Bibliographic record
Abstract
The physical characteristics of matter depend on both chemical composition and atomicscale structure. Materials with structure engineered at the atomic scale, or nanomaterials, differ markedly from bulk solids, and are believed to be the answer in the ongoing quest for materials with superior properties. With properties critically determined by the nanostructure, future advances hinge on developing an understanding of the growth processes and, particularly, on the ability to control and manipulate these processes. This thesis describes a number of advanced nanostructured infrared photonic devices with designable response characteristics, fabricated from vacuum evaporated amorphous silicon with the technique of glancing angle deposition. The technique enables continuous, controlled variation of the refractive index by introducing nanometer-scale oscillations of porosity of the material, thus allowing the manufacture of inhomogeneous photonic coatings such as rugate filters, graded-index broadband anti-reflection coatings and photonic crystals. In addition the thesis presents research that relates to the understanding of growth of glancing angle deposited thin films ranging from sub-nanometer to micron thickness. A number of experimental and analysis techniques used to study the films are described including scanning electron microscopy, atomic force microscopy, Monte Carlo simulations and both in situ and ex situ spectroscopic ellipsometry. The information obtained from this work is not only of fundamental importance but may ultimately lead to the ability to precisely engineer the behaviour and properties of materials of technological potential in the photonics industry.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".