Flood bend flow prediction in intermittent river reach using a 2D hydraulic model and stacking-ensemble-based LSTM technique DOI

Wen‐Dar Guo,

Wei‐Bo Chen,

Chih-Hsin Chang

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 18(1)

Published: Dec. 20, 2024

Language: Английский

Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants DOI Creative Commons

Muhammad Faisal Javed,

Bilal Siddiq,

Kennedy C. Onyelowe

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102637 - 102637

Published: July 29, 2024

Airborne contaminants pose significant environmental and health challenges. Titanium dioxide (TiO2) has emerged as a leading photocatalyst in the degradation of air compared to other photocatalysts due its inherent inertness, cost-effectiveness, photostability. To assess effectiveness, laboratory examinations are frequently employed measure photocatalytic rate TiO2. However, this approach involves time-consuming requirements, labor-intensive tasks, high costs. In literature, ensemble or standalone models commonly used for assessing performance TiO2 water contaminants. Nonetheless, application metaheuristic hybrid potential be more effective predictive accuracy efficiency. Accordingly, research utilized machine learning (ML) algorithms estimate photo-degradation constants organic pollutants using nanoparticles exposure ultraviolet light. Six metaheuristics optimization algorithms, namely, nuclear reaction (NRO), differential evolution algorithm (DEA), human felicity (HFA), lightning search (LSA), Harris hawks (HHA), tunicate swarm (TSA) were combined with random forest (RF) technique establish models. A database 200 data points was acquired from experimental studies model training testing. Furthermore, multiple statistical indicators 10-fold cross-validation examine established model's robustness. The TSA-RF demonstrated superior prediction among six suggested models, achieving an impressive correlation (R) 0.90 lower root mean square error (RMSE) 0.25. contrast, HFA-RF, HHA-RF, NRO-RF exhibited slightly R-value 0.88, RMSE scores 0.32. DEA-RF LSA-RF while effective, showed marginally 0.85, values 0.45 0.44, respectively. Moreover, SHapley Additive exPlanation (SHAP) results indicated that rates through photocatalysis most notably influenced by factors such reactor sizes, dosage, humidity, intensity.

Language: Английский

Citations

21
Progress in prediction of photocatalytic CO2 reduction using machine learning approach: A mini review DOI
Mir Mohammad Ali, Md. Arif Hossen, Azrina Abd Aziz

et al.

Next Materials, Journal Year: 2025, Volume and Issue: 8, P. 100522 - 100522

Published: Feb. 10, 2025

Language: Английский

Citations

2
Experimental and AI-driven enhancements in gas-phase photocatalytic CO2 conversion over synthesized highly ordered anodic TiO2 nanotubes DOI
Md. Arif Hossen, Md Munirul Hasan, Yunus Ahmed

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 327, P. 119544 - 119544

Published: Jan. 24, 2025

Language: Английский

Citations

2
Resilient 3D printed porous biodegradable Polylactic Acid coated with Bismuth Ferrite for Piezo Enhanced Photocatalysis degradation assisted by Machine Learning DOI

Manshu Dhillon,

Tushar Moitra,

Shivali Dhingra

et al.

Nano Energy, Journal Year: 2025, Volume and Issue: unknown, P. 111010 - 111010

Published: April 1, 2025

Language: Английский

Citations

1
Transformative strategies in photocatalyst design: merging computational methods and deep learning DOI Open Access
Jianqiao Liu, Liqian Liang, Baofeng Su

et al.

Journal of Materials Informatics, Journal Year: 2024, Volume and Issue: 4(4)

Published: Dec. 31, 2024

Photocatalysis is a unique technology that harnesses solar energy through in-situ processes, operating without the need for external inputs. It integral to advancing environmental, energy, chemical, and carbon-neutral objectives, promoting dual goals of pollution control carbon reduction. However, conventional approach photocatalyst design faces challenges such as inefficiency, high costs, low success rates, highlighting integrating modern technologies seeking new paradigms. Here, we demonstrate comprehensive overview transformative strategies in design, combining computational materials science with deep learning technologies. The review covers fundamental principles followed by examination methods workflow deep-learning-assisted design. Deep approaches are extensively reviewed, focusing on discovery novel photocatalysts, microstructure property optimization, approaches, application exploration, mechanistic insights into photocatalysis. Finally, highlight synergy between multidimensional computation learning, while discussing future directions development. This offers summary offering not only enhance development photocatalytic but also expand practical applications photocatalysis various domains.

Language: Английский

Citations

7
Optimized prediction modeling of micropollutant removal efficiency in forward osmosis membrane systems using explainable machine learning algorithms DOI
Ali Aldrees, Muhammad Faisal Javed, Majid Khan

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 66, P. 105937 - 105937

Published: Aug. 19, 2024

Language: Английский

Citations

5
Ti3C2Tx MXene-Based Hybrid Photocatalysts in Organic Dye Degradation: A Review DOI Creative Commons

Tank R. Seling,

Mackenzie Songsart-Power,

Amit Kumar Shringi

et al.

Molecules, Journal Year: 2025, Volume and Issue: 30(7), P. 1463 - 1463

Published: March 26, 2025

This review provides an overview of the fabrication methods for Ti3C2Tx MXene-based hybrid photocatalysts and evaluates their role in degrading organic dye pollutants. MXene has emerged as a promising material due to its high metallic conductivity, excellent hydrophilicity, strong molecular adsorption, efficient charge transfer. These properties facilitate faster separation minimize electron–hole recombination, leading exceptional photodegradation performance, long-term stability, significant attention degradation applications. significantly improve efficiency, evidenced by higher percentage reduced time compared conventional semiconducting materials. also highlights computational techniques employed assess enhance performance degradation. It identifies challenges associated with photocatalyst research proposes potential solutions, outlining future directions address these obstacles effectively.

Language: Английский

Citations

0
Deep Insights into the Integration of Artificial Neural Networks (ANNs) for Predicting the Photocatalytic Activities of Metal-Based Catalysts in Water Pollutant Reduction DOI
Mohd Adnan, Mohd Fadhil Majnis, Wan Nazirah Wan Md Adnan

et al.

Journal of environmental chemical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 116350 - 116350

Published: March 1, 2025

Language: Английский

Citations

0
Transformative Approaches in Photocatalytic CO2 Conversion: The Impact of AI and Computational Chemistry DOI
Nur Umisyuhada Mohd Nor,

Khaireddin Boukayouht,

Samir El Hankari

et al.

Current Opinion in Green and Sustainable Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 101027 - 101027

Published: April 1, 2025

Language: Английский

Citations

0
Synthesis, characterization, and photocatalytic degradation of methylene blue dye using Bi2S3/WS2/gC3N4-based heterojunction nanocomposite DOI

Priyanka Jangra,

Preeti Kumari, S. K. Sharma

et al.

Materials Science in Semiconductor Processing, Journal Year: 2025, Volume and Issue: 197, P. 109710 - 109710

Published: May 26, 2025

Language: Английский

Citations

0