Laser Fabricated Cu <sub>2</sub> O‐CuO/Ag Nanocomposite Films for SERS Application**
Bibliographic record
Abstract
Abstract Noble metals are widely used in surface‐enhanced Raman spectroscopy (SERS) because of the electromagnetic enhancement, which is from the surface plasma resonance on noble metal surface under the action of an electromagnetic field that can greatly enhance the Raman signal of the molecule. However, high cost limits their widespread use in realistic applications. Therefore, semiconductor nanomaterials have been introduced in SERS substrate. Here, we fabricate Cu 2 O‐CuO film on glass by direct laser writing and then mix with Ag NWs to form Cu 2 O‐CuO/Ag nanocomposite substrate. The composite substrate shows strong Raman resonance for both Rhodamine 6G (R6G) and 4‐mercaptobenzoic acid (4‐MBA), good uniformity and stability. LOD of R6G and 4‐MBA is 10 −10 and 10 −6 M, respectively. We further discuss the charge transfer process of metal‐semiconductor‐molecule (Ag‐Cu 2 O‐MBA and Ag‐CuO‐MBA) and Raman enhancement mechanism with XPS and UV‐vis diffuse reflectance spectroscopy validation. This work provides a facile and low‐cost method of fabricating Cu 2 O‐CuO/Ag SERS nanocomposite substrate.
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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) | 0.002 | 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".