Liquid Phase Acoustic Wave Exfoliation of Layered MoS<sub>2</sub>: Critical Impact of Electric Field in Efficiency
Bibliographic record
Abstract
Abstract Liquid phase exfoliation techniques of layered crystals establish the basis for high yield production of two-dimensional (2D) flakes suspension. However, such techniques generally require a long processing time. The recent demonstration of the piezoelectric phenomenon in noncentrosymmetric layered transition metal dichalcogenides, such as molybdenum disulfide (MoS2), leads to new opportunities for fast and efficient exfoliation processes. Here we use concomitant electric field and mechanical shear force for producing a suspension of MoS2 nanoflakes from exfoliation of their layered bulk powder particles. The electrical and mechanical fields are applied by a surface acoustic wave (SAW) microcentrifugation device. We show that the overall yield per unit of time of 3.816%/h can be achieved, which is at least an order of magnitude larger than previously reported liquid phase exfoliation methods. Simultaneously, the impressive monolayer yield is 58% in an excellent agreement with the computational estimation based on electric field assisted density functional theory calculations. The work therefore reports two major advancements. We show efficient exfoliation of layered MoS2. More importantly, we demonstrate the importance of the electric field in increasing the efficiency of liquid phase exfoliation. It is thus expected that these outcomes to fundamentally impact research activities focused on the exfoliation of piezoelectric 2D materials.
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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".