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Article Citation - WoS: 1Citation - Scopus: 1Effect of Atomic Charges on C2H2/Co2 Separation Performances of Covalent-Organic Framework Adsorbents(John Wiley and Sons Inc, 2025) Demir, H.; Erucar, I.A critical factor for the accuracy of computational screening studies is the method employed to assign atomic charges. While chemically meaningful atomic charges can be obtained using a quantum chemistry method-based charge assignment technique (density-derived electrostatic and chemical method (DDEC6)), its application to large material datasets remains computationally demanding. As an alternative, machine-learning (ML) models can offer the ability to determine atomic charges with high accuracy and speed. Herein, two ML models, Partial Atomic Charge Predicter for Porous Materials based on Graph Convolutional Neural Network (PACMAN) and Partial Atomic Charges in Metal-Organic Frameworks (PACMOF), are utilized to predict atomic charges in Clean, Uniform, Refined with Automatic Tracking from Experimental Database (CURATED) covalent-organic frameworks (COFs). The predicted atomic charges are used in simulations to assess COFs' C2H2/CO2/CH4 separation performances in comparison with reference DDEC6-based performances. Results show PACMAN charges can more effectively reproduce DDEC6-based charges and corresponding separation performance metrics, underscoring their suitability for high-throughput material screening. Additionally, the proportions of Coulombic interactions to van der Waals interactions are systematically analyzed, revealing substantial variation across both narrow and wide pores. This study highlights that ML models can be applied to obtain atomic charges that could enable attaining accurate material performance evaluations. © 2025 The Author(s). Advanced Theory and Simulations published by Wiley-VCH GmbH.Article Dual Zn/Zr Hybrid Framework-Integrated Membranes With Enhanced Proton Conductivity and Durability for High-Temperature PEM Fuel Cells(John Wiley and Sons Inc, 2025) Altınışık, H.; Devrim, Y.This study proposes an innovative strategy for fabricating advanced composite membranes based on a poly[2,2′-(m-phenylene)-5,5′-bibenzimidazole] (PBI) matrix for high-temperature proton exchange membrane fuel cells (HT-PEMFCs). A co-synthesized hybrid porous framework incorporating both Zn- and Zr-based nanostructures was integrated into the PBI backbone, ensuring uniform dispersion and strong interfacial bonding, as verified by comprehensive structural and morphological characterizations. This dual-framework architecture promoted the formation of continuous proton-conductive channels and enhanced membrane stability under demanding operating conditions. Furthermore, the membranes were utilized after acid doping, and the hybrid structure effectively mitigated the acid leaching issue, ensuring stable long-term proton conductivity. At 0.6 V and 170°C, the membranes achieved a current density of ≈630 mA/cm2, demonstrating the critical role of structural optimization in improving fuel cell efficiency. These findings offer valuable insights into designing scalable, durable, and thermally stable membranes for next-generation HT-PEMFC applications. © 2025 Society of Plastics Engineers.