizures make the prediction necessary. In this paper, a novel approach for epileptic seizure prediction based on successive variational mode decomposition (SVMD) and Transformers is proposed. SVMD is extended to multidimensional form for time-frequency analysis of multi-channel signals. It could adaptively extract common band-limited intrinsic modes among all channels on different time scales by solving a variational optimization problem. In the proposed seizure prediction method, data is firstly decomposed into multiple modes at different time scales by multivariate SVMD, and then irrelevant modes are removed for preprocessing. Finally, power spectrum of denoised data is input to a pre-trained Bidirectional Encoder Representations from Transformers (BERT) for prediction. The BERT could identify the mode information related to epileptic seizures in time-frequency domain. It shows fair prediction performance on an intracranial EEG dataset with the average sensitivity of 0.86 and FPR of 0.18/h.

Background Activated microglia play a key role in initiating the inflammatory cascade following ischemic stroke and exert proinflammatory or anti-inflammatory effects, depending on whether they are polarized toward the M1 or M2 phenotype. The present study investigated the regulatory effect of icaritin (ICT) on microglial polarization in rats after cerebral ischemia/reperfusion injury (CI/RI) and explored the possible anti-inflammatory mechanisms of ICT. Methods A rat model of transient middle cerebral artery occlusion (tMCAO) was established. Following treatment with ICT, a G protein-coupled estrogen receptor (GPER) inhibitor or an extracellular signal-regulated kinase (ERK) inhibitor, the Garcia scale and rotarod test were used to assess neurological and locomotor function. 2,3,5-Triphenyltetrazolium chloride (TTC) and Fluoro-Jade C (FJC) staining were used to evaluate the infarct volume and neuronal death. The levels of inflammatory factors in the ischemic penumbra were evaluated using enzyme-linked immunosorbent assays (ELISAs). In addition, western blotting, immunofluorescence staining and quantitative PCR (qPCR) were performed to measure the expression levels of markers of different microglial phenotypes and proteins related to the GPER–ERK–nuclear factor kappa B (NF-κB) signaling pathway. Results ICT treatment significantly decreased the cerebral infarct volume, brain water content and fluorescence intensity of FJC; improved the Garcia score; increased the latency to fall and rotation speed in the rotarod test; decreased the levels of interleukin-1 beta (IL-1β), tumor necrosis factor-alpha (TNF-α), Iba1, CD40, CD68 and p-P65-NF-κB; and increased the levels of CD206 and p-ERK. U0126 (an inhibitor of ERK) and G15 (a selective antagonist of GPER) antagonized these effects. Conclusions These findings indicate that ICT plays roles in inhibiting the inflammatory response and achieving neuroprotection by regulating GPER–ERK–NF-κB signaling and then inhibiting microglial activation and M1 polarization while promoting M2 polarization, which provides a new therapeutic for against cerebral ischemic stroke. Graphical Abstract

Functional brain network (FBN), estimated with functional magnetic resonance imaging (fMRI), has become a potentially useful way of diagnosing neurological disorders in their early stages by comparing the connectivity patterns between different brain regions across subjects. However, this depends, to a great extent, on the quality of the estimated FBNs, indicating that FBN estimation is a key step for the subsequent task of disorder identification. In the past decades, researchers have developed many methods to estimate FBNs, including Pearson's correlation and (regularized) partial correlation, etc. Despite their widespread applications in current studies, most of the existing methods estimate FBNs only based on the dependency between the measured blood oxygen level dependent (BOLD) signals, which ignores spatial relationship of signals associated with different brain regions. Due to the space and material parsimony principle of our brain, we believe that the spatial distance between brain regions has an important influence on FBN topology. Therefore, in this paper, we assume that spatially neighboring brain regions tend to have stronger connections and/or share similar connections with others; based on this assumption, we propose two novel methods to estimate FBNs by incorporating the information of brain region distance into the estimation model. To validate the effectiveness of the proposed methods, we use the estimated FBNs to identify subjects with mild cognitive impairment (MCI) from normal controls (NCs). Experimental results show that the proposed methods are better than the baseline methods in the sense of MCI identification accuracy.

