ArticleBiomicrofluidics2025
Design automation for deterministic lateral displacement by leveraging deep Q-network.
Article in Biomicrofluidics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- A 3D modeling framework for accurate trajectory-based prediction of critical diameter in deterministic lateral displacement microfluidics.Microsystems & nanoengineering · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the widespread application of microfluidic chips in research fields, such as cell biology, molecular biology, chemistry, and life sciences, the process of designing new chips for specific applications remains complex and time-consuming, often relying on experts. To accelerate the development of high-performance and high-throughput microfluidic chips, this paper proposes an automated Deterministic Lateral Displacement (DLD) chip design algorithm based on reinforcement learning. The design algorithm proposed in this paper treats the throughput and sorting efficiency of DLD chips as key optimization objectives, achieving multi-objective optimization. The algorithm integrates existing research results from our team, enabling rapid evaluation and scoring of DLD chip design parameters. Using this comprehensive performance evaluation system and deep Q-network technology, our algorithm can balance optimal separation efficiency and high throughput in the automated design process of DLD chips. Additionally, the quick execution capability of this algorithm effectively guides engineers in developing high-performance and high-throughput chips during the design phase.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.