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Record W2021281845 · doi:10.1021/ie801195b

Natural Convection Mass Transfer Behavior of Fixed Bed of Spheres in Relation to Catalytic and Electrochemical Reactor Design

2009· article· en· W2021281845 on OpenAlexaff
Ibrahim Hassan, Inderjit Nirdosh, Gomaa H. Sedahmed

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2009
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsLakehead University
Fundersnot available
KeywordsMass transferRaschig ringSPHERESLimiting currentNatural convectionElectrochemistryDiffusionMass transfer coefficientChemistryMass fractionThermodynamicsConvectionChemical engineeringMaterials sciencePacked bedAnalytical Chemistry (journal)ChromatographyElectrodePhysical chemistryPhysics

Abstract

fetched live from OpenAlex

The free convection mass transfer behavior of a fixed bed of spheres has been studied experimentally using an electrochemical technique which involved measuring the limiting current of the cathodic deposition of copper from acidified copper sulfate solution. Variables studied were sphere diameter, bed height, and physical properties of the solution. The mass transfer coefficient was found to decrease slightly with increasing bed height and independent of sphere diameter. The data were correlated for the conditions 5 × 10 6 < Sc · Gr < 5.4 × 10 8 by the equation Sh = 0.28( Sc · Gr ) 0.32 . A comparison between the present and some previous data at other packing geometries shows that the rate of natural convection mass transfer at a bed of spheres is higher than that at beds of Raschig rings and at cylinders. The importance of the present results for the design and operation of catalytic and electrochemical reactors used to conduct diffusion controlled liquid−solid reactions is 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.045
GPT teacher head0.281
Teacher spread0.236 · 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 designSimulation or modeling
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

Citations8
Published2009
Admission routes1
Has abstractyes

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