MétaCan
Menu
Back to cohort
Record W1992944078 · doi:10.1021/ie902036v

Analysis of Gaseous and Liquid Products from Pressurized Pyrolysis of Flax Straw in a Fixed Bed Reactor

2010· article· en· W1992944078 on OpenAlexafffund
Mohammad Shahed Hasan Khan Tushar, Nader Mahinpey, Pulikesi Murugan, Thilakavathi Mani

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2010
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of ReginaUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharPyrolysisChemistryPorosityStrawFurfuralYield (engineering)Chemical engineeringNitrogenScanning electron microscopeHydrogenOrganic chemistryNuclear chemistryMaterials scienceCatalysisInorganic chemistryComposite material

Abstract

fetched live from OpenAlex

Pyrolysis of flax straw was investigated under a nitrogen atmosphere using a tubular reactor at different pressures, ranging from 10 to 40 psi. The effects of pressure on the pyrolysis yields and the products obtained (gas, liquid, char) have been analyzed. The gaseous products were analyzed by online GC, and the bio-oils were characterized by gas chromatography/mass spectrometry (GC/MS). Scanning electron microscopy (SEM) was used to view the char surfaces in order to characterize the char and validate the presence of porosity. As the pyrolysis pressure increased, the yield of char and liquid decreased while the gas products increased. The gas products were mainly CO, H 2, CO 2, CH 4, and C 3 ’s. The bio-oils were mostly composed of phenolic compounds, carboxylic acids, and furfural. The pH and the density of the bio-oil revealed a moderate increase with increasing pressure. The SEM study confirms the porous nature of the char. Overall, bio-oil production was maximized at 10 psi; however, char and hydrogen production was maximized at 20 psi.

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)0.002
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 score0.002
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.269
Teacher spread0.237 · 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

Citations13
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicThermochemical Biomass Conversion ProcessesFrench-language works237,207