Purification principles and methodologies to produce high-purity tellurium
Bibliographic record
Abstract
This review paper reconstructs the current development of tellurium purification technologies with their setups to obtain high-purity tellurium.Since the uptrend of the solar industry in the early 2000s, the demand for tellurium, especially high-purity quality, has increased sharply to enable the production of e.g.solar modules, solar panels and semiconductor.This paper focuses on both primary as well as secondary routes of purification.Especially the recycling aspects can contribute to a close-to-zero-waste process and circular economy horizons.For this purpose, a general introduction on tellurium sources and global primary production along with price trends were provided to show the path up to high purity grades.Recovery processes through secondary sources such as CdTe and Bi 2 Te 3 were introduced, followed subsequently by a thorough review on two refining methods for tellurium.Vacuum distillation and zone refining processes were discussed in terms of their effectiveness in purifying tellurium as well as their challenges by reviewing relevant research.Through this review, several knowledge gaps can be identified to motivate further development of refining processes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".