Sintered porous Cu contains a heterogeneous particle-neck-pore architecture whose deformation depends strongly on loading mode. In this study, porous Cu structures prepared from small particles (184 nm) and large particles (1451 nm) were examined using micropillar compression, micro-cantilever bending, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and molecular dynamics (MD) simulations. Under compression, the large-particle structure exhibited a higher apparent elastic modulus (14.5 ± 0.9 GPa) than the small-particle structure (12.7 ± 0.5 GPa), whereas the small-particle structure showed an approximately 80% higher yield strength (364 ± 58 versus 202 ± 71 MPa). In contrast, the large-particle structure exhibited an approximately 245% higher conditional fracture toughness (5.56 ± 0.14 versus 1.61 ± 0.16 MPa⋅m^1/2) and 1270% higher total bending work (8823 ± 1245 versus 644 ± 164 pJ). Microstructural analyses revealed that the refined particle-neck network accommodated compression through distributed pore compaction, neck deformation, and dislocation accumulation, suppressing strain localization. The coarser network promoted intraparticle slip and interfacial sliding under compression, while enhancing remaining-ligament continuity and neck bridging during crack propagation. MD simulations reproduced these distinct compressive responses and defect-evolution characteristics. These findings provide guidance for optimizing particle-neck-pore architectures in reliable sintered Cu interconnects.

Finite element (FE) simulations of structures and materials are becoming increasingly accurate, but also more computationally expensive as a collateral result. This development occurs in parallel with a growing demand for data-driven design. To reconcile the two, a robust and data-efficient optimization method called Bayesian optimization (BO) has been previously established as a technique to optimize expensive objective functions. The mesh width of an FE model can be exploited to evaluate an objective at a lower or higher fidelity (cost & accuracy) level, which is the domain of multi-fidelity BO (MFBO) applications. However, BO and MFBO are usually not directly compared in the literature. Moreover, sampling quality and assessing design parameter sensitivity are often underrepresented parts of data-driven design. This paper combines global sensitivity analysis and (MF) BO into a novel, efficient Bayesian data-driven framework. We compare the performance of BO with that of MFBO by maximizing the energy absorption (EA) problem of spinodoid cellular structures. The findings show that similar or better designs are suggested by MFBO with 16% fewer expensive objective evaluations compared to BO when maximizing the EA. The results, which are made open-source, serve to support the utility of multi-fidelity techniques across expensive data-driven design problems.

Ultrasensitive and specific detection of low-abundance tumor biomarkers remains a major challenge for early and minimally invasive cancer diagnosis. Here, we present a high-performance biosensing platform that integrates a genetically engineered bacterial S-layer with an AlGaN/GaN high-electron-mobility transistor (HEMT) sensor for label-free detection of tumor antigens. As a proof-of-concept, the ovarian cancer antigen human epididymis protein 4 (HE4) was selected . Specifically, the S-layer protein rSbpA was fused with HE4-specific nanobody 1G8 to construct a bifunctional membrane capable of self-assembling into an ordered biorecognition layer on the sensor surface. Compared to conventional chemical crosslinking, S-layer-driven assembly increased antibody loading by 50 % and minimized nonspecific adsorption in plasma environments. The resulting HEMT sensor detected HE4 across a dynamic linear range (10⁻²¹ to 10⁻¹⁴ M), identifying patients with ovarian cancer with 100 % diagnostic accuracy (AUC = 1.0). This study establishes a versatile and modular biosensing strategy for ultra-low-abundance biomarker detection with broad potential applications in the precision diagnostics of cancer and other diseases.

The semiconductor industry is facing significant challenges, including physical limitations such as heat dissipation, rising manufacturing costs, and the difficulty of further shrinking transistor sizes while maintaining performance. To address these issues, chiplet technology has emerged, integrating smaller, specialized chips into a single package. This approach improves modularity, manufacturing yields, and allows mixing different technology nodes for optimal performance. However, challenges like ensuring high-speed, low-latency interconnects and effective thermal management persist.This study explores several key aspects of chiplet technology using a simplified dual-chip model, focusing on the integration of nanocopper materials. By comparing nanocopper with traditional solder, we analyze differences in mechanical behavior and thermal management. Additionally, we investigate variations in chiplet layout, examining the impact of spacing and the number of chiplets on performance under identical power conditions. Our findings indicate that nanocopper interconnects provide lower stress and better thermal conductivity than traditional solder. Adjusting chiplet spacing and increasing the number of chiplets both enhance heat dissipation. Future work will involve larger-scale simulations and fatigue analyses to optimize chiplet packaging for improved performance and reliability.

