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Lithium-ion batteries are now reaching the energy density limits set by their electrode materials, requiring new paradigms for Li + and electron hosting in solid-state electrodes. Reversible oxygen redox in the solid state in particular has the potential to enable high energy density as it can deliver excess capacity beyond the theoretical transition-metal redox-capacity at a high voltage. Nevertheless, the structural and chemical origin of the process is not understood, preventing the rational design of better cathode materials. Here, we demonstrate how very specific local Li-excess environments around oxygen atoms necessarily lead to labile oxygen electrons that can be more easily extracted and participate in the practical capacity of cathodes. The identification of the local structural components that create oxygen redox sets a new direction for the design of high-energy-density cathode materials. The chemistry of the transition metals within the oxide cathodes of lithium-ion batteries typically limits their capacity, however, reversible oxygen redox could potentially break this limit. It is now demonstrated that Li-excess and cation disorder create specific environments around oxygen atoms that lead to labile oxygen electrons that participate in the practical capacity of cathodes.

In this study, we explore the potential of using quantum natural language processing (QNLP) for property-guided inverse design of metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 450 hypothetical MOF structures consisting of 3 topologies, 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and CO 2 Henry’s constant values. We then compare various QNLP models (i.e., the bag-of-words, DisCoCat (Distributional Compositional Categorical), and sequence-based models) to identify the most effective approach to process the MOF dataset. Using a classical simulator provided by the IBM Qiskit, the bag-of-words model is identified to be the optimum model, achieving validation accuracies of 88.6% and 78.0% for binary classification tasks on pore volume and CO 2 Henry’s constant, respectively. Further, we developed multi-class classification models tailored to the probabilistic nature of quantum circuits, with average test accuracies of 92% and 80% across different classes for pore volume and CO 2 Henry’s constant datasets. Finally, the performance of generating MOF with target properties showed accuracies of 97.75% for pore volume and 90% for CO 2 Henry’s constant, respectively. Although our investigation covers only a fraction of the vast MOF search space, it marks a promising first step towards using quantum computing for materials design, offering a new perspective through which to explore the complex landscape of MOFs.

Pitting corrosion in seawater is one of the most difficult forms of corrosion to identify and control. A workhorse material for marine applications, 316L stainless steel (316L SS) is known to balance resistance to pitting with good mechanical properties. The advent of additive manufacturing (AM), particularly laser powder bed fusion (LPBF), has prompted numerous microstructural and mechanical investigations of LPBF 316L SS; however, the origins of pitting corrosion on as-built surfaces is unknown, despite their utmost importance for certification of LPBF 316L SS prior to fielding. Here, we show that Mn-rich silicate slags are responsible for pitting of the as-built LPBF material in sodium chloride due to their introduction of deleterious defects such as cracks or surface oxide heterogeneities. In addition, we explain how slags are formed in the liquid metal and deposited at the as-built surfaces using high-fidelity melt pool simulations. Our work uncovers how LPBF changes surface oxides due to rapid solidification and high-temperature oxidation, leading to fundamentally different pitting corrosion mechanisms. Mechanisms occurring during seawater corrosion of as-built laser powder bed fusion 316L stainless steels are largely unknown. Here, the authors show that Mn, Si-rich slags found in between laser tracks are responsible for corrosion.

Layered boron compounds have attracted significant interest in applications from energy storage to electronic materials to device applications, owing in part to a diversity of surface properties tied to specific arrangements of boron atoms. Here we report the energy landscape for surface atomic configurations of MgB 2 by combining first-principles calculations, global optimization, material synthesis and characterization. We demonstrate that contrary to previous assumptions, multiple disordered reconstructions are thermodynamically preferred and kinetically accessible within exposed B surfaces in MgB 2 and other layered metal diborides at low boron chemical potentials. Such a dynamic environment and intrinsic disordering of the B surface atoms present new opportunities to realize a diverse set of 2D boron structures. We validated the predicted surface disorder by characterizing exfoliated boron-terminated MgB 2 nanosheets. We further discuss application-relevant implications, with a particular view towards understanding the impact of boron surface heterogeneity on hydrogen storage performance. Layered boron compounds attract enormous interest in applications. This work reports first-principles calculations coupled with global optimization to show that the outer boron surface in MgB 2 nanosheets undergo disordering and clustering, which is experimentally confirmed in synthesized MgB 2 nanosheets.

Multivariate (MTV) porous materials exhibit unique structural complexities based on their diverse spatial arrangements of multiple building block combinations. These materials possess potential synergistic functionalities that exceed the sum of their individual components. However, the exponentially increasing design complexity of these materials poses significant challenges for accurate ground-state configuration prediction and design. To address this, we propose a Hamiltonian model for quantum computing that integrates compositional, structural, and balance constraints directly into the Hamiltonian, enabling efficient optimization of the MTV configurations. The model employs a graph-based representation to encode linker types as qubits. Our framework enables quantum encoding of a vast linker design space, allowing representation of exponentially many configurations with linearly scaling qubit resources, and facilitating efficient search for optimal structures based on predefined design variables. To validate our model, a variational quantum circuit was constructed and executed using the Sampling Variational Quantum Eigensolver (VQE) algorithm in the IBM Qiskit. Simulations on experimentally known MTV porous materials (e.g., Cu-THQ-HHTP, Py-MV-DBA-COF, MUF-7, and SIOC-COF2) successfully reproduced their ground-state configurations, demonstrating the validity of our model. Furthermore, VQE calculations were performed on a real IBM 127-qubit quantum hardware for validation purposes signaling a first step toward a practical quantum algorithm for the rational design of porous materials.

