Lithium-sulfur (Li-S) batteries hold great promise for next-generation high-energy storage but are challenged by sluggish lithium polysulfides (LiPSs) conversion, low sulfur utilization, and limited practical loading. Herein, we report an all-in-one ant-nest-like porous VN/B2O3 (VNBO) ceramic, constructed through a bottom-up sintering-diffusion process of VN nanoparticles coupled with the phase transition of B2O3. This integrated porous ceramic provides a continuous conductive framework with minimized interfacial resistance. The VN nano-units serve as highly active catalytic centers to accelerate LiPSs redox kinetics, while B2O3 component promotes the formation of the hierarchical ant-nest-like network and modulates the electronic structure of the VN/B2O3 heterointerface. This coupled electronic-catalytic regulation effectively suppresses LiPSs shuttling and enables fast, reversible LiPSs conversion. Benefiting from this synergistic architecture, the 2-VNBO@S cathode delivers outstanding electrochemical performances, achieving 1187.2 mAh g-1 at 0.5 C after 200 cycles and retaining 944.3 mAh g-1 over 300 cycles at 3 C with a capacity decay of only 0.054% per cycle. Even under a high sulfur loading of 4 mg cm-2, it maintains 557.9 mAh g-1 after 150 cycles. This work establishes a robust design strategy for high-energy and catalytically active sulfur cathodes.

The increasing automation of modern systems-across industry, healthcare, mobility, and beyond-has raised the demand for human reasoning and expertise, while alleviating the burden of repetitive tasks. This transformation is driving us toward Automation 5.0, a new paradigm aimed at unleashing human potential. Recently, the development of foundation models (FMs) has reinvigorated its realization, making it both urgent and critical to explore the concept of Automation 5.0 in this new era. In this article, we define Automation 5.0, discuss its significance, and emphasize its new world, thinking, and technology with the goal of achieving knowledge automation. A framework, based on business FMs, human-oriented operating systems, and scenarios engineering, is proposed, where biological, robotic, and digital humans work together in three modes: autonomous, parallel, and expert/emergency modes. Additionally, a diverse range of its scenarios and applications are summarized and discussed, such as Manufacturing 5.0, Healthcare 5.0, and Transportation 5.0. We believe that Automation 5.0 can drive the co-evolution of productivity and production relations across all domains, propelling society toward a "Safety, Security, Sustainability, Sensitivity, Service, Smartness (6S)" future. Note to Practitioners-This article is motivated by the question of effectively integrating human roles in automation systems. Automation 5.0 emerges as a new paradigm aimed at addressing this issue. In the new era of rapid advancement in foundation models, we explore a contemporary definition of Automation 5.0, along with its new world, thinking, and technology. Additionally, we propose its basic framework consisting of three enabling technologies, three kinds of humans, and three operational modes. This framework offers feasible methodologies to harness human distinctive intellectual strengths that cannot be substituted by machines in industrial systems, particularly in complex industrial environments. Finally, the article reviews its applications and analyzes emerging trends to provide insights into future directions for the development of human-centered and sustainable automation systems.

In multi-agent reinforcement learning environments, value decomposition methods are popularly applied to address the cooperation issue among agents. However, in some multi-agent value decomposition methods, the global action-value is usually approximated using upper and lower bounds, and leads to a lack of fine-grained cooperative actions. Furthermore, current state-of-the-art value decomposition approaches are predominantly confined to addressing cooperative learning problems involving small-scale multi-agent systems. As the number of agents increases, these methods may lead to difficulties in the convergence of the Q value function especially in more complex cooperative scenarios. To address the above two challenges, we propose a Group-based QMIX (GQMIX) method which learns to dynamically divide agents into multiple groups during exploration while applying Graph Attention Network (GAT) to simultaneously learn value decomposition under both global observation and local observation. This enables the subdivision of agents into different groups in large-scale settings, allowing the learning of the common subtasks in complex scenarios and improving the convergence efficiency of the value function. Experimental results demonstrate that our proposed algorithm is valid by providing better scheduling solutions for the extended flexible job shop scheduling problem. And it outperforms existing multi-agent reinforcement learning methods in terms of convergence and stability.

