Conductance,<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>I</mml:mi><mml:mo>−</mml:mo><mml:mi>V</mml:mi></mml:math>curves, and negative differential resistance of carbon atomic wires
Bibliographic record
Abstract
We report on a first-principles analysis of transport properties of carbon atomic wires in contact with two metallic electrodes under external bias. The equilibrium conductance of the atomic wires is found to be sensitive to two factors: charge transfer doping, which aligns the Fermi level of the electrodes to the lowest unoccupied molecular orbital (LUMO) of the carbon chain, and the overlapping of scattering states to the LUMO. The conductance is also affected by the crystalline orientation of the electrodes. The low-bias current-voltage $(I\ensuremath{-}V)$ characteristic is linear, but a negative differential resistance is observed at higher bias due to a shift of conduction channels relative to the states of the electrodes by the external bias potential. Our first-principles results give a clear physical picture of the molecule-electrode coupling, which is the controlling factor of electric conduction through the carbon atomic wires.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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".