To meet the growing demand for global electrical energy storage, high‐energy‐density electrode materials are required for Li‐ion batteries. To overcome the limit of the theoretical energy density in conventional electrode materials based solely on the transition metal redox reaction, the oxygen redox reaction in electrode materials has become an essential component because it can further increase the energy density by providing additional available electrons. However, the increase in the contribution of the oxygen redox reaction in a material is still limited due to the lack of understanding its controlled parameters. Here, it is first proposed that Li‐transition metals (TMs) inter‐diffusion between the phases in Li‐rich materials can be a key parameter for controlling the oxygen redox reaction in Li‐rich materials. The resulting Li‐rich materials can achieve fully exploited oxygen redox reaction and thereby can deliver the highest reversible capacity leading to the highest energy density, ≈1100 Wh kg−1 among Co‐free Li‐rich materials. The strategy of controlling Li/transition metals (TMs) inter‐diffusion between the phases in Li‐rich materials will provide feasible way for further achieving high‐energy‐density electrode materials via enhancing the oxygen redox reaction for high‐performance Li‐ion batteries. A new key parameter for fully exploiting the oxygen redox reaction by Li/transition metals (TMs) inter‐diffusion between the two phases in Co‐free Li‐rich materials is first proposed. The resulting Li‐rich materials can achieve fully exploited oxygen redox reaction and thereby can deliver the highest reversible capacity leading to the highest energy density, ≈1100 Wh kg−1 among Co‐free Li‐rich materials.

The quantification of interphase properties between metals and their corresponding hydrides is crucial for modeling the thermodynamics and kinetics of the hydrogenation processes in solid-state hydrogen storage materials. In particular, interphase boundary energies assume a pivotal role in determining the kinetics of nucleation, growth, and coarsening of hydrides, alongside accompanying morphological evolution during hydrogenation. The total interphase energy arises from both chemical bonding and mechanical strains in these solid-state systems. Since these contributions are usually coupled, it is challenging to distinguish via conventional computational approaches. Here, a comprehensive atomistic modeling methodology is developed to decouple chemical and mechanical energy contributions using first-principles calculations, of which feasibility is demonstrated by quantifying chemical and elastic strain energies of key interfaces within the FeTi metal-hydride system. Derived materials parameters are then employed for mesoscopic micromechanical analysis, predicting crystallographic orientations in line with experimental observations. The multiscale approach outlined verifies the importance of the chemo-mechanical interplay in the morphological evolution of growing hydride phases, and can be generalized to investigate other systems. In addition, it can streamline the design of atomistic models for the quantitative evaluation of interphase properties between dissimilar phases and allow for efficient predictions of their preferred phase boundary orientations.

This study classified the types of dining-out activities into three categories: visiting restaurants, using delivery services, and using take-out services to understand how customers’ various dining-out activities were carried out during the COVID-19 pandemic. The study used the Theory of Planed Behavior (TPB) model to analyze the structural relationship between the main factors and three dining-out activities. An online survey method was used to distribute and collect survey link addresses through respondents’ SNS and e-mail and a data analysis was performed on the final 429(85.8%) effective samples. A paired t-test and structural equation modeling (SEM) were used to investigate customers’ dining-out activities. This study is of significant contribution in that it compared and analyzed customers’ various dining-out activities using the TPB model, laid the theoretical foundation for related research, and suggested ways to help related industry workers establish marketing strategies under the pandemic.

Phospholipid fatty acid (PLFA) profiles in four full-scale activated sludge reactors (ASR1 ~ 4) treating municipal wastewater, South Korea, were monitored to evaluate the influence of influent water quality on microbial community structure (MCS) and the effect of the MCS on effluent water quality. In ASR1 ~ 3, PLFA profiles were very similar, regardless of the influent water quality and seasonal differences, and 16:17c/15:0iso2OH and 16:0 were dominant. PLFA profiles in ASR4 during summer and autumn were very similar to those in ASR1 ~ 3, but increases in specific fatty acids, 16:1ω5c, 11methyl18:1ω7c and 15:0iso3OH, were found in ASR4 during winter and spring, with relatively high total suspended solid (TSS) concentrations in the effluent. 16:1ω5c and 15:0iso3OH, possibly related with Flexibacter sp., caused a bulking problem in the activated sludge. The community diversity indices such as Shannon diversity and equability decreased in summer but increased in autumn in all the ASRs. Canonical correspondence analysis results suggested that the influent BOD concentration played the most important role in changing MCS, followed by influent TSS concentration. In addition, the TSS and total phosphorus concentrations in the effluent were significantly affected by the change of the MCS.

Purpose: To explore the evaluation between tourists’ and residents’ satisfaction with the tool of Importance-Satisfaction Analysis originally introduced by Importance-Performance Analysis. Design/methodology/approach: A survey was conducted at Anseong Matchum Land, the venue of the festival, and respondents were selected by the convenience sampling method. Also, rest areas at the festival site were used for data collection and 309 out of 400 were judged to be valid. Included in the questionnaires was a series of Likert-type questions about the respondent’s satisfaction with 18 attributes of the festival and the importance of these attributes to overall satisfaction with the festival. Findings: The results of this research show that the average degree of importance for the 18 attributes is 3.89, and the degree of satisfaction is 3.02. The result identified that parking lot, rest area, and washroom were found included in “Concentrate Here” quadrant of the ISA matrix as needed to prior management in this festival. Research limitations/implications: This study tried to look into local heritage festival regarding comparative perception between festival goers and local residents with ISA to provide that actual problems and potential solutions to the decision-makers of the city to make a sustainable festival. However, it is difficult to extend the results of this festival to other festivals and further research is needed in the future. Originality/value: This research tried to find out the gap between perceived Importance and Satisfaction and to identify actual management problems regarding facilities and services of the festival by ISA analysis originally introduced by Importance-Performance Analysis. This study suggested what festival organizers should prepare for the festival that will be activated after the Covid 19 pandemic.