Back

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

Navarro, I. B.; Datto, G.; Beni, L.; Barragan, D.; Mossburg, K. J.; Shen, S.; Hanna, A. R.; Cormode, D. P.; Issadore, D.

2026-05-14 bioengineering
10.64898/2026.05.12.724546 bioRxiv
Show 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 [&ge;]30 {micro}L per formulation at a rate of one sample every four seconds, representing an approximately 50x 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.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Lab on a Chip
88 papers in training set
Top 0.1%
34.6%
2
Advanced Materials
53 papers in training set
Top 0.4%
6.4%
3
Advanced Materials Technologies
27 papers in training set
Top 0.1%
6.4%
4
ACS Nano
99 papers in training set
Top 1.0%
4.0%
50% of probability mass above
5
Advanced Science
249 papers in training set
Top 5%
3.6%
6
Nature Communications
4913 papers in training set
Top 40%
3.6%
7
Nature Biomedical Engineering
42 papers in training set
Top 0.4%
3.3%
8
Analytical Chemistry
205 papers in training set
Top 1%
2.6%
9
The Analyst
15 papers in training set
Top 0.1%
2.6%
10
Advanced Healthcare Materials
71 papers in training set
Top 0.9%
2.1%
11
PLOS ONE
4510 papers in training set
Top 47%
2.1%
12
Advanced Functional Materials
41 papers in training set
Top 1%
1.9%
13
Biofabrication
32 papers in training set
Top 0.5%
1.7%
14
Scientific Reports
3102 papers in training set
Top 61%
1.5%
15
Science Advances
1098 papers in training set
Top 21%
1.3%
16
Small Methods
26 papers in training set
Top 0.6%
1.2%
17
Small
70 papers in training set
Top 0.8%
1.2%
18
HardwareX
16 papers in training set
Top 0.2%
1.2%
19
Bioengineering & Translational Medicine
21 papers in training set
Top 0.7%
1.0%
20
ACS Synthetic Biology
256 papers in training set
Top 3%
0.8%
21
Biosensors and Bioelectronics
52 papers in training set
Top 2%
0.7%
22
ACS Sensors
45 papers in training set
Top 1%
0.6%
23
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 47%
0.6%
24
Nature Biotechnology
147 papers in training set
Top 9%
0.6%
25
ACS Applied Materials & Interfaces
39 papers in training set
Top 1%
0.6%
26
ACS Biomaterials Science & Engineering
37 papers in training set
Top 1%
0.5%
27
Nature Nanotechnology
30 papers in training set
Top 1%
0.5%
28
Nano Letters
63 papers in training set
Top 4%
0.5%
29
Advanced Materials Interfaces
10 papers in training set
Top 0.5%
0.5%