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Thermogravimetric Analysis of Different Biomass Materials and the Primary Biomass Components

2012· article· en· W2057252764 on OpenAlexaff
Ya Bo Li, Xuming Zhang, Lei Zhao, Qiang Lü, Chang Qing Dong

Bibliographic record

VenueApplied Mechanics and Materials · 2012
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesKey Technologies Research and Development ProgramProgram for New Century Excellent Talents in University
KeywordsThermogravimetric analysisCellulosePyrolysisCharBiomass (ecology)LigninCombustionHemicelluloseChemical engineeringNitrogenMaterials scienceXylanPulp and paper industryLignocellulosic biomassChemistryWaste managementOrganic chemistryAgronomy

Abstract

fetched live from OpenAlex

Thermogravimetric analysis (TGA) was employed to study the pyrolysis and combustion characteristics of three primary biomass components (cellulose, xylan and lignin) and seven different biomass materials under both of nitrogen and air atmosphere. Based on the experimental results, the pyrolysis and combustion kinetics were calculated. The results indicated that the three primary biomass components exhibited different decomposition characteristics and pyrolysis kinetics. Xylan was the least thermal stable component, while the lignin would form much more solid residues than the cellulose and xylan under the nitrogen atmosphere. Moreover, the pyrolytic devolatilization process of the cellulose had high activation energy, so was the char combustion process of the lignin. The seven biomass materials showed similar pyrolysis characteristics, and poplar wood exhibited high activation energy values in both of pyrolysis and combustion processes.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.190
Teacher spread0.181 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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