A Simple Method for Synthesis of Chitosan Nanoparticles with Ionic Gelation and Homogenization
Bibliographic record
Abstract
Chitosan nanoparticles (CNPs) are known to have great utility in many fields (pharmaceutical, agricultural, food industry, wastewater treatment, etc.). In this study we aimed to synthesize sub-100 nm CNPs as a precursor of new biopolymer-based virus surrogates for water applications. We present a simple yet efficient synthesis procedure for obtaining high yield, monodisperse CNPs with size 68-77 nm. The CNPs were synthesized by ionic gelation using low molecular weight chitosan (deacetylation 75-85%) and tripolyphosphate as crosslinker, under rigorous homogenization to decrease size and increase uniformity, and purified by passing through 0.1 μm polyethersulfone syringe filters. The CNPs were characterized using dynamic light scattering, tunable resistive pulse sensing, and scanning electron microscopy. We demonstrate reproducibility of this method at two separate facilities. The effects of pH, ionic strength and three different purification methods on the size and polydispersity of CNP formation were examined. Larger CNPs (95-219) were produced under ionic strength and pH controls, and when purified using ultracentrifugation or size exclusion chromatography. Smaller CNPs (68-77 nm) were formulated using homogenization and filtration, and could readily interact with negatively charge proteins and DNA, making them an ideal precursor for the development of DNA-labelled, protein-coated virus surrogates for environmental water applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".