Multistimuli-Responsive Conductive Hydrogels for Information Encryption, Decryption, and Wearable Sensors
Bibliographic record
Abstract
Stimuli-responsive hydrogels have emerged as one of the most promising candidate materials in the fields of bionic materials and artificial intelligence due to their unique stimulus responsiveness and dynamic reversibility. To overcome the limitations of single-response factors and poor responsiveness in existing stimuli-responsive hydrogels, an ionically conductive hydrogel with excellent transmission properties and multistimuli-responsive information encryption and decryption at room temperature was developed. A temperature/ethanol/salt stimuli-responsive conductive hydrogel (PPSD) was synthesized by a one-pot method in a salt solvent, using SDS, NIPAm, PVA, and SGA 15 as raw materials. By incorporating SDS, known for its low-temperature sensitivity, together with a high-temperature-sensitive monomer featuring contrasting hydrophilic and hydrophobic structures, rapid dual-temperature responsiveness was achieved through intermolecular interactions at varying temperatures. When exposed to external solvents, the original intermolecular interactions were disrupted, causing shrinkage and aggregation among polymer chains, which, in turn, modified light scattering properties. This mechanism enabled ethanol and salt stimuli-responsiveness, evident through observable color changes. The directional migration of free ions in the PPSD hydrogel endowed it with excellent sensing capabilities.
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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 | 0.000 |
| 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".