Modeling the Electrochemical Synthesis of Zinc Oxide Nanoparticles Using Artificial Neural Networks.

Sławomir Francik, Michał Hajos, Beata Brzychczyk, Jakub Styks, Renata Francik, Zbigniew Ślipek

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Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

The Use of Artificial Neural Networks to Model Selected Strength Parameters of the Giant Miscanthus Stalk.Materials (Basel, Switzerland) · 2026
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Authors and funding

6 authors.

Sławomir FrancikDepartment of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Krakow, Poland.ORCID 0000-0002-4535-9450

Michał HajosDepartment of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Krakow, Poland.ORCID 0009-0004-4193-7542

Beata BrzychczykDepartment of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Krakow, Poland.ORCID 0000-0002-3873-1664

Jakub StyksDepartment of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Krakow, Poland.ORCID 0000-0003-3034-5774

Renata FrancikFaculty of Medicine and Health Sciences, University of Applied Sciences in Nowy Sacz, Kosciuszki 2G, 33-300 Nowy Sacz, Poland.ORCID 0000-0002-7071-8072

Zbigniew ŚlipekFaculty of Engineering Sciences, University of Applied Sciences in Nowy Sacz, Zamenhofa 1a, 33-300 Nowy Sacz, Poland.ORCID 0000-0002-3609-0680

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A neural model was developed to predict the distribution of ZnO nanoparticles obtained by electrochemical synthesis. It is a three-layer multilayer perceptron (MLP) artificial neural network (ANN) with five neurons in the input layer, eight neurons in the hidden layer, and one neuron in the output layer. This network has a hyperbolic tangent activation function for the neurons in the hidden layer and an exponential activation function for the neuron in the output layer. The input (independent) variables are particle size (nm), solvent type, and temperature (°C), and the output (dependent) variable is fraction share (%). The best neural model (ann08) has a root mean square error (RMSE) 0.84% for the training subset, 0.98% for the testing subset, and 1.27% for the validation subset. The RMSE values are therefore small, which enables practical use of the ANN model.

Indexed as

ANN modelArtificial Intelligenceelectrochemical synthesis of nanoparticlesnanoparticles size distributionZnO nanoparticle

Identifiers

PMID40942613

DOI10.3390/ma18174187

PMCPMC12430345

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.