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Record W4392128431 · doi:10.15376/frc.2009.2.1193

The Structure and Strength of Flocs of Precipitated Calcium Carbonate Induced by Various Polymers Used in Papermaking

2009· article· en· W4392128431 on OpenAlexafffund
Roger Gaudreault, Nicolas Di Cesare, Theo G. M. van de Ven, David A. Weitz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicBuilding materials and conservation
Canadian institutionsMcGill UniversityCascades (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaFPInnovationsHarvard University
KeywordsPapermakingPolymerCalcium carbonateScanning electron microscopeMaterials scienceComposite materialChemical engineeringFormamidePolymer chemistryChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

When the percentage of filler is increased in paper, the optical properties are improved and the production cost lowered. However, fillers weaken paper strength by decreasing the fibre-fibre bonded area. Little is known about the optimum filler floc size or filler floc properties to allow developing optimum paper characteristics. Consequently, the floc structure and strength of precipitated calcium carbonate (PCC) aggregates was studied using various polymers (flocculants and dry strength agents): by static light scattering/diffraction (SLS), real time fluorescent video imaging, image analysis, photometric dispersion analysis (PDA) and scanning electron microscopy (SEM). It was found that PEO/cofactor induced PCC aggregates were weaker at high shear and far more irreversible than those induced by the partially hydrolysed polyvinyl formamide copolymerised with acrylic acid (PVFA/NaAA) or C-starch. Flocs produced at low polymer dosages were smaller and weaker than those produced at higher dosages. The number of discrete PCC particles in aggregates was measured using real time fluorescent video imaging combined with image analysis.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.999

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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 designObservational
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
Published2009
Admission routes2
Has abstractyes

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