Supramolecular Zwitterionic Hydrogels for Information Encryption, Soft Electronics and Energy Storage at Icy Temperature
Bibliographic record
Abstract
Abstract Zwitterionic hydrogels have attracted intensive attentions for their exceptional hydration and anti‐fouling properties, while the exploration for rational structural designs to achieve multi‐function and superior anti‐freezing capability remains an ongoing mission. Herein, a new multi‐functional zwitterionic monomer (ACHPES) is designed and developed from which novel zwitterionic hydrogels are fabricated. The pyridinium‐sulfonate zwitterionic motif endows the hydrogel with discoloration in both visible light and fluorescence in response to pH stimulus, enabling unique multi‐channel information encryption and highly sensitive Cu2+ detection with a detection threshold of 0.64 ppm. The design of carbamate group fulfils hydrogel high bonding capacity with water, leading to superior anti‐freezing capability. As such, the hydrogels exhibit an extremely low water freezing point of −47.3 °C and a high breaking elongation of ≈500% at −20 °C. The outstanding anti‐freezing capability enables hydrogel sensors and electrolytes an excellent operational performance at freezing temperatures. This innovative design strategy offers a new avenue for functionalization and application of zwitterionic hydrogels at icy temperature.
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".