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Record W2950714235 · doi:10.1073/pnas.1904636116

Integration of renewable deep eutectic solvents with engineered biomass to achieve a closed-loop biorefinery

2019· article· en· W2950714235 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.

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of British Columbia
FundersNational Institutes of HealthKorea Institute of Science and TechnologyU.S. Department of Energy
KeywordsBiorefineryBiomass (ecology)Renewable energyLigninBiofuelBiochemical engineeringRenewable resourceLignocellulosic biomassPulp and paper industryProcess engineeringMaterials scienceEnvironmental scienceChemistryWaste managementOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Significance Deep eutectic solvents (DESs) have gained increasing attention due to their application-friendly properties, including universal solvating capabilities and wide tunability. Additionally, ease of synthesis and broad availability from inexpensive chemical components could render DESs more versatile solvents for biomass pretreatment, as compared with traditional ionic liquids. Because the long-term success of the biorefinery depends on the development of sustainable processes to convert lignocellulosics into biofuels, DESs derived from renewable sources such as lignin are highly desirable. We herein present our innovative process that integrates the use of low-recalcitrant engineered biomass with its pretreatment using lignin-derived DESs. The promising results described by near-theoretical sugar yield demonstrate the effectiveness of the integrated process, opening up opportunities toward a sustainable and circular bioeconomy.

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)0.000
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 score0.024
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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