Advanced Characterization of Lignin Nanoparticles by Asymmetric Flow‐Field Flow Fractionation
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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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".