Lecture

Tailored low cost electrochemical microfluidics for bioanalysis

  • 09.04.2024 at 15:30 - 16:00
  • ICM Saal 3
  • Language: English
  • Type: Lecture

Lecture description

Interfacing electrochemistry in micro-size environments provides an exciting approach for analytical (bio)sensing. Electrochemical detection offers inherent miniaturization, high compatibility with micro and nanotechnologies, high sensitivity, and cost efficiency. The appearance of nanomaterials has also opened new avenues in electrochemical (bio)sensing allowing improved analytical performance in terms of sensitivity, selectivity, and reproducibility. [1]

In this frame, electrochemical microfluidics has proven to be very suitable allowing the tailored integration of (bio)analytical steps toward the accurately controlled manipulation of fluidics in connection with the ultraminiaturized electrochemical biosensing [2]. However, the complexity and high cost of the microfabrication methods used in fabricating electrochemical microfluidic devices (EMDs) have historically limited the widespread use of these devices. Clean room-based techniques like photolithography and chemical vapor deposition can create PDMS-glass devices incorporating noble metal electrodes.

However, this microfabrication approach has a low throughput, is centralized, and yields expensive devices, hindering the rapid iteration of different fit-for-purpose designs. The incorporation of low-cost microfabrication approaches such as paper-based, xurography, and 3D printing provides cheap, user-friendly, and scalable alternative microfabrication methods which allow the in-lab tailored fabrication of these devices. [2]

In this invited talk, selected low-cost EMDs will be presented and conceptually discussed from a personal perspective.

References:
[1]J. F. Hernández-Rodríguez, D. Rojas, A. Escarpa, Anal. Chem. 2021, 93, 167 [2]J. F. Hernández-Rodríguez, M.A. López, D. Rojas, A. Escarpa, Lab Chip 2022, 22, 4805.

Acknowledgments:
The financial support of grant PID2020-118154GB-I00 funded by MCIN/AEI/ 10.13039/501100011033, and the Community of Madrid, grant number Y2020/NMT6312 (NEURO-CHIP-CM) is gratefully acknowledged.
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