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Record W4409191782 · doi:10.1016/j.ces.2025.121623

Utility of argon as a suppressant of triboelectrification in pressurized Gas-Solid fluidized beds

2025· article· en· W4409191782 on OpenAlexafffund
Nikhil Sridhar, Poupak Mehrani

Bibliographic record

VenueChemical Engineering Science · 2025
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUnivation Technologies
KeywordsArgonFluidized bedTriboelectric effectMaterials scienceFluidized bed combustionWaste managementNuclear engineeringChemistryEngineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Argon displays a significantly lower dielectric strength compared to nitrogen at ambient pressures, but this difference diminishes as the pressure of the gas increases. This work aimed to examine the usage of argon and its impact on particle charging and column-wall fouling in fluidized beds under pressurized conditions. This study tested the electrostatic charging behaviour of a commercially produced linear low-density polyethylene (LLDPE) resin and its column wall adhesion at pressures of 2600 kPa. Fluidization was performed using pure argon, pure nitrogen, and their equimolar mixtures. Fluidization with nitrogen served as the baseline for comparison. Argon reduced the fouling by 43 %, while the binary gas mixture reduced the fouling by 29 %. The particles’ bulk specific charge also showed a downward trend moving from pure nitrogen to pure argon. The study concludes that argon can be utilized as a triboblelectrification suppressant in pressurized gas–solid fluidized beds.

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)0.001
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 score0.180
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.005
GPT teacher head0.229
Teacher spread0.224 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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