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Record W2976350882 · doi:10.3390/polym11101558

Combined Treatments Consisting of Calcium Hydroxide and Activate Carbon for Purification of Xylo-Oligosaccharides of Pre-Hydrolysis Liquor

2019· article· en· W2976350882 on OpenAlexaff
Feng Xu, Jiachuan Chen, Guihua Yang, Xingxiang Ji, Qiang Wang, Shanshan Liu, Yonghao Ni

Bibliographic record

VenuePolymers · 2019
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsChemistryFurfuralLigninXyloseAcetic acidHydrolysisHemicelluloseAdsorptionCalcium hydroxideActivated carbonNuclear chemistryChromatographyZeta potentialOrganic chemistryFermentationChemical engineeringCatalysis

Abstract

fetched live from OpenAlex

In this study, the effect of a combined treatment consisting of calcium hydroxide (CH) followed by activated carbon (AC) on the purification of hemicellulose in the pre-hydrolysis liquor (PHL) from pulping process has been evaluated. The results show that lignin and furfural of PHL are efficiently removed, and the lignin removal is achieved by forming complexes onto CH particles in the CH treatment process, while acetic acid (acetate) is formed from the hydrolysis of acetyl groups present in the dissolved hemicelluloses in the PHL. The loss of xylo-oligosaccharides (XOS) is moderate, even at a high CH dosage of 0.8% while the xylose concentration is essentially unchanged. For the AC treatment, the optimal treating pH can enhance the interactions between AC and residual lignin and change the zeta potential of AC resulting in improved lignin adsorption onto AC. An increase of AC dosage has the tendency to adsorb more XOSDP>6 than XOSDP2~6. Overall, 66.9% of lignin and 70.1% of furfural removals are achieved under the optimal conditions of CH and AC treatment process, with a 5.9% total xylosugars loss. The present combination of CH and AC treatment process was more effective and selective for purification of xylosugars of PHL.

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 score0.010
Threshold uncertainty score0.336

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 head0.012
GPT teacher head0.223
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

Citations20
Published2019
Admission routes1
Has abstractyes

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