Nanostructured Copper Screen-Printed Electrodes as a Platform for Plasmon-Enhanced Spectroelectrochemistry
Bibliographic record
Abstract
Silver and gold are the most used plasmonic metals for surface-enhanced Raman spectroscopy (SERS), accounting for the vast majority of the published literature in this field. These two metals are preferred due to their excellent plasmonic enhancement, stability, and relative ease of synthesis and functionalization of their associated nanostructures. However, both silver and gold face earth abundance limitations, and so alternatives should be sought, particularly for large-scale plasmonic applications such as plasmon-enhanced photovoltaics or optical cloaking. In this work, a method to produce effective and scalable copper-based substrates for electrochemical SERS (EC-SERS) is introduced, utilizing commercially available carbon screen-printed electrodes (SPE) and physical vapor deposition (PVD). The carbon black particles present on the working electrode of the SPE serve as an efficient scaffold for the fabrication of copper nanostructures. Several test molecules were used to illustrate the performance of these sensors in the SERS analysis. This work also highlights the first reported formation of an electrochemically generated N -heterocyclic carbene (NHC) self-assembled monolayer (SAM) on a nanostructured copper surface under potential control in an aqueous electrolyte.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".