A QM:MM model for the interaction of DNA nucleotides with carbon nanotubes
Bibliographic record
Abstract
Hybrid materials formed by DNA and carbon nanotubes (CNTs) have shown very interesting properties, but their simulation in solution using quantum mechanical approaches is still a challenge in the computational chemistry community. In this paper, we developed a QM:MM model to study the interactions between charged DNA nucleotides and carbon nanotubes in solution. All four types of DNA nucleotides were taken to interact with two CNTs of similar diameter but different chiralities: (4,4) and (7,0). The nucleotides and CNTs were treated at the QM level, while added water and neutralizing ions were modeled at the MM level. ONIOM simulations were performed at the (M06-2X/6-31G(d):Amber) level for the hybrids, as well as for individually solvated CNTs and nucleotides, which allowed us to evaluate the energy of binding. Our binding energy (BE) values range from 146.60 to 503.43 kJ mol(-1), indicating strong physisorption of nucleotides on CNTs. The relatively large BE, compared with past studies on nucleobase-CNT binding in a vacuum, could be due to the larger size of nucleotides compared with nucleobases, the charges on the nucleotides, and the inclusion of solution which causes the release of water molecules upon hybridization.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".