Design and optimization of nanowire betavoltaic generators
Bibliographic record
Abstract
A model used to simulate and optimize the performance of nanowire-based betavoltaic generators is developed. The optimum nanowire array geometry is established for devices made of silicon, gallium arsenide, and gallium phosphide for both nickel-63 and tritium sources by computing the energy capture efficiency for each case. The captured power for nanowire devices is shown to be drastically greater compared to planar betavoltaic generators with maximum improvement factors of approximately 7, 3, 5, and 9 for devices utilizing radioisotope sources of nickel-63, nickel citrate, tritium, and tritiated butyl, respectively. In addition, nanowire devices do not suffer from self-shielding effects, a large limitation in conventional, planar betavoltaics. By coupling the spatial distribution of electron–hole pair generation rate calculated from Monte Carlo simulations and a semiconductor charge-transport model, the diode design is optimized for the maximum power output. The top performing devices, utilizing a tritium source, exhibited an output power of approximately 4, 6, and 2 μW cm−2 for silicon, gallium arsenide, and gallium phosphide, respectively. Overall device efficiencies were found to range from 4% to 10%, surpassing several betavoltaic devices reported in the literature. It was also found that, contrary to the traditional betavoltaic design, semiconductors with higher bandgaps do not necessarily result in the best device performance due to additional material parameters, such as surface recombination velocity. Potential improvements for nanowire-based betavoltaic generators are suggested for additional investigation.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".