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Record W2316090924 · doi:10.1021/je400874d

Thermal Behavior of Potassium C<sub>1</sub>–C<sub>12</sub> <i>n</i>-Alkanoates and Its Relevance to Fischer–Tropsch

2014· article· en· W2316090924 on OpenAlexaff
Ly H. Bui, Arno de Klerk

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2014
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChemistryPotassiumMelting pointFischer–Tropsch processThermal stabilityInorganic chemistryCatalysisOrganic chemistrySelectivity

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The thermal behavior of potassium C 1 –C 12 n -alkanoates (K-carboxylates) were studied over the temperature range T /K = (243 to 873). A number of problems in industrial Fischer–Tropsch facilities were attributed to these compounds, but this study also revealed some beneficial effect that may directly be related to the thermal behavior of potassium methanoate. The unusually low melting point of potassium methanoate, T /K = (442.2 ± 0.3), combined with its thermal stability to T /K ≈ (693), may explain the ease of distribution of the potassium promoter of iron-based Fischer–Tropsch catalysts during synthesis even when potassium promoter is added separately. The C 2 –C 12 K-carboxylates were all thermally stable at temperatures T /K ≤ (713), and significant mass loss was not observed at T /K ≤ (748). The thermal stability and high melting point of potassium propanoate, T /K = (636.9 ± 0.3) and potassium butanoate, T /K = (623.1 ± 0.3), in particular caused these compounds to be prone to cause pressure drop problems in refining units. The C 4 –C 12 K-carboxylates melted to a liquid crystal phase first, before clearing at higher temperature. One or more solid–solid transitions were observed in all of the K-carboxylates, with the exception of potassium hexanoate.

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 categoriesMeta-epidemiology (narrow)
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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

Explore more

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