Raman Spectroscopy as a Tool for Measuring Mutual-Diffusion Coefficients in Hydrogels
Bibliographic record
Abstract
Raman spectroscopy has been exploited to characterize the diffusion properties of solutes in hydrogels. Raman active vibrations were used as intrinsic probes of the solute concentration along gel cylinders. The resulting one-dimensional solute distribution, characterized as a function of both time and space, could be analyzed with a model based on Fick's diffusion law, and the mutual-diffusion coefficient (Dm) was then determined. To illustrate the potential of this approach, we measured the Dm of two polyethylene glycols (PEG) in Ca-alginate gels. In this case, the intensity of the CH stretching band was used to obtain the concentration profiles of PEGs, whereas the OH stretching band of water was used as an internal intensity standard. In addition to providing a straightforward approach to measuring diffusion coefficients, the Raman profile analysis provides information relative to the accessibility of gels to large molecules. As an example, it was found that the PEG penetration in Ca-alginate gels was restricted, a phenomenon that was dependent on PEG size. The Raman technique presented here effectively characterizes transport properties of solutes in gels, and such characterization is required for developing several technical applications of gels, such as their use as materials for controlled release of drugs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".