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Record W4417113077 · doi:10.1016/j.seppur.2025.136421

Experimental insights into surface chemistry and wetting properties of mineral particles in multiphase systems

2025· article· en· W4417113077 on OpenAlexafffund
Mohammed Zriki, Adrián Carrillo García, Louis Fradette, Jamal Chaouki

Bibliographic record

VenueSeparation and Purification Technology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsPolytechnique Montréal
FundersOCP GroupMitacs
KeywordsWettingContact angleMicroscale chemistryMineralSurface energyParticle (ecology)Aqueous solutionFroth flotation

Abstract

fetched live from OpenAlex

Mineral separation processes like froth flotation rely on differences in wettability, emphasizing the need to understand the interfacial properties and microscale processes occurring on material surfaces. Although the Young contact angle is the primary parameter for wettability quantification, it faces significant limitations when applied to fine particles due to their non-planar shapes and rough surfaces, making the analysis highly system-specific. This study explores the challenges related to the wettability of minerals, including carbonates, quartz, pHosphate minerals such as apatite, and rare-earth-bearing minerals like bastnaesite and monazite. Their respective wettability was determined using static and dynamic contact angle measurements, solvent extraction, and particle attachment-droplet coverage analysis. A higher tendency of apatite, monazite, and bastnaesite to attach to oil droplets under specific conditions (pH around 6) can be attributed to their greater hydrophobicity (higher contact angles >55°). This behavior likely arises from their surface chemical composition and crystalline structure, which favor lower surface energy and reduced affinity to the aqueous phase, while carbonate minerals and quartz, being more hydrophilic minerals, remained in the water phase. The analysis of mineral surface properties indicates the capability for processes like solid-stabilized emulsions to selectively separate and concentrate specific minerals.

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 score0.038
Threshold uncertainty score0.313

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 head0.012
GPT teacher head0.272
Teacher spread0.260 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueSeparation and Purification TechnologySame topicMinerals Flotation and Separation TechniquesFrench-language works237,207