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Record W3134273243 · doi:10.1002/cjce.24093

Preparation of silicon‐based soybean base oil by modified soybean oil by transesterification and hydrosilation

2021· article· en· W3134273243 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsHydrosilylationSoybean oilTransesterificationSilaneCatalysisPour pointSilanesOrganic chemistryBase oilBase (topology)Epoxidized soybean oilChemistryYield (engineering)Materials sciencePolymer chemistryRaw materialMathematicsComposite materialFood science

Abstract

fetched live from OpenAlex

Abstract A new green lubricating base oil was synthesized from soybean oil and triethyl silane by a combination of transesterification and hydrosilylation. The factors affecting the process of hydrosilylation were thoroughly investigated. AlCl 3 /C catalyst as catalyst for hydrosilylation showed better catalytic performance, and a higher yield of 36.79% was obtained under optimal condition of molar ratio of transesterified soybean oils/triethyl silane (1:1.1), reaction temperature 120°C, and reaction time 8 h. In addition, the performance of silicon‐based soybean base oil was tested according to industry standards. Compared with soybean oil, silicon‐based soybean base oil was obtained with high viscosity index, good pour point of −17°C, and excellent wear characteristic.

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 score0.151
Threshold uncertainty score0.432

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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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