Probing the Nanostructure, Interfacial Interaction, and Dynamics of Chitosan-Based Nanoparticles by Multiscale Solid-State NMR
Bibliographic record
Abstract
Chitosan-based nanoparticles (NPs) are widely used in drug and gene delivery, therapy, and medical imaging, but a molecular-level understanding of the internal morphology and nanostructure size, interface, and dynamics, which is critical for building fundamental knowledge for the precise design and efficient biological application of the NPs, remains a great challenge. Therefore, the availability of a multiscale (0.1-100 nm) and nondestructive analytical technique for examining such NPs is of great importance for nanotechnology. Herein, we present a new multiscale solid-state NMR approach to achieve this goal for the investigation of chitosan-poly(N-3-acrylamidophenylboronic acid) NPs. First, a recently developed (13)C multiple cross-polarization magic-angle spinning (MAS) method enabled fast quantitative determination of the NPs' composition and detection of conformational changes in chitosan. Then, using an improved (1)H spin-diffusion method with (13)C detection and theoretical simulations, the internal morphology and nanostructure size were quantitatively determined. The interfacial coordinated interaction between chitosan and phenylboronic acid was revealed by one-dimensional MAS and two-dimensional (2D) triple-quantum MAS (11)B NMR. Finally, dynamic-editing (13)C MAS and 2D (13)C-(1)H wide-line separation experiments provided details regarding the componential dynamics of the NPs in the solid and swollen states. On the basis of these NMR results, a model of the unique nanostructure, interfacial interaction, and componential dynamics of the NPs was proposed.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".