Genetic Algorithm Optimization of Core-Shell Nanowire Betavoltaic Generators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Numerical optimization has been used to determine the optimum junction design for core–shell nanowires used in betavoltaic generators. A genetic algorithm has been used to calculate the relative thickness, height, and doping of each segment within silicon, gallium arsenide, and gallium phosphide nanowires. Using the simulated spectra and energy deposition of nickel-63, nickel citrate, tritium, and tritiated butyl, devices with power output and overall efficiency up to 8 µ W.cm −2 and 12%, respectively, have been predicted. Compared to previously investigated axial nanowires, the core–shell structures simulated here have realized drastic improvements by reducing surface recombination for longer nanowires. In addition, core–shell nanowires are shown to be capable of nearly matching the ideal performance predicted for this device structure. A new approach for calculating the practical upper limit of betavoltaic performance is presented and additional methods for improvement are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 it