Bench-Top Fabrication of an All-PDMS Microfluidic Electrochemical Cell Sensor Integrating Micro/Nanostructured Electrodes
Bibliographic record
Abstract
In recent years, efforts in the development of lab-on-a-chip (LoC) devices for point-of-care (PoC) applications have increased to bring affordable, portable, and sensitive diagnostics to the patients' bedside. To reach this goal, research has shifted from using traditional microfabrication methods to more versatile, rapid, and low-cost options. This work focuses on the benchtop fabrication of a highly sensitive, fully transparent, and flexible poly (dimethylsiloxane) (PDMS) microfluidic (μF) electrochemical cell sensor. The μF device encapsulates 3D structured gold and platinum electrodes, fabricated using a shape-memory polymer shrinking method, which are used to set up an on-chip electrochemical cell. The PDMS to PDMS-structured electrode bonding protocol to fabricate the μF chip was optimized and found to have sufficient bond strength to withstand up to 100 mL/min flow rates. The sensing capabilities of the on-chip electrochemical cell were demonstrated by using cyclic voltammetry to monitor the adhesion of murine 3T3 fibroblasts in the presence of a redox reporter. The charge transfer across the working electrode was reduced upon cell adhesion, which was used as the detection mechanism, and allowed the detection of as few as 24 cells. The effective utilization of simple and low cost bench-top fabrication methods could accelerate the prototyping and development of LoC technologies and bring PoC diagnostics and personalized medicine to the patients' bedside.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".