In-sensor-memory computing (ISMC) resolves von Neumann bottlenecks via synergistic innovations across multi-dimensional functional materials, hybrid architectures, and algorithm-hardware co-design. This paradigm empowers ultra-low-latency edge applications, paving the way for autonomous systems, bio-integrated healthcare, and decentralized swarm intelligence. Translating ISMC into scalable commercialization necessitates global Industry-Academia-Research collaboration, application-centric benchmarking protocols, and cross-disciplinary ecosystem enablers.

Aiming at the reliability bottlenecks of power MOSFETs in such key application fields as new energy vehicles (NEVs), photovoltaic (PV) inverters, and industrial motor drives, this paper proposes an unsupervised anomaly detection method for power MOSFET degradation based on the Long Short-Term Memory Autoencoder (LSTM-AE). Without relying on labeled data or complex hardware modifications, the method can adaptively learn the health patterns and temporal features of devices, addressing the limitations of traditional methods such as difficulty in capturing long-term dependencies and poor noise immunity. Three different test conditions for power cycling tests were designed to collect operational data of power MOSFET devices. On-state resistance (RDS(on)) and body diode voltage drop(VSD) were selected as input features, and the Leave-One-Out Cross-Validation (LOOCV) method was adopted to verify the model performance. Experimental results demonstrate that the method can effectively capture weak degradation features during the stage of insignificant device performance changes, achieving an early warning prior to device failure. Under 3σ principle threshold, its detection performance outperforms the Multilayer Perceptron Autoencoder (MLP-AE) and traditional Mahalanobis Distance (MD) methods, with the optimal sequence size being 64. It achieves a high accuracy in identifying normal states and exhibits high recognition capability for severely degraded states, providing a robust, efficient, and practical solution for the health monitoring of power MOSFETs.

Sustainable hydrogen production via water electrolysis is pivotal to addressing global energy and environmental challenges. Among emerging materials, quantum dots (QDs) have garnered significant attention for the hydrogen evolution reaction (HER) due to their zero-dimensional nanostructure, high specific surface area, tunable electronic characteristics, and abundant active sites. This review provides a comprehensive overview of recent advancements in QD-based catalysts for electrocatalytic HER, focusing on the fundamental mechanisms that drive their enhanced performance. Key enhancement strategies—such as substrate dispersion, surface functionalization, defect engineering, and heteroatom doping—are critically discussed. Furthermore, the review explores the potential of QD-based catalysts for large-scale and industrial applications. By synthesizing current progress and challenges, this review offers critical insights into the rational design of next-generation HER catalysts to advance sustainable hydrogen energy.

Sintered Cu nanoparticles (Cu NPs) are promising interconnection materials for high-temperature power electronics, yet how their authentic three-dimensional pore architecture governs microscale deformation remains unclear. Here, synchrotron nano-computed tomography (nano-CT) was combined with in-situ micropillar compression, explicit dynamic elastoplastic finite element analysis, and TEM/TKD characterization to interrogate sintered Cu NPs. The nano-CT voxel size was 45 nm, and the reconstructed volume corresponded to a cylinder 16 µm in diameter and 10 µm in height. The average sectional porosity was 12.44%, with a systematic discrepancy between two-dimensional and three-dimensional porosity quantification. During loading, the porosity decreased to 9.55% while the pore aspect ratio increased from 1.82–2.35. Finite element analysis further showed pronounced pore-adjacent stress/strain localization at the elastic–plastic transition, with local stress and equivalent plastic strain reaching 650 MPa and 1.7 × 10⁻², compared with 250 MPa and 1.1 × 10⁻³ in adjacent regions. The GND density increased by 95.9% at a compressive strain of 26%, linking pore-induced strain gradients to dislocation accumulation. These results quantitatively connect authentic three-dimensional pore architecture, local deformation localization, and dislocation-mediated strengthening in sintered Cu NPs. Highlights Synchrotron nano-CT (45 nm voxel size) reconstructed a 16 × 10 µm cylindrical volume of sintered Cu NPs and resolved the authentic 3D pore network. Sectional porosity was 12.44%, and 2D/3D quantification showed a systematic discrepancy, with porosity decreasing to 9.55% and pore aspect ratio increasing from 1.82 to 2.35 during compression. Pore-adjacent localization was quantified at the elastic–plastic transition, with local stress/PEEQ reaching 650 MPa and 1.7 × 10⁻² versus 250 MPa and 1.1 × 10⁻³ in adjacent regions. A 95.9% increase in GND density at 26% compressive strain links pore-induced strain gradients to dislocation accumulation and strain-gradient-driven strengthening.

