WeChat Mini Program
Old Version Features

The Butterfly Effect of Tiny Density Difference in Microfluidic Channel

Dege Li, Ganggang Ni, Mengting Wang, Junyi Wu, Haining Zhang, Yingying Yang, Penghui Wang,Bo Chi, Shuo Ji,Yanzhen Zhang,Hao Zhang, Xiaofeng Wei

International Journal of Mechanical Sciences(2025)

Cited 0|Views4
Abstract
Microfluidic technology plays a vital role in modern industry and advanced scientific research due to its exceptional features. Owing to the small scales and minor density differences between multiphase fluids, the impact of microgravity on the droplet behavior has been largely overlooked. However, this work reveals that even subtle density differences can have a butterfly effect on the behavior of microdroplet. Droplets within microchannels exhibit three distinct motion regimes: (i) contacting mode: the droplet gradually descends during its movement along the channel and finally contacts the wall; (ii) floating mode: the droplet descends first but floats out maintaining a stable distance from the wall; (iii) bouncing mode: the droplet bounces off the wall at the moment of contacting it. This study uncovers the physical mechanisms of how the working parameters influence droplet behavior and establishes a phase diagram depicting the droplet behavior.
More
Translated text
Key words
Micro-gravity,Micro-channel,Droplet sinking,Droplet bouncing
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined