Mechanofluorescent Double Network Ionogels
Bibliographic record
Abstract
Mechanofluorescent gels, which are soft and capable of altering their fluorescent color in response to the applied external force, have garnered significant attention for applications spanning failure alert, crack visualization, information encryption, and adaptive camouflage. Most of the mechanofluorescent gels are obtained in the form of hydrogels, due to their facile preparation and diverse material design. However, the hydrogels suffer from compromised stability under elevated temperatures or desiccating conditions due to water evaporation. To tackle this challenge, we develop a mechanofluorescent double network (DN) ionogel that shows low volatility, high stress sensitivity, and excellent mechanical property. The ionogel is composed of two types of interpenetrating polymer networks and immobilized ionic liquids (IL) that can effectively swell the polymer chains. The resulting ionogel exhibits a reversible color change. Additionally, it demonstrates exceptional mass retention with a weight loss less than 1.5% over 15 days even at 80 °C. The mechanical property and mechanofluorescent sensitivity can be tailored via IL and mechanofluorophore content. The combination of double network structure and IL solvent overcomes the environmental instability bottleneck of mechanofluorescent gels and establishes a versatile platform for developing flexible fluorescent stress sensors.
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 | 0.000 |
| 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".