Cumulative Spectroscopic Detection for Taylor Dispersion Analysis of Nanoparticles
Bibliographic record
Abstract
Taylor dispersion analysis (TDA) provides a robust approach for determining the hydrodynamic diameter of molecular objects and nanoparticles (NPs), measuring diffusion of the analyte in laminar flow through a long capillary. TDA measurements are usually performed on capillary electrophoresis equipment with in-line absorbance detection. Here, we introduce a simple, yet versatile cumulative spectroscopic detection (CSD) technique for TDA, that precisely samples the radially averaged analyte concentration flowing out of the TDA capillary. CSD can be employed in different optical spectroscopic configurations, such as absorbance, fluorescence or resonant light scattering. A theoretical model of the measurement was formulated that combines Taylor's original expression with an extended injection and then accounts for the accumulation of the analyte in the detection cell. The model was validated by comparing the hydrodynamic radii of various nanoparticle samples measured by TDA-CSD to those obtained by differential dynamic microscopy and dynamic light scattering. Beyond TDA, CSD may be useful for accurate sampling of analyte peaks in other flow-based analysis techniques, such as liquid chromatography.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".