Electromechanically actuating molecules
Bibliographic record
Abstract
Controlled motion at the nanoscale is an emerging avenue for low powered electronics. The necessity for precision at the nanoscale makes organic chemistry an exciting addition to electronics, as organic synthesis is based upon the design and creation of nanoscale and sub-nanoscale structures. We have recently demonstrated the role of organic materials in the development of a nanoelectromechanical (NEM) switch that operates by electromechanical modulation of tunneling current through a switching gap defined by a few nanometer-thick organic molecular layer sandwiched between conductive contacts [1]. In this device, the molecular layer not only facilitates controlled formation of nanoscale switching gaps, but also avoids direct contact of the electrodes to minimize surface adhesion and provides force control at the nanoscale to prevent device failure due to stiction. Recent work has focused on the compression of the molecular layer by an applied electrostatic force between the two electrodes to reduce the tunneling gap. However, we envision next generation devices can contain advanced materials, which undergo electrochemically stimulated shape changes to modulate the tunneling distance and current. In order to achieve large current on-off ratios, the molecules must be capable of producing significant changes in dimension or shape upon electrical stimuli. Herein, we report a few examples of electromechanically actuating molecules.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".