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Record W4414279009 · doi:10.1021/acssuschemeng.5c05627

Mild Fractionation of Biomass into Three Binary-Component Fractions

2025· article· en· W4414279009 on OpenAlexaff
Tingjiao Wang, Qiongyao Su, Xinyuan Zhang, Yuchen Zeng, Chengyue Yuan, Jinguang Hu, Xiaoqiang Yu, Fei Shen, Dong Tian

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of Calgary
FundersNational Key Research and Development Program of ChinaDepartment of Science and Technology of Sichuan ProvinceNational Natural Science Foundation of China
KeywordsFractionationDeep eutectic solventXyloseBiorefineryArabinoxylanFurfuralHydrolysisSolvent

Abstract

fetched live from OpenAlex

The effective fractionation technology route is a crucial aspect for the full valorization of lignocellulose. The current fractionation technologies primarily focus on single-component fractionation of lignocellulose, with the technical dilemma between each component conversion availability and overall product yields, especially under harsh fractionation conditions. This work proposed the mild fractionation process of a mechanochemistry-assisted choline hydroxide-ethylene glycol alkaline deep eutectic solvent (Ch-Ely DES) to fractionate straw biomass, with the goal of higher-value binary-component fractions pursuing, i.e., holocellulose, lignin-carbohydrate complexes (LCC), and arabinoxylan (AX). Through the synergistic effect of mechanochemical deconstruction and alkaline deep eutectic solvent swelling, 93% holocellulose recovery, corresponding to a 53.2/100 Ar β-O-4 bond content in the LCC fraction, was achieved. The resulting holocellulose hydrolysis values at a 5 wt % solid loading were 96 and 55% for glucose and xylose respectively. The mild fractionation process could yield fractions with well-preserved structures by selective hydrogen bond network interaction and lignin-carbohydrate complex covalent bond preservation. The proposed binary-component fractionation route is promising to diversify current fractionation technologies and product streams.

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 categoriesMeta-epidemiology (narrow)
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 score0.047
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
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.195
Teacher spread0.192 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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