Resolving Polarization Switching Pathways of Sliding Ferroelectricity in Trilayer 3R-MoS2
Bibliographic record
Abstract
Exploring the pathways of polarization switching in 2D sliding ferroelectrics with multiple internal interfaces is crucial for understanding the switching mechanism and for enhancing their performance in memory-related applications. However, distinguishing the rich configurations of various stacking from a coexistence of polarization domains has remained challenging. In this investigation, we employ optical techniques to resolve the stacking degeneracy in a trilayer 3R-MoS2 across several polarization switching cycles. Through a comprehensive analysis of the unique excitonic response exhibited by different layers, we unveil multiple polarization switching pathways that are determined by the sequential release of domain walls initially pinned at various interfaces within the trilayer, providing an understanding of the switching mechanism in multilayered sliding ferroelectrics. Our study not only reveals the intricate dynamics of polarization switching, but also underscores the crucial role of controlling domain walls, pinning centers, and doping levels, offering new insights for enhancing the applications of these materials in sensing and computing.
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 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".