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Record W1988764433 · doi:10.1002/cjce.5450780609

Liquid‐solid mass transfer from horizontal woven screen packing in bubble column reactors

2000· article· en· W1988764433 on OpenAlexaffvenue
M. M. Zaki, I. Nirdosh, G.H. Sedahmed

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsLakehead University
Fundersnot available
KeywordsMass transferBubbleColumn (typography)Materials scienceMechanicsChromatographyPhysicsChemistryMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract The effect of gas sparging on the rate of mass transfer at horizontal single screen and stacks of closely packed horizontal screens was studied by an electrochemical technique which involved measuring the limiting current of the cathodic reduction of potassium ferricyanide. Variables studied were: distance between the sparger and the screen, screen characteristics, e.g., mesh number and wire diameter, physical properties of the solution and superficial gas velocity. Screen characteristics were found to have little effect on the rate of mass transfer. The mass transfer measurement at beds of closely packed screens revealed that the mass transfer coefficient decreased below the single screen value with increasing the number of screens per bed. Comparison of the present data with previous results showed that screens produce higher rates of mass transfer than other geometries under otherwise the same conditions. The importance of the present work to the design and operation of catalytic and electrochemical reactors was highlighted.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.159
Teacher spread0.155 · 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 source (direct Gemma or distilled Codex), 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

Citations14
Published2000
Admission routes2
Has abstractyes

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