Imperfection sensitivity of pressured buckling of biopolymer spherical shells
Bibliographic record
Abstract
Imperfection sensitivity is essential for mechanical behavior of biopolymer shells [such as ultrasound contrast agents (UCAs) and spherical viruses] characterized by high geometric heterogeneity. In this work, an imperfection sensitivity analysis is conducted based on a refined shell model recently developed for spherical biopolymer shells of high structural heterogeneity and thickness nonuniformity. The influence of related parameters (including the ratio of radius to average shell thickness, the ratio of transverse shear modulus to in-plane shear modulus, and the ratio of effective bending thickness to average shell thickness) on imperfection sensitivity is examined for pressured buckling. Our results show that the ratio of effective bending thickness to average shell thickness has a major effect on the imperfection sensitivity, while the effect of the ratio of transverse shear modulus to in-plane shear modulus is usually negligible. For example, with physically realistic parameters for typical imperfect spherical biopolymer shells, the present model predicts that actual maximum external pressure could be reduced to as low as 60% of that of a perfect UCA spherical shell or 55%-65% of that of a perfect spherical virus shell, respectively. The moderate imperfection sensitivity of spherical biopolymer shells with physically realistic imperfection is largely attributed to the fact that biopolymer shells are relatively thicker (defined by smaller radius-to-thickness ratio) and therefore practically realistic imperfection amplitude normalized by thickness is very small as compared to that of classical elastic thin shells which have much larger radius-to-thickness ratio.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".