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Current breakthroughs in the hardwood biorefineries: Hydrothermal processing for the co-production of xylooligosaccharides and bioethanol

2021· review· en· W3206127131 on OpenAlexaff
Pablo G. del Río, Beatriz Gullón, Jie Wu, Jack Saddler, Gil Garrote, Aloia Romaní

Bibliographic record

VenueBioresource Technology · 2021
Typereview
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiorefineryBiofuelPulp and paper industryRaw materialCelluloseRenewable energyBioenergyHardwoodEnzymatic hydrolysisChemistryCellulosic ethanolLigninHydrothermal circulationWaste managementSugarEnvironmental scienceHydrolysisFood scienceChemical engineeringEngineeringBotanyOrganic chemistry

Abstract

fetched live from OpenAlex

The development of lignocellulosic biorefineries requires a first stage of pretreatment which enables the efficient valorization of all fractions present in this renewable material. In this sense, this review aims to show the main advantages of hydrothermal treatment as a first step of a biorefinery infrastructure using hardwood as raw material, as well as, main drawback to overcome. Hydrothermal treatment of hardwood highlights for its high selectivity for hemicelluloses solubilization as xylooligosaccharides (XOS). Nevertheless, the suitable conditions for XOS production are inadequate to achieve an elevate cellulose to glucose conversion. Hence, several strategies namely the combination of hydrothermal treatment with delignification process, in situ modification of lignin and the mixture with another renewable resources (concretely, seaweeds, and by-products generated in the food industry with high sugar content) were pinpointed as promising alternative to increase the final ethanol concentration coupled with XOS recovery in the hydrolysate.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.044
GPT teacher head0.312
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations57
Published2021
Admission routes1
Has abstractyes

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