Experimental and theoretical study of the characteristics of LSPR peaks for metal NPs produced by controlling Ar ambient gas pressure to enhance the efficiency of solar cells
Bibliographic record
Abstract
In this study, silver (Ag) nanoparticle thin films were deposited on microscope slide glass and Si wafer substrates using the pulsed-laser deposition (PLD) technique in Ar ambient gas pressures of 1 × 10−3 and 7.5 × 10−1 mbar. AFM analysis has shown that the number of Ag nanoparticles reaching the substrate decreased with increasing Ar gas pressure. As a result of Ar ambient gas being allowed into the vacuum chamber, it was observed that the size and height of Ag nanoparticles decreased and the interparticle distances decreased. According to the absorption spectra taken by a UV–vis spectrometer, the wavelength where the localised surface plasmon resonance (LSPR) peak appeared was shifted towards the longer wavelength region in the solar spectrum as Ar background gas pressure was decreased. This experiment shows that LSPR wavelength can be tuned by adjusting the size of metal nanoparticles, which can be controlled by changing Ar gas pressure. The obtained extinction cross section spectra for Ag nanoparticle thin film was theoretically analysed and determined by using the metal nanoparticle–boundary element method (MNPBEM) toolbox simulation program. In this study, experimental spectrum and simulation data for metal nanoparticles were acquired, compared, and determined to be in agreement.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".