Chemical vapor deposition of anisotropic ultrathin gold films on optical fibers: real-time sensing by tilted fiber Bragg gratings and use of a dielectric pre-coating
Bibliographic record
Abstract
Tilted fiber Bragg gratings (TFBGs) are refractometry-based sensor platforms that have been employed herein as devices for the real-time monitoring of chemical vapour deposition (CVD) in the near-infrared range (NIR). The coreguided light launched within the TFBG core is back-reflected off a gold mirror sputtered onto the fiber-end and is scattered out into the cladding where it can interact with a nucleating thin film. Evanescent fields of the growing gold nanostructures behave differently depending on the polarization state of the core-guided light interrogating the growing film, therefore the resulting spectral profile is typically decomposed into two separate peak families for the orthogonal S- and P-polarizations. Wavelength shifts and attenuation profiles generated from gold films in the thickness regime of 5-100 nm are typically degenerate for deposition directly onto the TFBG. However, a polarization-dependence can be imposed by adding a thin dielectric pre-coating onto the TFBG prior to using the device for CVD monitoring of the ultrathin gold films. It is found that addition of the pre-coating enhances the sensitivity of the P-polarized peak family to the deposition of ultrathin gold films and renders the films optically anisotropic. It is shown herein that addition of the metal oxide coating can increase the peak-to-peak wavelength separation between orthogonal polarization modes as well as allow for easy resonance tracking during deposition. This is also the first reporting of anisotropic gold films generated from this particular gold precursor and CVD process. Using an ensemble of x-ray techniques, the local fine structure of the gold films deposited directly on the TFBG is compared to gold films of similar thicknesses deposited on the Al2O3 pre-coated TFBG and witness slides.
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".