Carbon Dots Infused 3D Printed Cephalopod Mimetic Bactericidal and Antioxidant Hydrogel for Uniaxial Mechano‐Fluorescent Tactile Sensor
Bibliographic record
Abstract
Cephalopods use stretchy skin and dynamic color-tuning organs for visual communication and camouflage. Inspired by these natural mechanisms, a fluorescent biomaterial for deformation-induced illumination and optical communication is proposed. This is the first report of 3D printed soft biomaterials infused with carbon dots hydrothermally derived from chitosan and benzalkonium chloride. These biomaterials exhibit a comprehensive array of properties, including significant uniaxial stretching, near-instantaneous response to tactile stimuli and pH, UV resistance, antibacterial, antioxidant, noncytotoxicity, and highlighting their potential as mechano-optical materials for biomedical applications. The hydrogel's durability is evaluated by cyclic stretching, folding, rolling, and twisting tests to ensure its integrity and good signal-to-noise ratio. The diffusion mechanism is determined by water imbibition kinetics, network parameters, and time-dependent breathing. Overcoming the common limitations of short lifespans and complex manufacturing processes in existing soft hybrids, this work demonstrates a straightforward method to produce durable, energy-independent, mechano-optical hydrogel. Combined with investigations, molecular dynamic modeling is used to understand the interactions of hydrogel components.
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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".