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This study investigates the H2O and CO2 sorption behavior of two chemically distinct polystyrene-divinylbenzene-based ion exchange sorbents: a primary amine and a permanently charged strong base quaternary ammonium (QA+) group with (bi)carbonate counter anions. We compare their distinct interactions with H2O and CO2 through simultaneous thermal gravimetric, calorimetric, gas analysis, and molecular modeling approaches to evaluate their performance for dilute CO2 separations like direct air capture. Thermal and hybrid (heat + low-temperature hydration) desorption experiments demonstrate that the QA+-based sorbent binds both water and CO2 more strongly than the amine counterparts but undergoes degradation at moderate temperatures, limiting its compatibility with thermal swing regeneration. However, a low-temperature moisture-driven regeneration pathway is uniquely effective for the QA+-based sorbent. To inform the energetics of a moisture-based CO2 separation (i.e., a moisture swing), we compare calorimetric water sorption enthalpies to Clausius–Clapeyron-derived total isosteric enthalpies. To our knowledge, this includes the first direct calorimetric measurement of water sorption enthalpy in a QA+-based sorbent. Both methods reveal monolayer-multilayer sorption behavior for both sorbents, with the QA+-based material having slightly higher water sorption enthalpies at the initially occupied strongest sorption sites. Molecular modeling supports this observation, showing higher water sorption energies and denser charge distributions in the QA+-based sorbent at λH2O = 1 mmol/mmolsite. Mixed gas experiments in the QA+-based sorbent show that not only does water influence CO2 binding, but CO2 influences water uptake through counterion-dependent hydration states, and that moisture swing responsiveness in this material causes hydration-induced CO2 release and drying-induced CO2 uptake, an important feature for low-energy CO2 separation under ambient conditions. Overall, the two classes of sorbents offer distinct pathways for the CO2 separation. 

Abstract A wide range of deep learning-based machine learning (ML) techniques are extensively applied to the design of high-entropy alloys (HEAs), yielding numerous valuable insights. Kolmogorov–Arnold networks (KAN) is a recently developed architecture that aims to improve both the accuracy and interpretability of input features. In this work, we explore three different datasets for HEA design and demonstrate the application of KAN for both classification and regression models. In the first example, we use a KAN classification model to predict the probability of single-phase formation in high-entropy carbide ceramics based on various properties such as mixing enthalpy and valence electron concentration. In the second example, we employ a KAN regression model to predict the yield strength and ultimate tensile strength of HEAs based on their chemical composition and process conditions including annealing time, cold rolling percentage, and homogenization temperature. The third example involves a KAN classification model to determine whether a certain composition is an HEA or non-HEA, followed by a KAN regressor model to predict the bulk modulus of the identified HEA, aiming to identify HEAs with high bulk modulus. In all three examples, KAN either outperform or match the performance in terms of accuracy such asF1 score for classification and mean square error, and coefficient of determination (R²) for regression of the multilayer perceptron by demonstrating the efficacy of KAN in handling both classification and regression tasks. We provide a promising direction for future research to explore advanced ML techniques, which lead to more accurate predictions and better interpretability of complex materials, ultimately accelerating the discovery and optimization of HEAs with desirable properties. 

Abstract Ti/TiN coatings are used in a wide range of engineering applications due to their superior properties such as high hardness and toughness. Doping Al into Ti/TiN can further enhance properties and lead to even higher performance. Therefore, studying the atomic‐level behavior of the TiAl/TiAlN interface is important. However, due to the large number of possible combinations for the 50 mol% Al‐doped Ti/TiN system, it is time‐consuming to use the DFT‐based Monte Carlo methods to find the optimal TiAl/TiAlN system with a high work of adhesion. In this study, we use a graph convolutional neural network as an interatomic potential, combined with reinforcement learning, to improve the efficiency of finding optimal structures with a high work of adhesion. By inspecting the features of structures in neural networks, we found that the optimal structures follow a certain pattern of doping Al near the interface. The electronic structure and bonding analysis indicate that the optimal TiAl/TiAlN structures have higher bonding strength. We expect our approach to significantly accelerate the design of advanced ceramic coatings, which can lead to more durable and efficient materials for engineering applications. 

Abstract Graphene-based electrodes have been extensively investigated for supercapacitor applications. However, their ion diffusion efficiency is often hindered by the graphene restacking phenomenon. Even though holey graphene is fabricated to address this issue by providing ion transport channels, those channels could still be blocked by densely stacked graphene nanosheets. To tackle this challenge, this research aims at improving the ion diffusion efficiency of microwave-synthesized holey graphene films by tuning the water interlayer spacer towards the improved supercapacitor performance. By controlling the vacuum filtration during graphene-based electrode fabrication, we obtain dry films with dense packing and wet films with sparse packing. The SEM images reveal that 20 times larger interlayer distance is constructed in the wet film compared to that in the dry counterpart. The holey graphene wet film delivers a specific capacitance of 239 F/g, ~82% enhancement over the dry film (131 F/g). By an integrated experimental and computational study, we quantitatively show that the interlayer spacing in combination with the nanoholes in the basal plane dominates the ion diffusion rate in holey graphene-based electrodes. Our study concludes that novel hierarchical structures should be further considered even in holey graphene thin films to fully exploit the superior advantages of graphene-based supercapacitors.