Tuning Gold Nanoparticle Self-Assembly for Optimum Coherent Anti-Stokes Raman Scattering and Second Harmonic Generation Response
Bibliographic record
Abstract
The research and development of new substrates for use in surface-enhanced spectroscopy is primarily motivated by the ability to tune such substrates to provide maximum signal enhancement and therefore lower detection limits. We examined a series of multilayer nanoparticle (NP) arrays with between 1 and 17 NP layers using two nonlinear optical (NLO) techniques: Coherent anti-Stokes Raman scattering (CARS) and second harmonic generation (SHG). The CARS signal of oxazine 720 was monitored at 1600 cm −1 using a 709-nm pump beam and an 800-nm stokes beam. Maximum signal was observed for 11 NP layers and is attributed to the matching of the CARS signal and the substrate surface plasmon excitation at 637 nm. The CARS signal for the 3000-cm −1 C−H stretching vibration showed maximum enhancement at 13 NP layers. The maximum SHG signal enhancement occurred at 13 NP layers, with a 50-fold overall enhancement of the SHG signal. We demonstrate that the NP-containing substrates can be tuned to provide maximum NLO response based on the number of NP depositions and the wavelength(s) involved in the NLO experiments. These multilayer NP arrays yield a stable and modular spectroscopic substrate advantageous for a variety of surface-enhanced spectroscopic techniques.
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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 | 0.001 |
| 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 | 0.000 | 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".