Laser powder bed fusion (LPBF) is an additive manufacturing process capable of producing intricate structures with high accuracy. Despite this capability, it struggles to achieve the required reliability for mass production-specifically the stability of a production run and repeatability across multiple runs. Parameter optimization, which adjusts process parameters to regulate a specific quantity of interest (QoI), is a crucial means of quality control. Existing methods, however, have not adequately addressed both random and systematic factors in the LPBF process. The stochastic nature of the process is often neglected under the assumption that identical parameter inputs will consistently yield the same QoI. This deviates from reality and is not intended to reduce potential variations in the QoI. Moreover, many studies do not incorporate the systematic neighboring effects between scan tracks into their optimization, so process reliability cannot be guaranteed. To address this issue, this study focuses on optimizing the probability distribution of the QoI. The key idea is not only to increase the likelihood of achieving the ideal QoI but also to reduce its variance. This is achieved by uncertainty-aware modeling and optimization of the LPBF process using machine learning. Specifically, the problem is formulated as maximizing the posterior distribution of scan parameters given an ideal QoI sequence and historical manufacturing data, yielding a large-scale constrained optimization problem. A stochastic, distributed, gradient-based method is proposed to solve this problem, where a coarse-to-fine strategy plays a critical role in accelerating convergence. A case study is then conducted to stabilize the melt pool volume by optimizing laser powers. The solutions are verified in a calibrated finite element-based simulation environment, in which the variations of the melt pool volume are effectively reduced both within a single run and across multiple runs. The implementation of our method is available at https://github.com/qihangGH/uncertainty_aware_param_optim_for_AM. Note to Practitioners-This paper is motivated by the critical reliability issues in laser powder bed fusion (LPBF) additive manufacturing, requiring that the process is stable within a run and repeatable across runs. Parameter optimization is an effective way to control a certain quantity of interest (QoI) to regulate the LPBF process. Existing methods, however, often ignore the stochastic variations and neighboring effects in the LPBF process. The objective solely focuses on minimizing the error between the predicted QoI and ideal one, under the assumption that the environment is deterministic. The optimized parameters, therefore, do not guarantee reliability under the influences of random factors. This paper intends to improve the reliability through uncertainty-aware modeling and optimization of the LPBF process. It focuses on optimizing the probability distribution of the QoI, encouraging the ideal QoI not only more likely to appear but also appear with less fluctuations. By incorporating important neighboring effects and bounded and smooth input constraints, a large-scale constrained optimization problem is formulated. To solve this problem efficiently, a stochastic and distributed gradient-based optimization algorithm is proposed, and a coarse-to-fine strategy is employed to accelerate the optimization process. For validation, a case study is conducted to stabilize the melt pool volume by regulating laser powers. The optimized laser powers are verified in a finite element-based simulation environment. Quantitative and qualitative results show that the variations of the melt pool volume within a single run and across repeated runs are reduced, indicating the potential of the uncertainty-aware optimization for enhancing the reliability of the LPBF process.

