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Record W2590366110 · doi:10.1134/s1070427216110173

Methanol enhanced catalytic viscosity-reduction of heavy oil by transition metal-Mannich base complex under low temperature

2016· article· en· W2590366110 on OpenAlexaff
Gang Chen, Weihua Yuan, Huijun Su, Jie Zhang, Xuefan Gu, Yun Bai, Ayodeji Jeje

Bibliographic record

VenueRussian Journal of Applied Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersEducation Department of Shaanxi ProvinceNational Science Foundation
KeywordsChemistryThermogravimetric analysisCatalysisMethanolFourier transform infrared spectroscopyBase oilTransition metalAsphalteneElemental analysisBase (topology)MetalViscosityNuclear chemistryMannich reactionOrganic chemistryChemical engineering

Abstract

fetched live from OpenAlex

A series of transition metal-Mannich base complex was synthesized and screened for catalytic aquathermolysis of heavy oil and the results showed that the Ni(II)-Mannich base complex is the most effective one. The structure of catalyst was characterized by Fourier transform infrared (FTIR), ultraviolet and visible spectrophotometer (UV), and the group compositions C/H/N elemental analysis (EL), and thermogravimetric analysis (TGA) of the heavy oil were also conducted. The viscosity of the heavy oil can be decreased by 82.6% using 1.0 wt % catalyst with 15 wt % methanol in a reaction under 180°С for 24 h, and the composition analysis shows that 9.0% asphaltene is converted to resin and saturated HC. The group composition and the element content of the heavy oil before and after the aquathermolysis reaction were tested and analyzed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations9
Published2016
Admission routes1
Has abstractyes

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