Bibliographic record
Abstract
Many of the physical properties of semiconductor materials depend on the presence of imperfections. A significant source of lattice imperfections is the inclusion of foreign atoms, or impurities. Since most semiconductor devices require accurate and repeatable results, highly pure materials are desired. In order to obtain high purity semiconductor metals, zone purification is commonly utilized as the final purification stage. The University of Victoria Crystal Growth Lab (CGL) Group is carrying out an optimization study of the zone refining process. To provide the required experimental platform for this study, a zone refining test bench (``CGL zone refiner'') was developed. The apparatus will be used to study the effects of zone geometry and mixing on the efficiency of the zone refining process. It also has the capability of zone refining, under an applied rotating magnetic field and an electric current, in order to examine their effect. A series of preliminary experiments were carried out with the CGL zone refiner prior to optimization testing. Samples were removed from the processed ingots and sent for glow discharge mass spectrometry (GDMS) analysis. The GDMS results indicated that the system operates efficiently and that, even with as few as three zone passes, the CGL zone refiner purified the material. A numerical thermal analysis for the zone refining of Te is also presented. In general, the numerical results were in agreement with experimental observations; the solid/liquid interface was convex(toward liquid) for small liquid zones, concave for large liquid zones and the system was thermally stable.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".