Nanostructured Thermionics for Conversion of Light to Electricity: Simultaneous Extraction of Device Parameters
Bibliographic record
Abstract
Thermionic conversion involves the direct conversion of heat, including light-induced heat, from a heat source, e.g., solar energy, to electricity. Although the concept is almost a hundred years old, the progress of thermionic convertors has been limited by issues such as the space-charge effect and availability of materials with desirable mechanical and electrical properties, while maintaining a low work function. Nanotechnology could help address some of the main challenges that thermionic convertors face. However, existing models, which were developed for macroscopic convertors, are not capable of describing all aspects of nanostructured devices. We present a method to evaluate the output characteristics of thermionic convertors with a higher precision than the existing models and the ability to simulate a broader range of parameters, including temperatures, active surface areas, interelectrode distances, and work functions. These features are crucial for the characterization of emergent devices due to the unknowns involved in their internal parameters; the model's high numerical precision and flexibility allows one to solve the reverse problem and to evaluate the internal parameters of the device from a set of simple experimental data. As an experimental case, a carbon nanotube forest was used as the emitter and locally heated to thermionic emission temperatures using a 50-mW-focused laser beam. The current-voltage characteristics were measured and used to solve the reverse problem to obtain the internal parameters of the device, which were shown to be consistent with the values obtained using other methods.
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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 | 0.000 | 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".