Nanoindentation of Isometric Viruses on Deterministically Corrugated Substrates
Bibliographic record
Abstract
It has been just over 100 years since inventor Joseph Coyle perfected the egg carton-a package format that has known very little changes since its first appearance ( Dhillon , S. B. C. Inventor Created Better Way to Carry Eggs. In The Globe and Mail Vancouver , 2013 ). In this article, we extend Coyle's old idea to the study of mechanical properties of viruses. Virus stiffness, strength, and breaking force obtained by force spectroscopy atomic force microscopy (AFM) provide the knowledge required for designing nanocontainers for applications in biotechnology and medicine, and for understanding the fundamentals of virus-host interaction such as virus translocation from one cellular compartment to another. In previous studies, virus particles adsorbed on flat surfaces from a physiological buffer were subjected to directional deformation by a known force exerted via a microscopic probe. The affinity between the virus shell and surface is required to be strong enough to anchor particles on the substrate while they are indented or imaged, yet sufficiently weak to preserve the native structure and interactions prior deformation. The specific question addressed here is whether an experimental scheme characterized by increased contact area and stable mechanical equilibrium under directional compression would provide a more reliable characterization than the traditional flat substrate approach.
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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 | 0.000 | 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".