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Article Citation - WoS: 6Citation - Scopus: 7Polyethyleneimine Functionalized Waste Tissue Paper@waste PET Composite for the Effective Adsorption and Filtration of Organic Dyes From Wastewater(Elsevier B.V., 2025) Radoor, Sabarish; Karayil, Jasila; Devrim, Yilser; Kim, HernThis study explores the potential of repurposing discarded plastic bottles and cellulosic paper waste to develop cost-effective and high-performance composites for dye removal applications. A novel composite, polyethyleneimine (PEI)-functionalized waste tissue integrated into waste polyethylene terephthalate (wPET) (PEIWT/wPET), was designed as an environmentally friendly adsorbent for wastewater treatment. Successful surface functionalization with PEI was confirmed through FTIR, EDX, and XPS analyses. The PEI-modified composite exhibited enhanced mechanical and thermal stability while demonstrating significantly improved dye adsorption/filtration performance. The composite was evaluated for the removal of both cationic (crystal violet, CV) and anionic (orange II, O II) dyes under optimized conditions; (10,000 mg/L and 1666 mg/L) adsorbent dosage, (11 and 1) pH, 10 mg/L initial dye concentration, and (180 min and 120 min) contact time for CV and O II respectively. Experimental results showed that PEIWT/wPET achieved maximum adsorption capacities of 3.94 mg/g for CV and 11.73 mg/g for O II, approximately five times higher than the unmodified composite (0.74 and 2.4 mg/g). Adsorption isotherm and kinetic studies indicated that the data aligned well with the Langmuir as well as Freundlich and pseudo-second order models. The membrane also exhibited filtration capability for both dyes, achieving a filtration efficiency of 78.69 % for anionic and 41.31 % for cationic dye separation. Overall, the PEIWT/wPET composite offers a promising, sustainable, and energy-efficient solution for the removal of organic pollutants.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.