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  1. Free, publicly-accessible full text available July 1, 2025
  2. Abstract

    The design of high‐entropy single‐atom catalysts (HESAC) with 5.2 times higher entropy compared to single‐atom catalysts (SAC) is proposed, by using four different metals (FeCoNiRu‐HESAC) for oxygen reduction reaction (ORR). Fe active sites with intermetallic distances of 6.1 Å exhibit a low ORR overpotential of 0.44 V, which originates from weakening the adsorption of OH intermediates. Based on density functional theory (DFT) findings, the FeCoNiRu‐HESAC with a nitrogen‐doped sample were synthesized. The atomic structures are confirmed with X‐ray photoelectron spectroscopy (XPS), X‐ray absorption (XAS), and scanning transmission electron microscopy (STEM). The predicted high catalytic activity is experimentally verified, finding that FeCoNiRu‐HESAC has overpotentials of 0.41 and 0.37 V with Tafel slopes of 101 and 210 mVdec−1at the current density of 1 mA cm−2and the kinetic current densities of 8.2 and 5.3 mA cm−2, respectively, in acidic and alkaline electrolytes. These results are comparable with Pt/C. The FeCoNiRu‐HESAC is used for Zinc–air battery applications with an open circuit potential of 1.39 V and power density of 0.16 W cm−2. Therefore, a strategy guided by DFT is provided for the rational design of HESAC which can be replaced with high‐cost Pt catalysts toward ORR and beyond.

     
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    Free, publicly-accessible full text available April 30, 2025
  3. Free, publicly-accessible full text available March 1, 2025
  4. Design optimization, and particularly adjoint-based multi-physics shape and topology optimization, is time-consuming and often requires expensive iterations to converge to desired designs. In response, researchers have developed Machine Learning (ML) approaches — often referred to as Inverse Design methods — to either replace or accelerate tools like Topology optimization (TO). However, these methods have their own hidden, non-trivial costs including that of data generation, training, and refinement of ML-produced designs. This begs the question: when is it actually worth learning Inverse Design, compared to just optimizing designs without ML assistance? This paper quantitatively addresses this question by comparing the costs and benefits of three different Inverse Design ML model families on a Topology Optimization (TO) task, compared to just running the optimizer by itself. We explore the relationship between the size of training data and the predictive power of each ML model, as well as the computational and training costs of the models and the extent to which they accelerate or hinder TO convergence. The results demonstrate that simpler models, such as K-Nearest Neighbors and Random Forests, are more effective for TO warmstarting with limited training data, while more complex models, such as Deconvolutional Neural Networks, are preferable with more data. We also emphasize the need to balance the benefits of using larger training sets with the costs of data generation when selecting the appropriate ID model. Finally, the paper addresses some challenges that arise when using ML predictions to warmstart optimization, and provides some suggestions for budget and resource management. 
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    Free, publicly-accessible full text available August 20, 2024
  5. Free, publicly-accessible full text available September 1, 2024
  6. Abstract

    Mineralization is a long-lasting method commonly used by biological materials to selectively strengthen in response to site specific mechanical stress. Achieving a similar form of toughening in synthetic polymer composites remains challenging. In previous work, we developed methods to promote chemical reactions via the piezoelectrochemical effect with mechanical responses of inorganic, ZnO nanoparticles. Herein, we report a distinct example of a mechanically-mediated reaction in which the spherical ZnO nanoparticles react themselves leading to the formation of microrods composed of a Zn/S mineral inside an organogel. The microrods can be used to selectively create mineral deposits within the material resulting in the strengthening of the overall resulting composite.

     
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