(Invited) Spectroscopy and Photocurrents in All-Carbon Molecular Electronic Devices
Bibliographic record
Abstract
One or both contacts in all-carbon molecular junctions can be sufficiently transparent to permit optical spectroscopy as a probe of device structure, and photocurrent generation to investigate photon-induced electron transport. Current-voltage (JV) curves for aromatic molecules depends strongly on molecular structure when molecular layers are more than 5 nm thick, 1 and photocurrents were used to determine structural factors determining JV behavior. 2 As shown in the left figure below, the photocurrent spectrum tracks the molecular absorption spectrum determined directly in the completed molecular junction. A molecular bilayer consisting of an electron donor and acceptor layers yields significantly higher photocurrents, which depend on the order of the layer deposition (right image below). 3 The use of photocurrents and Raman spectroscopy for characterization of molecular junction structure and operation will be discussed. 4 (1) Morteza Najarian, A.; McCreery, R. L.; Structure Controlled Long-Range Sequential Tunneling in Carbon-Based Molecular Junctions; ACS Nano 2017, 11, 3542. (2) Morteza Najarian, A.; Bayat, A.; McCreery, R. L.; Orbital Control of Photocurrents in Large Area All-Carbon Molecular Junctions; Journal of the American Chemical Society 2018, 140, 1900. (3) Smith, S. R.; McCreery, R.; Photocurrent, Photovoltage and Rectification in Large-Area Bilayer Molecular Electronic Junctions submitted 2018. (4) Supur, M.; Smith, S. R.; McCreery, R. L.; Characterization of Growth Patterns of Nanoscale Organic Films on Carbon Electrodes by Surface Enhanced Raman Spectroscopy; Analytical Chemistry 2017, 89, 6463. Figure 1
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".