Interface chemistry of two-dimensional heterostructures – fundamentals to applications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".