http://hdl.handle.net/10563/1000003 2026-07-23T17:57:40Z 2026-07-23T17:57:40Z Hamza Ul Haq Yasir, Muhammad Aslam Khan, Muhammad Nouman Gul, Jawad Zubair, Mukarram Ali, Hassan Sedlařík, Vladimír Ahmad, Nasir. M. http://hdl.handle.net/10563/1012830 2026-07-09T23:10:54Z 2026-01-01T00:00:00Z

Optimized adsorption removal and capacity prediction of anionic pollutants using a hybrid strategy of machine learning algorithms Hamza Ul Haq; Yasir, Muhammad; Aslam Khan, Muhammad Nouman; Gul, Jawad; Zubair, Mukarram; Ali, Hassan; Sedlařík, Vladimír; Ahmad, Nasir. M. Accurate prediction of adsorption performance is crucial for optimizing wastewater treatment systems, however, the complex interactions among operational variables and adsorbent properties often limit conventional modelling approaches. In this study, a machine learning framework was developed to predict the adsorption removal efficiency and kinetic capacity of anionic pollutants in aqueous systems. A comprehensive experimental dataset was generated using four representative pollutants, i.e., bovine serum albumin, methyl orange, sulfate, and nitrate and four adsorbent materials, including powdered activated carbon (PAC), thermally modified PAC, chemically modified PAC, and ion-exchange chitosan beads. Key operational parameters, including pH, contact time, adsorbent dosage, BET surface area, solution volume, and pollutant concentration, were used as input features. Four ML algorithms, i.e., Decision Tree (DT), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Ensemble Learning Tree (ELT), were developed and further optimized using Bayesian optimization to improve predictive performance. Among the evaluated models, the optimized ELT model demonstrated the highest predictive accuracy with a coefficient of determination (R2) of 0.78, indicating its strong capability in capturing nonlinear adsorption behavior. Model interpretation through partial dependence plots revealed significant influences of pH, adsorbent dosage, BET surface area, and initial pollutant concentration on adsorption performance, while Sobol sensitivity analysis confirmed the dominant role of initial concentration. Experimental validation using jojoba-derived biochar for the removal of methyl orange and Eriochrome Black T dyes showed strong agreement with model predictions. The developed ML models provide a reliable tool for predicting adsorption performance and designing efficient adsorbent-based wastewater treatment systems.

2026-01-01T00:00:00Z Batool, Mehwish Haidar, Usman Khan, Asim Laeeq Aslam, Muhammad Alsubaie Abdullah Saad Yasir, Muhammad Zaheen, Aqsa Asad Abbas, M. Batool, Iram Ahmad, Nasir M. http://hdl.handle.net/10563/1012829 2026-07-09T23:04:15Z 2026-01-01T00:00:00Z

Aminopropyltriethoxysilane-Enhanced Activated Carbon with Polyether Sulfone-Cellulose Acetate Mixed Matrix Nanofiltration Membranes for Water Purification Batool, Mehwish; Haidar, Usman; Khan, Asim Laeeq; Aslam, Muhammad; Alsubaie Abdullah Saad; Yasir, Muhammad; Zaheen, Aqsa; Asad Abbas, M.; Batool, Iram; Ahmad, Nasir M.

2026-01-01T00:00:00Z Pokorný, Tomáš Macháč, Petr Moravec, Zdeněk Šimoníková, Lucie Leonová, Lucie Hlavenková, Zuzana Škoda, David Pacultová, Kateřina Karásková, Kateřina Stýskalík, Aleš http://hdl.handle.net/10563/1012821 2026-07-09T22:07:16Z 2026-01-01T00:00:00Z

Ni- and Zn-doping effects on Cu/SiO2 catalysts in nonoxidative ethanol dehydrogenation Pokorný, Tomáš; Macháč, Petr; Moravec, Zdeněk; Šimoníková, Lucie; Leonová, Lucie; Hlavenková, Zuzana; Škoda, David; Pacultová, Kateřina; Karásková, Kateřina; Stýskalík, Aleš Nonoxidative ethanol dehydrogenation opens a pathway for the sustainable production of acetaldehyde and butadiene. One crucial aspect of producing butadiene by the Lebedev process is the high-temperature stability of ethanol to acetaldehyde conversion. However, copper-based catalysts, despite exhibiting high activity and selectivity, suffer from sintering and coking and need to be improved for successful industrial applications. Herein, we show Cu-based (similar to 2.5 wt %) catalysts doped with Ni and Zn (0.028-0.36 wt %) to improve the catalytic performance of nanoparticles. The catalysts were prepared by hydrolytic sol-gel and dry impregnation methods. STEM analysis determined the nanoparticle sizes in the 1.9-2.8 nm range. Ni-doped catalysts outperformed the parent Cu catalysts in ethanol dehydrogenation activity at lower temperatures (185-220 degrees C) but suffered from faster deactivation. The Zn-doped catalysts exhibited improved high-temperature stability. For these materials, acetaldehyde selectivity fluctuated around similar to 90% and acetaldehyde productivity reached 3.63 g g-1 h-1 at 290 degrees C and a WHSV of 4.73 h-1. The improved stability of the Zn-doped samples was correlated with lower coke formation (XPS, TG analysis, and Raman spectroscopy).

2026-01-01T00:00:00Z Muchová, Monika Münster, Lukáš Kolařík, Roman Víchová, Zdenka Vašíček, Ondřej Humpolíček, Petr Vícha, Jan http://hdl.handle.net/10563/1012822 2026-07-09T22:07:17Z 2026-01-01T00:00:00Z

Simultaneous cross-linking and nanoparticle anchoring by dialdehyde cellulose in injectable composite chitosan/polypyrrole hydrogels Muchová, Monika; Münster, Lukáš; Kolařík, Roman; Víchová, Zdenka; Vašíček, Ondřej; Humpolíček, Petr; Vícha, Jan The injectable composite hydrogel with covalently bound polypyrrole (PPy) has been prepared using dialdehyde cellulose (DAC) as a bifunctional cross-linker, forming dynamic imine bonds with water-soluble half acetylated chitosan (SCN) and simultaneously tethering the PPy nanoparticles by aldol condensation. The novelty lies in translating this dual chemistry into an injectable, self-healing hydrogel system, for the first time fully utilizing dynamic Schiff base cross-linking in combination with covalent PPy anchoring. PPy is also involved both in hydrogel cross-linking, altering its rheological behavior, but also providing antioxidative and anti-inflammatory effects. The resulting hydrogels exhibited shear-thinning behavior, rapid self-healing, and storage moduli ranging from 25 to 47 Pa, allowing for injection through 21 G needles. All formulations were noncytotoxic toward NIH/3T3 fibroblasts and RAW 264.7 macrophages. In scratch assays, SCN_DAC_20_PPy significantly accelerated wound closure, with the residual wound area to 39 ± 2% after 10 h versus 83 ± 7% for controls and 65 ± 3% for the corresponding PPy-free hydrogel. In LPS-stimulated macrophages, all hydrogels decreased nitric oxide production, and PPy-containing hydrogels additionally reduced IL-6 secretion. The SCN/DAC/PPy injectable hydrogels thus exhibit cytocompatibility, self-healing properties, and anti-inflammatory activity, representing a promising platform for the future development of advanced wound dressings.

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