Advanced Characterization of Lignin Nanoparticles by Asymmetric Flow‐Field Flow Fractionation
Why this work is in the frame
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Bibliographic record
Abstract
The unique properties of lignin nanoparticles (LNPs) - uniform shape, surface charge and nanoscale - carry great potential for the desired material utilization of technical lignins. Especially, the particle size distribution and dispersity of LNPs are the key for their successful valorization. However, characterization of LNPs usually requires a rather elaborate combination of light scattering and microscopy techniques which moreover provide only average values, are often limited in sampling size and require tedious sample preparation. Here we introduce a method based on asymmetric flow field-flow fractionation (AF4) coupled with multi angle laser light scattering (MALLS), dynamic light scattering (DLS) and refractive index (RI) detection for the analysis of size and shape of LNPs. Exploiting the separation power of AF4 in combination with MALLS, DLS, and RI allowed us to obtain enhanced particle size distributions of LNP that are comparable to batch DLS and AFM measurements. Moreover, we discuss the influence of the particle size on the MALLS and DLS signals and determination of the shape factor ρ of LNPs.
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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 | 0.001 |
| 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 it