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Record W4308179940 · doi:10.1039/d2ra03253h

Valorization of camelina oil to biobased materials and biofuels for new industrial uses: a review

2022· review· en· W4308179940 on OpenAlexafffund
Muhammad Arshad, Amar K. Mohanty, Rene Van Acker, Rachel Riddle, Jim Todd, Hamdy Khalil, Manjusri Misra

Bibliographic record

VenueRSC Advances · 2022
Typereview
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsLethbridge CollegeMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaOntario Agri-Food Innovation AllianceUniversity of ManitobaMinistero dello Sviluppo EconomicoOntario Ministry of Agriculture, Food and Rural AffairsUniversity of WaterlooOntario Ministry of Economic Development, Job Creation and TradeUniversity of TorontoArboraNanoRoyal Society of CanadaAgriculture and Agri-Food CanadaRoyal SocietyMinistry of Agriculture, Food and Rural AffairsArrell Food Institute, University of GuelphUniversity of Guelph
KeywordsCamelinaBiofuelCamelina sativaPulp and paper industryBiochemical engineeringEnvironmental scienceWaste managementChemistryFood scienceEngineeringAgronomyBiologyCrop

Abstract

fetched live from OpenAlex

, generally known as camelina, which has limited use as a food oil and so is currently being explored as a feedstock for various industrial applications in both Europe and North America. Camelina oil is highly unsaturated, making it an ideal potential AGH feedstock for the manufacture of lower carbon footprint, biobased products that reduce our dependency on petroleum resources and thus help to combat climate change. This review presents a brief description of camelina highlighting its composition and its production in comparison with traditional plant oils. The main focus is to summarize recent data on valorization of camelina oil by various chemical means, with specific emphasis on their industrial applications in biofuels, adhesives and coatings, biopolymers and bio-composites, alkyd resins, cosmetics, and agriculture. The review concludes with a discussion on current challenges and future opportunities of camelina oil valorization into various industrial products.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.105
GPT teacher head0.338
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), 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

Citations51
Published2022
Admission routes2
Has abstractyes

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