Generally Cylindrical Orthotropic Constitutive Properties Modeling of Matrix-filled Single-walled Nanotubes: Axial Mechanical Properties
Bibliographic record
Abstract
The technology of filling hollow carbon nanotubes with desirable materials has created tremendous interest in recent years. The objective of this study is to introduce analytical solutions for effective longitudinal Young's modulus and major Poisson's ratio of matrix-filled single-walled nanotubes (SWNTs). In this work, both SWNT and its filler material are considered generally cylindrical orthotropic. Analytical solutions are obtained and accordingly reduced to transversely isotropic as well as isotropic cases for both tube and filler materials, and then the results are compared with the existing solutions. For further validation, a 3-D model of a matrix-filled single-walled carbon nanotube (SWCNT) is generated and solved for displacement and strain results numerically, using the finite element method. The finite element numerical analysis is employed to verify the accuracy of the results obtained from the analytical approach for generally cylindrical orthotropic materials. Excellent agreement is achieved between the results obtained from the analytical and numerical methods. Furthermore, a parametric study is also conducted to investigate the effective properties variations of the matrix-filled nanotubes based on the variations of nanotube/filler geometry and material properties.
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 | 0.000 |
| 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 | 0.001 | 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 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".