Cobalt-zinc carbides embedded in N-doped porous carbon nanospheres as polysulfide mediators for efficient lithium-sulfur batteries.

Developing high-efficiency interlayer catalysts is a promising tactic for improving the cycling performance of rechargeable lithium-sulfur (Li-S) batteries. Herein, using the Prussian blue analogue as the precursor, cobalt-zinc carbide nanocrystal-embedded N-doped porous carbon (CoZnC@NC) is synthesized simple post-carbonization. The obtained CoZnC@NC nanospheres exhibit a robust core-shell structure showing good conductivity, high porosity and available metal active sites, favoring the interfacial charge transfer and the electron transport upon electrochemical reactions.

Port environmental path planning based on key obstacles.

This paper proposes an improved hybrid algorithm for automated guided vehicles (AGVs) in port environments based on the concept of key obstacles for the JPS and DWA algorithms. Given the complexity of the port environment and the abundance of obstacles, the traditional heuristic function of the JPS algorithm is improved by adding the key obstacle heuristic function. Simultaneously, improvements are made to the evaluation function of the traditional DWA algorithm, where the braking distance is segmented into key obstacle distance and non-key obstacle distance, utilizing the concept of key obstacles.

Designing van der Waals magnetic tunnel junctions with high tunnel magnetoresistance Brillouin zone filtering.

Magnetic tunnel junctions (MTJs) consisting of two-dimensional (2D) van der Waals heterostructures have no inter-layer chemical bonds; therefore, their spin tunneling is determined solely by the Brillouin zone (BZ) filtering effect. To obtain high tunnel magnetoresistance (TMR), they should possess transversal momentum-resolved conduction channels for the electrodes and transmission channels for the barriers. Here, we investigate 2D magnets as electrodes whose Curie temperatures approach room temperature and also hexagonal 2D insulators as the barrier.

Effect of the Surface Peak-Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature.

This study explores the kinetic behavior of droplets impacting microtextured surfaces under a Leidenfrost temperature, employing high-speed photography and picosecond laser micromachining techniques. The investigation focuses on two types of microtextured surfaces with totally different surface peak-valley features: a negatively skewed surface with micropit arrays ( < 0) and a positively skewed surface with micropillar arrays ( > 0). The results indicate that both microtextured surfaces contribute to a higher Leidenfrost temperature compared with the original smooth surface, which is consistent with previous studies.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data.

Environmental assessments are critical for ensuring the sustainable development of human civilization. The integration of artificial intelligence (AI) in these assessments has shown great promise, yet the "black box" nature of AI models often undermines trust due to the lack of transparency in their decision-making processes, even when these models demonstrate high accuracy. To address this challenge, we evaluated the performance of a transformer model against other AI approaches, utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.

An investigation of CO generation path upon loading coal from perspective of macromolecular simulation.

CO is a hazardous and pollutant gas that can be produced in many scenarios of coal-related operations. The study mainly investigated CO production process and mechanism when coal is subject to external forces. The effects of coal type, particle size, temperature, and inlet atmosphere on CO production from coal body fragmentation were investigated through coal loading experiments.

Cross-domain zero-shot learning for enhanced fault diagnosis in high-voltage circuit breakers.

Ensuring the stability of high-voltage circuit breakers (HVCBs) is crucial for maintaining an uninterrupted supply of electricity. Existing fault diagnosis methods typically rely on extensive labeled datasets, which are challenging to obtain due to the unique operational contexts and complex mechanical structures of HVCBs. Additionally, these methods often cater to specific HVCB models and lack generalizability across different types, limiting their practical applicability.

Plasmonic Pd-Sb nanosheets for photothermal CH conversion to HCHO and therapy.

Photothermal catalysis effectively increases catalytic activity by using the photothermal effect of metal nanomaterials; however, the combination of strong light absorption and high catalytic performance remains a challenge. Here, we demonstrate hexagonal ~5-nanometer-thick palladium antimony (chemical formula as PdSb) nanosheets (NSs) that exhibit strong light absorption within full spectral and localized surface plasmon resonance (LSPR) effects in the visible region. Such LSPR features lead to strong photothermal effects, and PdSb NSs aqueous dispersion enables enhanced photothermal methane (CH) conversion to formaldehyde (HCHO) under full-spectrum light irradiation at 1.

Tool State Recognition Based on POGNN-GRU under Unbalanced Data.

Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently.

Adaptive Control for a Two-Axis Semi-Strapdown Stabilized Platform Based on Disturbance Transformation and LWOA-PID.

