Photolithographically assembled polyelectrolyte complexes as shape-directing templates for thermoreversible gels
Bibliographic record
Abstract
Preparation of soft materials with diverse, customized shapes has been a topic of intense research interest. To this end, we have recently demonstrated photolithographic directed assembly as a strategy for customizing polyelectrolyte complex (PEC) shape. This process uses in situ photopolymerization of an anionic monomer in the presence of a cationic polymer, which drives localized PEC formation at the irradiation sites. Here, we show how such photolithographically assembled PECs can serve as structure-directing templates for tailoring the shapes of other soft materials; namely, thermoreversible gels. These templated hydrogels are prepared by adding a thermogelling polymer (agarose) to the anionic monomer/cationic polymer/photoinitiator precursor solutions so that, upon irradiation, custom-shaped PECs form within agarose gel matrices. Once these PECs are formed, the surrounding agarose gels are melted (through heating) and washed away which, upon returning the samples to room temperature, produces interpenetrating PEC/agarose gel networks with photopatterned shapes and dimensions. Dissolution of these sacrificial PEC templates in concentrated NaCl solutions then generates photolithographically templated agarose gels, whose shapes and dimensions match those of their PEC templates. Besides tuning their shapes and sizes, the mechanical properties of these gels can be easily tailored by varying the initial agarose concentrations used. Moreover, this PEC-templated gel synthesis appears to not adversely affect hydrogel cytocompatibility, suggesting its potential suitability for biological and biomedical applications. Though the present study uses only agarose as the model gel system, this PEC-based strategy for customizing gel shape can likely also be applied to other thermoreversible gel networks (e.g., those based on methylcellulose, poloxamers or thermoresponsive chitosan derivatives) and could have many attractive applications, ranging from drug delivery and tissue engineering, to sensing and soft robotics.
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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 | 0.001 |
| 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".