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Record W2288430644 · doi:10.1021/acs.langmuir.5b00508

Controlling the Shell Formation in Hydrothermally Reduced Graphene Hydrogel

2015· article· en· W2288430644 on OpenAlexafffund
Kaiwen Hu, Xingyi Xie, Marta Cerruti, Thomas Szkopek

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcGill University
FundersSichuan UniversityNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsGrapheneMaterials scienceOxideSelf-healing hydrogelsAdsorptionGraphene oxide paperGraphene foamNanotechnologyChemical engineeringShell (structure)Hydrothermal circulationPorosityComposite materialChemistryPolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Graphene hydrogels/aerogels are emerging three-dimensional graphene macroscopic assemblies of potential use in many applications including energy storage, pollutant adsorption, and gas sensing. In this Letter, we identify, characterize and control the formation of the exterior shell structure of graphene hydrogels prepared via hydrothermal reduction of graphene oxide. Unlike the porous bulk of the hydrogel, the shell is a compact, highly ordered layer with a higher electrical conductivity. Shell formation is dependent upon the surface anchoring of graphene oxide at the liquid-air and liquid-container interfaces. By purposefully weakening surface anchoring of graphene oxide using mild thermal or chemical prereduction method prior to hydrothermal reduction, we have succeeded in completely suppressing shell formation in the graphene hydrogel. The resulting graphene hydrogel shows a lower volume reduction with a porous bulk structure immediately accessible from the surface, in contrast to graphene hydrogels prepared under conventional conditions.

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 score0.016
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.240
Teacher spread0.210 · 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

Citations33
Published2015
Admission routes2
Has abstractyes

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