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Record W4386245295 · doi:10.3390/polym15173575

Foaming Properties of Lignosulfonates in the Flotation Process

2023· article· en· W4386245295 on OpenAlexaff
Jhon Chique, Lina Uribe, Marek Pawlik, Andrés Ramírez, Leopoldo Gutiérrez

Bibliographic record

VenuePolymers · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of British Columbia
FundersFondo de Fomento al Desarrollo Científico y TecnológicoAgencia Nacional de Investigación y DesarrolloFondo de Financiamiento de Centros de Investigación en Áreas PrioritariasCentro de Recursos Hídricos para la Agricultura y la Minería
KeywordsLignosulfonatesChemistryDodecylbenzeneSurface tensionAlkalinityChemical engineeringCopperChromatographyBubbleLigninSodiumSulfonateOrganic chemistry

Abstract

fetched live from OpenAlex

S) and bad odors. Previous studies showed that molybdenite flotation can be depressed by the action of lignosulfonates, but no significant progress has been made in studying the effect that these reagents have on the foaming/frothing phenomena in flotation. The objective of this work was to investigate the foaming properties of three samples of lignosulfonates through measurements of surface tension, foamability, bubble size distributions, and water recovery. A sugared sodium lignosulfonate (NaLS), a calcium lignosulfonate (CaLS), and a sample prepared by sulphomethylation of kraft lignin (KLS) were tested. It was found that all lignosulfonates displayed surface activity that decreased with pH and was related to the degree of anionicity and molecular weight. The NaLS lignosulfonate showed the highest dynamic foamability index (DFI) value, compared to that of the CaLS and KLS samples. The lignosulfonates tested in this study strongly affected bubble size. Water recovery tests performed using flotation experiments in a two-phase system showed that the KLS and NaLS samples had the strongest effect, which correlated with the surface tension, foamability, and bubble size results.

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 score0.075
Threshold uncertainty score0.179

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 head0.026
GPT teacher head0.274
Teacher spread0.247 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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