Cu-Ag composite sintered pastes are promising die-attach materials for wide bandgap power modules, yet their electrothermal performance is governed by 3D phase connectivity and interfacial morphology. In this work, a workflow for better predicting the thermal and electrical conductivity was presented, using 2D SEM processing, 3D reconstruction with a self-learning optimized QSGS algorithm, and electrothermal finite element simulations in COMSOL Multiphysics. A Hashin Shtrikman composition baseline, together with closed-form structure-corrected equations, was further introduced for better prediction accuracy, which enables rapid screening and microstructure-informed design of composite sintering materials for die-attach.

Ion implantation and subsequent annealing reshape the defect landscape and stress state of compound semiconductors, yet the temperature-dependent mechanisms in SiC remain incompletely understood. Here, we utilize molecular dynamics (MD) simulations and confocal micro-Raman measurements to resolve how implantation temperature and post-annealing regulate lattice disorder, amorphization kinetics, and residual-stress evolution in chemical vapor deposited (CVD) 4H-SiC. MD reveals surface-nucleated amorphization that propagates inward, whereas elevated implantation temperatures activate defect recombination pathways that suppress amorphous-layer formation. Raman signatures of optical-phonon shifts, linewidth broadening, and amorphization bands track the coupled evolution of lattice disorder and stress. Experimentally, increasing implantation temperature smooths the surface (S_a 0.133 → 0.101 nm) and reduces the amorphous-layer thickness (from ∼700 nm at 25°C to undetectable at 500°C), while driving more compressive residual stress (−57 → −132 MPa). Post-annealing largely restores phonon lifetimes and eliminates amorphization signatures, consistent with the recovery trends predicted by MD. These results delineate a thermal-treatment window that controls amorphization and residual stress in 4H-SiC, providing a transferable Raman-based methodology for nondestructive assessment of implantation-induced damage in compound semiconductors.

This study is motivated by a conceptual inconsistency in the physical interpretation of eight-chain hyperelastic theory, which arises from the combined effect of two distinct issues: the use of the marginal projection distribution p_z(|r_z|) as a surrogate for the full probability density of end-to-end distance p_r̄(r̄), and the subsequent reliance on a root mean square (RMS) approximation step in the micro–macro averaging of chain stretch. We first revisit this probabilistic mismatch by reformulating the probability density function of freely-jointed chains (FJCs) in terms of the squared end-to-end vector r², thereby restoring consistency on chain-level statistics. Building on this formulation, the micro–macro mapping averaging of chain conformational free energy is constructed directly in terms of r², leading to a one-step mean-field approximation that avoids RMS averaging. The modified probability transformation is examined by Monte Carlo sampling at the microscopic level. To account for interchain interactions, q-mean statistical description of micro tube confinement was incorporated, leading to the appearance of the general invariant I_q=λ₁^q+λ₂^q+λ₃^q. The resulting continuum constitutive model is assessed against multiaxial experimental data for several polymer networks, including vulcanized natural rubber, Entec Enflex S4035A thermoplastic elastomer, Tetra-PEG, and isoprene rubber vulcanizate. Comparisons with three existing hyperelastic strain energy formulations, the extended eight-chain, extended tube models, and the four-parameter ”comprehensive” model, demonstrate comparable phenomenological accuracy of the current model while providing a clearer and more consistent micro–macro physical interpretation of model parameters. A parametric study further illustrates how the dimensionless parameters n and q govern the shape of the macroscopic stress–strain responses. The present formulation provides a consistent theoretical basis within the scope of hyperelasticity and admits potential extensions toward more complex irreversible phenomena.

We report a bioinspired, transparent green plant-like window that integrates passive cooling, thermal insulation, and solar-driven waste heat recovery to reduce building energy consumption. By mimicking leaf transpiration, the system uses a carbon quantum dot-doped CPPB hydrogel to achieve high visible transmittance (~92%) while blocking UV and NIR radiation. A self-powered water circulation layer repurposes solar heat for domestic water heating. Outdoor field tests and 168-h solar simulator exposure confirm long-term thermal stability and cooling up to 21.9°C. Simulations across 30 global cities show annual cooling energy savings up to 569.1 MJ/m ². This scalable, multifunctional window offers a sustainable path toward net-zero energy buildings.

Two-dimensional materials (2DMs)-based devices exhibit aerospace potential due to their superior properties. However, the operational reliability of 2DMs-based devices in space environments is significantly influenced by charged-particle radiation, necessitating rigorous ground-based radiation tolerance assessments. Current research on radiation effects in 2DMs is primarily experimental, yet such methodologies are inherently time-consuming, resource-intensive, and limited in throughput. To address these challenges, computational modeling and simulation techniques are increasingly being integrated with experimental characterization to accelerate materials design and unravel underlying physical mechanisms. This review systematically evaluates the state-of-the-art multiscale computational frameworks for 2DMs research, focusing on recent advancements, technical challenges, and emerging opportunities. A novel integrative approach is proposed, combining density functional theory, molecular dynamics, Monte Carlo, finite element analysis, and machine learning techniques. Particular emphasis is placed on addressing challenges in multiscale modeling, including accurate representation of complex phenomena across spatial and temporal scales under extreme environmental conditions. Conversely, opportunities for enhancing predictive capabilities are highlighted, with implications for expediting materials discovery in electronics, photonics, energy storage, catalysis, and nanomechanical systems. This comprehensive survey provides a strategic roadmap for future research directions in multiscale computational modeling of 2DMs, emphasizing interdisciplinary methodologies that bridge atomistic simulations with macroscale engineering applications. The insights presented herein aim to advance the development of radiation-hardened 2DMs-based devices for next-generation aerospace systems.