A two-axis semi-strapdown stabilized platform is a device designed to eliminate aircraft disturbances and ensure the stability of the sensor's orientation. A traditional two-axis semi-strapdown stabilization platform for aircraft can effectively control disturbance in pitch and yaw channel, but it cannot achieve ideal disturbance control in the roll channel. In order to solve this problem, an adaptive control method based on disturbance transformation and LWOA-PID is proposed.

Integrated mental stress smartwatch based on sweat cortisol and HRV sensors.

Mental stress, a human's common emotion that is difficult to recognize and describe, can give rise to serious psychological disorders. Skin and sweat are easily accessible sources of biomarkers and bio-signals that contain information about mental stress. It is challenging for current wearable devices to monitor psychological stress in real-time with a non-invasive manner.

Strain-Amplified Exciton Chirality in Organic-Inorganic Hybrid Materials.

Chiral organic-inorganic hybrids combining chirality of organic molecules and semiconducting properties of inorganic frameworks generate chiral excitons without external spin injection, creating the potential for chiroptoelectronics. However, the relationship between molecular chirality and exciton chirality is still unclear. Here we show the strain-amplified exciton chirality in one-dimensional chiral metal halides.

Enhanced photocatalytic CO conversion of a CdS/Co-BDC nanocomposite Co(ii)/Co(iii) redox cycling.

Photocatalytic CO reduction into value-added chemical fuels using sunlight as the energy input has been a thorny, challenging and long-term project in the environment/energy fields because of to its low efficiency. Herein, a series of CdS/Co-BDC composite photocatalysts were constructed by incorporating CdS nanoparticles into Co-BDC using a dual-solvent growth strategy for improving photocatalytic CO reduction efficiency. The composites were characterized through XRD, SEM, TEM, XPS, DRS and EPR techniques in detail.

Coordinated Multi-Scenario Optimization Strategy for Park Photovoltaic Storage Based on Master-Slave Game.

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master-slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks.

Design and simulation of a compact polarization beam splitter based on dual-core photonic crystal fiber with elliptical gold layer.

For the polarization multiplexing requirements in all-optical networks, this work presents a compact all-fiber polarization beam splitter (PBS) based on dual-core photonic crystal fiber (PCF) and an elliptical gold layer. Numerical analysis using the finite element method (FEM) demonstrates that the mode modulation effect of the central gold layer effectively reduces the dimensions of the proposed PBS. By determining reasonable structural parameters of the proposed PCF, the coupling length ratio (CLR) between X- and Y-polarized super-modes can approach 2, achieving a minimal device length of 0.

Stepwise reconstruction of higher-order networks from dynamics.

Higher-order networks present great promise in network modeling, analysis, and control. However, reconstructing higher-order interactions remains an open problem. A significant challenge is the exponential growth in the number of potential interactions that need to be modeled as the maximum possible node number in an interaction increases, making the reconstruction exceedingly difficult.

An Optimal Spatio-Temporal Hybrid Model Based on Wavelet Transform for Early Fault Detection.

An optimal spatio-temporal hybrid model (STHM) based on wavelet transform (WT) is proposed to improve the sensitivity and accuracy of detecting slowly evolving faults that occur in the early stage and easily submerge with noise in complex industrial production systems. Specifically, a WT is performed to denoise the original data, thus reducing the influence of background noise. Then, a principal component analysis (PCA) and the sliding window algorithm are used to acquire the nearest neighbors in both spatial and time dimensions.

Deep Learning-Based State-of-Health Estimation of Proton-Exchange Membrane Fuel Cells under Dynamic Operation Conditions.

Proton-exchange membrane fuel cells (PEMFCs) play a crucial role in the transition to sustainable energy systems. Accurately estimating the state of health (SOH) of PEMFCs under dynamic operating conditions is essential for ensuring their reliability and longevity. This study designed dynamic operating conditions for fuel cells and conducted durability tests using both crack-free fuel cells and fuel cells with uniform cracks.

Deep learning-based deformable image registration with bilateral pyramid to align pre-operative and follow-up magnetic resonance imaging (MRI) scans.

Background: The evaluation of brain tumor recurrence after surgery is based on the comparison between tumor regions on pre-operative and follow-up magnetic resonance imaging (MRI) scans in clinical practice. Accurate alignment of MRI scans is important in this evaluation process. However, existing methods often fail to yield accurate alignment due to substantial appearance and shape changes of tumor regions.

Visualization and standardized quantification of surface charge density for triboelectric materials.

Triboelectric nanogenerator (TENG) operates on the principle of utilizing contact electrification and electrostatic induction. However, visualization and standardized quantification of surface charges for triboelectric materials remain challenging. Here, we report a surface charge visualization and standardized quantification method using electrostatic surface potential measured by Kevin probe and the iterative regularization strategy.