Characterization of Variant Soft Nanoparticle Structure and Morphology in Solution by NMR Spectroscopy
Bibliographic record
Abstract
The physicochemical properties of soft nanoparticles, most notably size, morphology, and ligand interactions, typically depend upon the solution environment in which they are suspended. Comprehensive characterization in a given environment is therefore essential. We have employed high-resolution solution nuclear magnetic resonance (NMR) spectroscopy, nuclear spin relaxation measurements, and diffusion ordered NMR spectroscopy (DOSY) techniques to thoroughly characterize polymeric nanoparticles formed by salt-induced collapse of a methacrylic acid–ethyl acrylate copolymer stabilized by ultraviolet (UV) irradiation. UV-dose-dependent production of new chemical species is apparent from 1 H and 13 C chemical shift patterns. 1 H– 13 C correlation spectroscopy reveals that cross-linking is likely responsible for nanoparticle structural integrity. Paramagnetic relaxation enhancement (PRE) unambiguously shows protection of photochemically derived moieties from solvent, with a UV-dose-dependent decrease in particle size. Temperature-dependent swelling and solvent-induced contraction demonstrate that increased UV dose leads to an increase in the proportion of compact, solvent protected particle core relative to more dynamic, solvent accessible shell. Correlation of disparate solution-state NMR observables allowed for these conclusions and is readily generalizable to the in situ characterization of the exact state of a wide variety of soft nanoparticles as a function of environment.
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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 | 0.001 |
| 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".