MétaCan
Menu
Back to cohort
Record W2747635092 · doi:10.11159/ffhmt17.194

Fabrication of Low Cost Composite Spargers and Their Performance in Polar and Non-Polar Liquids

2017· article· en· W2747635092 on OpenAlexvenueno aff
Behnam Mostajeran Goortani, Elham Khoshandam

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsnot available
FundersUniversity of Isfahan
KeywordsPolarFabricationComposite numberMaterials scienceComputer scienceComposite materialPhysicsAstronomy

Abstract

fetched live from OpenAlex

Spargers are porous devices used for the continuous injection of gas bubbles into liquids. They have many applications like effective aeration in bio reactors, enhanced oil recovery, flotation, filtration and water treatment. In this study low cost spargers are fabricated, utilizing a new method. The bubble sizes and distributions are determined in an experimental setup comprising a bubble column equipped with a semi-professional camera to record the sizes of the bubbles in the column and resulting bubbles are photographed at different gas flow rates. First substrate of glass-bead spargers are fabricated. They are then covered by a layer of copper. The effect of reaction temperature and fluid properties are investigated on the size and the distribution of the produced bubbles. The results showed that the pore size of flat composite sample is decreased to 100 nm by coating by plasma focus deposition device; consequently, all bubbles produced by this sample are less than 0.1 mm inside kerosene. Comparison of BSD for all samples indicated that the smallest bubbles are produced in kerosene. By controlling the sintering conditions and through our innovative reaction and sintering method, we fabricated flat and conical composite spargers that produce 100% bubbles of less than 0.1mm diameter, and in kerosene a foamy bubble column is formed.

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 score0.023
Threshold uncertainty score0.496

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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicAdvanced ceramic materials synthesisFrench-language works237,207