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. 2025 Sep 29;15(1):33422.
doi: 10.1038/s41598-025-18837-7.

Modeling and optimization of argon-based floating helix electrode cold plasma

G Divya Deepak  1 , Gajanan Anne  1 , Subraya Krishna Bhat  2
Affiliations

Modeling and optimization of argon-based floating helix electrode cold plasma

G Divya Deepak et al. Sci Rep. .

Abstract

Cold atmospheric pressure plasma (CAP) technology has vast potential in several technological domains, including biomedical engineering. CAP, also known as non-thermal plasma, is characterized by high-energy electrons while the bulk gas remains near room temperature, allowing for effective plasma treatment without thermal damage-critical for biomedical applications. This paper presents a coupled machine learning and statistical technique-based process modeling and optimization approach for a novel floating-helix electrode-based cold plasma device, operating strictly within the cold plasma regime. An artificial neural network (ANN) model was developed to describe the relationship between the process parameters-supply voltage (SV) and frequency (SF)-and performance parameters-power consumption (P), and jet lengths with and without an end ring (JwER and JwoER). The generality and robustness of the ANN model were confirmed through experimental validation and extrapolative predictions. For multi-response optimization, the composite desirability method was employed. Finally, machine learning models for logistic regression-namely, ANN classifier, K-Nearest Neighbor, and Support Vector Machine-were developed to classify the discharge type within the cold plasma operating range, ensuring its suitability for biomedical applications. The proposed system may hold potential for biomedical use, contingent upon further validation through biological testing.

Keywords: Argon; Biomedical devices; Cold plasma; Machine learning.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Artificial neural network – applications in process modeling and optimization.
Fig. 2
Fig. 2
Argon plasma jet obtained using the floating helix based APCPJ.
Fig. 3
Fig. 3
Experimental arrangement of the floating helix electrode configuration with end ring.
Fig. 4
Fig. 4
ANN development process.
Fig. 5
Fig. 5
‘2-3-3’ ANN topology.
Fig. 6
Fig. 6
Identification of optimum number of neurons.
Fig. 7
Fig. 7
ANN predictions compared with experimental results: (a) JwER, (b) JwoER and (c) P.
Fig. 8
Fig. 8
MSE variation during backpropagation.
Fig. 9
Fig. 9
Pearson’s correlation coefficient (R) plots for the optimized ANN.
Fig. 10
Fig. 10
Main effect plot of CDI.
Fig. 11
Fig. 11
ANN model sensitivity on (a) SF and (b) SV.
Fig. 12
Fig. 12
Pearson’s correlation coefficient matrix for experimental results.
Fig. 13
Fig. 13
Confusion matrix for (a) KNN, (b) SVM and (c) ANNC models.
Fig. 14
Fig. 14
ROC for transition from glow to arc discharge (right column) & glow discharge (left column) with respect to (a) KNN, (b) SVM and (c) ANNC models.
Fig. 15
Fig. 15
LIME coefficient scores representing relative importance of each parameter on model predictions in: (a) KNN, (b) SVM and (c) ANNC models.
Fig. 16
Fig. 16
3D surface plots of experimental data shown in Table 1 for (a) JwER, (b) JwoER, (c) P with respect to SV and SF.
See this image and copyright information in PMC

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