Cluster Size of Amylopectin and Nanosized Amylopectin Fragments Characterized by Pyrene Excimer Formation
Bibliographic record
Abstract
Amylopectin from waxy corn and the three nanosized amylopectin fragments (NAFs)—NAF(56), NAF(20), and NAF(8)—from waxy corn starch with a hydrodynamic diameter of 227, 56, 20, and 8 nm, respectively, were randomly labeled with 1-pyrenebutyric acid. The efficiency of these pyrene-labeled amylopectin-based polysaccharides (Py-AbPS) for pyrene excimer formation (PEF) upon diffusive encounter between an excited and a ground-state pyrene increased with increasing concentration of unlabeled NAF(56) in Py-AbPS dispersions in DMSO. Fluorescence decay analysis of the Py-AbPS dispersions in DMSO prepared with increasing [NAF(56)] yielded the maximum number (Nblobexp) of anhydroglucose units (AGUs) separating two pyrene-labeled AGUs while still allowing PEF. Comparison of Nblobexp with Nblobtheo, obtained by conducting molecular mechanics optimizations on helical oligosaccharide constructs with HyperChem, led to a relationship between the interhelical distance (dh-h) in a cluster of oligosaccharide helices, [NAF(56)], and the number of helices in a cluster. It was found that the AbPSs were composed of building blocks made of 3.5 (±0.9) helices that self-assembled into increasingly larger clusters with increasing [NAF(56)]. The ability of PEF-based experiments to yield the cluster size of AbPSs provides a new experimental means to probe the interior of AbPSs at the molecular level.
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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".