ArticleMicrosystems & nanoengineering2026
Intelligent label-free droplet microfluidic sorting system for single-cell encapsulation and morphology-guided screening.
Article in Microsystems & nanoengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Machine vision-enabled ODThe Analyst · 2026Article
- Decoding Cellular Heterogeneity with Microfluidic Single-Cell Secretion Analysis Tools.ACS omega · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The label-free extraction of cellular morphological data from within droplet microenvironments, and its subsequent translation into reliable, high-precision sorting, continues to pose a central challenge in the field of droplet microfluidics. Here, we develop an intelligent label-free droplet sorting (ILFDS) system by integrating droplet microfluidics, real-time image recognition, and dielectrophoresis (DEP) sorting. The developed system operates at low voltages (250-350 V) by using the innovative liquid-metal electrodes, which enable real-time sorting while minimizing droplet deformation and preserving cell integrity. In experiments on sorting single-target encapsulated droplets, the ILFDS system achieves detection accuracies of >98% and sorting efficiencies of >85% for particles, Haematococcus pluvialis, and Scenedesmus quadricauda, with the proportion of single-target droplets increasing by approximately 3.15-fold, 11.37-fold, and 4.59-fold, respectively, after sorting. Furthermore, the ILFDS system demonstrates high-precision sorting of targets from mixed samples based on morphological features, achieving detection accuracies of above 90% and sorting efficiencies of over 89% for mixed Haematococcus pluvialis and Euglena gracilis samples. These results highlight the system's robustness in handling heterogeneous samples and its capability to overcome key limitations associated with conventional droplet encapsulation and detection. By enabling scalable, high-throughput, and label-free sorting based on image recognition, our ILFDS system offers a versatile platform for a wide range of droplet-based analytical and screening applications.
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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.