A Graphene Nanoribbon Electrode-Based Porphyrin Molecular Device for DNA Sequencing
Bibliographic record
Abstract
We propose a DNA nucleobase sequencing device composed of zigzag graphene nanoribbon electrodes connected with a porphyrin molecule via carbon chains (GEPM). The connecting geometry between the nanoribbons with an even width number and the carbon chains is laterally symmetric to filter out electrons of specific modes. Various properties of the GEPM and of the GEPM + nucleobase systems, such as interaction energies, charge density differences, spin-differential electronic densities, and electric currents, are investigated using the density functional theory (DFT) combined with the non-equilibrium Green’s function (NEGF) method. The results show that the GEPM device holds promise for DNA sequencing with the measurement of the electric signals through it. The four nucleobases—adenine (A), cytosine (C), guanine (G), and thymine (T)—can be efficiently distinguished based on the conductance and current sensitivity when they are located on the porphyrin molecule of the GEPM device. The symmetry of the connecting geometry between the carbon chains and the nanoribbons selects Bloch states with specific symmetry to pass through the device and results in broad transmission valleys or gaps. In addition, the edge magnetism of graphene nanoribbons can further manipulate the transmission and then the sequencing effects. The device exhibits extremely high conductance sensitivity in the parallel magnetic configuration. This study explores the possible advantage of this technology compared with conventional nanopore sequencing devices and potentially expands the variety of available sequencing structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".