Quantitative Mechanical Properties of Carbon-based Surfaces Examined through Analysis of Cantilever Dynamics in Atomic Force Microscopy
Bibliographic record
Abstract
Mechanical properties of nanomaterials have been at the forefront of recent scientific interest, as a reduction in the feature size of these materials can introduce a significant improvement in their mechanical properties. To investigate the nanoscale mechanical properties, researchers have employed a variety of instruments and techniques such as nanoindentation and dynamic atomic force microscopy (AFM). Although these techniques have been successful in providing a qualitative assessment of the mechanical properties of the surfaces, quantification of the experimental data has been hindered due to the lack of precisely calibrated data. Additionally, novel applications of nanomaterials require high spatial and temporal resolution in their mechanical properties, which have not been achieved in current characterization methods. To begin to address these issues, dynamic AFM was operated under ultrahigh vacuum (UHV) conditions to determine the elastic modulus of the novel materials down to the atomic length-scale. Considering the cantilever shape in a dynamic contact, calibration of the experimental data was implemented to extract a quantitative elastic modulus and a spatially-resolved map of this value on graphite and graphene surface. The developed experimental technique and calibration method were verified through the comparison with both analytical and simulation models of the surface. Following the quantification of the high spatial resolution of mechanical properties of two-dimensional materials, an advanced technique is introduced and developed to measure the variation of the mechanical properties through the spectral/frequency analysis of the conventional static cantilever bending data acquired at high sampling rates on the order of ∼1 MHz. The results of this dissertation are promising as they confirm the ability of these techniques to provide high-fidelity in spatial resolution of the mechanical properties of nanomaterials. Furthermore, they can be used in several industries such as aerospace, design and manufacturing, and microelectromechanical systems (MEMS), where the techniques can be utilized for the more efficient assessment of the functionality of nanomaterials in such applications. Furthermore, the results of this research demonstrate the importance of frequency analysis in advanced microscopy techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".