Wei Cai

High Resolution Image Download MS PowerPoint Slide Conspectus Polymer-based commercial products have been manufactured globally at annual scales of hundreds of millions of tons, occupying an important position in people’s daily life and industrial progress, attributed to their light weight, workability, and excellent properties. However, due to their organic composition, polymer materials are sensitive to high temperatures and are easily ignited, frequently leading to serious fire accidents. To address this huge challenge, nano flame retardants with high efficiency and multifunctionality have been regarded as an available approach. First, nano flame retardants significantly increase the melt strength of polymer materials by their extremely large specific surface area and strong interfacial interactions. Second, based on a well-dispersed state, nano flame retardants, especially with layered structures, establish a labyrinth effect to suppress the convective delivery of heat and pyrolysis products. Third, metal-based nano flame retardants chelate with the lone-pair electrons of polymer chains to form a transitional ring structure, thus decreasing the energy barrier to promote the carbonization process. Attracted by these advantages, a large amount of research work has already been dedicated to developing nano flame retardants for enhancing the fire safety of polymer materials. Herein, this Account not only introduces the early discoveries of nano flame retardants but also provides a comprehensive overview of our recent advances in the development of nano flame retardants, the exploration of mechanisms, and applications in emerging frontiers. We first illustrate the initial discovery of nano flame retardants by demonstrating the fundamental mechanisms, synergistic effect with traditional flame retardants, and essential issues in nanocomposite manufacturing. In order to solve the low efficiency of nanomaterials added individually, our research attempts to design nanostructured architectures that integrate multiple nanomaterials with specific mechanisms to produce synergistic effects under complex combustion conditions, thereby further enhancing the flame retardancy efficiency. To enhance sustainability and reduce complex experimental efforts, our research adopts biobased resources and machine learning approaches, respectively, to design novel nano flame retardants. Within the in-depth condensed-phase mechanism, we propose multiple strategies, including the catalytic effect of transition metals and the interfacial charring strategy, which preferentially promote the conversion of pyrolysis products into char layers rather than smoke particles. Building on the intrinsic properties of nanomaterials, our studies further expand the application of nano flame retardants into emerging frontiers such as energy storage, thermal management, and electromagnetic interference shielding, thereby shifting the research perspective from traditional polymer systems toward real-world functional applications. Finally, we outline the future design direction of nano flame retardants, concentrating on artificial intelligence and sustainability, and propose the critical challenges in their practical application.

The flammability and poor recyclability of epoxy thermosets present urgent challenges for sustainable polymer use. Flame retardant epoxy vitrimers offer a promising solution by addressing both issues simultaneously. However, conventional synthesis routes typically rely on complex procedures, catalysts, and solvents, and cannot process existing epoxy waste, limiting their sustainability. Here, we propose a fully solid-state, catalyst-free mechanochemical “waste-to-wealth” strategy to upcycle epoxy thermosets into flame retardant vitrimers. Black phosphorus (BP), synthesized via high-energy ball milling of red phosphorus, was subjected to UV-assisted controllable oxidation, with DFT calculations confirming the energy barrier was reduced by 16.35 kcal/mol under irradiation. The resulting oxidized BP (oBP), rich in P–OH groups, was employed for hot-pressing based vitrimerization of anhydride-cured epoxy waste. Through mixed transesterification, an organic–inorganic interfacial dynamic covalent network was constructed. The upcycled vitrimers exhibited remarkable dynamic adaptability with the lowest flow activation energy of 68.0 kJ/mol and the highest recycle efficiency of 89.3%, efficient infrared-triggered self-healing, and outstanding flame retardancy with V-0 rating in UL-94 test and limit oxygen index improved by 10%. Importantly, the oxidized structures of oBP were found to play a pivotal role in vitrimerization and to act synergistically with elemental phosphorus in enhancing fire resistance. This work provides new insights into the green upcycling of epoxy waste into multifunctional vitrimer composites, advancing both sustainability and performance.

Atomistic simulations are often restricted by timescale limitations when systems become trapped in metastable energy basins. The transitions between these metastable states are rare events and they dictate the long-term evolution of the system. We present an importance sampling framework to accelerate the time scale of Langevin dynamics simulations. The framework uses a neural network parameterized importance function to bias the dynamics, enhancing the efficiency of rare-transition sampling while preserving relative probabilities between transition paths. We provide a rigorous mathematical formulation to recover the original transition rates between metastable states from the biased dynamics, and use a branching random walk algorithm to control the statistical variance of the estimated rates. We validate the framework on 2- and 14-dimensional problems, as well as the system of 7 Lennard-Jones discs as a benchmark atomistic model. The framework provides a scalable foundation for accelerating the simulation of rare events in atomistic systems.

