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Record W2378043091

Effect of ionic liquid pretreatment on lignocellulosic biomass from oilseeds

2013· article· en· W2378043091 on OpenAlexaff
Liyang Liu

Bibliographic record

VenueHuagong xuebao · 2013
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsScience North
Fundersnot available
KeywordsIonic liquidChemistryStrawLignocellulosic biomassChlorideEnzymatic hydrolysisYield (engineering)XyloseNuclear chemistryRaw materialCelluloseHydrolysisOrganic chemistryInorganic chemistryMaterials scienceCatalysisFermentationMetallurgy
DOInot available

Abstract

fetched live from OpenAlex

In this study,three kinds of ionic liquid,including 1-butyl-3-methylimidazolium chloride([Bmim]Cl),1-butyl-3-methylimidazolium bromide([Bmim]Br)and 1-octyl-3-methylimidazolium chloride([Omim]Cl),were selected to pretreat the lignocellulosic parts of oilseeds:peanut husk,peanut straw and cole straw.The untreated and pretreated materials were investigated through the compositional,enzymatic hydrolysis and structural analysis.Among the untreated materials,peanut straw with the highest sugar yield 54.31% and the lowest lignin content was considered as the preferable substrate for biofuels production.After ionic liquid pretreatment,the effect of[Bmim]Cl on sugar yield was more significant, which lead to 85.43%sugar yield for peanut straw.The structural changes were also analyzed by scanning electron microscope(SEM)and Fourier transform-infrared(FT-IR).Among the raw materials,peanut straw's morphological structure was distinctive with broken surface,incompact structural and lower crystallinity.After pretreatment,all material turned to be more porous and rough than before.On the basis,the mechanism of lignocellulose's dissolution by ionic liquid with different cation and anion were also discussed. The results showed that the chlorine and[Bmim]+were vital on the effect of ionic liquid pretreatment.

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 categoriesInsufficient payload (model declined to judge)
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.194
Teacher spread0.188 · 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.

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

Citations7
Published2013
Admission routes1
Has abstractyes

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