Co-Deposition of Gold Nanoparticles and Metalloporphyrin Using the Langmuir–Blodgett (LB) Technique for Surface-Enhanced Raman Scattering (SERS)
Bibliographic record
Abstract
The synergistic effect produced by metallic nanoparticles when incorporated into different systems empowers a research field that is growing rapidly. In addition, organometallic materials are at the center of intensive research with diverse applications such as light-emitting devices, transistors, solar cells, and sensors. The Langmuir-Blodgett (LB) technique has proven to be suitable to address challenges inherent to organic devices, since the film properties can be tuned at the molecular level. Here we report a strategy to incorporate gold nanoparticles (AuNPs) into the LB film by co-deposition in order to achieve surface-enhanced Raman scattering (SERS) of the zinc(II)-protoporphyrin (IX) dimethyl ester (ZnPPIX-DME). Prior to the LB co-deposition, the properties of the Langmuir monolayer of ZnPPIX-DME at the air-water interface, containing AuNPs in the subphase, are studied through the surface-pressure versus mean molecular area (π-A) isotherms. The ZnPPIX-DME+AuNPs π-A isotherm presented a significant shift to higher molecular area, suggesting an interaction between both ZnPPIX-DME molecules and AuNPs. Those interactions are a key factor allowing the co-deposition of both AuNPs and ZnPPIX-DME molecules onto a solid substrate, thus forming the LB film. SERS of ZnPPIX-DME was successfully attained, ensuring the spatial distribution of the AuNPs. Higher enhancement factors were found at AuNP aggregates, as a result of the intense local electromagnetic field found in the metal nanoparticle aggregates. The main vibrational bands observed in the SERS spectra suggest a physical adsorption of the ZnPPIX-DME onto the surface of AuNPs. The latter is not only in agreement with the interactions pointed out by the π-A isotherms but also suggests that this interaction is kept upon LB film co-deposition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".