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Record W2049918550 · doi:10.1021/ie030711g

Hydrolysis of Ferric Sulfate in the Presence of Zinc Sulfate at 200 °C:  Precipitation Kinetics and Product Characterization

2004· article· en· W2049918550 on OpenAlexafffund
Terry C. Cheng, George P. Demopoulos

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistrySulfateHematiteFerricInorganic chemistryZincStoichiometryPrecipitationHydrolysisReaction rate constantKineticsNuclear chemistryMineralogyPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The hydrolysis of ferric sulfate was studied in a batch reactor at 200 °C with the initial Fe(III) concentration ranging from 0.10 to 0.80 M in the presence/absence of zinc sulfate (1.2 M ZnSO 4 ) with 3 h retention time. Regardless of the presence or not of ZnSO 4, the hydrolytic precipitation kinetics at 200 °C was determined to be first-order with respect to Fe(III), and expressed by the following rate equation, r hyd = k hyd ( C Fe(III) − C Fe(III),eq ), where the apparent kinetic constant, k hyd, was determined to be 10 -2 min -1 . Two reaction stoichiometries were established: one leading to the formation of kinetically favored basic ferric sulfate at C Fe(III),initial ≥ 0.4 M in the absence of ZnSO 4 or at C Fe(III),initial ≥ 0.7 M in the presence of ZnSO 4, while the other led to the production of hematite. The hydrothermally produced hematite material possessed very high specific surface area (50−80 m 2 /g) and contained around 1% S (as SO 4 via chemisorption), 0.2% Zn (when ZnSO 4 was present), and about 4.5% H 2 O/OH. After excluding the SO 4 and Zn content, the following stoichiometric formula for hematite was determined: Fe 2 O 3 - x (OH) 2 x · y H 2 O where x = 0.08 and y = 0.29.

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 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)0.001
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 score0.014
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.302
Teacher spread0.252 · 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 teacher head, 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

Citations47
Published2004
Admission routes2
Has abstractyes

Explore more

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