MétaCan
Menu
Back to cohort
Record W2094365974 · doi:10.1515/htmp-2012-0100

Thermodynamics of Phosphorus Removal from Silicon in Solvent Refining of Silicon

2012· article· en· W2094365974 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHigh Temperature Materials and Processes · 2012
Typearticle
Languageen
FieldEngineering
TopicSilicon and Solar Cell Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSiliconRefining (metallurgy)ImpurityMaterials scienceGetterPhosphorusAlloyRecrystallization (geology)MetallurgySolventChemical engineeringChemistryOrganic chemistryOptoelectronicsGeology

Abstract

fetched live from OpenAlex

Abstract Refining of silicon for solar applications using metallurgical approaches has attracted a considerable attention in the recent years. The present study involves employing solvent refining as a purification technique in which silicon recrystallization takes place from an iron-silicon alloy melt. It is believed that iron will perform as the impurity “getter” and purified silicon crystals grow from the alloy melt, while the impurities are segregated to the liquid alloy. The focus of this article is on removal of phosphorus, as a critical impurity in solar silicon. In order to assess the feasibility and efficiency of phosphorus removal through solvent refining, the distribution of phosphorus between solid Si and Fe-Si melt at 1483–1583 K (1210–1310 °C) was measured. Interaction parameter between phosphorus and iron was calculated by varying the concentration of phosphorus at each temperature.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.202
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it