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Record W2330326358 · doi:10.1021/acssuschemeng.5b00124

Enhancing Hemicellulose Recovery and the Enzymatic Hydrolysis of Cellulose by Adding Lignosulfonates during the Two-Stage Steam Pretreatment of Poplar

2015· article· en· W2330326358 on OpenAlexafffund
Richard P. Chandra, Keith Gourlay, Changsoo Kim, J. N. Saddler

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLignosulfonatesHemicelluloseChemistryCelluloseHydrolysisXyloseXylanLigninAdsorptionEnzymatic hydrolysisOrganic chemistrySteam explosionChromatographyNuclear chemistryPulp and paper industry

Abstract

fetched live from OpenAlex

To enhance overall sugar recovery, poplar was steam pretreated in two stages. First, under mild pretreatment conditions (160 °C, 15 min) using various acid catalysts (H 2 SO 4, SO 2, oxalic, citric) to assess optimum hemicellulose recovery, followed by an uncatalyzed, second pretreatment (200 °C, 5 min), to facilitate enzymatic hydrolysis of the cellulose. The first stage solubilized and recovered about 50% of the xylan, as compared to the 30% obtained in the more severe, single-stage pretreatment. As surfactants are known to increase hemicellulose accessibility during pretreatments, lignosulfonates were added to birch xylan and poplar, increasing xylose yields and retaining 60 mmol/kg strong acid groups, even after the second pretreatment stage and extensive washing, suggesting that lignosulfonates adsorbed to the substrate and enhanced cellulose accessibility. This was confirmed by the increased water retention values and Direct Orange dye adsorption. A two-stage steam pretreatment, incorporating lignosulfonate addition, increased cellulose hydrolysis from 75 to 92%.

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 score0.040
Threshold uncertainty score0.755

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 head0.003
GPT teacher head0.164
Teacher spread0.162 · 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

Citations55
Published2015
Admission routes2
Has abstractyes

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