Effects of Thickness and Indenter Tip Geometry in Nanoindentation of Nickel Films
Bibliographic record
Abstract
Nanoindentation has become a widely used technique to measure the mechanical properties of materials. Due to its capability to deform materials in micro- and nano-scale, nanoindentation has found more applications in characterizing the deformation behavior and determining the mechanical properties of thin films and coatings. This research deals with the characterization of samples received from Center for Advanced Microstructures and Devices (CAMD) and Integran Technologies Inc., Toronto, Canada and the objective of this investigation was to utilize the experimental data obtained from nanoindentation to determine the deformation behavior, mechanical properties of thin films on substrates and bulk materials, and the effect of geometrically different indenters (Berkovich, cubecorner, and conical). X-ray diffraction (XRD), transmission electron microscope (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM) analysis were performed on these materials to determine the crystal orientation, grain size of the material, and also to measure any substrate effects like pile-up or sin-in respectively. The results indicate that indentation size effect (ISE) strongly depends on shape of the indenter and less sensitive to penetration depth where as the hardness measurements depends on shape of indenter and depth of penetration. There is a negligible strain rate dependency of hardness at deeper depths and a significant increase in the hardness due to the decrease in grain size and results also indicate that there is no significant substrate effect on thin films for 10% and 20% of film thicknesses. Nanocrystalline material could not validate a dislocation based mechanisms deformation for indentation made by cubecorner and conical indenters in depths less than 1mm.
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 | 0.001 |
| 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".