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Record W2174132549 · doi:10.18331/brj2015.2.4.6

Pyrolysis characteristic of kenaf studied with separated tissues, alkali pulp, and alkali lignin

2015· article· en· W2174132549 on OpenAlexvenueno aff
Yasuo Kojima, Yoshiaki Kato, Minami Akazawa, Seung-Lak Yoon, Myong-Ku Lee

Bibliographic record

VenueBiofuel Research Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsKenafLigninPulp (tooth)ChemistryPyrolysisAlkali metalOrganic chemistryFiber

Abstract

fetched live from OpenAlex

To estimate the potential of kenaf as a new biomass source, analytical pyrolysis was performed using various kenaf tissues, i.e., alkali lignin and alkali pulp. The distribution of the pyrolysis products from the whole kenaf was similar to that obtained from hardwood, with syringol, 4-vinylsyringol, guaiacol, and 4-vinylguaiacol as the major products. The phenols content in the pyrolysate from the kenaf core was higher than that from the kenaf cuticle, reflecting the higher lignin content of the kenaf core. The ratios of the syringyl and guaiacyl compounds in the pyrolysates from the core and cuticle samples were 2.79 and 6.83, respectively. Levoglucosan was the major pyrolysis product obtained from the kenaf alkali pulp, although glycol aldehyde and acetol were also produced in high yields, as previously observed for other cellulosic materials. Moreover, the pathways for the formation of the major pyrolysis products from alkali lignin and alkali pulp were also described, and new pyrolysis pathways for carbohydrates have been proposed herein. The end groups of carbohydrates bearing hemiacetal groups were subjected to ring opening and then they underwent further reactions, including further thermal degradation or ring reclosing. Variation of the ring-closing position resulted in the production of different compounds, such as furans, furanones, and cyclopentenones.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.068
GPT teacher head0.323
Teacher spread0.255 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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