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Record W2102260325 · doi:10.1002/cjce.20600

Process intensification of synthesis of magnetite using spinning disc reactor

2011· article· en· W2102260325 on OpenAlexvenueno aff
Rupesh G. Moharir, Parag R. Gogate, Virendra K. Rathod

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2011
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetiteSpinningMaterials scienceProcess engineeringVolumetric flow rateRotational speedProcess (computing)Particle (ecology)Particle sizePrecipitationAnalyserChemical engineeringNuclear engineeringMechanical engineeringMechanicsComposite materialChemistryComputer sciencePhysicsChromatographyMetallurgyEngineering

Abstract

fetched live from OpenAlex

Abstract An instantaneous co‐precipitation reaction for the synthesis of magnetite particles has been investigated in conventional mechanically agitated reactor and novel spinning disc reactor (SDR) with an objective of process intensification. Characteristics of the particles have been analysed using Coulter Counter particle analyser. It has been observed that the particle size distribution is more uniform with overall lower power consumption in the SDR as compared to the conventional reactors. With a viewpoint of improving the synthesis process in terms of the obtained conversion levels in the SDR, effect of different operating parameters viz. rotational speed, diameter and type of the disc, flow rate of the reactants and the operating temperature on the synthesis process has also been investigated. It has been observed that the flow rate of the reactants as well as disc characteristics have a significant influence on the extent of conversion. Overall, it has been established that the SDR gives excellent particle size distribution characteristics as compared to the conventional approaches and hence results in process improvement/intensification for magnetite synthesis process at comparatively lower energy inputs.

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 score0.037
Threshold uncertainty score0.253

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 head0.020
GPT teacher head0.200
Teacher spread0.179 · 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

Citations12
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicAerosol Filtration and Electrostatic PrecipitationFrench-language works237,207