Effect of Molecular Weight on the Exponential Growth and Morphology of Hyaluronan/Chitosan Multilayers: A Surface Plasmon Resonance Spectroscopy and Atomic Force Microscopy Investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The layer-by-layer growth of multilayer assemblies of two polysaccharides, the polyanion hyaluronan (HA) and the polycation chitosan (CH), was investigated using atomic force microscopy (AFM) and surface plasmon resonance (SPR) spectroscopy, with primary emphasis on the effect of the polysaccharide molecular weights on the film thickness and surface morphology. The HA/CH multilayers exhibit an exponential increase of the optical film thickness with the number of deposited bilayers. We show that the multilayer thickness at a given stage depends on the size of both CH, the diffusing polyelectrolyte, and HA, the non-diffusing species. Assemblies (12 bilayers) of high molecular weight polysaccharides (HA, 360,000; CH, 160,000) were twice as thick (approximately 900 nm vs approximately 450 nm) as those obtained with low molecular weight polymers (HA, 30,000; CH, 31,000), as assessed by AFM scratch tests. The exponential growth rate is the same for the high and low molecular weight pairs; the larger film thicknesses observed by SPR and by AFM arising from an earlier onset of the steep exponential growth phase in the case of the high molecular weight pair. In all cases, isolated islets form during the deposition of the first CH layer onto the underlying HA. Upon further film growth, individual islets coalesce into larger vermiculate features. The transition from distinct islands to vermiculate structures depends on the molecular weights of the polysaccharides and the lower molecular weight construct presents larger worm-like surface domains than the high molecular weight pair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it