Evidence map›Paper›PMID 42504485›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2026

Navigating Lipid Nanostructure Design Space Through Continuous Microfluidic Automation.

Bradley Diggines, Marcus Fletcher, Manuel Bibrowski, Karnyart Samnuan, Rongjun Chen, David J Peeler, Amjad Abouselo, Morag R Hunter, Molly M Stevens, Nicholas J Brooks and 1 more

Abstract read
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Bradley DigginesDepartment of Chemical Engineering, Imperial College London, London, UK.ORCID https://orcid.org/0009-0008-4185-5538
Marcus FletcherDepartment of Chemical Engineering, Imperial College London, London, UK.ORCID https://orcid.org/0000-0003-3036-1168
Manuel BibrowskiDepartment of Chemical Engineering, Imperial College London, London, UK.ORCID https://orcid.org/0009-0002-8330-0692
Karnyart SamnuanDepartment of Chemical Engineering, Imperial College London, London, UK.ORCID https://orcid.org/0000-0002-2361-6954
Rongjun ChenDepartment of Chemical Engineering, Imperial College London, London, UK.ORCID https://orcid.org/0000-0002-8133-5472
David J PeelerDepartment of Materials, Department of Bioengineering and Institute for Biomedical Engineering, Imperial College London, London, UK.ORCID https://orcid.org/0000-0003-2441-6409
Amjad AbouseloEarly Product Development & Manufacturing, Pharmaceutical Sciences, R&D, AstraZeneca, Macclesfield, UK.
Morag R HunterCentre For Genomics Research, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.ORCID https://orcid.org/0000-0003-1226-9923
Molly M StevensDepartment of Materials, Department of Bioengineering and Institute for Biomedical Engineering, Imperial College London, London, UK.ORCID https://orcid.org/0000-0002-7335-266X
Nicholas J BrooksDepartment of Chemistry, Imperial College London, London, UK.ORCID https://orcid.org/0000-0002-1346-9559
Yuval ElaniDepartment of Chemical Engineering, Imperial College London, London, UK.ORCID https://orcid.org/0000-0002-9603-2490

Funding

Biotechnology and Biological Sciences Research Council BB/Z514895/1Department of Science, Innovation and TechnologyEPSRC EP/V048651/1EPSRC EP/Y530529/1Imperial College LondonRoyal Academy of Engineering CiET2021∖94UK Research and Innovation (UKRI) Future Leaders Fellowship MR/S031537/1
6 · The paper itself

Abstract

The versatility of nanoscale lipid particles has positioned them as the scaffold of choice for biomedical delivery, synthetic membrane engineering, and fundamental biophysical exploration. Across these fields, rational particle design has become a major bottleneck. Lipid composition, stoichiometry, size, morphology, phase behavior, and biophysical properties combine into an enormous, high‑dimensional space that is inherently difficult to explore, making conventional optimization approaches slow and limited. Identifying optimal lipid compositions in this vast space necessitates high-throughput screening based on efficient low-cost automation. Here we introduce a high-throughput microfluidic platform for rapid screening of lipid nanoparticles, offering precise, programmable control over composition and morphology. The system integrates on-chip microfluidic metering of lipid stocks with continuous particle self-assembly, followed by robotic collection into 96-well plates. This workflow enables the generation of over 200 unique formulations per hour, delivering a several-orders-of-magnitude increase in throughput relative to conventional approaches. We apply the platform to systematically map biophysical space at unprecedented resolution, validate compositional trends in transfection efficiency, and identify non-lamellar formulations with optimal functional performance. By coupling scalable synthesis with automated screening, we expect this platform to provide a robust foundation for data-driven and AI-integrated discovery of self-assembled nanomaterials including lipid, polymeric, and biomolecular assemblies.

Indexed as

automationbottleneckcomputer sciencemicrofluidicsnanomaterialsnanoscopic scalenanotechnologyparticlethroughputworkflow

Identifiers

PMID42504485
PMCPMC13579175

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.