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This paper proposes a performance-based microstructure design methodology specifically tailored for gas diffusion layers (GDLs). The generative machine learning denoising diffusion probabilistic model (DDPM) is trained and used for performance-based microstructure design. This study demonstrates significant progress in performance-based design by incorporating DDPM into the microstructure design process, offering a promising approach for GDL microstructure design. This methodology provides the advantage of generating 2D microstructures with desired permeability and volume fractions. Moreover, it is not limited to a specific performance criterion, making it adaptable to target other metrics. To train the DDPM, a 2D GDL microstructure dataset is constructed using the Lattice Boltzmann Method (LBM) for permeability estimation. Subsequently, we employ the DDPM with U-net architecture, which leverages positional encoding to learn the microstructure's volume fraction and permeability effectively. The input label pair of permeability and volume fraction is generated considering the inherent relationship between these two parameters to ensure the generation of meaningful microstructures. This relationship is supposed to ensure that the resulting microstructures align with realistic and physically meaningful characteristics. The simulated performance results obtained from the generated microstructures using the proposed methodology demonstrate a strong consistency with the targeted performance objectives. [ABSTRACT FROM AUTHOR]

It is a challenge to develop low-cost, efficient, and stable multi-functional electrocatalysts for the oxygen evolution reaction (OER), overall water splitting (OWS), oxygen reduction reaction (ORR), and zinc–air batteries (ZABs). To address such issues, for the first time, we rationally design and facilely construct iron-single-atom-coupled Fe3C on P-, F-, N-triply doped carbon nanotubes (FeSAs-PFN-CNTs). Herein, the modulation of the Fe atom coordination environment, low loading of Fe3C nanoparticles, and inclusion of heteroatom-doped CNTs facilitated ample proton-feeding and charge-transfer processes. As a result, the FeSAs-PFN-CNT catalyst delivers fast electrokinetics, exhibiting efficient OER (an overpotential (η10) of 310 mV with 24 h stability), admirable OWS anode (325 mV (η10) with 10 h stability), excellent ORR (an half-wave potential (E1/2) of 0.88 V with 6 h stability), and outstanding ZAB (an open-circuit voltage of 1.49 V, high specific capacity of 781 mA h gZn−1 (93.5% of the theoretical capacity of 835 mA h gZn−1), and ultrahigh power density of 241 mW cm−2) performances, which is superior to those of benchmark electrocatalysts. In this regard, this work provides an excellent scalable multifunctional catalyst for the commercialization of low-cost, efficient, and stable electrocatalysts for application in oxygen catalysis and Zn–air batteries. [ABSTRACT FROM AUTHOR]

Nowadays, self-healing materials have been studied actively in electronics, soft robotics, aerospace, and automobiles because they can prolong the life span of the materials. However, overcoming the trade-off relationship between mechanical properties and self-healing performance is challenging. Herein, graphene oxide-polyaniline (GO-PANI) filler was introduced to overcome this challenge because GO has a highly excellent modulus, and nitrogen atoms in PANI can endow a self-healing ability through hydrogen bonds. Aside from the hydrogen bond in PANI, the hydrogen bond in the carbonyl group and the disulfide exchange bond in the epoxy matrix also helped the materials heal efficiently. Therefore, the modulus of SV-GPN1 (Self-healing Vitrimer-GO-PANI1) reached 770 MPa, and a 65.0% healing efficiency was demonstrated. The modulus and self-healing efficiency were enhanced after adding GO-PANI filler. The self-healing ability, however, deteriorated when adding more GO-PANI filler because it hindered the collision between the molecules. Meanwhile, SV-GPN1 was excellent in reproducibility, which was proven by the experiment that 16.50 mm thick SV-GPN1 also displayed a self-healing ability. Thus, SV-GPN1 can be applied to structural materials in industries like aerospace because of its self-healing ability, excellent modulus, and reproducibility. [ABSTRACT FROM AUTHOR]

Carbon fibers (CFs) are experiencing a growing demand owing to their low specific weight, exceptional mechanical properties, superior temperature, and corrosion resistance, however, their sustainability and energy consumption during manufacturing is still a challenge. Therefore, reclamation of waste CFs and their reformatting has gained significant attention. Herein, we synthesized a chemically degradable vitrimer matrix by curing bisphenol‐A diglycidyl ether (BADGE) with 2‐aminophenyl disulfide (2‐AFD) and further utilized the matrix for the development of CF reinforced composites (CFRCs) through vacuum‐assisted resin infusion molding (VARIM) process. The obtained vitrimeric system and its composites show excellent mechanical, self‐adhering, shape‐memory, and reprocessing properties. Meanwhile, the developed CFRP vitrimer composites can be rapidly dissolved in thiol solvent (1‐octanethiol), resulting in the efficient recycling of CFs. X‐ray diffraction, scanning electron microscopy, and Raman spectroscopy validate that the chemical structure of the recycled fibers closely resembles the structure of the original CFs. The recycled CFs were further used to prepare second generation composite materials with excellent thermal, dynamic, and mechanical properties for nonstructural applications (e.g., sports, automotive, etc.). Thus, with an effective CF recycling method, this study can assist in preparing reliable, long‐term functional, recyclable, and high‐performance composites. [ABSTRACT FROM AUTHOR]

This paper proposes a microstructure reconstruction framework with denoising diffusion models for the first time. The novelty and strength of the proposed model lie in its universality and generality for the microstructure characterization and reconstruction (MCR) that can be applied to various types of composite materials. The applicability of the diffusion-based models is validated with several types of microstructures (e.g., polycrystalline alloy, carbonate, ceramics, copolymer, fiber composite, etc.) that have different morphological characteristics. Moreover, an implicit probabilistic model (which yields non-Markovian diffusion processes) is formulated to accelerate the sampling process, thereby controlling the computational cost considering the practicability and reliability. [ABSTRACT FROM AUTHOR]

This paper presents a multiscale progressive fatigue damage model to predict the fatigue life of composite laminates. The multi-level damage model was employed considering the interfacial debonding effect, and effective material properties were calculated through homogenization. Damage variables and damage slopes were defined at the constituent level, and fatigue damage parameters were obtained using the residual stiffness data with the chaotic firefly algorithm. The model was implemented into ABAQUS, then validated with flat-bar and pin-loaded specimens of AS4/3501-6 composite. The numerical results corresponded well with the experimental data and showed the ability to capture the failure propagation of composite laminates. [ABSTRACT FROM AUTHOR]