Multiparametric Characterization of Individual Suspended Nanoparticles Using Confocal Fluorescence and Interferometric Scattering Microscopy with Microfluidic Confinement
Bibliographic record
Abstract
Detailed characterization of the size, mass, payload, and structure of suspended mRNA-lipid nanoparticles (LNPs) is necessary to improve our understanding of how these heterogeneous properties influence therapeutic efficacy and potency. Methods currently in use face limitations in reporting ensemble-average particle properties or requiring dedicated home-built microscopes that are beyond the reach of nanoparticle developers. In this work, we overcome these limitations by combining a commercially available confocal microscope and a convex lens-induced confinement (CLiC) instrument to achieve simultaneous characterization and correlation of the size, mass, refractive index, and nucleic acid payload of individual LNPs. We established the accuracy and precision of our method using nanosized beads and used it to investigate the size, payload, and water content of LNPs in different solvent pH. By employing readily available microscopy tools, we open the door to widespread adoption of our quantitative, in-solution nanoparticle characterization method.
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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 | 0.001 | 0.001 |
| 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".