Label‐free detection of enhanced saccharide binding at pH 7.4 to nanoparticulate benzoboroxole based receptor units
Bibliographic record
Abstract
Nanoparticles modified with either 6-amino-1-hydroxy-2,1-benzoxaborolane (3-aminobenzoboroxole) or 3-aminophenylboronic acid were prepared by nucleophilic substitution of a styrene-co-DVB-co-vinylbenzylchloride latex (25 nm). Isothermal titration calorimetry (ITC) was used as a label-free detection method for the analysis of the binding between monosaccharides and these two differently derivatized nanoparticle systems at pH 7.4. Because ITC reveals, thermodynamical parameters such as the changes in enthalpy ΔH, free energy ΔG, and entropy ΔS, possible explanations for the higher binding constants can be derived in terms of entropy and enthalpy changes. In case of the modified nanoparticles, the free energy of binding is dominated by the entropy term. This shows that interfacial effects, besides the intrinsic affinity, lead to a higher binding constant compared with the free ligand. The highest binding constant was found for fructose binding to the benzoboroxole modified nanoparticles: Its value of 1150 M(-1) is twice as high as for the free benzoboroxole and five times as high as with phenylboronic acid or 3-aminophenylboronic acid. In contrast to the binding of fructose to free boronic acids, which is an enthalpically driven process, the binding of fructose to the modified nanoparticles is dominated by the positive entropy term.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".