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The last 20 years have seen many publications investigating porous solids for gas adsorption and separation. The abundance of adsorbent materials (this work identifies 1608 materials for CO2/N2 separation alone) provides a challenge to obtaining a comprehensive view of the field, identifying leading design strategies, and selecting materials for process modeling. In 2021, the empirical bound visualization technique was applied, analogous to the Robeson upper bound from membrane science, to alkane/alkene adsorbents. These bound visualizations reveal that adsorbent materials are limited by design trade-offs between capacity, selectivity, and heat of adsorption. The current work applies the bound visualization to adsorbents for a wider range of gas pairs, including CO2, N2, CH4, H2, Xe, O2, and Kr. How this visual tool can identify leading materials and place new material discoveries in the context of the wider field is presented. The most promising current strategies for breaking design trade-offs are discussed, along with reproducibility of published adsorption literature, and the limitations of bound visualizations. It is hoped that this work inspires new materials that push the bounds of traditional trade-offs while also considering practical aspects critical to the use of materials on an industrial scale such as cost, stability, and sustainability. [ABSTRACT FROM AUTHOR]

Introduction: Tuberculosis (TB) stands as a paramount global health concern, contributing significantly to worldwide mortality rates. Effective containment of TB requires deployment of cost-efficient screening method with limited resources. To enhance the precision of resource allocation in the global fight against TB, this research proposed chest X-ray radiography (CXR) based machine learning screening algorithms with optimization, benchmarking and tuning for the best TB subclassification tasks for clinical application., Methods: This investigation delves into the development and evaluation of a robust ensemble deep learning framework, comprising 43 distinct models, tailored for the identification of active TB cases and the categorization of their clinical subtypes. The proposed framework is essentially an ensemble model with multiple feature extractors and one of three fusion strategies-voting, attention-based, or concatenation methods-in the fusion stage before a final classification. The comprised de-identified dataset contains records of 915 active TB patients alongside 1,276 healthy controls with subtype-specific information. Thus, the realizations of our framework are capable for diagnosis with subclass identification. The subclass tags include: secondary tuberculosis/tuberculous pleurisy; non-cavity/cavity; secondary tuberculosis only/secondary tuberculosis and tuberculous pleurisy; tuberculous pleurisy only/secondary tuberculosis and tuberculous pleurisy., Results: Based on the dataset and model selection and tuning, ensemble models show their capability with self-correction capability of subclass identification with rendering robust clinical predictions. The best double-CNN-extractor model with concatenation/attention fusion strategies may potentially be the successful model for subclass tasks in real application. With visualization techniques, in-depth analysis of the ensemble model's performance across different fusion strategies are verified., Discussion: The findings underscore the potential of such ensemble approaches in augmenting TB diagnostics with subclassification. Even with limited dataset, the self-correction within the ensemble models still guarantees the accuracies to some level for potential clinical decision-making processes in TB management. Ultimately, this study shows a direction for better TB screening in the future TB response strategy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Sun, Xing, Wan, Ding, Wang, Zhong, Zhou, Gong, Li and Zhang.)