ArticleMicrosystems & nanoengineering2025
On-demand droplet formation at a T-junction: modelling and validation.
Article in Microsystems & nanoengineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Droplet microfluidics have found increasing applications across many fields. While droplet generation at a T-junction is a common method, its reliance on trial-and-error operation imposes undesirable constraints on its performance and applicability. In this study, we demonstrate a simple method for on-demand droplet formation at a T-junction with precise temporal control over individual droplet formation. Based on experimental observations, we also develop a physical model to describe the relationships among pressures, droplet generation, device geometry, and interfacial properties. Experimental validation demonstrates excellent performance of the model in predicting the pressure thresholds for switching droplet generation on and off. To address parameter uncertainties arising from real-world complexities, we show that monitoring droplet generation frequency provides a rapid, in situ approach for optimising experimental conditions. Our findings offer valuable guidelines for the design and automation of robust droplet-on-demand microfluidic systems, which can be readily implemented in conventional laboratories for a broad range of applications.
Identifiers
What OpenQuestion holds
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.