Fabricating high-strength ceramics with high precision intricate shapes from photosensitive ceramic slurry is an exciting yet challenging due to limited control over sub-grain structures, performance and sustainable manufacturing. Herein, to address these issues, a high solid content 50 vol% zirconia-reinforced alumina (ZRA) ceramic suspension with printable built-in functionality is proposed, enabling customizable target recognition, and enhanced flexural strength performance. Using stereolithography 3D printing with an optimized low shear rate suspension, precise printing control over tuned physical properties, morphology engineered structures and significantly enhanced performance with different holding time were obtained. With the ZrO2 reinforcement strategy, this study not only demonstrates high flexural strength and hardness but also minimization of shrinkage. After sintering at 1550 degrees C for 6 h, the density reached a maximum of 98.7 % with shrinkage 7.57 % along the XY direction and 16.33 % along Z direction. Remarkably, the sintered ZRA ceramic exhibited a flexural strength of 371.8 +/- 5 MPa, Vickers hardness of 1198. 2 +/- 1.6 HV, and compressive strength of 33.54 MPa. Microscopic and tomographic analysis revealed a two-phase microstructural nature that enhances toughness and promotes good distribution. Benchmark strength is enhanced because of the improved interfacial bonding and fine-grained structure, which is the most dominant contributor to the mechanical properties. The findings set a guideline for high strength property-structure relationship of DLP-3D printed alumina reinforced composites in the field of advanced ceramic industry.

Preparing free-base porphyrinoid radicals that can function as coordination ligands is a challenging task.Here we report the synthesis of a stable,free-base benzocorrole(BC)radical containing only two inner NH protons via a retro-Diels-Alder conversion.The radical character of BC was fully supported by crystallographic analysis,spectroscopic evidence,and theoretical calculations.This neutral radical ligand allowed easy insertion of Zn(II),Ga(III),and Pd(II)ions to produce radical complexes.All these radicals exhibited luminescence-on responses under weak reducing atmosphere,corresponding to the conversion to their aromatic anions.The red fluorescence was observed for BC and its Zn(II)and Ga(III)complexes,and the near-infrared phosphorescence(>900nm)was detected for Pd(II)complex at room temperature.Furthermore,Ga(III)corrole exhibited a variation in fluorescence in response to axial coordination.Our findings provide a promising radical platform for coordination and developing novel functional materials with switchable spin and emission.

Quantum-correlated photon pairs are crucial resources for modern quantum information science. Similarly, the reliable generation of nonclassical phonon pairs is vital for advancing engineerable solid-state quantum devices and hybrid quantum networks based on phonons. Here, we present a novel approach to generate quantum-correlated phonon pairs in a suspended silicon microstructure initialized in its motional ground state. By simultaneously implementing red- and blue-detuned laser pulses, equivalent high-order optomechanical nonlinearity–specifically, an effective optomechanical four-wave mixing process–is achieved for generating a nonclassical phonon pair, which is then read out via a subsequent red-detuned pulse. We demonstrate the nonclassical nature of the generated phonon pair through the violation of the Cauchy-Schwarz inequality. Our experimentally observed phonon pair violates the classical bound by more than 5 standard deviations and maintains a decoherence time of 132 ns. This work reveals novel quantum manipulation of phonon states enabled by equivalent high-order optomechanical nonlinearity within a pulse scheme and provides a valuable quantum resource for mechanical quantum computing.

Here, we experimentally demonstrate a new type of phonon-based frequency comb with at least 260 comblines and a self-injection locking phonon laser in a silicon optomechanical crystal cavity. Our demonstration offers a low-phase-noise oscillator, and an alternative optomechanical frequency comb for sensing, timing, and metrology applications.

Passive daytime radiative cooling (PDRC), as a sustainable technology, has garnered significant attention. However, the necessity for high whiteness often compromises the aesthetic appeal of radiative cooling materials. Drawing inspiration from printing technology, this study integrates radiative cooling with alternating current electroluminescent (ACEL) devices to enhance the fabric's aesthetic qualities while simultaneously achieving a seamless combination of passive cooling and active display. Initially, a highly elastic, waterproof, and breathable radiatively cooled nanomembrane (RCNM) was developed through a one-step electrospinning process. By manipulating the fiber diameter, sunlight can be effectively scattered, resulting in an impressive solar reflectivity of up to 95.6 %. Additionally, atmospheric window radiation is amplified through chemical bond vibrations, resulting in an emissivity of 93.2 %. The RCNM produces a notable cooling effect of 6 degrees C when applied directly to the skin. The ACEL devices, built upon the foundation of RCNM, achieve a brightness of 40.16 cd/m2 at 188 V and 5 kHz while maintaining luminosity after undergoing durability tests, including washing and exposure to high temperatures and humidity. Notably, the electrospinning process generates an emissive layer that forms a robust three-dimensional network structure, characterized by excellent deformability and structural integrity under mechanical stress. In comparison to conventional polyester/cotton (T/C) fabrics, those incorporating integrated RCNM/ACEL dual-functional fabrics exhibit a temperature reduction of 9 degrees C under similar sunlight conditions, highlighting the potential benefits of combining RC and ACEL technologies. This innovation is expected to broaden the practical applications of multifunctional fabrics and open new avenues for advancements in textile development.

