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<p>Herein, Pristine barium oxide (BaO) and zinc-doped barium oxide nanorods (Zn–BaO NRs) were fabricated using a facile chemical co-precipitation approach. Density of states (DOS) analysis shows that Zn incorporation in BaO introduces electronic states near the Fermi level, enhancing charge carrier mobility and promoting efficient charge separation. In photocatalytic tests, Zn–BaO NRs demonstrated superior activity under UV irradiation, achieving 99.05% degradation within 70&#xa0;min, with a kinetic rate constant of 0.03626&#xa0;min<sup>−1</sup> and an excellent fit (R<sup>2</sup> = 0.99279). The Least Squares Boosting (LSBoost) algorithm was employed to model and predict the photocatalytic performance with high accuracy. Consequently, excellent agreement between experimental results and LSBoost predictions was achieved at optimal conditions of 45&#xa0;mg catalyst dosage, 10&#xa0;mg/L&#xa0;MB concentration, and 250&#xa0;rpm stirring speed. As demonstrated in this study, integrating artificial intelligence with photocatalytic nanomaterials offers efficient, cost-effective, and optimized micropollutant removal from water.</p>

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Electronic structure engineering of Zn-doped BaO nanorods for enhanced photocatalytic degradation of methylene blue: AI-assisted optimization of the degradation process

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Shoaib Siddique,
  • Bo-Tau Liu,
  • Tehreem Tariq,
  • Muhammad Hilal

摘要

Herein, Pristine barium oxide (BaO) and zinc-doped barium oxide nanorods (Zn–BaO NRs) were fabricated using a facile chemical co-precipitation approach. Density of states (DOS) analysis shows that Zn incorporation in BaO introduces electronic states near the Fermi level, enhancing charge carrier mobility and promoting efficient charge separation. In photocatalytic tests, Zn–BaO NRs demonstrated superior activity under UV irradiation, achieving 99.05% degradation within 70 min, with a kinetic rate constant of 0.03626 min−1 and an excellent fit (R2 = 0.99279). The Least Squares Boosting (LSBoost) algorithm was employed to model and predict the photocatalytic performance with high accuracy. Consequently, excellent agreement between experimental results and LSBoost predictions was achieved at optimal conditions of 45 mg catalyst dosage, 10 mg/L MB concentration, and 250 rpm stirring speed. As demonstrated in this study, integrating artificial intelligence with photocatalytic nanomaterials offers efficient, cost-effective, and optimized micropollutant removal from water.