Plasmonic thermochromism based on a reversible redox reaction of Ag<sup>+</sup>/Ag on Au nanorods
Bibliographic record
Abstract
Reversible redox reaction-based thermochromism using plasmonic nanocrystals is challenging due to the requirements set based on the complexity of the reaction system where the oxidizing and reducing agents must not interfere with each other, and both should possess temperature sensitivity. Herein, we demonstrate plasmonic thermochromism based on a reversible redox reaction of Ag+/Ag on Au nanorods (AuNRs) by incorporating temperature-sensitive reducing and oxidizing agents into the same system. The competition between reduction and oxidation is solely dependent on temperature. When the temperature is above (below) the transition temperature, the reduction of Ag+ (oxidation of Ag) dominates on the surface of AuNRs, and the thermochromic nanostructure solution appears green (red). An experimental study on the mechanism reveals that HOCl produced at low concentrations by H2O2 is the source of the observed temperature dependence of the Ag oxidation. Rationally tuning the transition temperature in a range from 27 to 40 °C can be realized by changing the concentration of some key chemical compounds in the solution. The thermochromic solution can be standalone-functional within multiple cycles of heating and cooling and long-term storage without any additional reagents. Our study provides new insight into plasmonic thermochromism and may pave the way for fabricating smart thermochromic materials.
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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".