Temporal soliton mode-locking in coherently pumped microcavities provides a promising platform for miniaturized frequency comb systems. While significant progress has been made, achieving high conversion efficiency in such microcombs remains a critical challenge. Soliton generation through pulse pumping has emerged as an effective strategy to improve conversion efficiency. However, the on-chip integration of pulse generation with dissipative Kerr soliton (DKS) formation within the photonic chip has not yet been realized. In this work, we demonstrate a photonic chip-based soliton microcomb with high conversion efficiency, achieved by integrating on-chip pulse generation and DKS generation. The pulsed laser, fabricated on a lithium niobate-on-insulator (LNOI) platform, delivers a 35.5GHz repetition rate with broadly tunable center frequencies. By coupling these on-chip pulses to a silicon nitride microresonator, we achieve stable DKS generation with a pump-to-soliton conversion efficiency of 43.9 architecture establishes a viable pathway toward chip-scale soliton microcombs with unprecedented efficiency, opening up new possibilities for optical communications, precision spectroscopy, and photonic sensing.

Metals with high thermal and electrical conductivity, such as copper, have limited tensile strength, which restricts their broad application in industry. The integration of functional and structural components can be achieved through the design and fabrication of multi-material structures. This study investigates the mechanical and physical properties of copper-stainless steel (Cu-SS) multi-material structures fabricated using blue laser directed energy deposition (BL-DED) technology. By optimizing process parameters, high relative densities of Cu and SS were achieved. The performance differences of Cu-SS multi-materials under various Cu-to-SS ratios were revealed through comprehensive analysis of the interfacial microstructure, elemental distribution, as well as thermal conductivity, electrical conductivity, and mechanical properties. The results indicate that as the content of SS decreases, the thermal conductivity of the Cu-SS structures is significantly enhanced, while the mechanical properties are notably reduced. Due to the substantial difference in electrical conductivity between Cu and SS, the eddy current electrical conductivity exhibits distinct measurement-side-dependence. Furthermore, a balance relationship for the performance of Cu-SS multi-material structures has been established through theoretical modeling and experimental validation, providing an optimization framework for achieving high thermal conductivity, high electrical conductivity, and high strength in multi-material structures. This study offers new insights into the design and fabrication of functional and structural integration in advanced engineering components.

To achieve the practical application of photocatalysis, various carbon materials are testified to trap photoinduced electrons (e-) and enhance the photocatalytic efficiency of catalysts. Compared with other carbon materials, biomass-derived carbon (BDC) with sustainability and affordability is regarded as an appropriate e- acceptor to fabricate highly active photocatalysts on a large scale. However, biomass materials exhibit significant diversity in their chemical structures and physical properties, which critically determines the morphology, composition, and physicochemical performance of the resulting BDC products. Therefore, a variety of strategies are developed to prepare highly photoactive BDC-based photocatalysts. This review summarizes a large amount of related works have been reported in the last two decades and divides the synthesis methodology into the bottom-up and top-down methods. In particular, the bottom-up method primarily employs hydrosoluble carbohydrates to synthesize four hydrothermal carbonization (HTC) composites or HTC-derived photocatalysts. As for the top-down method, the existing natural biomass precursors are utilized to fabricate three biochar-based photocatalysts. Consequently, this review comprehensively describes the designing mentality, preparation approach, architectural feature, photocatalytic application, and photocatalytic mechanism of different BDC-based materials. That will stimulate to design and develop more highly active BDC-based photocatalysts for the potential practical applications in the future.

