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Record W4319755378 · doi:10.1016/j.dib.2023.108953

Dataset of methane pyrolysis products in a batch reactor as a function of time at high temperatures and pressures

2023· article· en· W4319755378 on OpenAlexafffund
James Tatum, Ambuj Punia, Larry W. Kostiuk, Marc Secanell, Jason S. Olfert

Bibliographic record

VenueData in Brief · 2023
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsCarleton UniversityUniversity of Alberta
FundersCanada First Research Excellence Fund
KeywordsMethaneHydrogenChemistryCarbon dioxidePyrolysisVolume (thermodynamics)Batch reactorAnalytical Chemistry (journal)QuartzMaterials scienceCatalysisChromatographyOrganic chemistryThermodynamicsComposite material

Abstract

fetched live from OpenAlex

Methane pyrolysis is a process used to generate hydrogen gas and carbon black without the creation of carbon dioxide. Methane pyrolysis in a constant volume batch reactor was investigated at temperatures of 892, 1093, and 1292 K with reaction times of 15, 30, 60, 180, and 300 s at an initial pressure of 399 kPa. A quartz vessel (32 mL) was placed inside an oven where it was heated to high temperatures. At the beginning of the process, the quartz vessel was vacuumed, then flushed with nitrogen before being vacuumed again prior to every experiment. Pressurized methane was then injected into the vessel for an allocated reaction time and collected in a sample bag post reaction for analysis. The molar concentration of the product gas was analyzed using gas chromatography. Hydrogen molar concentration increased as temperature and reaction time increased. For experiments completed at 892 K the hydrogen molar concentration varied from 10.0 ± 5.9% with a 15 s reaction time to 26.5 ± 0.8% for a 300 s reaction time. For experiments completed at 1093 K the hydrogen molar concentration varied from 21.8 ± 3.7% for a 15 s reaction time to 53.0 ± 2.9% for a 300 s reaction time. For experiments completed at 1292 K the hydrogen molar concentration varied from 31.5 ± 1.7% for a 15 s reaction time to 53.0 ± 2.4% for a 300 s reaction time.

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 score0.027
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.229
Teacher spread0.215 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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