Peer-reviewedCC BYRSC Advances
Green synthesis of Ag/MgO nanocomposites from red pepper seed waste for efficient sunlight-driven diclofenac degradation: experimental investigation and EEFO-optimized DT_LSBoost predictive modeling
Abstract
), while residual analysis confirmed its robustness and generalization ability. A MATLAB-based graphical user interface was further developed to enable rapid prediction of photocatalytic performance under different operating conditions. The integration of green-synthesized Ag/MgO NCs with an EEFO-optimized DT-LSBoost model provides a novel and efficient framework for predicting solar-driven diclofenac degradation. This integrated strategy highlights the potential of combining green nanotechnology with explainable machine learning to support the design, optimization, and practical implementation of sustainable photocatalytic water-treatment systems.
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BibTeX
@article{mechati2026green,
title = {{Green synthesis of Ag/MgO nanocomposites from red pepper seed waste for efficient sunlight-driven diclofenac degradation: experimental investigation and EEFO-optimized DT_LSBoost predictive modeling}},
author = {Sabrina Mechati and Meriem Zamouche and Hichem Tahraoui and Nasreddine Hadjadj and Mohammed Kebir and Sonia Cherif and Jie Zhang and Lotfi Mouni and Abdeltif Amrane},
journal = {RSC Advances},
year = {2026},
month = oct,
doi = {10.1039/d6ra06722k},
url = {https://hal.science/hal-05781147v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Green synthesis of Ag/MgO nanocomposites from red pepper seed waste for efficient sunlight-driven diclofenac degradation: experimental investigation and EEFO-optimized DT_LSBoost predictive modeling
AU - Sabrina Mechati
AU - Meriem Zamouche
AU - Hichem Tahraoui
AU - Nasreddine Hadjadj
AU - Mohammed Kebir
AU - Sonia Cherif
AU - Jie Zhang
AU - Lotfi Mouni
AU - Abdeltif Amrane
PY - 2026
DA - 2026/10/06
JO - RSC Advances
DO - 10.1039/d6ra06722k
UR - https://hal.science/hal-05781147v1
AB - ), while residual analysis confirmed its robustness and generalization ability. A MATLAB-based graphical user interface was further developed to enable rapid prediction of photocatalytic performance under different operating conditions. The integration of green-synthesized Ag/MgO NCs with an EEFO-optimized DT-LSBoost model provides a novel and efficient framework for predicting solar-driven diclofenac degradation. This integrated strategy highlights the potential of combining green nanotechnology with explainable machine learning to support the design, optimization, and practical implementation of sustainable photocatalytic water-treatment systems.
SN - 2046-2069
ER -