Bibliographic record
Abstract
The potential use of two-dimensional (2D) materials as solid lubricants in micro-and nano-scale mechanical systems draws significant attention, mainly due to the fact that liquid based lubrication schemes fail at such small length scales. Within this context, the lamellar material molybdenum disulfide (MoS2), in the form of a single or few layers, emerges as a promising candidate for the solid lubrication of small-scale mechanical systems.Motivated as above, this thesis focuses on the nanotribological properties of mechanically exfoliated MoS2, explored via state-of-the-art atomic force microscopy (AFM) experiments. First, the dependence of friction force on sliding speed is investigated for single-layer and bulk MoS2 samples. The results demonstrate that (i) friction forces increase logarithmically with respect to sliding speed, (ii) there is no correlation between the speed dependence of friction and the number of layers of MoS2, and (iii) changes in the speed dependence of friction can be attributed to changes in the physical characteristics of the AFM probe. The direction dependence (i.e. anisotropy) of friction on MoS2 is studied next. In particular, high-resolution AFM measurements conducted by our collaborators at McGill University lead to the direct imaging of atomic-scale ripples on few-layer MoS2 samples, allowing to explain the various symmetries for friction anisotropy that are observed in our experiments as a function of scan size. Finally, the nanotribological properties of Re-doped MoS2 are studied, revealing a surprising, inverse dependence of friction force on number of layers, in contradiction with the seemingly universal trend of decreasing friction with increasing number of layers on 2D materials. Attempts are made to uncover the physical mechanisms behind this striking observation by way of roughness and adhesion measurements.In summary, the results reported in this thesis contribute to the formation of a comprehensive, mechanistic understanding of the nanotribological properties of MoS2 in particular, and 2D materials, in general. While the speed dependence and anisotropy results are relatively self-contained, further work needs to be conducted in order to explain the inverse layer-dependence of friction observed on Re-doped MoS2.
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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".