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Record W4213284230 · doi:10.1021/acs.jchemed.1c00957

Methanation of Synthesis Gas to Produce Methane: A Hands-On Catalysis Experiment

2022· article· en· W4213284230 on OpenAlexaff
Yun-Hua Li, Cuixue Chen, Meiling Ye, Alexander Luis Imbault

Bibliographic record

VenueJournal of Chemical Education · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsMethanationSyngasMethaneRenewable energyProcess engineeringCatalysisChemical reaction engineeringChemistryProcess (computing)Environmental scienceChemical engineeringWaste managementBiochemical engineeringOrganic chemistryEngineeringComputer science

Abstract

fetched live from OpenAlex

Organizing undergraduates in chemistry, energy engineering, and chemical engineering specialties to participate in an energy transformation experiment can encourage them to think about the impact of human activities on nature and stimulate them to learn more about the topic. This laboratory experiment demonstrates a general approach to the methanation of syngas in a fixed bed reactor with online chromatographic monitoring. Undergraduates gain the opportunity to operate temperature controllers, mass flow meters, fixed bed reactors, gas chromatography, and cold traps as well as broaden their knowledge of renewable CO/CO 2 conversion. The experiment also offers a discussion about the chemical principles in catalytic conversion of syngas to methane, viable alternatives to green fuel sources, and the catalytic reaction process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.013
GPT teacher head0.285
Teacher spread0.273 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2022
Admission routes1
Has abstractyes

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