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Data-Driven Optimization of TiO2 Sol-Gel Synthesis: Insights from Statistical and Machine Learning Approaches

by Gladys Egyir, Terfa Jude Igba, Henry Makinde, Victor Stanley Francis, Jeffrey Christian Ayerh, Frederick Adrah, Dennis Opoku Boakye
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 49
Year of Publication: 2025
Authors: Gladys Egyir, Terfa Jude Igba, Henry Makinde, Victor Stanley Francis, Jeffrey Christian Ayerh, Frederick Adrah, Dennis Opoku Boakye
10.5120/ijca2025925846

Gladys Egyir, Terfa Jude Igba, Henry Makinde, Victor Stanley Francis, Jeffrey Christian Ayerh, Frederick Adrah, Dennis Opoku Boakye . Data-Driven Optimization of TiO2 Sol-Gel Synthesis: Insights from Statistical and Machine Learning Approaches. International Journal of Computer Applications. 187, 49 ( Oct 2025), 62-66. DOI=10.5120/ijca2025925846

@article{ 10.5120/ijca2025925846,
author = { Gladys Egyir, Terfa Jude Igba, Henry Makinde, Victor Stanley Francis, Jeffrey Christian Ayerh, Frederick Adrah, Dennis Opoku Boakye },
title = { Data-Driven Optimization of TiO2 Sol-Gel Synthesis: Insights from Statistical and Machine Learning Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2025 },
volume = { 187 },
number = { 49 },
month = { Oct },
year = { 2025 },
issn = { 0975-8887 },
pages = { 62-66 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number49/data-driven-optimization-of-tio2-sol-gel-synthesis-insights-from-statistical-and-machine-learning-approaches/ },
doi = { 10.5120/ijca2025925846 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-10-23T00:18:28.978132+05:30
%A Gladys Egyir
%A Terfa Jude Igba
%A Henry Makinde
%A Victor Stanley Francis
%A Jeffrey Christian Ayerh
%A Frederick Adrah
%A Dennis Opoku Boakye
%T Data-Driven Optimization of TiO2 Sol-Gel Synthesis: Insights from Statistical and Machine Learning Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 49
%P 62-66
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Titanium dioxide (TiO₂) is used extensively in products from pigments and sunscreens to optical components. The sol–gel synthesis of TiO₂ is controlled by an intricate set of interactive parameters of which optimization is an important issue. A set of 290 experimental conditions was studied in detail to model and optimize yield of TiO₂ by means of statistical and machine learning methodologies. Out of the methodologies studied, polynomial regression and optimized random forest models showed best predictive capability achieving coefficient of determination (R²) of 0.9522 and 0.9314, respectively, in comparison to linear regression. Feature importance analysis identified precursor concentration and hydrolysis ratio (water-to-precursor ratio) to play key role by having predominant influence, with secondary influence being aging time and pH. The paper highlights the value of data-based methodologies for synthesis design guidance, improved reproducibility, and expedited advances in materials chemistry.

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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Materials Synthesis

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