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Record W3128163673 · doi:10.21203/rs.3.rs-188518/v1

Fabrication of Reduced Graphene Oxide-Cellulose Nanofibers Based Hybrid Film with Good Hydrophilicity and Conductivity as Electrodes of Supercapacitor

2021· preprint· en· W3128163673 on OpenAlexaff
Chuanyin Xiong, Congmin Zheng, Shuangxi Nie, Chengrong Qin, Lei Dai, Yongjian Xu, Yonghao Ni

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of New Brunswick
FundersShaanxi University of Science and TechnologyChina Postdoctoral Science FoundationNatural Science Foundation of Shaanxi ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceSupercapacitorGrapheneNanofiberOxideCapacitanceFabricationConductivityCelluloseSubstrate (aquarium)ElectrodeComposite materialNanotechnologyChemical engineering

Abstract

fetched live from OpenAlex

Abstract In this research, a kind of non-carbonized reduced graphene oxide (RGO)-cellulose nanofibers (CNF) film is constructed by a combination of filtration and chemical reduction. In the hybrid, RGO enhances conductivity of CNF, and CNF as an idea spacer not only prevents stacking of RGO but also gives the film good mechanical properties including mechanical strength and flexibility. The synergistic effect of both endows the film with good electrochemical storage performance and outstanding mechanical properties. The hybrid film can be directly assembled into a symmetrical supercapacitor that shows an outstanding cycle stability, excellent rate performance, good mechanical strength and flexibility. Moreover, the film presents a high specific capacitance of 120 mF cm − 2 , energy density of 536 µWh cm − 2 (32 Wh kg − 1 ) and power density of 193 mW cm − 2 (53 kW kg − 1 ). Additionally, the film can be used as a substrate to graft other components.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.035
GPT teacher head0.305
Teacher spread0.271 · 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.

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

Citations15
Published2021
Admission routes1
Has abstractyes

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