BACKGROUND Traumatic elbow varus posteromedial rotatory instability can involve an anteromedial coronoid fracture, proximal avulsion of the lateral collateral ligaments, and a tear of the ulnar collateral ligament posterior bundle, leading to chronic elbow instability, cartilage damage, and osteoarthritis. This retrospective study evaluated postoperative outcomes at 6 months in 9 patients with elbow varus posteromedial rotatory instability following an anteromedial surgical approach using a steel plate and high-strength suture. MATERIAL AND METHODS This retrospective study analyzed the data of 9 patients (6 females, 3 males; mean age 50±22.97 years) who underwent surgical treatment for varus posteromedial rotatory instability between April 2017 and January 2024. The procedure involved repairing varus posteromedial rotatory instability using high-strength sutures and steel-plate fixation via an anteromedial approach. Postoperative elbow function was assessed using the Mayo elbow performance score (MEPS). RESULTS During the 8.5-19.5 (12.38±3.43) month postoperative period, we treated 9 patients. Within 6-14 weeks, fracture healing occurred. Although 2 patients developed mild heterotopic ossification, there were no obvious postoperative complications such as elbow joint instability, infection, or vascular or nerve damage. Assessment of elbow joint function was conducted using MEPS, with results indicating 8 cases rated as excellent and 1 case as good. CONCLUSIONS In varus posteromedial rotatory instability, the anteromedial approach allows direct visualization of the anteromedial fracture and the medial collateral ligament. The combination of high-strength sutures and plate fixation effectively stabilizes small anteromedial bone fragments. This approach provides a reference for surgical management of similar injuries.

Defect-functionalized graphene offers considerable promise for enhancing the pavement performance of asphalt in pavement engineering, due to its unique nanostructural characteristics and surface chemistry. Nevertheless, the underlying mechanism of asphalt binder modified with defect-functionalized graphene is still unclear. Herein, the influence of defect density on graphene-modified asphalt binder is comprehensively explored by multiscale analyses. Rheological tests show that defect-functionalized graphene with higher defect density is beneficial for improving the high- and low-temperature performance of modified asphalt, and the rutting factor of high defect-functionalized graphene-modified asphalt binder increases by 16.24% compared to that of low defect-functionalized graphene. Theoretical calculations indicate that a higher density of defects leads to an increase in the number of sites available for vacancy adsorption, thereby improving the interaction between graphene and nonpolar molecules present in asphalt. Higher defect density leads to a more uneven charge distribution on the graphene surface, giving it greater polarity. Subsequently, the interaction of van der Waals forces between graphene and nonpolar molecules present in asphalt is further intensified, leading to improved thermal-mechanical characteristics of asphalt binder modified with defect-functionalized graphene. These findings provide valuable insights for advancing the use of graphene in asphalt to bolster the durability of pavement.

Abstract Iron nitride‐based composites with different morphologies and magnetic properties are obtained by controlling the nitriding process, thus constructing heterogeneous interfaces conducive to microwave absorption. During the low‐temperature nitridation process for synthesizing the permanent magnetic Fe 16 N 2 phase, a stable soft magnetic Fe 4 N phase tends to form on the surface of particles. Consequently, the porous structured Fe 16 N 2 /Fe 4 N composites composed of nano‐units are prepared by spray pyrolysis and nitriding processes. The microwave‐absorbing mechanisms of permanent/soft magnetic composite iron nitride with porous structure has been elucidated by analyzing the microstructure and electromagnetic properties of samples with varying contents of permanent and soft magnetic materials. The composite of permanent and soft magnetic materials not only creates a heterogeneous interface but also generates a magnetic exchange coupling effect, which improves the impedance matching and increases the interface polarization and dipolar polarization. Eventually, through the coordinated action of excellent dielectric and magnetic loss, good microwave absorption properties are achieved: the minimum reflection loss (RL min ) is −48.27 dB and the widest effective absorption bandwidth (EAB max ) is 4.08 GHz with the matching thickness of 1.3 mm. This study provides new insight for further exploration of the application of nano‐magnetic materials in the field of microwave absorption.

