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Record W1965333006 · doi:10.1002/cjce.21767

Effects of sweating temperature on the purification of phosphoric acid hemihydrate crystal in dry‐sweating process

2012· article· en· W1965333006 on OpenAlexvenueno aff
Baoming Wang, Jun Li, Jianhong Luo, Kun Zhou, Jin Yang, Yabing Qi, Chunlei Ma

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldEngineering
TopicFreezing and Crystallization Processes
Canadian institutionsnot available
Fundersnot available
KeywordsPhosphoric acidArrhenius equationActivation energyHemihydrateChemistryCrystal (programming language)Analytical Chemistry (journal)Nuclear chemistryChromatographyMaterials sciencePhysical chemistryMetallurgyOrganic chemistryGypsum

Abstract

fetched live from OpenAlex

Abstract In order to purify phosphoric acid hemihydrate (H3PO4·0.5H2O) crystal further, the effects of sweating time and sweating temperature on purification are investigated in a dry‐sweating process. Experimental results indicate the content of Mg, As, Sb and Pb in the H3PO4·0.5H2O crystal decreases with increasing sweating time and sweating temperature during sweating process and show the same tendency to decrease with time at different sweating temperatures. The purification rate coefficients of H3PO4·0.5H2O crystals for Mg, As, Sb and Pb are determined. They increase with an increase in sweating temperature. The Arrhenius equation can be successfully used to describe the relationship between the purification rate coefficient and the absolute sweating temperature. The activation energy of sweating for Mg, As, Sb and Pb in the H3PO4·0.5H2O crystal are calculated using these experimental data. These values for Mg, As, Sb and Pb are 130.13, 193.59, 98.51 and 102.29 kJ mol−1, respectively.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.171
Teacher spread0.167 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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