Electronic properties of metal-molecular nanojunctions and networks
Bibliographic record
Abstract
Electronics based on individual molecules is often considered the ultimate form of miniaturization for future "beyond CMOS" technologies and hybrid integrated circuits. In this work, we investigate nanoscale metal-molecular junctions and networks composed of interconnected molecules and metallic clusters. Molecular modeling via Austin Model 1 (AMI) semi-empirical methods is used to study the electronic properties of several classes of metal-molecular nanojunctions and networks, including linear chains and multi-terminal networks. The HOMO (highest occupied molecular orbital)-LUMO (lowest unoccupied molecular orbital) gaps of the molecular systems decrease by several eV after the introduction of Al clusters. Molecular orbitals near the HOMO-LUMO gap of benzenedithiol molecular networks show good delocalization whereas those composed of alkanedithiol molecules were mainly localized to the metallic clusters. In addition, it was found that the frontier orbital level spacing decreased as the size of the molecular networks increased, approaching band formation for the largest structures studied. The HOMO-LUMO gap was also found to decrease with increasing network size while both HOMO and LUMO level shifts for larger structures indicated a decreased barrier to electron transport. These results provide an avenue for engineering electronics at the molecular level by using superstructures of different molecules and topologies.
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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 | 0.000 | 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) | 0.001 | 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".