Evidence map›Paper›PMID 42182207›Full record

ArticlebioRxiv : the preprint server for biology2026

Open-source robotic chip-to-plate interface for high-throughput microfluidic generation of materials libraries.

Isabel B Navarro, Gregory Datto, Lameck Beni, Diego Barragan, Katherine J Mossburg, Shiny Shen, Andrew R Hanna, David P Cormode, David Issadore

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

9 authors.

Isabel B NavarroDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.
Gregory DattoDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.
Lameck BeniDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.
Diego BarraganDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.
Katherine J MossburgDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104.ORCID 0000-0003-1317-4061
Shiny ShenDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.
Andrew R HannaDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.ORCID 0009-0009-3220-4378
David P CormodeDepartment of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.ORCID 0000-0002-8391-9500
David IssadoreDepartment of Chemical Engineering and Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.ORCID 0000-0002-5461-8653

Funding

Silver Telluride Nanoparticle Dual-Energy Mammography Contrast Agents For Breast Cancer ScreeningR01CA291880 · NCI · UNIVERSITY OF PENNSYLVANIA · PI David Peter Cormode · 2025 to 2026
$1.2M
Enhanced treatment of breast cancer via biodegradable polymer nanoparticlesR21CA297703 · NCI · UNIVERSITY OF PENNSYLVANIA · PI CORMODE, DAVID PETER · 2025 to 2025
$392k
NCI NIH HHS R01 CA291880NCI NIH HHS R21 CA297703
6 · The paper itself

Abstract

Data-driven materials development requires large, well-characterized libraries of precisely defined formulations. While microfluidic platforms excel at generating highly controlled materials, their throughput is often limited by the challenge of efficiently interfacing device outputs with standard well plates. This bottleneck frequently necessitates manual transfer or non-microfluidic workflows, constraining both throughput and reproducibility. Here, we present LMNOP-bot (Libraries of Micro- and Nano-materials, OPen-source bot), an open-source robotic platform for the automated generation and collection of micro- and nanomaterial libraries from serial microfluidic outputs. Using synchronized, pressure-driven flow, LMNOP-bot enables continuous formulation and direct deposition into standard well plates. The system is low-cost (<$700, excluding pressure regulators), constructed from readily available or easily fabricated components, and designed for broad accessibility. LMNOP-bot collects ≥30 μL per formulation at a rate of one sample every four seconds, representing an approximately 50× increase in throughput over existing serial microfluidic workflows, and operates robustly for over 10,000 runs without maintenance. We demonstrate compatibility with both PDMS/glass and commercial polycarbonate devices, with seamless interfacing to 96- and 384-well plates. Repeated sampling confirms high precision and reproducibility. By removing a key bottleneck in microfluidic library generation, LMNOP-bot enables rapid, scalable, and accessible exploration of material design spaces.

Identifiers

PMID42182207
PMCPMC13193029

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.