Abstract Machine‐learned force fields (MLFFs) promise to offer a computationally efficient alternative to ab initio simulations for complex molecular systems. However, ensuring their generalizability beyond training data is crucial for their wide application in studying solid materials. This work investigates the ability of a graph neural network (GNN)‐based MLFF, trained on Lennard–Jones Argon, to describe solid‐state phenomena not explicitly included during training. The MLFF's performance is assessed in predicting phonon density of states (PDOS) for a perfect face‐centered cubic (FCC) crystal structure at both zero and finite temperatures. Additionally, vacancy migration rates and energy barriers are evaluated in an imperfect crystal using direct molecular dynamics (MD) simulations and the string method. Notably, vacancy configurations are absent from the training data. These results demonstrate the MLFF's capability to capture essential solid‐state properties with good agreement to reference data, even for unseen configurations. Data engineering strategies are further discussed to enhance the generalizability of MLFFs. The proposed set of benchmark tests and workflow for evaluating MLFF performance in describing perfect and imperfect crystals pave the way for reliable application of MLFFs in studying complex solid‐state materials.

A nitrogen and iron co-incorporated CoO nano-electrocatalyst has been designed to enhance water splitting under a magnetic field, which originates from the magnetohydrodynamic effect and electron spin polarization.

Textile, printing, and dyeing industries in China are expanding annually, resulting in the discharge of significant volumes of wastewater. These effluents have complex compositions and contain diverse pollutants that pose severe hazards to aquatic systems, ecological environments, and nearby flora, fauna, and human populations. The inadequate or rudimentary treatment of these effluents can cause substantial environmental damage. Current technologies for treating textile dyeing wastewater (TDW) include physical, chemical, and biological methods, with biological treatment being noted for its low cost and environmental sustainability. In the realm of biotechnological treatment, microorganisms, such as bacteria, fungi, and algae, exhibit significant potential. This review highlights the urgent need for effective treatment of textile dyeing wastewater (TDW), which poses severe environmental and health risks. It provides a comparative analysis of physical, chemical, and biological treatment methods, with a focus on the unique advantages of biological approaches, such as biodegradation and biosorption, for sustainable wastewater management. Key findings include recent advancements in microbial applications, challenges in scaling up, and integration into existing treatment systems. This review aims to guide future research and practical applications in achieving eco-friendly and cost-effective solutions for TDW remediation.

Jogs, atomic-scale steps on dislocations, play an important role in crystal plasticity, yet they are often ignored in discrete dislocation dynamics (DDD) simulations due to their small sizes. While jogs on screw dislocations are known to move non-conservatively (i.e. climb) accompanied by vacancy emission, jogs on edge dislocations are commonly expected to move conservatively (i.e. glide) with the dislocation under ambient conditions. Here we report unexpected findings from molecular dynamics simulations of an edge dislocation containing a pair of unit jogs in face-centered cubic nickel at 300K. While the jogs glide conservatively with the edge dislocation at low stresses, we observe that one of the jogs climbs and emits vacancies intermittently at higher stresses. This observation is unexpected at such a low temperature, as climb is typically associated with temperatures closer to the creep temperature (roughly half of the melting temperature). Our results highlight the significance of the complex interplay between point defects (i.e., vacancies) and dislocations in room-temperature plasticity, suggesting that these interactions may be more significant than previously thought.

Machine-learned force fields (MLFFs) promise to offer a computationally efficient alternative to ab initio simulations for complex molecular systems. However, ensuring their generalizability beyond training data is crucial for their wide application in studying solid materials. This work investigates the ability of a graph neural network (GNN)-based MLFF, trained on Lennard-Jones Argon, to describe solid-state phenomena not explicitly included during training. We assess the MLFF's performance in predicting phonon density of states (PDOS) for a perfect face-centered cubic (FCC) crystal structure at both zero and finite temperatures. Additionally, we evaluate vacancy migration rates and energy barriers in an imperfect crystal using direct molecular dynamics (MD) simulations and the string method. Notably, vacancy configurations were absent from the training data. Our results demonstrate the MLFF's capability to capture essential solid-state properties with good agreement to reference data, even for unseen configurations. We further discuss data engineering strategies to enhance the generalizability of MLFFs. The proposed set of benchmark tests and workflow for evaluating MLFF performance in describing perfect and imperfect crystals pave the way for reliable application of MLFFs in studying complex solid-state materials.

Camouflaged Object Detection (COD) is an important task in the field of computer vision, which refers to the process of identifying and segmenting objects that seamlessly blend with their surroundings. Applying diffusion models to camouflaged object detection can yield more precise detection results. However, due to the large network architecture and multiple iteration steps of diffusion models, the training speed of these models tends to be slow. This paper proposes a lightweight method for camouflaged object detection based on diffusion models. Firstly, a lightweight U-shaped network is designed. Subsequently, a lightweight Fusion Upsample Enhancement (FUE) module is introduced to enhance, refine, and upsample the extracted features. Experiments show that the proposed LDiffCOD network, when compared with 10 other benchmark algorithms on two COD datasets, achieves the best performance or equal to the optimal results across the board.

