Two‐dimensional Nanosheets by Liquid Metal Exfoliation
Bibliographic record
Abstract
Abstract Liquid exfoliation is a scalable and effective method for synthesizing 2D nanosheets (NSs) but often induces contamination and defects. Here, liquid metal gallium (Ga) is used to exfoliate bulk layered materials into 2D NSs at near room temperature, utilizing the liquid surface tension and Ga intercalation to disrupt Van der Waals (vdW) forces. In addition, the process can transform the 2H‐phase of transition metal dichalcogenides into the 1T’‐phase under ambient conditions. This method produces high aspect ratio, surfactant‐free 2D‐NSs for more than 10 types of 2D materials that include h‐BN, graphene, MoTe 2 , MoSe 2 , layered minerals, etc. The subsequent Ga separation via ethanol dispersion avoids the formation of additional defects and surfactant contamination. By adjusting initial defect levels of the layered materials, customize the metallicity and/or defectiveness of 2D NSs can be customized for applications such as birefringence‐tunable modulators with exfoliated h‐BN, and enhanced hydrogen evolution with defective MoS 2 . This approach offers a strategy to optimize liquid metal/2D interfaces, preserving intrinsic properties and enabling practical applications, potentially transforming optics, energy conversion, and beyond.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".