Physical Chemistry Chemical Physics, volume 25, issue 23, pages 15744-15755

Predicting drop coalescence in microfluidic device with a deep learning generative model

Kewei Zhu ¹

Sibo Cheng ²

Nina Kovalchuk ³

Mark Simmons ³

Yi-Ke Guo ²

Omar K Matar ⁴

Rossella Arcucci ⁵

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Department of Computer Science, University of York, UK |

Data Science Institute, Department of Computing, Imperial College London, UK |

School of Chemical Engineering, University of Birmingham, UK |

⁴

Department of Chemical engineering, Imperial College London, UK |

⁵

Department of Earth Science & Engineering, Imperial College London, UK |

Publication type: Journal Article

Publication date: 2023-04-27

Royal Society of Chemistry (RSC)

Journal: Physical Chemistry Chemical Physics

scimago Q2

SJR: 0.721

CiteScore: 5.5

Impact factor: 2.9

ISSN: 14639076, 14639084

DOI: 10.1039/d2cp05975d

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Physical and Theoretical Chemistry

General Physics and Astronomy

Abstract

Predicting drop coalescence based on process parameters is crucial for experiment design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the...

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Publisher

Royal Society of Chemistry (RSC)

Journal

Physical Chemistry Chemical Physics

scimago Q2

SJR

0.721

CiteScore

5.5

Impact factor

2.9

ISSN

14639076 (Print)

14639084 (Electronic)

Predicting drop coalescence in microfluidic device with a deep learning generative model

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