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Record W3131306747 · doi:10.1039/d0cs01070g

Interface chemistry of two-dimensional heterostructures – fundamentals to applications

2021· review· en· W3131306747 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemical Society Reviews · 2021
Typereview
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsKensington Health
Fundersnot available
KeywordsHeterojunctionInterface (matter)NanotechnologyScale (ratio)ChemistryComputer scienceEngineering physicsMaterials scienceOptoelectronicsPhysicsMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

Two-dimensional heterostructures (2D HSs) have emerged as a new class of materials where dissimilar 2D materials are combined to synergise their advantages and alleviate shortcomings. Such a combination of dissimilar components into 2D HSs offers fascinating properties and intriguing functionalities attributed to the newly formed heterointerface of constituent components. Understanding the nature of the surface and the complex heterointerface of HSs at the atomic level is crucial for realising the desired properties, designing innovative 2D HSs, and ultimately unlocking their full potential for practical applications. Therefore, this review provides the recent progress in the field of 2D HSs with a focus on the discussion of the fundamentals and the chemistry of heterointerfaces based on van der Waals (vdW) and covalent interactions. It also explains the challenges associated with the scalable synthesis and introduces possible methodologies to produce large quantities with good control over the heterointerface. Subsequently, it highlights the specialised characterisation techniques to reveal the heterointerface formation, chemistry and nature. Afterwards, we give an overview of the role of 2D HSs in various emerging applications, particularly in high-power batteries, bifunctional catalysts, electronics, and sensors. In the end, we present conclusions with the possible solutions to the associated challenges with the heterointerfaces and potential opportunities that can be adopted for innovative applications.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.379
Teacher spread0.326 · 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