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Record W2081912009 · doi:10.1179/cmq.2006.45.2.199

APPLICATION OF A MODIFIED WATER GLASS IN A CATIONIC FLOTATION OF CALCITE AND DOLOMITE

2006· article· en· W2081912009 on OpenAlexfundno aff
Kejian Ding, J. Laskowski

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCalciteDolomiteChemistrySodium silicateSilicateMineralogyAmmonium bromideCationic polymerizationZeta potentialNuclear chemistryInorganic chemistryChemical engineeringPulmonary surfactantPolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A series of modified water glasses was prepared by reacting polyvalent metal salts with water glass. The effect of the products on cationic flotation of calcite and dolomite was investigated. The tests showed that water glass and even more so the modified water glass activated the flotation of calcite and dolomite for a cationic flotation with DTAB (dodecyltrimethyl ammonium bromide). The zeta potential measurements revealed that the addition of water glass (or modified water glass) made the mineral surfaces more negative and increased adsorption of DTAB on both calcite and dolomite.On a préparé une série de silicates de sodium modifiés en faisant réagir des sels métalliques polyvalents avec du silicate de sodium. On a étudié l'effet des produits sur la flottation cationique de calcite et de dolomie. Les essais ont montré que le silicate de sodium ainsi que le silicate de sodium modifié activaient la flottation de la calcite et de la dolomie pour une flottation cationique avec du DTAB (bromure d'ammonium dodécyltriméthyle). Les mesures de potentiel zéta ont révélé que l'addition de silicate de sodium (ou de silicate de sodium modifié) rendait la surface du minéral plus négative et augmentait l'adsorption du DTAB tant sur la calcite que sur la dolomie.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
Threshold uncertainty score0.886

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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

Citations25
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Metallurgical QuarterlySame topicMinerals Flotation and Separation TechniquesFrench-language works237,207