What Appeals to the Chinese Customers? Content Analysis of Chinese Advertisements in Newspaper and on TV
Bibliographic record
Abstract
Polyelectrolyte complexation, the combination of anionically and cationically charged polymers through ionic interactions, can be used to form hydrogel networks. These networks can be used to encapsulate and release cargo, but the release of cargo is typically rapid, occurring over a period of hours to a few days and they often exhibit weak, fluid-like mechanical properties. Here we report the preparation and study of polyelectrolyte complexes (PECs) from sodium hyaluronate (HA) and poly[tris(hydroxypropyl)(4-vinylbenzyl)phosphonium chloride], poly[triphenyl(4-vinylbenzyl)phosphonium chloride], poly[tri(<i>n</i>-butyl)(4-vinylbenzyl)phosphonium chloride], or poly[triethyl(4-vinylbenzyl)phosphonium chloride]. The networks were compacted by ultracentrifugation, then their composition, swelling, rheological, and self-healing properties were studied. Their properties depended on the structure of the phosphonium polymer and the salt concentration, but in general, they exhibited predominantly gel-like behavior with relaxation times greater than 40 s and self-healing over 2-18 h. Anionic molecules, including fluorescein, diclofenac, and adenosine-5'-triphosphate, were encapsulated into the PECs with high loading capacities of up to 16 wt %. Fluorescein and diclofenac were slowly released over 60 days, which was attributed to a combination of hydrophobic and ionic interactions with the dense PEC network. The cytotoxicities of the polymers and their corresponding networks with HA to C2C12 mouse myoblast cells was investigated and found to depend on the structure of the polymer and the properties of the network. Overall, this work demonstrates the utility of polyphosphonium-HA networks for the loading and slow release of ionic drugs and that their physical and biological properties can be readily tuned according to the structure of the phosphonium polymer.
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How this classification was reachedexpand
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 | 0.002 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".