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Cellulose fiber drainage improvement via citric acid crosslinking

2024· article· en· W4403155180 on OpenAlexaff
Fuchun Huang, Zhongjian Tian, Yingchao Wang, Xingxiang Ji, Dongxing Wang, Pedram Fatehi

Bibliographic record

VenueInternational Journal of Biological Macromolecules · 2024
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsLakehead University
Fundersnot available
KeywordsCelluloseCitric acidCellulose fiberFiberDrainageChemistryChemical engineeringPulp and paper industryPolymer chemistryPolymer scienceMaterials scienceBiochemistryOrganic chemistryEcologyBiologyEngineering

Abstract

fetched live from OpenAlex

Wheat straw, as a non-wood fiber waste, is available worldwide and can be used in cellulosic matric production, promoting the application of sustainable materials. However, poor fiber properties and water drainage are the primary obstacles to its utilization. In this study, wheat straw pulp fibers were chemically crosslinked by citric acid (CA) in an environmentally friendly process. X-ray photoelectron spectroscopy and Fourier transform infrared spectra confirmed that the chemical treatment introduced carboxylic groups to cellulose fibers. Meanwhile, X-ray diffraction patterns showed that the crystallinity of cellulose was reduced. The average fiber length and water retention value of the pulp decreased with increasing CA dosage under the conditions of 3 mL/g CA 4 (4 wt% CA), and the drainage performance of the cellulose pulp improved by 21 %. Also, the crosslinking of fibers contributed to the mechanical properties of the cellulosic matrix, increasing the dry and wet strength by 21 % and 282 %, respectively. These results demonstrated that citric acid could be a sustainable method for improving the properties of wheat straw fibers, thereby promoting its application in fabricating sustainable materials.

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 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 score0.049
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.319
Teacher spread0.296 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

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