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Record W1977945564 · doi:10.1038/srep04021

High performance catalytic distillation using CNTs-based holistic catalyst for production of high quality biodiesel

2014· article· en· W1977945564 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

VenueScientific Reports · 2014
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsMinistry of Education and Child Care
FundersSINOPEC Shanghai Research Institute of Petrochemical TechnologyNational Natural Science Foundation of ChinaMinistry of Science and Technology of the People's Republic of ChinaNational Science Foundation
KeywordsGlycerolBiodieselBiodiesel productionCatalysisTransesterificationDistillationProcess engineeringProcess (computing)Materials scienceChemical engineeringBiochemical engineeringPulp and paper industryChemistryOrganic chemistryComputer scienceEngineering

Abstract

fetched live from OpenAlex

For production of biodiesel from bio oils by heterogeneous catalysis, high performance catalysts of transesterification and the further utilization of glycerol have been the two points of research. The process seemed easy, however, has never been well established. Here we report a novel design of catalytic distillation using hierachically integrated CNTs-based holistic catalyst to figure out the two points in one process, which shows high performance both for the conversion of bio oils to biodiesel and, unexpectedly, for the conversion of glycerol to more valuable chemicals at the same time. The method, with integration of nano, meso to macro reactor, has overwhelming advantages over common technologies using liquid acids or bases to catalyze the reactions, which suffer from the high cost of separation and unsolved utilization of glycerol.

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)0.002
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 score0.169
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.029
GPT teacher head0.261
Teacher spread0.232 · 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