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Integrating Artificial intelligence within sustainable smart analytical chemistry for analyzing the divisor impact on UV-spectrophotometric efficiency of solifenacin and mirabegron combination

Hayam M. Lotfy et al · Nature Portfolio · 2026

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Abstract This study examines the influence of divisor selection on the efficacy of advanced analytical spectrophotometric methods that integrate artificial intelligence (AI), green-chemistry principles, and white-analytical-chemistry (WAC) frameworks for pharmaceutical investigation. Advanced analytical chemistry, which combines environmental sustainability, analytical practice and computational cleverness, was employed to create innovative spectrophotometric techniques for the concurrent quantification of solifenacin succinate (SOF), and mirabegron (MIR), both utilized in the treatment of overactive bladder. Three divisor approaches were evaluated within complementary smart resolution strategies based on high impact amplitude manipulation method (HIAM) using normalized divisor of MIR, concentration-dependent divisor of MIR at 3.0, 8.0, and 14.0 µg/mL, as well as extracted zero order spectra of MIR obtained by absorbance resolution method (AR). Linearity for SOF was observed from 2.5 to 25.0 µg/mL using first derivative D1at 222 nm, while MIR exhibited linearity from 1.5 to 15.0 µg/mL at its maxima 249.0 nm. To assess the robustness and risk, cumulative validation score; CVS, was calculated, serving as instrumental sign for evaluating analytical reliability and method performance. We introduce Sustainable & Smart Analytical Chemistry (SSAC), conjoining Green Analytical Chemistry (GAC), WAC, and AI to develop analytical methods that are efficient, environmentally responsible, and consistent with the multiple Sustainable Development Goals; SDGs. The Sustainability of Analytical Methods Index (SAMI) was applied to evaluate the method’s holistic alignment with the 17 SDGs. Using the Multi-Color Assessment Tool (MA), the method’s greenness, realism, presentation, and novelty were evaluated, demonstrating its sustainability and global impact.

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APA 7

al, H. M. L. E. (2026). Integrating Artificial intelligence within sustainable smart analytical chemistry for analyzing the divisor impact on UV-spectrophotometric efficiency of solifenacin and mirabegron combination. https://doi.org/10.1038/s41598-026-44688-x

MLA

al, Hayam M. Lotfy et. "Integrating Artificial intelligence within sustainable smart analytical chemistry for analyzing the divisor impact on UV-spectrophotometric efficiency of solifenacin and mirabegron combination." 2026. https://doi.org/10.1038/s41598-026-44688-x.

Chicago

al, Hayam M. Lotfy et. 2026. "Integrating Artificial intelligence within sustainable smart analytical chemistry for analyzing the divisor impact on UV-spectrophotometric efficiency of solifenacin and mirabegron combination.". https://doi.org/10.1038/s41598-026-44688-x.

Harvard

al, H. M. L. E. 2026, Integrating Artificial intelligence within sustainable smart analytical chemistry for analyzing the divisor impact on UV-spectrophotometric efficiency of solifenacin and mirabegron combination, Nature Portfolio, available at: https://doi.org/10.1038/s41598-026-44688-x [Accessed 8 Aug. 2026].

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Titolo
Integrating Artificial intelligence within sustainable smart analytical chemistry for analyzing the divisor impact on UV-spectrophotometric efficiency of solifenacin and mirabegron combination
Autore / collaboratori
Hayam M. Lotfy et al
Editore
Nature Portfolio
Anno di pubblicazione
2026
ISSN
2045-2322
ISSN
2045-2322
Lingua
Inglés

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