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Record W2356073050

Thermogravimetric Analysis and Combustion Performance of 10 Common Types of Tree Leaves in Maoer Mountain Region

2014· article· en· W2356073050 on OpenAlexaff
Zhang Yi-xi

Bibliographic record

VenueAnhui nongye kexue · 2014
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsScience North
Fundersnot available
KeywordsThermogravimetric analysisPyrolysisCombustibilityCombustionHemicelluloseCelluloseLigninCarbonizationActivation energyHeat of combustionPulp and paper industryChemistryMaterials scienceChemical engineeringOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

By thermogravimetric analysis method,this paper studied pyrolysis characteristics and kinetics of 10 kinds of representative trees species grown in Maoer Mountain of Heilongjiang Province. Besides,it analyzed basic pyrolysis process of fuel using TG-DTG curve. Through pyrolysis parameters,it made quantitative comparison of pyrolysis characteristics of the different plant fuel and obtained the relation between lignin,hemicellulose and cellulose. By grading reaction kinetics model( Coats-Redfem method),it obtained their activation energy E and frequency factor A. All samples of pyrolysis in nitrogen atmosphere underwent three major stages,namely,water precipitation,fast pyrolysis and carbonization. In addition,Mongolian scotch pine and Black pinus tabulaeformis carr have better fireproof performance than other leaves with ignition temperature of 275. 17 ℃ and 274. 38 ℃ and activation energy of 44. 188 6 KJ / mol and 42. 864 3 KJ / mol respectively. Further,with the aid of the technology of relative limited oxygen index( LOI),it measured the LOI of fuel. Its numerical value can reflect the fuel combustibility. Oxygen index of elm is 26. 4% and belongs to flame resistant level,while black pinus tabulaeformis oxygen index is 20. 2% and belongs to inflammables.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.369

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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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