(Invited) Custom Low-Dimensional Material Systems Explored at the Atomic Scale
Bibliographic record
Abstract
Innovative technologies have a history of capitalizing on the discovery of new physical phenomena, often at the confluence of advances in material characterization techniques and innovations in design and controlled synthesis of high-quality materials. Pioneered by the discovery of graphene, atomically thin materials (2D materials) hold the promise for realizing physical systems with distinct properties, previously inaccessible. In this talk I will describe experiments that seek to uncover the novel physical phenomena in 2D materials by using scanning tunneling microscopy and spectroscopy (STM/STS) and supporting spectroscopic techniques. Firstly, I will discuss the 1T polymorph of TaS2, which has one of the richest phase diagrams among the layered transition metal dichalcogenides and address the question of crossover from bulk to few layers. Secondly, I will describe atomic-scale characterization of the in-plane anisotropic semiconductor ReS2. We demonstrate that rhenium atoms form diamond-shaped clusters, organized in disjointed chains, and characterize the semiconducting electronic band gap by STS. By spatially mapping the local density of states around defects in ReS2, we explore their origin and electrostatic nature.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.026 |
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".