Systematic study of low energy geometries of copper nano-junctions exposed to water and to species that can result from dissociation of water
Bibliographic record
Abstract
Abstract A detailed computational analysis has been performed, considering copper atomic contacts that are exposed directly to water molecules, hydroxyl groups, and monatomic as well as molecular hydrogen and oxygen species. The optimized physical bonding structure, electrical conductance and inelastic tunneling spectra (IETS) have been determined theoretically for moderately large structures by performing appropriate ab initio and semi-empirical calculations. By considering the aforementioned properties, it has been possible to determine that some of the molecular bridging structures may be regarded as being highly-probable outcomes, resulting from the exposure of copper electrodes to the atomic/molecular contaminants. We specifically identify the conductance properties of a variety of configurations including examples with very high and very low conductance values. This is done in order to identify junction geometries that may be realized experimentally and their conductance and IETS signatures. By reporting geometries with very high and very low conductance values here, we intend to provide a wider perspective view than previous studies of copper-molecular junctions that have focused on high conductance structures. In addition, we explore the properties of metal junctions with multiple molecules, a class of systems for which little theoretical work has been available in the molecular electronics literature. We find that water molecules surrounding the junction can influence the bonding geometry of the molecules within the junction and consequently can affect strongly the calculated conductances of such junctions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".