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Record W4390745198 · doi:10.1038/s41467-024-44692-7

Bidirectionally promoting assembly order for ultrastiff and highly thermally conductive graphene fibres

2024· article· en· W4390745198 on OpenAlexfundno aff
Li Peng, Ziqiu Wang, Yuxiang Qi, Gangfeng Cai, Yingjie Zhao, Xin Ming, Zizhen Lin, Weigang Ma, Jiahao Lin, Hang Li, Kai Shen, Yingjun Liu, Zhen Xu, Zhiping Xu, Chao Gao

Bibliographic record

VenueNature Communications · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
FundersInstitute of Nutrition, Metabolism and DiabetesNational Key Research and Development Program of ChinaNatural Science Foundation of Zhejiang ProvinceFundamental Research Funds for the Central UniversitiesNanjing UniversityZhejiang UniversityNanjing University of Aeronautics and AstronauticsNational Natural Science Foundation of China
KeywordsGrapheneMaterials scienceNanotechnologyOxidePolymerMicrostructureThermal conductivityComposite material

Abstract

fetched live from OpenAlex

Abstract Macroscopic fibres assembled from two-dimensional (2D) nanosheets are new and impressing type of fibre materials besides those from one-dimensional (1D) polymers, such as graphene fibres. However, the preparation and property-enhancing technologies of these fibres follow those from 1D polymers by improving the orientation along the fibre axis, leading to non-optimized microstructures and low integrated performances. Here, we show a concept of bidirectionally promoting the assembly order, making graphene fibres achieve synergistically improved mechanical and thermal properties. Concentric arrangement of graphene oxide sheets in the cross-section and alignment along fibre axis are realized by multiple shear-flow fields, which bidirectionally promotes the sheet-order of graphene sheets in solid fibres, generates densified and crystalline graphitic structures, and produces graphene fibres with ultrahigh modulus (901 GPa) and thermal conductivity (1660 W m −1 K −1 ). We believe that the concept would enhance both scientific and technological cognition of the assembly process of 2D nanosheets.

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)0.001
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 score0.283
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.028
GPT teacher head0.336
Teacher spread0.308 · 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

Citations86
Published2024
Admission routes1
Has abstractyes

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