Machine learning predicts electrospray particle size

Wang, Fanjin; Elbadawi, Moe; Tsilova, Scheilly Liu; Gaisford, Simon; Basit, Abdul W.; Parhizkar, Maryam

doi:10.1016/j.matdes.2022.110735

Cited by 18 publications

(14 citation statements)

References 67 publications

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“…These results show that flow rate and conductivity are the most important features for predicting drop diameters during electrospray. This is consistent with the findings of Wang et al [ 17 ] who also reported flow rate as the most important feature and voltage as another major feature in determining the diameter of ejected droplet. For predicting current in electrospray, features like voltage and nozzle diameter are the most important, while nozzle–substrate distance, permittivity, and surface tension are comparatively less important in the prediction of spray current.…”

Section: Resultssupporting

confidence: 93%

“…In addition to predicting nanofiber diameters, ML techniques have also been used in classifying produced nanofibers into homogeneous (anomaly‐free) and nonhomogeneous (with defects) products, based on a hybrid unsupervised and supervised learning algorithm. [ 16 ] Wang et al [ 17 ] applied different ML models to predict the diameter of droplets produced by electrospraying considering applied voltage, solution flow rate, nozzle–substrate distance, nozzle diameter, and solution properties. Based on the testing performance, Wang et al [ 17 ] reported that tree‐based algorithm (particularly XGBoost) exhibits best results.…”

Section: Introductionmentioning

confidence: 99%

“…[ 16 ] Wang et al [ 17 ] applied different ML models to predict the diameter of droplets produced by electrospraying considering applied voltage, solution flow rate, nozzle–substrate distance, nozzle diameter, and solution properties. Based on the testing performance, Wang et al [ 17 ] reported that tree‐based algorithm (particularly XGBoost) exhibits best results. Recently, Akbari et al [ 18 ] developed a comprehensive ML framework called MeltpoolNet for predicting melt pool geometry and defects from metal additive manufacturing.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning‐Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Singh,

Rai,

Subramanian

2023

Adv Eng Mater

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Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high‐quality datasets at appropriate volumes. This article extends the suitability of such ML‐enabled approaches to a new technological domain of EHD spraying and drop‐on‐demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree‐based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.

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Section: Resultssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning‐Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Singh,

Rai,

Subramanian

2023

Adv Eng Mater

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“…This model could predict Dp 50 at known experimental conditions or setting the process variables to achieve a given size, which was useful for a system capable of precise particle formation and scaling up BUDP. Besides, the modelling relationship between electrospraying product diameter and key experiment parameters were established by machine learning ( Wang et al, 2022b ), which could expedite the optimization process by accurately predicting particle diameter for both nano- and micron-sized particles.…”

Section: Characterization Of Zein-based Nanoparticlesmentioning

confidence: 99%

Zein-based nanoparticles: Preparation, characterization, and pharmaceutical application

Liu

et al. 2023

Front. Pharmacol.

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Zein, as one of the natural and GRAS proteins in plant, is renewable, nontoxic, biocompatible and biodegradable. Over the past decade, many research efforts have been devoted to zein-based biomaterials for several industrial applications. Combining with research experiences in our research group, the preparation methods, characterizations and pharmaceutical applications of zein-based nanoparticles were summarized in this review. Zein NPs with different particle nanostructures have been prepared by chemical crosslinking, desolvating, dispersing and micromixing strategies. The pharmaceutical applications of zein NPs are mainly focus on the drug delivery. Zein NPs can improve the drug stability, increase the oral bioavailability, control the drug release and enhance the drug targeting, thereby improving the pharmaceutical effect effectively. More efforts are required to analyze the relationship among preparation methods, particle nanostructures and pharmaceutical properties in virtue of quality by design approach, and further promote the scale-up production and clinical application of zein NPs.

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“…In healthcare, ML is used in clinical trials, diagnostics and surgery (Giorgio et al, 2022;Halamka et al, 2022;Myszczynska et al, 2020;Shah et al, 2019;Zame et al, 2020). In pharmaceutics, ML models have been applied to model drug-food interactions, drug-microbiome interactions, and formulation development (Gavins et al, 2022;McCoubrey et al, 2021;Wang et al, 2022). For 3D printing medicines, ML has been demonstrated to predict printability, drug release rate, and accelerating quality control (Elbadawi et al, 2020a;Muñiz Castro et al, 2021;O'Reilly et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products

Abdalla

Elbadawi

et al. 2023

International Journal of Pharmaceutics

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Machine learning predicts electrospray particle size

Cited by 18 publications

References 67 publications

Machine Learning‐Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Machine Learning‐Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Zein-based nanoparticles: Preparation, characterization, and pharmaceutical application

Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products

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