Monodispersed Renewable Particles by Cascade and Density Gradient Size Fractionation to Advance Lignin Nanotechnologies
Bibliographic record
Abstract
Control over particle size and shape heterogeneity is highly relevant to the design of photonic coatings and supracolloidal assemblies. Most developments in the area have relied on mineral and petroleum-derived polymers that achieve well-defined chemical and dimensional characteristics. Unfortunately, it is challenging to attain such control when considering renewable nanoparticles. Herein, a pathway toward selectable biobased particle size and physicochemical profiles is proposed. Specifically, lignin is fractionated, a widely available heterogeneous polymer that can be dissolved in aqueous solution, to obtain a variety of monodispersed particle fractions. A two-stage cascade and density gradient centrifugation that relieves the need for solvent pre-extraction or other pretreatments but achieves particle bins of uniform size (~60 to 860 nm and polydispersity, PDI<0.06, dynamic light scattering) along with characteristic surface chemical features is introduced. It is found that the properties and associated colloidal behavior of the particles are suitably classified in distinctive size populations, namely, i) nanoscale (50-100 nm), ii) photonic (100-300 nm) and iii) near-micron (300-1000 nm). The strong correlation that exists between size and physicochemical characteristics (molar mass, surface charge, bonding and functional groups, among others) is introduced as a powerful pathway to identify nanotechnological uses that benefit from the functionality and cost-effectiveness of biogenic particles.
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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".