A further look at the nano/micro-indentation method for measuring and ranking Young’s modulus and hardness of materials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Determination of the intrinsic Young’s modulus ( E ) is essential for material design and applications. However, the commonly used micro/nano-indentation method does not give accurate intrinsic Young’s modulus, since the measured modulus comes from the damaged zone under the indent tip. In this study, we analyze the intrinsic Young’s modulus or that without local damage caused by indentation, and determine that the intrinsic Young’s modulus can be determined by extrapolation of the E ∼ load curve as the indentation load approaches zero. To support this finding, indentation behaviors of five ceramic materials (Al 2 O 3 , Si 3 N 4 , ZrO 2 , glass and cemented WC/Co) were analyzed and compared with those determined using an acoustic method. The intrinsic Young’s modulus measured, e.g., using the acoustic method, are appropriate for material ranking, while Young’s moduli of different materials measured by indentation under the same load could give misleading information because of different degrees of local damage to the materials under the indenter. Underlying mechanisms for the observed phenomena shown in this novel and unique study are elucidated based on the interatomic bonding. Hardness versus load curves show trends similar to those of Young’s modulus. However, unlike the Young’s modulus, the hardness values measured under the same load can be directly used to rank materials; the reason behind is also discussed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it