Enhanced Photothermal Conversion in Vertically Oriented Gallium Arsenide Nanowire Arrays
Bibliographic record
Abstract
The photothermal properties of vertically etched gallium arsenide nanowire arrays are examined using Raman spectroscopy. The nanowires are arranged in square lattices with a constant pitch of 400 nm and diameters ranging from 50 to 155 nm. The arrays were illuminated using a 532 nm laser with an incident energy density of 10 W/mm(2). Nanowire temperatures were highly dependent on the nanowire diameter and were determined by measuring the spectral red-shift for both TO and LO phonons. The highest temperatures were observed for 95 nm diameter nanowires, whose top facets and sidewalls heated up to 600 and 440 K, respectively, and decreased significantly for the smaller or larger diameters studied. The diameter-dependent heating is explained by resonant coupling of the incident laser light into optical modes of the nanowires, resulting in increased absorption. Photothermal activity in a given nanowire diameter can be optimized by proper wavelength selection, as confirmed using computer simulations. This demonstrates that the photothermal properties of GaAs nanowires can be enhanced and tuned by using a photonic lattice structure and that smaller nanowire diameters are not necessarily better to achieve efficient photothermal conversion. The diameter and wavelength dependence of the optical coupling could allow for localized temperature gradients by creating arrays which consist of different diameters.
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.
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) | 0.000 | 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 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".