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Record W7000562826

Frothers and gas dispersion: A review of the structure-property-function relationship

2018· article· en· W7000562826 on OpenAlexfundno aff

Bibliographic record

VenuePrace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu · 2018
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaShell CanadaBarrick Gold Corporation
KeywordsAlkylEthylene oxideBubbleCoalescence (physics)Propylene oxideMoleculeChain (unit)
DOInot available

Abstract

fetched live from OpenAlex

Over the past 20 years quantitative measures of frother functions have been developed to try to replace such qualitative descriptors as "weak" and "strong".One of these metrics is the critical coalescence concentration (CCC) that quantifies a frother's ability to reduce bubble size; another is the concentration at minimum velocity (CMV) that quantifies a frother's ability to reduce bubble rise velocity.The experimental procedure for the two measures is briefly outlined and the measures are shown to be related.Using CMV, based on more than 50 surfactants from the two main frother families, alcohols and polyglycols, the frother structure-property-function link is investigated.The structure variables were: in alcohols, alkyl chain length, and position of the methyl branch and hydroxyl group(s); and in polyglycols, alkyl chain length, and number of propylene oxide (PO) or ethylene oxide (EO) groups.On the argument that low CMV represents the desired outcome, the main findings are: the dominant effect of alkyl chain length in both alcohols and polyglycols; that for alcohols branched-chain isomers are superior to straight chain, with the best combination being OH at the terminus and the methyl branch as far away as possible; and for polyglycols, PO-based are superior to EO-based.Interpretation of these observations included the effect of structure on the following properties: surface activity, mass transfer rate, H-bonding, and molecule packing.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.673

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 head0.008
GPT teacher head0.199
Teacher spread0.190 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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