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Record W2047302068 · doi:10.1080/07349340302262

Coal Reverse Flotation. Part I. Adsorption of Dodecyltrimethyl Ammonium Bromide and Humic Acids onto Coal and Silica

2003· article· en· W2047302068 on OpenAlexaff
Marek Pawlik, J. Laskowski

Bibliographic record

VenueCoal Preparation · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAdsorptionChemistryAmmonium bromidePulmonary surfactantCoalContact angleChemical engineeringCationic polymerizationInorganic chemistryBromideBituminous coalOrganic chemistryAmmoniumMolecule

Abstract

fetched live from OpenAlex

Adsorption of dodecyltrimethyl ammonium bromide (DTAB) and humic acids (HA) on bituminous, oxidized bituminous, and subbituminous coals, as well as on silica, was studied through direct adsorption, electrokinetic, and contact angle measurements. It was concluded that the adsorption of DTAB and HA on a hydrophobic coal surface takes place through hydrophobic interactions between the hydrocarbon chains of these compounds and the hydrophobic coal surface. The adsorbed DTAB molecules cause a gradual decrease of coal hydrophobicity due to their tail-to-surface orientation. The adsorption density of DTAB on a hydrophilic/oxidized coal is much higher than on a bituminous coal. The results indicate that the adsorption mechanism involves some strong interactions between the cationic head group of the surfactant and the negatively charged oxygen groups on the coal surface. Despite the apparent head-to-surface orientation, the adsorbed DTAB molecules do not render the surfaces hydrophobic; this probably results from the fairly chaotic orientation of DTAB molecules on the surface of oxidized coal. The well-ordered conformation of the DTAB ions adsorbed onto the negatively charged silica surface, combined with significant adsorption of this surfactant at the solution/air interface makes the silica surface very hydrophobic.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.613

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 communication0.0000.001
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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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

Citations31
Published2003
Admission routes1
Has abstractyes

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