Multifunctional 3D Hierarchical Bioactive Green Carbon-Based Nanocomposites
Bibliographic record
Abstract
Carbon-based nanocarriers such as multiwall carbon nanotubes (MWCNTs) and reduced graphene oxide (rGO) have shown promising delivery capabilities due to their low immunogenicity, superior internalization, and suitable cell penetration efficiency. Herein, a molecular engineering strategy is advanced for the one-pot synthesized rGO/MWCNT/Fe 3 O 4 /ZnO to enhance the stability of the nanocarrier in the biological matrix; green synthesized ZnO was responsible for water uptake and reduced cytotoxicity, while Fe 3 O 4 controlled the cellular internalization for gene delivery. Surface morphology of the ensuing nanocomposite was correlated with photocatalytic and gene delivery (CRISPR/Cas9) features. For the first time, a complete physical interaction between CRISPR/Cas9 and nanomaterial is evidenced via atomic force microscopy (AFM), demonstrating an increase in green fluorescence protein (EGFP) up to 11%. Furthermore, the enhanced photocatalytic activity is displayed in complete degradation of the methylene blue dye under 10 min with an efficiency of over 98%. The cytotoxicity of the nanocomposite is enhanced by ZnO on treatment with the PC12 and HEK-293 cell lines subsequent to 24, 48, and 72 h of exposure, with more than 88, 79, and 80% cell viabilities for PC12 and more than 88, 80, and 85% cell viabilities for HEK-293 in the maximum ratio of material to CRISPR (W R of nM/CC being 100). Furthermore, the nanocomposite showed an antibacterial activity against both Staphylococcus aureus and Escherichia coli bacteria (MZI values of 24 and 21 mm, respectively). The surface chemistry of the optimized system opens up new prospects to codeliver therapeutic agents for useful clinical applications.
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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".