Progress with Molecular Electronic Junctions: Meeting Experimental Challenges in Design and Fabrication
Bibliographic record
Abstract
Molecular electronics seeks to incorporate molecular components as functional elements in electronic devices. There are numerous strategies reported to date for the fabrication, design, and characterization of such devices, but a broadly accepted example showing structure-dependent conductance behavior has not yet emerged. This progress report focuses on experimental methods for making both single-molecule and ensemble molecular junctions, and highlights key results from these efforts. Based on some general objectives of the field, particular experiments are presented to show progress in several important areas, and also to define those areas that still need attention. Some of the variable behavior of ostensibly similar junctions reported in the literature is attributable to differences in the way the junctions are fabricated. These differences are due, in part, to the multitude of methods for supporting the molecular layer on the substrate, including methods that utilize physical adsorption and covalent bonds, and to the numerous strategies for making top contacts. After discussing recent experimental progress in molecular electronics, an assessment of the current state of the field is presented, along with a proposed road map that can be used to assess progress in the future.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".