Bibliographic record
Abstract
Background: Tunability in resonance wavelength and the enhancement of the electromagnetic field intensities around the surface are two unique properties which make metal as a plasmonic material. A theoretical investigation on the LSPR and field enhancement for heterogeneous dimer–trimer metallic nanostructure by constituting Al and Au as two different plamsonic materials has been studied. Since electrons in Al exhibit free behavior for LSPR of Au, therefore, they influence the electric field magnitude generated by Au LSPR. Methods: The electromagnetic simulations reported in this paper were performed using the FDTD Solutions (version 7.5.1), a product of Lumerical Solutions Inc., Vancouver, Canada. We adopted a cubic Yee cell of 1 nm side and a time step Δt= 1.31•10-18 s, bounded by Courant condition. Results: The extinction spectrum shows LSPR peak over UV-visible region for isotropic nanostructure which shifts to NIR region for anisotropic shape nanostructure. The spherical shape hetero dimer nanostructure shows enhancement factor ~ 3.9 X 105 whereas it increases to ~ 6.2 X 106 for anisotropic shape at 610 nm. The field distribution corresponding to the trimer nanostructure reveals a large dipolar field distribution on each of the three nanoparticles, oscillating approximately in-phase. The spherical shape Al-Au-Al shows enhancement factor ~ 8.5 X 106 at 571 nm. The anisotropic shape increase the enhancement factor to ~ 2.4 X 107 at peak wavelength 700 nm i.e. tuning the plasmon wavelength towards NIR region. Conclusion: The tunability in plasmon wavelength and field enhancement factor has been evaluated for heterogeneous nanostructure over wider spectrum range i.e. DUV-Visible-NIR using Au-Al dimer and trimer nanostructure. The isotropic shape Au-Al hetero nanostructure shows larger enhancement in the UV-visible region, whereas the anisotropic shape nanostructure contributes towards the NIR region.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".