Extracellular Vesicle Refractive Index Derivation Utilizing Orthogonal Characterization
Bibliographic record
Abstract
The analysis of small particles, including extracellular vesicles and viruses, is contingent on their ability to scatter sufficient light to be detected. These detection methods include flow cytometry, nanoparticle tracking analysis, and single particle reflective image sensing. To standardize measurements and enable orthogonal comparisons between platforms, a quantifiable limit of detection is required. The main parameters that dictate the amount of light scattered by particles include size, morphology, and refractive index. To date, there has been a lack of accessible techniques for measuring the refractive index of nanoparticles at a single-particle level. Here, we demonstrate two methods of deriving a small particle refractive index using orthogonal measurements with commercially available platforms. These methods can be applied at either a single-particle or population level, enabling the integration of diameter and scattering cross section values to derive the refractive index using Mie theory.
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 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".