The widespread application of metal additive manufacturing (AM) is limited by the ability to control the complex interactions between the energy source and the feedstock material. Here, we develop a generalizable process to introduce nanoscale grooves to the surface of metal powders which increases the powder absorptivity by up to 70% during laser powder bed fusion. Absorptivity enhancements in copper, copper-silver, and tungsten enable energy-efficient manufacturing, with printing of pure copper at relative densities up to 92% using laser energy densities as low as 83 joules per cubic millimeter. Simulations show that the enhanced powder absorptivity results from plasmon-enabled light concentration in nanoscale grooves combined with multiple scattering events. The approach taken here demonstrates a general method to enhance the absorptivity and printability of reflective and refractory metal powders by changing the surface morphology of the feedstock without altering its composition.

2D Boron Nitride for Deep-Ultraviolet Light Modulation Light modulation is one of the most crucial operations in optics. In article number 2307330, Baofu Ding, Wei Cai, Bilu Liu, and co-workers made a deep-ultraviolet light modulator based on sensitive electro-optical Kerr effect of 2D boron nitride inorganic liquid crystals. This modulator can realize solar-blind ultraviolet light communication. The work can benefit numerous applications that require deep-ultraviolet light modulation

Tunable nonlinearity facilitates the creation of reconfigurable nonlinear metasurfaces, enabling innovative applications in signal processing, light switching, and sensing. This paper presents a novel approach to electrically modulate SHG from a lithium niobate (LN) metasurface, exploiting the electro-optical (EO) effect. By fabricating a nanohole array metasurface on a thin LN film and applying an electric field, we demonstrate the alteration of the material's refractive index, resulting in resonance shifts and modulation of SHG intensity at specific wavelengths. Our findings provide valuable insights for the development of electrically tunable nonlinear light sources, quantum optics, dynamic nonlinear holography, and nonlinear information processing.

Recent studies have established a connection between the macroscopic mechanical response of polymeric materials and the statistics of the shortest path (SP) length between distant nodes in the polymer network. Since these statistics can be costly to compute and difficult to study theoretically, we introduce a branching random walk (BRW) model to describe the SP statistics from the coarse-grained molecular dynamics (CGMD) simulations of polymer networks. We postulate that the first passage time (FPT) of the BRW to a given termination site can be used to approximate the statistics of the SP between distant nodes in the polymer network. We develop a theoretical framework for studying the FPT of spatial branching processes and obtain an analytical expression for estimating the FPT distribution as a function of the cross-link density. We demonstrate by extensive numerical calculations that the distribution of the FPT of the BRW model agrees well with the SP distribution from the CGMD simulations. The theoretical estimate and the corresponding numerical implementations of BRW provide an efficient way of approximating the SP distribution in a polymer network. Our results have the physical meaning that by accounting for the realistic topology of polymer networks, extensive bond-breaking is expected to occur at a much smaller stretch than that expected from idealized models assuming periodic network structures. Our work presents the first analysis of polymer networks as a BRW and sets the framework for developing a generalizable spatial branching model for studying the macroscopic evolution of polymeric systems.

Understanding plastic deformation of crystals in terms of the fundamental physics of dislocations has remained a grand challenge in materials science for decades. To overcome this, the Discrete Dislocation Dynamics (DDD) method has been developed, but its lack of atomistic resolution leaves open the possibility that certain key mechanisms may be overlooked. By comparing large-scale Molecular Dynamics (MD) with DDD simulations performed under identical conditions we uncover significant discrepancies in the predicted strength and microstructure evolution in BCC crytals under high-strain rate conditions. These are traced to unexpected behaviors of dislocation network nodes forming at dislocation intersections, that can move in ways not previously anticipated as revealed by MD. Once these newfound freedoms of nodal motion are incorporated, DDD simulations begin to closely match plastic evolution observed in MD. This additional mechanism of motion whereby non-screw dislocations can change their glide plane profoundly affects fundamental processes of dislocation multiplication, recovery and storage that define strength of metals.

Abstract Achieving light modulation in the spectral range of 200–280 nm is a prerequisite for solar‐blind ultraviolet communication, where current technologies are mainly based on the electro‐luminescent self‐modulation of the ultraviolet source. External light modulation through the electro‐birefringence control of liquid crystal (LC) devices has shown success in the visible‐to‐infrared regions. However, the poor stability of conventional LCs against ultraviolet irradiation and their weak electro‐optical response make it challenging to modulate ultraviolet light. Here, an external ultraviolet light modulator is demonstrated using two‐dimensional boron nitride LC. It exhibits robust ultraviolet stability and a record‐high specific electro‐optical Kerr coefficient of 5.1 × 10⁻ 2 m V −2 , being three orders of magnitude higher than those of other known electro‐optical media that are transparent (or potentially transparent) in the ultraviolent spectral range. The sensitive response enables fabricating transmissive and stable ultraviolet‐C electro‐optical Kerr modulators for solar‐blind ultraviolet light. An M‐ary coding array with high transmission density is also demonstrated for solar‐blind ultraviolet communication.

