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Record W2056664618 · doi:10.1021/jp911949w

Gas−Liquid Flow Solid-Catalyzed Reactions in Magnetic-Field Emulated Microgravity

2010· article· en· W2056664618 on OpenAlex
Faı̈çal Larachi, Mugurel Catalin Munteanu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMagnetic and Electromagnetic Effects
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsDiamagnetismSelectivityMagnetic fieldCatalysisWettingPhenylacetyleneMagnetMaterials scienceChemical engineeringChemical physicsChemistryAnalytical Chemistry (journal)ChromatographyPhysicsOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The catalytic hydrogenation of α-methylstyrene and phenylacetylene were used to study the conversion and selectivity behaviors of wetting-sensitive solid-catalyzed gas−liquid flowing systems in artificial gravity generated in strong inhomogeneous static magnetic fields. Such microgravity environment maintained in a 9 T magnet for hours down to 10 −4 g for co-currently flowing diamagnetic gas/diamagnetic liquid systems was realized by matching the gas and liquid mass magnetic susceptibilities. Conversion of α-methylstyrene and selectivity of styrene were measured with and without magnetic fields at constant wetting efficiency and contact time. It was shown that magnetic fields affect conversion and selectivity of catalytic reactions exclusively via hydrodynamic phenomena. Hence, this method could prove useful to generate artificial microgravity in Earth-bound facilities to emulate passive/reactive catalytic multiphase flows in lunar/Martian gravity or in other microgravity conditions.

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.

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 score0.002
Threshold uncertainty score0.500

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 integrity0.0000.001
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 head0.003
GPT teacher head0.242
Teacher spread0.239 · 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