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Investigation on Preparation of Mesophase Pitch by the Cocarbonization of Naphthenic Pitch and Polystyrene

2016· article· en· W2287219589 on OpenAlexaff
Dong Liu, Ming Li, Fengjiao Qu, Ran Yu, Bin Lou, Chongchong Wu, Niu Jianping, Guangkai Chang

Bibliographic record

VenueEnergy & Fuels · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Calgary
FundersFundamental Research Funds for the Central UniversitiesMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsMesophasePolystyreneMaterials scienceFourier transform infrared spectroscopyScanning electron microscopeMesogenTexture (cosmology)Elemental analysisChemical engineeringLiquid crystalOrganic chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

Two modified pitches with softening points of 70 °C (LPA) and 110 °C (HPA) produced from modification of naphthenic vacuum residue were cocarbonized with polystyrene (PS), respectively, and the mesophase pitches were obtained. The effects of reaction time, PS addition, pressure, and properties of raw materials on cocarbonization behavior were analyzed by H/C, quinoline insoluble content (QI), carbon residue value, density, polarized optical images, X-ray diffraction pattern (XRD), scanning electron microscopy (SEM) analysis, Fourier transform infrared (FTIR) spectra, and 1 H nuclear magnetic resonance ( 1 H NMR) analysis. Comparing with HPA, the cocarbonization process showed more suitable for LPA to prepare the mesophase pitch due to the favorable occurrence of radical chain transfer reactions between LPA and PS components which contributed to the existence of aliphatic side-chains and naphthenic structures in mesogens as well as the formation of a well-developed mesophase pitch. Mesophase pitch with larger domains optical texture and better crystal structure was obtained from cocarbonization with 5 wt % PS under the optimal conditions of 410 °C, 6 h, and 6 MPa.

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 score0.005
Threshold uncertainty score0.268

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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

Citations32
Published2016
Admission routes1
Has abstractyes

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