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Record W2801413889 · doi:10.3389/fchem.2018.00117

Two-Step Thermochemical Cellulose Hydrolysis With Partial Neutralization for Glucose Production

2018· article· en· W2801413889 on OpenAlexafffund
James Kong-Win Chang, Xavier Duret, Véronique Berberi, Hassan Zahedi-Niaki, Jean‐Michel Lavoie

Bibliographic record

VenueFrontiers in Chemistry · 2018
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsCRB Innovations (Canada)Université de Sherbrooke
FundersCRB InnovationsUniversité de SherbrookeMinistère de l'Énergie et des Ressources NaturellesMitacsEnerkem
KeywordsCelluloseCellulosic ethanolHydrolysisChemistryYield (engineering)DilutionNeutralizationChromatographyChemical engineeringNuclear chemistryOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Cellulose hydrolysis processes using concentrated acid usually involve two steps in order to obtain high glucose yields. The first step (pre-treatment) decrystallizes cellulose while the second step (post-hydrolysis) converts the amorphous cellulose to glucose. The two-step process developed by the Industrial Research Chair on Cellulosic Ethanol and Biocommodities and its industrial partner CRB Innovations Inc., includes an intermediate partial neutralization step, whose purpose is to decrease the amount of dilution water to be added for post-hydrolysis thus minimizing handling costs. In this work, the effect of several operating parameters on the glucose yield of this process was investigated using triticale cellulose and the best conditions yielding fermentable glucose (close to 100%) were determined. These conditions involve pretreating cellulose at 30ºC using 72 wt% H2SO4 with a H2SO4/dry cellulose mass ratio of 36 over 2 h, followed by a partial neutralization using 20 wt% NaOH at an H+/OH- molar ratio of 2.3-2.5 and a post-hydrolysis at 121ºC for 10 min.

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 score0.031
Threshold uncertainty score0.493

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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

Citations87
Published2018
Admission routes2
Has abstractyes

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