Thermoplasmonic Response of Semiconductor Nanoparticles: A Comparison with Metals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it