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Record W2565955359 · doi:10.1002/cjce.22764

Preparation of high quality Microgranulate CrO<sub>3</sub> based on green process design

2016· article· en· W2565955359 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldChemistry
TopicPigment Synthesis and Properties
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsQuality (philosophy)Process (computing)Reliability engineeringComputer scienceProcess engineeringEngineeringPhysicsOperating system

Abstract

fetched live from OpenAlex

Abstract A mild crystallization process was proposed to prepare chromium trioxide (CrO 3 ) by reaction between K 2 Cr 2 O 7 and HNO 3 aqueous solutions. Through ICP, XRPD, SEM, and EDS analysis, key factors and mechanisms that influenced the preparation of CrO 3 were studied. Large spherical particles of CrO 3 (d 50 ≥ 300 µm) with high purity (CrO 3 ≥ 0.99 g/g, K ≤ 2 mg/g) were prepared when the initial concentration of K 2 Cr 2 O 7 was kept at 80 g/100 mL HNO 3 , the acid feeding rate and the cooling rate were set at 1 mL · min −1 and 0.1 °C · min −1 , respectively, and the direct recovery of Cr 6+ was up to more than 95 %. Kinetic analysis indicated that low nucleation rate and high growth rate would favour the increase of CrO 3 particle size in this process. As a high‐valued byproduct, Cr(VI)‐free KNO 3 was further prepared with the designed crystallization steps. Additionally, green characteristics of the process were also discussed.

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 score0.003
Threshold uncertainty score0.280

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 head0.019
GPT teacher head0.226
Teacher spread0.208 · 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