Characterization of the internal parameters of nanostructured light induced thermionic emission devices for energy conversion
Bibliographic record
Abstract
We propose a method to calculate the output current-voltage characteristics of a light induced thermionic emission device. This approach improves on the existing methods by having both a higher precision and higher range in evaluating the associated integrals, resulting in simulated device characteristics with a wider range of parameters. This method represents a significant step towards the characterization of emergent devices due to the unknowns involved in their internal parameters. More importantly, its high numerical precision and flexibility allow one to solve the reverse problem and evaluate the internal parameters of the device such as workfunction, from experimental current-voltage curves. As an experimental case, a carbon nanotube forest was used as the emitter of a thermionic device and locally heated to about 2,000 K using a 50-mW focused laser light. The current-voltage characteristics were measured and fitted to simulation data to obtain the internal parameters of the device. The obtained parameters were consistent with the values obtained with other methods. The estimation of these parameters was previously not feasible with one single type of experiment.
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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 | 0.001 |
| 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 | 0.000 |
| 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".