A yield surface of a material is a set of critical stress conditions beyond which macroscopic plastic deformation begins. For crystalline solids, plastic deformation occurs through the motion of dislocations, which can be captured by discrete dislocation dynamics (DDD) simulations. In this paper, we predict the yield surfaces and strain-hardening behaviors using DDD simulations and a geometric manifold learning approach. The yield surfaces in the three-dimensional space of plane stress are constructed for single-crystal copper subjected to uniaxial loading along the $[100]$ and $[110]$ directions, respectively. With increasing plastic deformation under $[100]$ loading, the yield surface expands nearly uniformly in all directions, corresponding to isotropic hardening. In contrast, under $[110]$ loading, latent hardening is observed, where the yield surface remains nearly unchanged in the orientations in the vicinity of the loading direction itself, but expands in other directions, resulting in an asymmetric shape. This difference in hardening behaviors is attributed to the different dislocation multiplication behaviors on various slip systems under the two loading conditions.

Driver Monitoring System (DMS), usually equipped with a camera, is an emerging vehicle safety system that can monitor driver attentiveness and trigger timely alarms when signs of inattention are detected. Since a single indicator (e.g., eye blink rate) is insufficient and unreliable to analyze driver attentiveness, almost all existing solutions train several independent models to identify driver facial states, such as face landmark, head pose, yawning, eye state, etc. However, apart from neglecting the inherent correlations between these related tasks, multiple models also raise challenges for vehicle safety-critical systems (e.g., hardware resources, software compatibility, and real-time response). In this paper, we propose a multi-task learning CNN framework (DANet) to unify the relevant tasks into one model and simultaneously output various driver facial states. By sharing the common features and parameters of highly related tasks, DANet avoids repetitive computations and mitigates single task overfitting. More importantly, the model provides a comprehensive overview of facial states while maintaining low complexity. We also propose two novel designs: (1) Dual-loss Block, which decomposes the pose estimation task into pose classification and coarse-to-fine regression; (2) Head Pose Penalization, which constrains the network to predict gaze direction based on predicted head pose. Our method achieves compelling results in both speed and accuracy on a vehicle computing platform, marking a momentous step in this field.

In the field of computational physics and material science, the efficient sampling of rare events occurring at atomic scale is crucial. It aids in understanding mechanisms behind a wide range of important phenomena, including protein folding, conformal changes, chemical reactions and materials diffusion and deformation. Traditional simulation methods, such as Molecular Dynamics and Monte Carlo, often prove inefficient in capturing the timescale of these rare events by brute force. In this paper, we introduce a practical approach by combining the idea of importance sampling with deep neural networks (DNNs) that enhance the sampling of these rare events. In particular, we approximate the variance-free bias potential function with DNNs which is trained to maximize the probability of rare event transition under the importance potential function. This method is easily scalable to high-dimensional problems and provides robust statistical guarantees on the accuracy of the estimated probability of rare event transition. Furthermore, our algorithm can actively generate and learn from any successful samples, which is a novel improvement over existing methods. Using a 2D system as a test bed, we provide comparisons between results obtained from different training strategies, traditional Monte Carlo sampling and numerically solved optimal bias potential function under different temperatures. Our numerical results demonstrate the efficacy of the DNN-based importance sampling of rare events.

Understanding the atomistic mechanisms of inelastic deformation in metallic glasses (MGs) remains challenging due to their amorphous structure, where local carriers of plasticity cannot be easily defined. Using molecular dynamics (MD) simulations, we analyzed the onset of inelastic deformation in CuZr MGs, specifically the temperature dependence of the elastic limit, in terms of localized shear transformation (ST) events. We find that although the ST events initiate at lower strain with increasing temperature, the elastic limit increases with temperature in certain temperature ranges. We explain this anomalous behavior through the framework of an energy-strain landscape (ESL) constructed from high-throughput strain-dependent energy barrier calculations for the ST events identified in the MD simulations. The ESL reveals that the anomalous behavior is caused by the transition of ST events from irreversible to reversible with increasing temperature. An analytical formulation is developed to predict this transition and the temperature dependence of the elastic limit.