Ultrastable Plasmonic Cu-Based Core–Shell Nanoparticles
Bibliographic record
Abstract
Cu is the cheapest plasmonic metal showing plasmonic resonance in the visible region, which makes it highly attractive in various fields (e.g., sensing, surface-enhanced Raman scattering, and photocatalysis). However, its poor chemical stability severely restricts its application. Herein, we develop a seed-mediated approach to synthesize ultrastable Cu-based nanoparticles (NPs) stabilized with a thin, completely covered shell. By precisely controlling the reaction conditions, we are able to achieve uniform plasmonic Cu–Au core–shell NPs with significantly enhanced chemical stability even in a harsh environment in the presence of a strong oxidizing acid (HNO 3 ) solution. In-depth characterizations and analysis allow us to identify the critical role of the external crystalline Au layer, as compared to the AuCu alloy layer, in achieving superior stability. Furthermore, a deeper understanding of the plasmonic spectra was obtained by correlating the theoretical calculations on NPs of different core–shell dimensions with experimental results. Transient absorption measurements reveal that the plasmon dynamics and the heat transfer coefficients are not affected with the shell formation. As a proof of concept, these NPs demonstrate high photothermal efficiency and chemical stability for solar steam generation. This work offers a general strategy for the synthesis of ultrastable cost-effective, plasmonic Cu-based NPs, which show great potential in catalysis, electronics, and optics.
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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) | 0.001 | 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".