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Effects of Washing, Autoclaving, and Surfactants on the Enzymatic Hydrolysis of Negatively Valued Paper Mill Sludge for Sugar Production

2019· article· en· W2912784624 on OpenAlexafffund
Suiyi Zhu, Jun Feng Sui, Ya Liu, Shufeng Ye, Chuanxin Wang, Mingxin Huo, Yang Yu

Bibliographic record

VenueEnergy & Fuels · 2019
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsChemistryHydrolysisEnzymatic hydrolysisRaw materialCellulosic ethanolPEG ratioChromatographySugarPulp and paper industryCelluloseNuclear chemistryFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Paper mill sludge (PMS) is a paper industry waste but can be a potential feedstock for cellulosic sugar production. In this study, washing, autoclaving, and surfactants were investigated for PMS pretreatment before enzymatic hydrolysis to produce cellulosic sugars. It was demonstrated that washing and autoclaving had a limited impact on improving the enzymatic hydrolysis of PMS but washing reduced the ash content, resulting in less acid being used in neutralization. Adding nonionic surfactants of Triton X-100, Tween 80, and PEG 8000 improved the conversion of PMS, and the highest rates were 56.3% and 55.4%, achieved by adding 1% Triton X-100 and 5% PEG 8000, respectively. The lowest conversion rates were produced by 1% and 5% Tween 80, probably because it had a hydrophobic alkyl chain. After the optimization of the enzyme and PMS concentrations in hydrolysis via supplementation with PEG 8000, the highest PMS conversion of 74.7% was achieved by 10% PMS and 3% enzymes. With the addition of PEG 8000, the conversion of PMS was reduced at high concentrations of enzyme and PMS compared with that of the non-PEG control, which was more significant at the later stage of hydrolysis. We proposed that the combined negative effects of end products and surfactants were more significant on hydrolysis than the effects of end products alone.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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

Labeled directly by 2 models reading the full record.

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

Citations15
Published2019
Admission routes2
Has abstractyes

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