Benchtop Fabricated Nano‐Roughened Microstructured Electrodes for Electrochemical and Surface‐Enhanced Raman Scattering Sensing
Bibliographic record
Abstract
Abstract Tailoring a material's surface with hierarchical structures from the micro‐ to the nanoscale is key for fabricating highly sensitive detection platforms. To achieve this, the fabrication method should be simple, inexpensive, and yield materials with a high density of surface features. Here, using benchtop fabrication techniques, gold surfaces with hierarchically structured roughness are generated for sensing applications. Hierarchical gold electrodes are prepared on pre‐stressed polystyrene substrates via electroless deposition and amperometric pulsing. Electrodes fabricated using 1 mm H[AuCl₄] and roughened with 80 pulses revealed the highest electroactive surface area. These electrodes are used for enzyme‐free detection of glucose in the presence of bovine serum albumin and achieved a limit of detection of 0.36 mm, below glucose concentrations in human blood. The surfaces nanoroughened with 100 pulses also showed excellent surface‐enhanced Raman scattering (SERS) response for the detection of rhodamine 6G, with an enhancement factor of ≈2 × 106 compared to detection in solution, and for the detection of a self‐assembled monolayer of thiophenol, with an enhancement factor of ≈30 compared to the response from microstructured gold surfaces. It is envisioned that the simplicity and low fabrication cost of these gold‐roughened structures will expedite the development of electrochemical and SERS sensing devices.
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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 | 0.001 | 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".