Simple and Inexpensive Fabrication of High Surface-Area Paper-Based Gold Electrodes for Electrochemical and Surface-Enhanced Raman Scattering Sensing
Bibliographic record
Abstract
Paper has emerged as an excellent alternative to create environmentally benign disposable electrochemical sensing devices. The critical step to fabricating electrochemical sensors is making paper conductive. In this work, paper-based electrodes with a high electroactive surface area (ESA) were fabricated using a simple electroless deposition technique. The polymerization time of a polydopamine adhesion layer and the gold salt concentration during the electroless deposition step were optimized to obtain uniformly conductive paper-based electrodes. The optimization of these fabrication parameters was key to obtaining the highest ESA possible. Roughening factors ( R f ) of 7.2 and 2.3 were obtained when cyclic voltammetry was done in sulfuric acid and potassium ferricyanide, respectively, demonstrating a surface prone to fast electron transfer. As a proof of concept, mercury detection was done through anodic stripping, achieving a limit of quantification (LOQ) of 0.9 ppb. By changing the metal deposition conditions, the roughness of the metalized papers could also be tuned for their use as surface-enhanced Raman scattering (SERS) sensors. Metallized papers with the highest SERS signal for thiophenol detection yielded a LOQ of 10 ppb. We anticipate that this method of fabricating nanostructured paper-based electrodes can accelerate the development of simple, cost-effective, and highly sensitive electrochemical and SERS sensing platforms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".