Synthesis of Highly Ordered Amphiphilic Polymer Conetwork Hydrogels via the Topologically Precise Interconnection of Two Highly Incompatible Polymers
Bibliographic record
Abstract
Here, hydrophobic polyisobutylene and hydrophilic poly-(ethylene glycol), both of reasonably high molar masses, have been end-linked, yielding amphiphilic polymer conetwork (APCN) hydrogels that can self-organize in water into well-ordered lamellar structures. The cross-linking of hydrophobic and hydrophilic polymer segments produces networks that typically exhibit sphere-like nanodomains in water and in the bulk, but the orderly interconnection of relatively large and highly incompatible polymers leads to hydrogels that internally assemble into lamellae. This unprecedented result may be attributed to the weak force-field established by the presence of a minimal concentration of homogeneously distributed cross-links in the case of the present system, which must be contrasted to a higher concentration of randomly placed cross-linking points, which destroy long-range ordering in conventional APCN hydrogels. Significantly, the presently developed APCN hydrogels maintain good tensile mechanical properties, with their strain-at-break reaching a value of 800%. This study puts forward the design concepts for attaining highly ordered hydrogels, which would confer upon them better transport and mechanical properties and broaden their utility in biomedical and energy applications.
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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) | 0.000 | 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 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".