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Record W2809277978 · doi:10.1002/cjce.23259

Activation and deactivation scenarios in a plasma‐synthesized Co/C catalyst for Fischer‐Tropsch synthesis

2018· article· en· W2809277978 on OpenAlexaffvenue
James Aluha, Nicolas Abatzoglou

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCatalysisFischer–Tropsch processSqualaneChemistrySpace velocityCobaltPhase (matter)Nuclear chemistryAnalytical Chemistry (journal)Chemical engineeringChromatographyInorganic chemistryOrganic chemistrySelectivity

Abstract

fetched live from OpenAlex

ABSTRACT A carbon‐supported cobalt nano‐catalyst (Co/C) synthesized through plasma was tested for Fischer‐Tropsch activity. Catalyst deactivation and activation protocols studied included in situ sample pre‐treatment at 673 K in gas flowing at the rate of 250 cm 3 · min −1 in: (a) H 2 only, (b) CO only, and (c) CO followed by H 2 , with each cycle lasting 24 h. The so‐treated catalyst samples were then tested for FTS activity for over 50 h of time‐on‐stream (TOS), at 2.2 MPa pressure and 493 or 518 K temperature in a 3‐phase continuously‐stirred tank slurry reactor (3‐φ‐CSTSR) using squalane (C 30 H 62 ) as the carrier liquid phase. The feed gas composition of molar H 2 :CO = 1.7 comprising 50 % H 2 , 30 % CO, and 10 % CO 2 balanced in Ar for mass balance determination, flowing at a gas hourly space velocity (GHSV) of 3360 cm 3 · h −1 · g −1 of catalyst. Fresh catalysts and those reduced by CO‐only were completely inactive at 493 K and initial inactivity was attributed to the excessive C matrix formed around the metal nanoparticles during catalyst synthesis. The sample that was reduced by H 2 only was the most active, although it showed the fastest declining activity due to cumulative high H 2 O vapour pressure in the FTS reactor, while the sample pre‐treated in both CO and H 2 demonstrated a higher degree of stability with TOS.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.041
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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