Boron and nitrogen co-doped reduced graphene oxide (BN-rGO) materials were prepared via a facile dielectric barrier discharge plasma treatment method. X-ray photoelectron spectroscopy results demonstrated that the boron content in the boron-doped rGO (B-rGO) and BN-rGO is 1.21 at.% and 1.41 at.%, while the nitrogen content in the nitrogen-doped rGO (N-rGO) and BN-rGO is 2.12 at.% and 2.69 at.%, respectively. The doping of heteroatoms significantly improves the capacitance of the as-synthesized materials, giving BN-rGO a highly enhanced capacitance of 350 F g−1 at a current density of 0.5 A g−1, which is 2.36, 1.46 and 1.21 times higher than that of rGO, B-rGO or N-rGO, respectively.

In order to improve the accuracy of the parameters needed in the discrete element method (DEM) simulation process of wet-sticky feed raw materials, the JKR contact model in DEM was used to calibrate and verify the physical parameters of wet-sticky feed raw materials. Firstly, the parameters that have a significant effect on the angle of repose were screened using a Plackett–Burman design, and the screened parameters were: MM rolling friction coefficient, MM static friction coefficient, and JKR surface energy. Then, the three screened parameters were selected as the influencing factors and the accumulation angle of repose was selected as evaluating indicator; thus, the performance optimization experiments were carried out with the quadratic orthogonal rotation design. Taking the experimentally measured angle of repose value of 54.25°as the target value, the significance parameters were optimized, and the optimal combination was obtained : MM rolling friction factor was 0.21, MM static friction factor was 0.51, and JKR surface energy was 0.65. Finally, the angle of repose and SPP tests were compared under the calibrated parameters. The results showed that the relative error of experimental and simulated tests in angle of repose was 0.57%, and the compression displacement and compression ratio of the experimental and simulated tests in SPP were 1.01% and 0.95%, respectively, which improved the reliability of the simulated results. The research findings provide a reference basis for simulation study and optimal design of related equipment for feed raw materials.

Liquid crystal elastomers (LCEs) are a class of programmable polymer materials that can deform reversibly under diverse stimuli such as light, heat, electric field, and magnetic field. LCEs have demonstrated their potential applications in soft robots, soft actuators, artificial muscles, etc. How to align the mesogen orientation and achieve actuations of LCEs are the two most important issues for the studies on LCEs. At present, varied alignment methods and versatile actuations for LCEs have developed rapidly. However, few reviews are addressing these two important issues simultaneously. In this review, three types of alignment methods for LCEs as mechanical stress‐induced alignment, external field‐induced alignment, and surface effect‐induced alignment are summarized, and comparing their programmability toward the mesogen orientation is focused on. The key factors influencing the versatile actuations of LCEs are their orientation structure (intrinsic factor) and the applied external stimuli (extrinsic factor). Thus, actuations are classified into two types on the basis of programmable orientation structure and selective external stimuli, respectively, and focus on comparing their deformation controllability. Finally, an outlook for the future key technologies to develop versatile, precise, and fast‐responsive deformation actuations for LCEs is proposed. Aligning the mesogen orientation and achieving versatile actuations of liquid crystal elastomers (LCEs) are always the central research objects in the field of LCEs. This review summarizes three types of LCE alignment methods and classifies two types of versatile actuations. The key technologies to develop versatile, precise, and fast‐responsive deformation actuations for LCEs are discussed.

There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily depends on the artificial experience (or domain knowledge) and is highly subjective. To address these issues, in this paper we propose an end-to-end epileptic seizure prediction approach based on the long short-term memory network (LSTM). In the new method, only the gamma band of raw electroencephalography (EEG) signals is extracted as network input directly for seizure prediction, thus avoiding subjective and expensive feature design process. Despite its simplicity, the proposed method achieves the mean sensitivity of 91.76% and false prediction rate (FPR) of 0.29/h on Children’s Hospital Boston-MIT (CHB-MIT) scalp EEG Database, respectively, when identifying the preictal stage from the EEG signals. Furthermore, different from traditional methods that only consider the classification of preictal and interictal EEG, we introduce the postictal stage as an extra class in the proposed method. As a result, the performance of seizure prediction is further improved, obtaining a higher sensitivity of 92.17% and a low FPR of 0.27/h. The mean warning time is 44.46 min, which suggests that sufficient time is reserved for patients to take intervention measures by this prediction method.