High-purity single-molecule modification of carbon nanotubes by stochastic distribution of DNA
Bibliographic record
Abstract
Single-walled carbon nanotubes (SWCNTs) show promise for probing molecular interactions at single-molecule resolution, but precise and uniform surface modifications needed for single-molecule SWCNT applications remain challenging due to the stochastic nature of chemical reactions. This study presents a batch-scale synthesis and separation of single-molecule modified SWCNTs, leveraging the stochastic distribution of single-stranded DNA (ssDNA) on SWCNTs and biology-inspired separation strategies. Specifically, SWCNTs were dispersed with ssDNA mixed with a small fraction of decorated ssDNA (m-ssDNA). We developed a stochastic ssDNA-SWCNT binding model to predict the distribution of ssDNA and m-ssDNA on SWCNT at distinct mixing ratios and successfully implemented our model to produce SWCNT nanoparticles predominately containing only one m-ssDNA tag. These singly modified SWCNTs were isolated from unmodified SWCNTs using magnetic bead exploiting biotin-streptavidin interactions, achieving 0.05 μg yield at 0.25% m-ssDNA ratio, corresponding to 97.6% purity of singly modified populations. Finally, the single-molecule modification at the predicted m-ssDNA ratios was confirmed using a fluorophore as a model, enabling precise determination of SWCNT molar concentration. Ultimately, our approach provides a batch-scale method for incorporating a single molecular tag per SWCNT, supporting diverse future applications in SWCNT-based nanotechnologies.
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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) | 0.000 | 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".