Designing Injectable Hydrogel Biomaterials with Highly-Tunable Properties
Bibliographic record
Abstract
Chemically cross-linked hydrogels (chemical gels) offer a number of enhanced properties over their physical counterparts, particularly in biomedical applications such as drug delivery, tissue engineering, and cell encapsulation. Conventional chemical gels are generally too elastic to be introduced into the body without requiring surgical implantation, making them challenging to use in a clinical context. In response, this thesis is focused on developing injectable analogues of conventional hydrogel-based biomaterials as well as advanced, engineered injectable hydrogels, enabling the facile use of these hydrogels in biomedical applications. Cross-linking is achieved using hydrazone chemistry, in which one precursor is functionalized with aldehyde groups and the other is functionalized with hydrazide groups. Following coextrusion of the reactive precursors, a stable hydrogel network spontaneously forms within seconds. By employing this chemistry as a standard in all of this work, a number of injectable hydrogel systems with well-defined properties (including swelling, drug loading and release, optical properties, gel formation and degradation kinetics, response to the temperature of the surrounding environment, and tissue response) have been generated that can be tuned by rationally varying the charge content in the precursor polymers, the number of cross-linking functional groups used, the reactivity of the electrophilic cross-linking units, and the length and number of hydrophobic affinity domains present within the gels. This work therefore presents a series of independent methods for customizing hydrogels so that they may be adapted to a number of different biomedical applications.
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.
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 | 0.001 |
| 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".