Dynamic Deformation of Soft Particle in Dual-trap Optical Tweezers
Bibliographic record
Abstract
The dual-trap optical tweezers is used for deforming and measuring the elasticity of soft particles suspended in aquatic buffer. In the experiment the trapped particle was the Red Blood Cell (RBC) swollen to a spherical form. The 3D radiation stress distribution was computed by ray tracing, the generalized Lorentz-Mie scattering theory with the Tmatrix and the FDTD via the Maxwell stress tensor. The 3D deformation of the cells was then computed with the elastic membrane theory. The calculated deformation can fit to experimental data resulting in cell’s elasticity coefficient. The static approach is valid only for small deformation (5-10%). For a large deformation such as that of the RBC, we consider re-distribution of the radiation stress on the morphologically deformed cell. This stress re-distribution in turn induces subsequent deformation of the deformed cell and new stress re-distribution. The recursive process continues until a final equilibrium state is achieved. This iterative computation was implemented with the finite element method using the COMSOL TM multi-physics models. The deformation results can fit to the experimental data for cell’s deformation up to 20%.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".