MétaCan
Menu
Back to cohort
Record W7010690523

Interaction between fine particles and colloidal gas aphrons (CGA)

2015· dissertation· en· W7010690523 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsMcGill University
FundersMcGill University
KeywordsPulmonary surfactantZeta potentialIron oxideAdsorptionParticle (ecology)ColloidSilicon dioxideOxideIron oxide nanoparticles
DOInot available

Abstract

fetched live from OpenAlex

Interactions between fine particles (-10 m) and colloidal gas aphrons (CGA) were predicted from zeta potential measurements, which were then compared to flotation experiments.CGAs, which are microbubbles coated and stabilized by a surfactant, were used as to selectively recover fine iron oxide or silica particles.Surfactant adsorption was measured by the changes in zeta potential as a function of surfactant addition.Using synthetic mineral (-10 m) particles, four sets of recovery experiments were conducted using negatively charged CGA and positively charged CGA ( iron (III) oxide and sodium dodecylsulfate (SDS) CGA, silicon dioxide and SDS CGA, iron (III) oxide and hexadecyltrimethylammonium bromide (HTAB) CGA, and silicon dioxide and HTAB CGA).In addition, the recovery of natural fine mineral particles (hematite and quartz) was tested using an identical experiment setups.Both sets of synthetic particles and natural minerals showed positive correlations between recovery experiments and surfactant adsorption experiments.Therefore, high levels of particle recovery is related to good adsorption of surfactant onto the surface of particles, which is in agreement with electrostatic interactions of particles and CGA.Conversely, low levels of particle recovery is associated with poor

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.583
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.284
Teacher spread0.258 · 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.

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

Citations0
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicMinerals Flotation and Separation TechniquesFrench-language works237,207