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Record W2518408394 · doi:10.1021/acs.iecr.5b01402

Fischer–Tropsch Mechanism: <sup>13</sup>C<sup>18</sup>O Tracer Studies on a Ceria–Silica Supported Cobalt Catalyst and a Doubly Promoted Iron Catalyst

2015· article· en· W2518408394 on OpenAlexafffund
Debanjan Chakrabarti, Muthu Kumaran Gnanamani, Wilson D. Shafer, Mauro C. Ribeiro, Dennis E. Sparks, Vinay Prasad, Arno de Klerk, Burtron H. Davis

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Alberta
FundersNatural Resources CanadaHelmholtz-Alberta InitiativeHelmholtz-GemeinschaftUniversity of Alberta
KeywordsFischer–Tropsch processCatalysisCobaltChemistryMethanolSyngasWater-gas shift reactionInorganic chemistryOrganic chemistrySelectivity

Abstract

fetched live from OpenAlex

Tracer studies were performed on cobalt and iron Fischer–Tropsch catalysts using a synthesis gas containing a 20:80 mixture of 13 C 18 O and 12 C 16 O. The objective of the work was to investigate the antecedents of the C–O bonds in alcohols and CO 2 formed during Fischer–Tropsch (FT) synthesis. It was found that chain growth proceeded by a CO insertion mechanism over both cobalt and iron catalysts. Over the cobalt catalyst, the dominant pathway for methanol synthesis involved a partial hydrogenation of CO as well as CO 2 by a reaction pathway separate from the Fischer–Tropsch pathway. Over the iron catalyst, the majority of the methanol was formed by partial hydrogenation of only CO through the FT reaction pathway. Iron is active for water gas shift conversion, which produced CO 2 . Oxygen exchange reactions of CO 2 were likely over both catalysts and complicated the interpretation of the results.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.038
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
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 head0.134
GPT teacher head0.347
Teacher spread0.213 · 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.

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

Citations20
Published2015
Admission routes2
Has abstractyes

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