Effect of Process Parameters on the Dynamic Modulus, Damping and Energy Absorption of Vertically Aligned Carbon Nano-Tube (VACNT) Forest Structures
Bibliographic record
Abstract
Functionally graded materials (FGMs) are a new generation of engineered materials wherein the micro-structural details are spatially varied through non-uniform distribution of the reinforcement phase(s). The dynamic mechanical behavior and high-strain rate response characteristics of a functionally graded material system consisting of vertically aligned carbon nanotube ensembles grown on silicon wafer substrate (VACNT-Si) and processed at various temperatures have been characterized. Flexural rigidity (storage modulus) and the loss factor (damping) were measured with a dynamic mechanical analyzer in an oscillatory three-point bending mode. It was found that the functionally graded VACNT-Si processed at 770°C and 820°C exhibited higher damping without sacrificing flexural rigidity. A Split-Hokinson Pressure Bar (SHPB) was used for determining the dynamic response under high-strain rate compressive loading. It was again observed that the VACNT-Si specimens processed at 770°C and 820°C showed a large increase in specific energy absorption, compared with those processed at 720°C. Interfacial friction between individual VACNTs, caused by their alignment/entanglements under cyclic deformation, is believed to be the primary energy dissipation mechanism for such large improvement in loss factor (compared to base Silicon wafer substrate). The larger height of VACNT forest depositions for specimens processed at 770°C and 820°C may have caused more entanglements, as reflected in higher damping and specific energy absorption. It appears that the optimal processing temperature may be around 770°C for attaining the highest damping and specific energy absorption, in terms of height and alignment/entanglement of the VACNTs grown on Si-wafer substrate. doi:10.12783/issn. 2168-4286/2.2/Mantena
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".