Super-anti-freezing, tough and adhesive titanium carbide and L-ornithine-enhanced hydrogels
Bibliographic record
Abstract
Hydrogels are highly porous three-dimensional crosslinked polymer networks consisting of hydrophilic polymers, employed most practically in medicine and industry, often as biosensors. Simple hydrogels suffer limitations in their mechanical properties, such as tensile and compression, and freeze at sub-zero temperatures, which compromise their ability as useful biosensors. In this study, the incorporation of L-ornithine-based zwitterionic monomer (OZM), titanium carbide (MXene), and glycerol within polyacrylamide hydrogels was used to prepare a novel polyacrylamide/polyL-ornithine-based zwitterion/MXene (PAM/Porn/MXene) hydrogel to improve the mechanical, adhesion, and anti-freezing properties of pure polyacrylamide hydrogels. This study also analyzed the mechanical strength (tensile and compression), adhesion, and anti-freezing properties of a novel PAM/Porn/MXene hydrogel at 1%, 4%, and 10% MXene concentrations to establish to what extent the conductive MXene material enhanced these properties and concluded that the tensile and compressive properties improved linearly with the increase in the concentrations of MXene, adhesion decreased with the increased MXene concentrations, and synergistic interaction between MXene and OZM significantly improved the anti-freezing properties up to –80 °C.
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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) | 0.000 | 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 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".