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Record W3154991897 · doi:10.24908/iqurcp.10728

Fracta-like Microreactor for Hydrogen Production

2018· article· en· W3154991897 on OpenAlexvenueno aff
Jonas Gerson

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMicroreactorHydrogenStack (abstract data type)Hydrogen productionPressure dropMethanolChannel (broadcasting)Drop (telecommunication)Materials scienceYield (engineering)CatalysisChemical engineeringNuclear engineeringChemistryMechanicsElectrical engineeringComposite materialEngineeringComputer sciencePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

A catalyst micro­channel reactor using Cu/Alumina as the catalyst was designed in order to produce hydrogen from methanol in order to provide a fuel source to a hydrogen fuel cell to meet a power demand of 100W. To do this a branched channel micro­reactor design was employed. The reasoning for this was that the increased pressure drop due to the branching network and increased surface area of the reactor would produce a greater hydrogen yield then that of a straight channel reactor. The branched channel reactor showed a 10% greater hydrogen yield then that of the straight channel under three different conditions which the initial flow rate of methanol was varied. The final design of the reactor to meet the 100W power demand was a stack of 48 single branch disk units, each 17.5 mm in diameter, 1 mm thick, a constant channel depth of 250 µm, and a outlet channel width of 100 µm

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.083
GPT teacher head0.342
Teacher spread0.259 · 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
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

Citations0
Published2018
Admission routes1
Has abstractyes

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