Fabrication of microstructured electrodes via electroless metal deposition onto polydopamine‐coated polystyrene substrates and thermal shrinking
Bibliographic record
Abstract
Abstract The ability to provide high sensitivity with small footprints makes miniaturized electrodes key components of biosensing, wearable electronics and lab‐on‐a‐chip devices. Recently, thin film deposition onto polystyrene films, followed by thermal shrinking has been used to produce microstructured electrodes (MSEs) with high electroactive surface area (ESA). Nevertheless, the high cost associated with film deposition through evaporation used in microfabrication and the variability in performance of screen‐printed electrodes (SPEs) remain key barriers that limit their widespread deployment. Here, a simple and inexpensive method is developed for the solution‐based patterning of high‐quality metallic films on polystyrene substrates for MSE fabrication. The ESA of electrodes produced through this method is 2 × and 12 × larger than that of microstructured and planar electrodes produced through sputtering, respectively, and their cost is only 20% of sputtered ones. This methodology allows the fabrication of on‐chip microstructured electrochemical cells (SMECs) with excellent analytical performance (3% RSD inter‐day reproducibility and 0.3% RSD repeatability), superior to that of commercially available SPEs. In addition, the ESA of SMECs is significantly higher than that of SPEs, and they show excellent response toward dopamine detection. We anticipate that this solution‐based fabrication approach will expedite the development of miniaturized sensing platforms for point‐of‐care applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".