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Record W2751062572 · doi:10.1021/acs.iecr.7b01708

Conversion of Wood-Based Hemicellulose Prehydrolysate into Succinic Acid Using a Heterogeneous Acid Catalyst in a Biphasic System

2017· article· en· W2751062572 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2017
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsLakehead University
FundersCanada Research ChairsCanada Foundation for Innovation
KeywordsFurfuralHemicelluloseSuccinic acidChemistryAcetic acidYield (engineering)Organic chemistryCatalysisTolueneCelluloseMaterials science

Abstract

fetched live from OpenAlex

A novel approach for the conversion of biomass-based hemicellulose prehydrolysate to high-value succinic acid has been investigated using a heterogeneous acid catalyst, Amberlyst 15 and hydrogen peroxide, in a biphasic system. A vital intermediate in this process, furfural, was oxidized in the biphasic system to produce succinic acid. Production of furfural in good yields is a limiting step in such processes for a number of reasons. Among the organic solvents evaluated, toluene was found to be an ideal solvent for furfural extraction and to facilitate the conversion of furfural to succinic acid. Simultaneous extraction of furfural into the organic solvent as it was produced increased the overall yield. It was observed that the developed method resulted in a succinic acid yield of 52.34% from the furfural obtained from hemicellulose prehydrolysate. It was found that 50 mg of Amberlyst 15 per millimole of furfural resulted in 100% furfural conversion in less time.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.207
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.057
GPT teacher head0.296
Teacher spread0.239 · 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