Conventional and emerging strategies for the fabrication and functionalization of PDMS-based microfluidic devices
Bibliographic record
Abstract
Microfluidics is an emerging and multidisciplinary field that is of great interest to manufacturers in medicine, biotechnology, and chemistry, as it provides unique tools for the development of point-of-care diagnostics, organs-on-chip systems, and biosensors. Polymeric microfluidics, unlike glass and silicon, offer several advantages such as low-cost mass manufacturing and a wide range of beneficial material properties, which make them the material of choice for commercial applications and high-throughput systems. Among polymers used for the fabrication of microfluidic devices, polydimethylsiloxane (PDMS) still remains the most widely used material in academia due to its advantageous properties, such as excellent transparency and biocompatibility. However, commercialization of PDMS has been a challenge mostly due to the high cost of the current fabrication strategies. Moreover, specific surface modification and functionalization steps are required to tailor the surface chemistry of PDMS channels (e.g. biomolecule immobilization, surface hydrophobicity and antifouling properties) with respect to the desired application. While significant research has been reported in the field of PDMS microfluidics, functionalization of PDMS surfaces remains a critical step in the fabrication process that is difficult to navigate. This review first offers a thorough illustration of existing fabrication methods for PDMS-based microfluidic devices, providing several recent advancements in this field with the aim of reducing the cost and time for mass production of these devices. Next, various conventional and emerging approaches for engineering the surface chemistry of PDMS are discussed in detail. We provide a wide range of functionalization techniques rendering PDMS microchannels highly biocompatible for physical or covalent immobilization of various biological entities while preventing non-specific interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".