Explainable AI models for predicting drop coalescence in microfluidics device DOI Creative Commons
Jin-Wei Hu, Kewei Zhu, Sibo Cheng

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 481, P. 148465 - 148465

Published: Jan. 2, 2024

In the field of chemical engineering, understanding dynamics and probability drop coalescence is not just an academic pursuit, but a critical requirement for advancing process design by applying energy only where it needed to build necessary interfacial structures, increasing efficiency towards Net Zero manufacture. This research applies machine learning predictive models unravel sophisticated relationships embedded in experimental data on microfluidics device. Through deployment SHapley Additive exPlanations values, features relevant processes are consistently identified. Comprehensive feature ablation tests further delineate robustness susceptibility each model. Furthermore, incorporation Local Interpretable Model-agnostic Explanations local interpretability offers elucidative perspective, clarifying intricate decision-making mechanisms inherent model's predictions. As result, this provides relative importance outcome interactions. It also underscores pivotal role model reinforcing confidence predictions complex physical phenomena that central engineering applications.

Language: Английский

Integrated technologies for continuous monitoring of organs-on-chips: Current challenges and potential solutions DOI
Jonathan Sabaté del Río, Jooyoung Ro, Heejeong Yoon

et al.

Biosensors and Bioelectronics, Journal Year: 2023, Volume and Issue: 224, P. 115057 - 115057

Published: Jan. 2, 2023

Language: Английский

Citations

26

Functions and applications of artificial intelligence in droplet microfluidics DOI
Huan Liu, Lang Nan, Feng Chen

et al.

Lab on a Chip, Journal Year: 2023, Volume and Issue: 23(11), P. 2497 - 2513

Published: Jan. 1, 2023

This review summarizes the implementations of droplet microfluidics based on AI, including generation, biological analysis, and material synthesis.

Language: Английский

Citations

26

Prospects of Microfluidic Technology in Nucleic Acid Detection Approaches DOI Creative Commons
Zilwa Mumtaz, Zubia Rashid, Ashaq Ali

et al.

Biosensors, Journal Year: 2023, Volume and Issue: 13(6), P. 584 - 584

Published: May 27, 2023

Conventional diagnostic techniques are based on the utilization of analyte sampling, sensing and signaling separate platforms for detection purposes, which must be integrated to a single step procedure in point care (POC) testing devices. Due expeditious nature microfluidic platforms, trend has been shifted toward implementation these systems analytes biochemical, clinical food technology. Microfluidic molded with substances such as polymers or glass offer specific sensitive infectious noninfectious diseases by providing innumerable benefits, including less cost, good biological affinity, strong capillary action simple process fabrication. In case nanosensors nucleic acid detection, some challenges need addressed, cellular lysis, isolation amplification before its detection. To avoid laborious steps executing processes, advances have deployed this perspective on-chip sample preparation, introduction an emerging field modular microfluidics that multiple advantages over microfluidics. This review emphasizes significance technology non-infectious diseases. The isothermal conjunction lateral flow assay greatly increases binding efficiency nanoparticles biomolecules improves limit sensitivity. Most importantly, deployment paper-based material made cellulose reduces overall cost. discussed explicating applications different fields. Next-generation methods can improved using CRISPR/Cas systems. concludes comparison future prospects various systems, plasma separation used

Language: Английский

Citations

25

Microsystem Advances through Integration with Artificial Intelligence DOI Creative Commons
Hsieh‐Fu Tsai, Soumyajit Podder, Pin‐Yuan Chen

et al.

Micromachines, Journal Year: 2023, Volume and Issue: 14(4), P. 826 - 826

Published: April 8, 2023

Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale volume, typically on the of micro- or nanoliters. Under larger surface-to-volume ratio, advantages low reagent consumption, faster reaction kinetics, more compact systems are evident in microfluidics. However, miniaturization microfluidic chips introduces challenges stricter tolerances designing controlling them for interdisciplinary applications. Recent advances artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, optimization bioanalysis data analytics. In microfluidics, Navier-Stokes equations, which partial differential equations describing viscous fluid motion complete form known not general analytical solution, can be simplified fair performance through numerical approximation due inertia laminar flow. Approximation using neural networks trained by rules physical knowledge new possibility predict physicochemical nature. The combination automation produce large amounts data, where features patterns difficult discern human extracted machine learning. Therefore, integration with AI potential revolutionize workflow enabling precision control analysis. Deployment smart may tremendously beneficial various applications future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), personalized medicine. this review, we summarize key integrated discuss outlook possibilities combining

Language: Английский

Citations

24

Explainable AI models for predicting drop coalescence in microfluidics device DOI Creative Commons
Jin-Wei Hu, Kewei Zhu, Sibo Cheng

et al.

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 481, P. 148465 - 148465

Published: Jan. 2, 2024

In the field of chemical engineering, understanding dynamics and probability drop coalescence is not just an academic pursuit, but a critical requirement for advancing process design by applying energy only where it needed to build necessary interfacial structures, increasing efficiency towards Net Zero manufacture. This research applies machine learning predictive models unravel sophisticated relationships embedded in experimental data on microfluidics device. Through deployment SHapley Additive exPlanations values, features relevant processes are consistently identified. Comprehensive feature ablation tests further delineate robustness susceptibility each model. Furthermore, incorporation Local Interpretable Model-agnostic Explanations local interpretability offers elucidative perspective, clarifying intricate decision-making mechanisms inherent model's predictions. As result, this provides relative importance outcome interactions. It also underscores pivotal role model reinforcing confidence predictions complex physical phenomena that central engineering applications.

Language: Английский

Citations

15