<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>characteristics and differential conductance fluctuations of Au nanowires
Bibliographic record
Abstract
Electronic transport properties of the Au nanostructure are investigated using both experimental and theoretical analysis. Experimentally, stable Au nanowires were created using a mechanically controllable break junction in air, and simultaneous current-voltage $(I\ensuremath{-}V)$ and differential conductance $\ensuremath{\delta}I/\ensuremath{\delta}V$ data were measured. The atomic device scale structures are mechanically very stable up to bias voltage ${V}_{b}\ensuremath{\sim}0.6 \mathrm{V}$ and have a lifetime of a few minutes. Facilitated by a shape function data analysis technique which finger prints electronic properties of the atomic device, our data show clearly differential conductance fluctuations with an amplitude $>1%$ at room temperature and a nonlinear $I\ensuremath{-}V$ characteristics. To understand the transport features of these atomic scale conductors, we carried out ab initio calculations on various Au atomic wires. The theoretical results demonstrate that transport properties of these systems crucially depend on the electronic properties of the scattering region, the leads, and most importantly the interaction of the scattering region with the leads. For ideal, clean Au contacts, the theoretical results indicate a linear $I\ensuremath{-}V$ behavior for bias voltage ${V}_{b}<0.5 \mathrm{V}.$ When sulfur impurities exist at the contact junction, nonlinear $I\ensuremath{-}V$ curves emerge due to a tunneling barrier established in the presence of the S atom. The most striking observation is that even a single S atom can cause a qualitative change of the $I\ensuremath{-}V$ curve from linear to nonlinear. A quantitatively favorable comparison between experimental data and theoretical results is obtained. We also report other results concerning quantum transport through Au atomic contacts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.174 | 0.044 |
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".