Thermoplasmonic Response of Semiconductor Nanoparticles: A Comparison with Metals
Bibliographic record
Abstract
Abstract A number of applications in nanoplasmonics utilize noble metals, gold (Au) and silver (Ag), as the materials of choice. However, these materials suffer from problems of poor thermal and chemical stability with significant dissipative losses under high‐temperature conditions. In this regard, semiconductor nanoparticles have attracted attention with their promising characteristics of highly tunable plasmonic resonances, low ohmic losses, and greater thermochemical stability. Here, the size‐dependent thermoplasmonic properties of semiconducting silicon and gallium arsenide nanoparticles are investigated to compare them with Au nanoparticles using Mie theory. To this end, experimentally estimated models of dielectric permittivity are employed. Among the various permittivity models for Au, the Drude–Lorentz (DL) and the Drude and critical points (DCP) models are further compared. Results show a redshift in the scattering and absorption resonances for the DL model while the DCP model presents a blueshift. A massive Drude broadening contributes strongly to the damping of resonances in Au nanoparticles at elevated temperatures. In contrast, the semiconductor nanoparticles do not exhibit significant deterioration in their scattering and absorption resonances at high temperatures. In combination with low dissipative damping, this makes the semiconductor nanoparticles better suited for high‐temperature applications in nanoplasmonics wherein the noble metals suffer from excessive heating.
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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".