Comprehensive study on the Mn–ZnO/SiO2 nanocomposites: from the digital colorimetry to the visible-light photocatalysis

A series of manganese-doped ZnO/SiO 2 nanocomposites was studied to establish the structure-property relationships governing visible-light photocatalysis and to assess whether digital colorimetry, chemometrics and machine learning can provide rapid, nondestructive composition control for doped semiconductor/support systems, using the antiviral drug lopinavir as a model pollutant. The novelty lies in coupling machine learning, chemometrics-supported spectroscopy, and digital colorimetry with conventional characterization to enable rapid, nondestructive compositional quality control of this doped oxide/support system.Nanocomposites containing 3, 6, and 9 mmol of Mn–ZnO per gram of SiO 2 were prepared by a wet chemical route followed by thermal treatment at 600 °C. They were characterized by X-ray diffraction, Raman spectroscopy, scanning electron microscopy with AI-assisted image segmentation, nitrogen adsorption–desorption, atomic force microscopy, UV-visible spectroscopy and photoluminescence. Digital images were classified using convolutional neural network (Inception v3) embeddings combined with a support vector machine, and infrared spectra (FTIR and IR-ATR) were modeled by principal component analysis and NIPALS regression.Hexagonal wurtzite ZnO formed, with crystallite sizes of 27.7–31.0 nm. Changes in the Raman A 1 (LO) mode were consistent with defect-related lattice distortion, including contributions from oxygen vacancy- and zinc-interstitial-related states. The predominantly mesoporous solids exhibited specific surface areas of 110–160 m 2 g −1 and enhanced visible-light absorption with defect-mediated photoluminescence. The classification pipeline accurately discriminated the samples, and IR-ATR provided improved compositional predictions compared with conventional FTIR. The most active composite, 9-Mn–ZnO/SiO 2 , removed 96 % of lopinavir within 1 h under visible light ( k 1 = 0.0465 min −1 ), with • OH and O 2 −• implicated as the dominant reactive species and stable performance over four reuse cycles.This integrated approach provides mechanistic insight into structure-property relationships and practical, non-destructive quality-control tools for visible-light-active doped metal oxide/support photocatalysts.

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Journal
Journal of Materials Science
Published
2026-10-13
DOI
https://doi.org/10.1007/s10853-026-13831-4
Primary Topic
Advanced Photocatalysis Techniques
Type
article
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article

Comprehensive study on the Mn–ZnO/SiO2 nanocomposites: from the digital colorimetry to the visible-light photocatalysis

João F. Chaves, Rogério Navarro Correia de Siqueira, Bojan A. Marinković, Olena Artiushenko et al.
Journal of Materials Science
Advanced Photocatalysis Techniques
article

Comprehensive study on the Mn–ZnO/SiO2 nanocomposites: from the digital colorimetry to the visible-light photocatalysis

João F. Chaves, Rogério Navarro Correia de Siqueira, Bojan A. Marinković, Olena Artiushenko, Mariia V. Galaburda, Esteban Camilo Moreno Diaz, Braulio Soares Archanjo, Gustavo Chacón, Bożena Czech, Danian A. Dugato, R.J. Caraballo-Vivas, Michael Nazarkovsky, Artur Sokołowski, Enrique Roy Dionisio Calderon, Nilson Francisco Ladaim de Paula, A. M. G. Pimenta, R. G. Costa
article en

Abstract

A series of manganese-doped ZnO/SiO 2 nanocomposites was studied to establish the structure-property relationships governing visible-light photocatalysis and to assess whether digital colorimetry, chemometrics and machine learning can provide rapid, nondestructive composition control for doped semiconductor/support systems, using the antiviral drug lopinavir as a model pollutant. The novelty lies in coupling machine learning, chemometrics-supported spectroscopy, and digital colorimetry with conventional characterization to enable rapid, nondestructive compositional quality control of this doped oxide/support system.Nanocomposites containing 3, 6, and 9 mmol of Mn–ZnO per gram of SiO 2 were prepared by a wet chemical route followed by thermal treatment at 600 °C. They were characterized by X-ray diffraction, Raman spectroscopy, scanning electron microscopy with AI-assisted image segmentation, nitrogen adsorption–desorption, atomic force microscopy, UV-visible spectroscopy and photoluminescence. Digital images were classified using convolutional neural network (Inception v3) embeddings combined with a support vector machine, and infrared spectra (FTIR and IR-ATR) were modeled by principal component analysis and NIPALS regression.Hexagonal wurtzite ZnO formed, with crystallite sizes of 27.7–31.0 nm. Changes in the Raman A 1 (LO) mode were consistent with defect-related lattice distortion, including contributions from oxygen vacancy- and zinc-interstitial-related states. The predominantly mesoporous solids exhibited specific surface areas of 110–160 m 2 g −1 and enhanced visible-light absorption with defect-mediated photoluminescence. The classification pipeline accurately discriminated the samples, and IR-ATR provided improved compositional predictions compared with conventional FTIR. The most active composite, 9-Mn–ZnO/SiO 2 , removed 96 % of lopinavir within 1 h under visible light ( k 1 = 0.0465 min −1 ), with • OH and O 2 −• implicated as the dominant reactive species and stable performance over four reuse cycles.This integrated approach provides mechanistic insight into structure-property relationships and practical, non-destructive quality-control tools for visible-light-active doped metal oxide/support photocatalysts.

Journal of Materials Science
Maria Curie-Skłodowska University (PL), Pontifícia Universidade Católica do Rio de Janeiro (BR), Instituto Nacional de Metrologia, Qualidade e Tecnologia (BR), Chuiko Institute of Surface Chemistry (UA), Centro Brasileiro de Pesquisas Físicas (BR), Instituto de Tecnología Química (ES)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Hebrew University of Jerusalem, Narodowe Centrum Nauki, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Universidade Federal do Rio Grande do Sul, Fundación General CSIC, Consejo Superior de Investigaciones Científicas
Openalex Percentile: Top 34%
Advanced Photocatalysis Techniques
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