DFT investigation of 5-fluorouracil interactions with aluminum and gallium-doped SWCNTs (8,8): enhanced drug delivery potential
Bibliographic record
Abstract
Carbon nanotubes (CNTs) have gained considerable attention in drug delivery applications due to their high surface area and exceptional electronic and mechanical properties. Doping CNTs with metals like aluminum (Al) and gallium (Ga) can modify their electronic, chemical, and stability characteristics, enhancing their suitability for drug delivery. This study investigates the interaction of the drug molecule 5-fluorouracil (5-FU) with pristine and doped (8,8) CNTs using density functional theory (DFT) calculations with the B3LYP functional and the 6-31 G basis set. The results reveal significant changes in the electronic properties after doping. Aluminum doping increases the energy gap, reducing system reactivity, while gallium doping decreases the energy gap, promoting stronger interactions with the drug. Aluminum doping improves the structural stability of CNTs with 5-FU, whereas gallium enhances the chemical potential and hardness, favoring interactions with the drug molecule. In conclusion, aluminum- and gallium-doped CNTs exhibit different behaviors in drug interactions, allowing for optimization of targeted drug delivery systems. Based on energy gap, chemical potential, and chemical hardness values, both doping types can enhance drug delivery properties, with the specific doping choice dependent on the therapeutic application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".