Self-Diffusion and Mutual Diffusion of Small Molecules in High-Set Curdlan Hydrogels Studied by <sup>31</sup>P NMR
Bibliographic record
Abstract
Self-diffusion and mutual diffusion are two different transport mechanisms experimentally characterized on different length and time scales. NMR spectroscopy is a highly suitable technique to characterize these two phenomena as both mechanisms can be studied on the same system and in the same experimental conditions. Pulsed field gradient (PFG) NMR was used to measure the self-diffusion whereas (31)P NMR profiling provided an approach to determine the mutual diffusion coefficients. We have characterized the diffusion of phosphate, trimetaphosphate, alendronate, and d-glucose-6-phosphate in hydrogels prepared with 10% (w/v) curdlan, a bacterial polysaccharide built of linear (1-->3)-beta-d-glucose repeating units. These solutes are small compared to the average pore size of the hydrogel, as inferred from environmental scanning electron microscopy (eSEM). Our results show that the self- and mutual-diffusion coefficients of small molecules in curdlan hydrogels are similar and are reduced by 30% compared to those measured in aqueous solutions. These observations are validated for the complete series of investigated analytes. It is therefore concluded that, for this system, the analyte diffusion in the gel is essentially reduced because of interactions at the molecular level and that the open structure of this gel has a very limited influence at the mesoscopic length scale. A literature survey indicates that these conditions prevail for the large majority of the systems that have been investigated up to now.
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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) | 0.000 | 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 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".