Back

Laser Scanning Confocal printing, a new simple method for micro-device fabrication (LSCprint method).

Megias, D.; Alonso, J. G.; Julia, J. O.; Mata, G.; Martinez, M. P.

2024-02-09 bioengineering
10.1101/2024.02.08.579477 bioRxiv
Show abstract

Micro-devices and lab-on-a-chip technologies have revolutionized cell biology research, enabling a plethora of applications from single-cell sorting to organ-on-a-chip assays. However, their construction remains laborious, requiring specialized equipment and trained personnel, thereby restricting their accessibility to specialized laboratories. The conventional protocol for micro-device printing involves intricate steps, including master cast/mold production and device fabrication, leading to high costs and time consumption. Here, we present a novel, simplified method utilizing a laser scanning confocal microscope (LSCM) and commercially available photosensitive resins. By using the UV or violet excitation laser lines of an LSCM, we eliminate the need for external suppliers and complex equipment, enabling any conventional cell biology laboratory to fabricate micro-devices swiftly and inexpensively. Our method not only enhances the capabilities of standard confocal microscopes but also democratises microfluidic device fabrication, making it accessible to non-specialized laboratories. With minimal reagent consumption and high scalability, our approach offers a cost-effective solution for rapid prototyping and production of micro-devices, circumventing previous barriers to widespread adoption. Moreover, our method allows direct printing of micro-devices onto substrates, eliminating the need for molds and intermediate steps, thus facilitating greater design flexibility and accessibility for non-specialized laboratories.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.