Electron transport in all-carbon molecular electronic devices
Bibliographic record
Abstract
Carbon has always been an important electrode material for electrochemical applications, and the relatively recent development of carbon nanotubes and graphene as electrodes has significantly increased interest in the field. Carbon solids, both sp(2) and sp(3) hybridized, are unique in their combination of electronic conductivity and the ability to form strong bonds to a variety of other elements and molecules. The Faraday Discussion included broad concepts and applications of carbon materials in electrochemistry, including analysis, energy storage, materials science, and solid-state electronics. This introductory paper describes some of the special properties of carbon materials useful in electrochemistry, with particular illustrations in the realm of molecular electronics. The strong bond between sp(2) conducting carbon and aromatic organic molecules enables not only strong electronic interactions across the interface between the two materials, but also provides sufficient stability for practical applications. The last section of the paper discusses several factors which affect the electron transfer kinetics at highly ordered pyrolytic graphite, some of which are currently controversial. These issues bear on the general question of how the structure and electronic properties of the carbon electrode material control its utility in electrochemistry and electron transport, which are the core principles of electrochemistry using carbon electrodes.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".