In recent years, 3D printing technology has played an increasingly important role in a growing number of industries. However, as a relatively new technology, it tends to exhibit more defects during the printing process compared to traditional manufacturing methods. These defects can significantly impact the performance of the final product. Given that 3D printed parts typically have complex and highly optimized geometric shapes, traditional detection technologies struggle to meet the demands for precision and efficiency. To address this challenge, this paper introduces a 3D printing defect detection algorithm based on an improved version of YOLOv5. The algorithm makes extensive refinements to the YOLOv5 model, achieving model lightweighting by replacing the loss function and introducing an attention mechanism. The newly designed detection system is characterized by a smaller parameter scale, rapid inference speed, high detection accuracy, and strong robustness. Compared to the original YOLOv5s model, the improved lightweight model has achieved a detection accuracy of 94.2%, and has nearly halved the parameter scale. This advancement not only enhances detection efficiency but also provides an effective technical solution for 3D printing defect detection and fault diagnosis.

Navigating autonomous surface vehicles in dynamic marine environments, where uncertainties and disturbances like static or moving obstacles, ocean currents, and waves abound, poses a formidable challenge. Recent advancements in Deep Reinforcement Learning (DRL) have shown promising results in terms of adaptivity and timeliness through interaction with the environment. However, effectively addressing zero safety violations while achieving sample efficiency remains a dual challenge in practical applications. In this paper, we strive to ensure both safety and learning efficiency by combining the advantages of the Dynamic Window Approach (DWA) and safe reinforcement learning. First, a customized simulator for diverse marine conditions is developed, where various types of marine scenarios and algorithms are trained and testified. Then, the problem is formulated as a constrained Markov decision process and the DWA-based safe RL (DWAS-RL) approach is proposed. Specifically, to guarantee safety in the exploration process, we utilize DWA to observe and generate prudent actions by predicting potential near-future hazards, then utilize the safe RL framework for exploration and training. To improve sample efficiency, the technique called Hindsight Experience Replay is utilized to accelerate the training process. Simulation experiments demonstrate the effectiveness of our approach on the metrics of kinematics performance, safety and sample efficiency compared to the state-of-the-art DRL algorithms. These findings highlight the robustness and superiority of our approach, suggesting that our approach holds promise for addressing challenges in complex marine environments.

The challenges of fabricating low-loss waveguides and the reliance on bulky external magnets hinder the miniaturization of Faraday isolators. Now, researchers have overcome this limitation by femtosecond laser writing of waveguides within latched garnet.

In 3D printing material defect detection, environmental variations frequently induce false positives and missed detections by automated systems. Current research addresses dynamic environments by fine-tuning models during the detection phase. However, existing methods suffer from delayed adaptation: models require prolonged iterations to achieve stable performance in new environments. Furthermore, noise-contaminated pseudo-labels during domain adaptation exacerbate error accumulation and catastrophic forgetting, leading to severe performance degradation of the model. To address these challenges, we propose a robust and rapid adaptive detection framework tailored for dynamic environments. First, we innovatively employ the Gram matrix of model feature layers to quantify environmental shifts, endowing the model with real-time environmental awareness. Second, the dynamically maintained sample buffer ensures that stored samples satisfy three critical properties: pseudo-label reliability, class-balanced distribution, and diverse environmental representation. This mechanism selects samples most representative of new environments for fine-tuning, significantly accelerating adaptation. Experimental results demonstrate that our method achieves superior detection accuracy on real-world 3D printing material datasets under complex scenarios (e.g., sudden illumination changes and environmental shifts). Compared to baseline Test-Time Adaptation (TTA) methods, it exhibits enhanced adaptability and robustness.

