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Record W4286544450 · doi:10.1039/d2sc01786e

Fabrication of graphene-based porous materials: traditional and emerging approaches

2022· review· en· W4286544450 on OpenAlexafffund
Heidi Jahandideh, Jun‐Ray Macairan, Aram Bahmani, Mathieu Lapointe, Nathalie Tufenkji

Bibliographic record

VenueChemical Science · 2022
Typereview
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcGill University
KeywordsFabricationGrapheneNanotechnologyMaterials sciencePorosityPorous mediumAnisotropyComposite materialPhysics

Abstract

fetched live from OpenAlex

The anisotropic nature of 'graphenic' nanosheets enables them to form stable three-dimensional porous materials. The use of these porous structures has been explored in several applications including electronics and batteries, environmental remediation, energy storage, sensors, catalysis, tissue engineering, and many more. As method of fabrication greatly influences the final pore architecture, and chemical and mechanical characteristics and performance of these porous materials, it is essential to identify and address the correlation between property and function. In this review, we report detailed analyses of the different methods of fabricating porous graphene-based structures - with a focus on graphene oxide as the base material - and relate these with the resultant morphologies, mechanical responses, and common applications of use. We discuss the feasibility of the synthesis approaches and relate the GO concentrations used in each methodology against their corresponding pore sizes to identify the areas not explored to date.

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: Review · Consensus signal: Review
Teacher disagreement score0.428
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.178
GPT teacher head0.335
Teacher spread0.157 · 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
GenreReview

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
Published2022
Admission routes2
Has abstractyes

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