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High-throughput Protein Expression Generator Using a Microfluidic Platform
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Published on: August 23, 2012

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

Isabel B Navarro1, Gregory Datto1,2, Lameck Beni1

  • 1Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104.

Biorxiv : the Preprint Server for Biology
|May 25, 2026
PubMed
Summary

LMNOP-bot automates the generation and collection of micro- and nanomaterial libraries from microfluidic devices, significantly increasing throughput. This open-source platform enables rapid, reproducible, and accessible materials development for diverse applications.

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Area of Science:

  • Materials Science
  • Robotics
  • Chemical Engineering

Background:

  • Data-driven materials development necessitates large, well-characterized formulation libraries.
  • Microfluidic platforms offer precise material control but face throughput limitations due to output interfacing challenges.
  • Manual transfers or non-microfluidic methods hinder throughput and reproducibility in microfluidic library generation.

Purpose of the Study:

  • To develop an open-source robotic platform for automated micro- and nanomaterial library generation and collection.
  • To overcome the bottleneck of interfacing microfluidic device outputs with standard well plates.
  • To enhance the throughput, reproducibility, and accessibility of data-driven materials development.

Main Methods:

  • Introduction of LMNOP-bot (Libraries of Micro- and Nano-materials, OPen-source bot), a low-cost, open-source robotic platform.
  • Utilizing synchronized, pressure-driven flow for continuous formulation and direct deposition into well plates.
  • Demonstrating compatibility with PDMS/glass and polycarbonate microfluidic devices and 96-/384-well plates.

Main Results:

  • LMNOP-bot achieves a throughput of one sample every four seconds, a ~50x increase over existing serial microfluidic workflows.
  • The system collects ≥30 µL per formulation and operates robustly for over 10,000 runs without maintenance.
  • High precision and reproducibility were confirmed through repeated sampling, with seamless interfacing to standard well plates.

Conclusions:

  • LMNOP-bot effectively removes a key bottleneck in microfluidic library generation.
  • The platform enables rapid, scalable, and accessible exploration of material design spaces.
  • This open-source solution promotes broader adoption of automated microfluidic material synthesis for research and development.