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Understanding the Precipitated Calcium Carbonate Flocculation Mechanism Induced by Starch through Population Balance Modeling

2011· article· en· W1988595851 on OpenAlex
Yi Zhou Sang, Nayef M. Al Saifi, Peter Englezos

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

VenueAdvanced materials research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicCoagulation and Flocculation Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFlocculationBreakageChemical engineeringPopulationCalcium carbonateCationic polymerizationMaterials scienceChemistryShear rateComposite materialRheologyPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

The precipitated calcium carbonate (PCC) flocculation kinetics and floc structures induced by cationic tapioca starch were recorded by the Malvern Mastersizer 2000 (Malvern Instruments Inc, Malvern, UK). Of particular interest, a population balance model for PCC flocculation was employed to extract the flocculation constants, namely collision efficiency, magnitude of energy dissipation rate and restructuring rate. The model made an attempt to take aggregation, breakage and flocs restructuring into account simultaneously to describe the PCC flocculation by aggregation and breakage mechanism. Through a response surface methodology (RSM) involving a central composite design, the effects of temperature, polymer dosage, ionic strength, and shear rate on flocculation parameters were investigated in this paper.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.365
GPT teacher head0.386
Teacher spread0.022 · 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