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Record W3015065504 · doi:10.1515/nanoph-2019-0556

MXenes: focus on optical and electronic properties and corresponding applications

2020· article· en· W3015065504 on OpenAlexaff
Yifan Wang, Yanheng Xu, Menglei Hu, Han Ling, Xi Zhu

Bibliographic record

VenueNanophotonics · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMXenesCarbideMaterials scienceNanotechnologyGrapheneNanomaterialsElectronicsNitrideCharacterization (materials science)Engineering physicsChemistryLayer (electronics)PhysicsMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The discovery of graphene, the first two‐dimensional (2D) material, has caused an upsurge, as this kind of material revealed a tremendous potential of application in areas such as energy storage, electronics, and gas separation. MXenes are referred to as a family of 2D transition metal carbides, carbonitrides, and nitrides. After the synthesis of Ti 3 C 2 from Ti 3 AlC 2 in 2011, about 30 new compositions have been reported. These materials have been widely discussed, synthesized, and investigated by many research groups, as they have many advantages over traditional 2D materials. This review covers the structures of MXenes, discusses various synthesis routines, analyzes the properties, especially optical and electronic properties, and summarizes their applications and potential, which may give readers an overview of these popular materials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.030
GPT teacher head0.238
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations162
Published2020
Admission routes1
Has abstractyes

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