Solder joint reliability related to failures due to thermomechanical loading is a critically important yet physically complex engineering problem. As a result, simulated behavior is oftentimes computationally expensive. In an increasingly data-driven world, it is popular to use efficient data-driven design schemes. Among the family of efficient optimization methods, Bayesian optimization with Gaussian process regression is a key representative. The authors argue that additional computational savings can be obtained from exploiting thorough surrogate modeling and selecting a design candidate based on multiple acquisition functions. This is feasible due to the relatively low computational cost, compared to the expensive simulation objective. This paper presents a novel heuristic framework for performing Bayesian optimization with adaptive hyperparameters across multiple optimization iterations. A comparative study shows the ability of adaptive Bayesian optimization to save on expensive objective evaluations with respect to the worst-performing regular Bayesian optimization scheme. As an engineering use case, the solder joint reliability problem is tackled by minimizing the accumulated non-linear creep strain under a cyclic thermal load. Results show that adaptive Bayesian optimization can at least match the performance of regular Bayesian optimization in terms of raw objective performance, but achieves this with half of the computational expense budget. This practical result underlines the methodological potential of the novel adaptive Bayesian data-driven methodology to achieve more efficient results and significantly cut optimization-related expenses. Lastly, to promote the reproducibility of the results, the data-driven implementations are made available on an open-source basis.

As 2.5D/3D advanced packaging moves toward higher interconnect density, glass substrates are attractive for their electrical performance and dimensional stability. However, high aspect ratio, fine pitch through-glass vias (TGVs) are prone to thermo-mechanical failures under long-term thermal shock, and microscale reliability assessment is hindered by costly microstructural characterization and the lack of models capturing long-term microstructural evolution and property degradation. Here, fully copper-filled TGV-Cu structures were fabricated on Schott AF32 glass and subjected to thermal-shock cycling from - 40 ℃ to 125 ℃ (0, 700, 1400, and 2100 cycles). Results show a non-monotonic, location-dependent hardness evolution with pronounced heterogeneity, where the mid-via region exhibits stronger late-stage softening by nanoindentation. EBSD shows grain diameter increases from 0.61 μm to 0.91 μm, while GND density decreases by recovery and then re-accumulates preferentially near the via bottom, evidencing competition between grain-growth softening and dislocation hardening. Hybrid Potts-phase field simulations further predict continuous grain growth and stress relaxation (declining von Mises stress) inside the via; including the glass substrate and interface constraints raises the stress level but preserves the relaxation trend. Moreover, the phase field simulations results of equivalent plastic strain (PEEQ) reveal progressively intensified cyclic plastic localization near the top/bottom caps (Cu/glass intersections), providing mechanistic support for cracking localized at the via mouths.

Particle morphology is a critical structural variable in pressure-assisted sintering because it controls packing, pore topology, interparticle bonding and load transfer. Here, copper (Cu) was used as a model system to examine how monomodal spherical, bimodal spherical and flake-shaped particle assemblies, processed under identical conditions, form porous structures with distinct mechanical responses. Micro-pillar compression reveals low effective elastic moduli of 7.5–12.5 GPa and high yield strengths of 403–450 MPa. The deformation pathways are strongly morphology dependent. The monomodal structure accommodates strain through distributed pore collapse and particle deformation, leading to progressive densification hardening. The bimodal structure exhibits size-partitioned deformation, with large particles forming the main load-bearing backbone and smaller particles accommodating local rearrangement, embedding and shear compaction. The flake-shaped structure undergoes geometry-guided deformation, where extended face-to-face bonding enhances local load bearing, while inter-flake misalignment concentrates strain and promotes shear localization. Post-compression transmission electron microscopy (TEM) and transmission Kikuchi diffraction (TKD) analyses link these modes to pore collapse, neck deformation and grain-scale strain accommodation. TKD further gives average Geometrically Necessary Dislocations (GND) densities of 4.36×10¹⁴ m⁻², 3.69×10¹⁴ m⁻² and 4.11×10¹⁴ m⁻² for the monomodal, bimodal and flake-shaped structures, respectively. Molecular dynamics (MD) simulations reproduce the corresponding strain-localization patterns and reveal morphology-controlled load-transfer pathways dominated by Shockley partial dislocations. These results establish particle morphology as a design parameter for tuning stiffness, strength and damage tolerance in sintered porous metals.

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.

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.

Flexible strain sensors are key components in emerging fields such as soft robotics and wearable electronics, where robust interfacial adhesion, low electrical resistance, and mechanical stability under repeated deformation are essential. Conventional metal films on polymer substrates often exhibit poor reliability due to thermal expansion mismatch and weak interfacial bonding. Therefore, silver sintering has emerged as a promising low-temperature interconnect material with high conductivity and flexibility, yet controlling porosity during sintering and ensuring strong adhesion to polymers remain critical challenges. This work presents a strategy to expand sintered silver for flexible strain sensor applications. On the one hand, the silver (Ag) porosity was controlled from 20% to 40% through a pressureless sintering process; On the other hand, the Ag-polymer interface was reinforced via a nanoscale surface modulation method using a two-step etching treatments, while the 40 min etched sample demonstrated the most uniform performance, with the lowest electrical conductivity average degradation of 2.97%. The proposed approach effectively improves the microstructural integrity and interfacial reliability, paving the way for high-performance, durable, flexible sensors suitable for the next-generation soft electronic systems.