Borophene Based 3D Extrusion Printed Nanocomposite Hydrogel for Antibacterial and Controlled Release Application
Bibliographic record
Abstract
Abstract Herein, a borophene/zinc oxide (BZ) nanocomposite is synthesized through a straightforward one‐step solvothermal process, avoiding the need for any rigorous reducing agent. The BZ nanocomposites are introduced into semi‐interpenetrating polymer networks to form hydrogels via in situ UV triggered free radical gelation during three‐dimensional (3D) microextrusion printing. The hydrogels exhibit mechanical robustness, high compressibility, pH sensitivity, and microporosity. The diffusion behavior of the hydrogel shows a combination of swelling and molecular chain relaxation based on its water uptake kinetics. Hydrogels are tested in rigorous pH environments over multiple cycles to ensure structural integrity. The rheological assessment of the hydrogels proves their high elasticity. The uniaxial mechanical properties support its mechanical toughness and zero permanent set resulting elastomeric soft matrix. The cyclic compression test up to 100 cycles has negligible data deviations in calculating compression moduli (≈24 kPa). The hydrogels are non‐toxic and found to be effective bactericidal materials for both Gram‐positive and Gram‐negative bacteria. The hydrogel demonstrates pH‐responsive time‐dependent payload release behavior, suggesting its potential as a soft matrix drug carrier in biomedical research. To the best of the authors knowledge, this is the first report of BZ‐based soft biomaterial with antibacterial properties serving as an excellent controlled drug delivery device.
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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".