Evaluation of an Electrodeposited Bimetallic Cu/Ag Nanostructured Screen Printed Electrode for Electrochemical Surface-Enhanced Raman Spectroscopy (EC-SERS) Investigations
Bibliographic record
Abstract
The field of plasmonics has experienced rapid growth over the past decade with a host of emerging applications including single molecule sensing and plasmon-assisted catalysis. The vast majority of these applications use either silver or gold as the plasmonic metal, which are both high cost and face earth-abundance limitations in the next 100 years. Recent efforts have focused on taking advantage of the plasmonic properties of copper, a more abundant and low cost coinage metal as a sustainable route for plasmonic applications. In particular, there has been great interest in developing copper substrates capable of reliable and efficient enhancement of Raman signals for use in surface-enhanced Raman spectroscopy (SERS) sensing. Herein we describe a sequential electrodeposition technique whereby highly functional and robust Cu/Ag bimetallic SERS-active screen printed electrodes can be produced rapidly and at low cost, which display excellent plasmonic performance and are capable of supporting surface-plasmon assisted catalysis (SPAC). This modified screen printed electrode allows for the in situ spectroelectrochemical investigation of surface redox processes using a sustainable alternative to traditional monometallic electrodes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".