pH Dependent Equilibria of Poly(anilineboronic acid)‐Saccharide Complexation in Thin Films
Bibliographic record
Abstract
Abstract Summary: The complexation of saccharides with poly(anilineboronic acid) as a function of pH has been studied with simultaneous measurements of open‐circuit potential and mass change. This study provides an insight into this reaction as well as the relationship between complexation and open‐circuit potential and the optimum pH for complexation of D‐fructose and D‐glucose. The optimum pH values for poly(anilineboronic acid)‐D‐fructose and ‐D‐glucose complexation are near the pKa values of the complex reported in homogeneous solution. At physiological pH (7.4), the apparent binding constant of D‐fructose and D‐glucose with poly(anilineboronic acid) is 19.2 and 0.2 M−1, respectively. In contrast, at pH 9.0, the apparent binding constant of D‐glucose with poly(anilineboronic acid) is 12 M−1, double than that of D‐fructose. The decrease in complexation in the polymer films at pH values above the pKa of the complexes is in contrast to the behavior in homogeneous solutions. This trend is observed for both D‐fructose and D‐glucose using open‐circuit potential, mass change and polarization modulated infrared reflection absorption spectroscopic measurements. Also, the complexation is limited in the polymer film, i.e., ≤23% boron is involved. These results suggest that steric and/or electrostatic interactions may play an important role in complexation within polymer films. magnified image
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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) | 0.001 | 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".