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Record W4379095408 · doi:10.1021/acs.biomac.3c00053

Formation and Identification of Lignin–Carbohydrate Complexes in Pre-hydrolysis Liquors

2023· article· en· W4379095408 on OpenAlexafffund
Nianjie Feng, Shaowen She, Fei Tang, Xiangdong Zhao, Jingqian Chen, Peng Wang, Qian Wu, Orlando J. Rojas

Bibliographic record

VenueBiomacromolecules · 2023
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British Columbia
FundersHubei UniversityUniversity of British ColumbiaCanada Excellence Research Chairs, Government of CanadaNational Natural Science Foundation of ChinaCanada Foundation for InnovationChina Scholarship CouncilHubei University of TechnologyCanada Research Chairs
KeywordsHydrolysisChemistryLigninFractionationBiomass (ecology)Organic chemistryEtherDegradation (telecommunications)CarbohydrateChromatography

Abstract

fetched live from OpenAlex

The lignin-carbohydrate complexes (LCCs) typically present in the liquors produced in the pre-hydrolysis of biomass cause severe difficulties in downstream fractionation. To address this issue, a series of LCC samples were accessed from solutions obtained from the pre-hydrolysis of extractive-free pine wood meal (H-LCC) and compared with LCC obtained from the corresponding residues (B-LCC). Chromatographic and spectroscopic techniques revealed that 8.2% of the lignins were degraded at 160 °C, resulting from the breakage of β-O-4' linkages during pre-hydrolysis. Meanwhile, (reactive) hemicelluloses were mainly removed from the fibers' cell walls. Some hemicelluloses in the pre-hydrolysis liquor, such as glucomannans, were associated with degraded lignin fragments via ether and ester bonds. However, the newly formed LCCs were pH-labile and underwent rapid hydrolysis. Overall, we reveal details about LCC formation and degradation during pre-hydrolysis at given temperatures, critically important in efforts to improve biomass processing and valorization.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.340

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)0.0000.000

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 head0.007
GPT teacher head0.218
Teacher spread0.211 · 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

Citations46
Published2023
Admission routes2
Has abstractyes

Explore more

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