Porphyrins represent fundamental molecules in phototherapy, with their derivatives clinically approved as photosensitizers for photodynamic therapy (PDT). Their applications have expanded from traditional fluorescence imaging and PDT to diverse approaches, including photoacoustic imaging and photothermal therapy. Compared with visible light, near-infrared (NIR) light offers enhanced safety and efficiency in phototheranostics due to its greater tissue penetration depth. However, conventional porphyrins lack NIR absorption; furthermore, they suffer from aggregation-caused quenching and limited photostability. This review systematically summarizes recent advances in addressing these problems through molecular design of porphyrins, employing ligand π-system engineering, metal coordination, and nanoassembly to achieve precise control over their photophysical properties. We focus on achieving bathochromic shifts with enhanced intensity in absorption/emission spectra in porphyrins and on regulating energy conversion pathways to selectively improve NIR luminescence, reactive oxygen species generation, or photothermal conversion. These advances facilitate the development of highly efficient and controllable NIR-activatable agents, highlighting the promising role of porphyrin-based materials and paving the way for their clinical translation in precision multifunctional phototheranostics.

Digital light processing (DLP) 3D printing has a huge potential for manufacturing intricate and customized ceramic parts with high precision and cost-effectiveness. Research in this field contributes to material innovation, opening the avenues for new process designs in both scientific and industrial sectors. However, the implementation of DLP 3D printing technology in ceramic research has not yet reached the maturity level as that in polymer and tissue engineering. Necessarily, a holistic in-depth literature reporting the successful integration of alumina ceramics within DLP 3D printing technology is urgently needed. This review, systematic examines recent progress in DLP technology, focusing on photopolymer resins that incorporate UV-sensitive monomers, photoinitiators, and dispersants, as well as their synergistic effects on achieving high-quality printing, desirable material properties, and enhanced performance. Further, the review discusses key factors including post-processing characteristics such as debinding and sintering, which influence microstructure, and defect formation including microcracks, porosity and voids. Finally, the challenges associated with printing and sintering are highlighted, aiming to identify focused focused development pathways and potential solution to optimize outcomes. This analysis clarifies existing challenges and also propsoes future applications for DLP technology in the alumina-ceramic field.

Steel coil recognition in factories is important for an intelligent crane. In recent years, the deep neural network has become a popular tool in industry. The YOLO family of object detection methods has been widely applied. However, the original YOLOv8 has certain limitations in dealing with light change, occlusive targets, and small long-distance targets. To achieve a better recognition effect and improve the working efficiency, in this paper we make some improvements. Firstly, Space-to-depth (SPD) and Large Selective Kernel Networks (LSK) are added to the network Backbone. Secondly, the BiFormer attention mechanism is added to the neck part of the network, and the self-attention mechanism is used to obtain the global relationship between input to improve the network performance, so that the network can receive information at any time and autonomously adjust its receptive field in the spatial dimension. The improved YOLOv8 has better recognition accuracy for low-resolution occluded objects and small target objects. By experiments, the improved YOLOv8 network has 84.3% mAP on the self-built datasets, which is better than the 82.1% mAP of the original YOLOv8 network.

Recently, multi-layer perceptron-based implicit representations have achieved remarkable successes in hand modeling. Compared with previous explicit mesh-based representation methods, implicit methods are more compact shape representations. However, it is expensive to obtain explicit geometry surfaces from implicit functions with Marching Cubes, which limits the real-time performance in surface reconstruction applications. To explore a more effective and efficient hand representation, we present a skeleton-driven method to represent a human hand with a point cloud. To achieve this goal, we propose a Tri-Axis Modeling method to model the motion pattern of the xyz coordinate of a patch of point cloud, and an Order Encoding strategy to construct a parameter-sharing and geometry-disentangled network. These two effective strategies make our method run in real-time and has super-high fidelity close to implicit methods. Qualitative and quantitative experiments on public datasets demonstrate the efficiency, effectiveness, and robustness of our method against state-of-the-art approaches.