Sintered copper (Cu) particles are widely used in catalysis, biomedical devices, energy systems and electronic packaging, yet the role of particle morphology in governing their mechanical response remains insufficiently resolved. Here, we investigate porous sintered Cu produced from monomodal spherical, bimodal spherical and flake-shaped particles by combining nanoindentation, scanning electron microscopy (SEM) and molecular dynamics (MD) simulations. Both spherical and Berkovich indenters were used to interrogate morphology-dependent deformation mechanisms. The bimodal spherical network exhibited the highest elastic modulus, whereas the monomodal spherical network showed the highest indentation yield strength. In contrast, the flake-derived network displayed substantially reduced elastic modulus and strength due to weak through-thickness connectivity. Correlative SEM images and MD simulations reveal morphology-dependent deformation mechanisms: distributed neck plasticity in the monomodal network, hierarchical load sharing between coarse and fine particles in the bimodal network, and anisotropic strain localization along interlamellar interfaces in the flake-derived network. Indenter geometry further modulates the response, with spherical indentation promoting distributed compaction and Berkovich indentation promoting stress concentration at weak interfaces. These findings highlight particle morphology as a critical factor governing the topology-dependent mechanical performance of porous sintered Cu.

Accurate lifetime prediction of power semiconductor devices is critical for the reliability of power electronic systems. Power cycling tests generate only a small number of failure samples due to their high cost and long duration. This data scarcity makes it difficult to train reliable prediction models. At the same time, modern deep learning models require significant AI expertise to configure and tune, which creates a barrier for reliability engineers. To address these two problems, this paper proposes a domain-constrained multi-agent framework for automated remaining useful life (RUL) prediction of power semiconductor devices. The framework integrates three collaborative agents: a Stats Agent for statistical reliability assessment, a Physics Agent for physical plausibility validation, and a Model Agent for algorithm selection and hyperparameter tuning. A Central Controller manages the workflow and coordinates agent communication. The agents interact through a Propose-Critique-Refine (PCR) mechanism, in which the Model Agent proposes configurations and the other two agents critique them from statistical and physical perspectives. This iterative negotiation eliminates the need for manual parameter tuning. The framework is validated on the NASA IGBT accelerated aging dataset under Leave-One-Device-Out Cross-Validation. Results show that the framework achieves an R2 of 0.914 at a 50% observation ratio and remains above 0.79 even at 30%, demonstrating strong generalization under limited data conditions.

Pressureless sintered Ag pastes are promising die-attach materials for power electronics, yet practical sintering-profile optimization still relies heavily on trial-and-error, and the link from thermal kinetics to fracture-relevant microstructure and strength remains insufficiently established. In this work, a thermal-kinetics-guided workflow was developed to design paste-specific pressureless sintering profiles for two commercial Ag pastes (a spherical-particle paste and a flake-based paste) and to interpret the resulting strength–fracture response using SEM-based, heterogeneity-aware learning. Multi-heating-rate TGA was used to define the conversion fraction (α), while DSC was used to identify the dominant thermal-event window and extract the characteristic peak temperature for Arrhenius pairing. The TGA-defined conversion evolution was then modeled using a JMAK/Avrami form with Arrhenius temperature dependence to predict the isothermal holding time required to reach a target conversion at a selected dwell temperature. Relative to supplier-recommended profiles, the optimized profiles increased die-shear strength by 35% for the spherical-particle paste and by 206% for the flake-based paste, and promoted a fracture-mode transition from interfacial debonding toward mixed/cohesive fracture. Pearson/Ridge baselines and attention-based multiple-instance learning (MIL) linked strength and fracture-mode distribution to porosity/connectivity-related descriptors; paste-wise normalization mitigated paste-specific baselines and enabled MIL to reveal profile-induced microstructure–performance co-variation. Overall, this study establishes a practical workflow that couples thermal-kinetics-guided profile design with mechanical and fractographic validation and interpretable microstructure–property attribution, supporting mechanism-informed optimization of pressureless sintered Ag die-attach pastes.

This study investigates the corrosion mechanisms at the interfaces of sintered joints exposed to H2S for offshore applications. Through finite element modeling and experimental validation, the galvanic corrosion behavior of four sintered joint configurations was analyzed. Simulation results identified the interface as the primary corrosion zone, with accelerated degradation driven by galvanic coupling. Polarization and electrical conductivity measurements further confirmed that sintered silver maintains corrosion resistance and stable electrical properties after corrosion. This work focused on the interfacial corrosion mechanism and provides insights for offshore power module packages.