Injectable, Self-Healing Hydrogel with Tunable Optical, Mechanical, and Antimicrobial Properties
Bibliographic record
Abstract
Injectable self-healing hydrogels, as implanted materials, have received great attention over the past decades. The tunable optical and mechanical properties as well as the ability to lower the risk of inflammatory responses are essential considerations for their applications in diverse bioengineering processes. In this work, we report a novel injectable self-healing hydrogel with tunable optical, mechanical, and antimicrobial properties, fabricated by a multifunctional ABA triblock copolymer gelator, poly{(4-formylphenyl methacrylate)-co-[[2-(methacryloyloxy)ethyl] trimethylammonium chloride]}-b-poly(N-isopropylacrylamide)-b-poly{(4-formylphenyl methacrylate)-co-[[2-(methacryloyloxy)ethyl] trimethylammonium chloride]} and polyethylenimine. The self-healing capability of the hydrogel was demonstrated by rheology tests, and quantitative force measurements using a surface forces apparatus (SFA) provided molecular insights into the self-healing mechanism of Schiff base reaction. Additionally, the optical and mechanical properties of the hydrogel can be fine-tuned in a sensitive temperature-responsive manner because of the local nano-hydrophobic domains formed through the phase transition of the ABA triblock copolymer gelator. The hydrogel also demonstrated multiple sol–gel transitions subjected to pH change. Moreover, the hydrogel can also effectively inhibit the growth of both Gram-negative and Gram-positive bacteria (Escherichia coli and Staphylococcus aureus), while showing low cytotoxicity to both fibroblast and cancer cells (MRC-5 and HeLa). The novel multifunctional injectable self-healing hydrogel with tunable optical, mechanical, and excellent antimicrobial properties shows great potential in various bioengineering 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 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 | 0.000 |
| 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".