Long Carbon Nanotubes Functionalized with DNA and Implications for Spintronics
Bibliographic record
Abstract
Helical molecules such as DNA have recently been found to behave as an efficient source and detector of spin-polarized charge carriers. This phenomenon, often dubbed as chirality-induced spin selectivity or CISS, could be used to significantly improve the performance of spintronic devices, which utilize carrier spins (rather than charge) to realize electronic and sensing functions. Recently, it has been reported that carbon nanotubes, helically wrapped with DNA, can also act as an efficient source and detector of spin-polarized carriers, by virtue of spin-orbit coupling originating from the helical potential. It has been postulated that spin polarization should increase with the length of the wrapped tubes. However, in literature, most fabrication processes yield tubes with submicron lengths, which can produce ∼70% spin polarization. In an effort to enhance this effect further, here, we report a fabrication process that can yield DNA-wrapped nanotubes of length ∼1-4 microns. Detailed characterization of these devices, using atomic force microscopy, Raman, UV-vis, and temperature-dependent transport, has been presented. Initial transport measurements indicate the presence of strong magnetoresistance in these tubes, which could be attributed to spin-dependent effects. Systematic fabrication of long DNA-wrapped nanotubes, which has hitherto not been reported, is expected to enable further investigation into the spin-dependent properties of these ultimate one-dimensional nanoscale hybrids and may have a significant impact on nanoscale spintronics.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".