Low-pressure plasma-enhanced behavior of thermionic converters
Bibliographic record
Abstract
High-pressure plasmas have historically been used in thermionic energy converters both to reduce the electrode workfunctions and to mitigate the space-charge effect. The behavior of such devices has been studied extensively, but low-pressure thermionic converters are far less understood. Advances in nanotechnology, such as the possibility to intercalate nanomaterials-based electrodes with alkali metals in order to reduce workfunction, may alleviate the need for high gas pressures; low-pressure devices may thus play a significant role in future if they can address the space-charge problem. Here, we develop the physics of low-pressure thermionic converters by solving the Vlasov-Poisson system of equations self-consistently. We demonstrate that various possibilities arise due to intricate interactions between the spatially varying electron and ion concentrations, leading to phenomena such as plasma oscillations at higher ion fluxes. We show that even a relatively low ion flux density (∼5×10−4 times the flux density of electrons) reduces space-charge significantly and increases the electron current density by a factor of 7. We further extend the model by including electron and ion emission from both the cathode and anode electrodes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".