Rapid prototyping of microfluidic devices with integrated wrinkled gold micro-/nano textured electrodes for electrochemical analysis
Bibliographic record
Abstract
Fully-integrated electro-fluidic systems with micro-/nano-scale features have a wide range of applications in lab-on-a-chip systems used for biosensing, biological sample processing, and environmental monitoring. Rapid prototyping of application-specific electro-fluidic systems is envisioned to facilitate the testing, validation, and market translation of several lab-on-a-chip systems. Towards this goal, we developed a rapid prototyping process for creating wrinkled micro-/nano-textured electrodes on shrink memory polymers, fabricating microfluidics using molds patterned by a craft-cutter, and bonding electrical and fluidic circuitries using a PDMS partial curing method optimized for creating void-free bonds at the side walls and surfaces of tall (>5 μm) micro-/nano-textured wrinkled electrodes. The resulting electro-fluidic devices, featuring closely spaced high topography electrodes for electrochemical analysis, can withstand flow-rates and burst pressures larger than 25 mL min(-1) and 125 kPa, respectively. In addition, the fully-integrated electrochemical flow-cell developed here demonstrates excellent electrochemical behaviour, with negligible scan to scan variation for over 100 cyclic voltammetry scans, and expected redox signatures measured under various voltage scan rates and fluidic flow rates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".