Simulation and optimization of current generation in gallium phosphide nanowire betavoltaic devices
Bibliographic record
Abstract
The geometry of a gallium phosphide nanowire (NW) array has been optimized for maximum current generation in a betavoltaic (BV) device. The energy capture efficiency for various device geometries with different radioisotope source compounds was calculated in GEANT4. A validation of GEANT4 for BV device simulation was performed by comparing a model output with the available bulk semiconductor BV performance data, followed by predictions of the performance of NW-based devices. The pitch and the diameter of the NWs were found to have the most significant impact on the β-generated current density, with the optimum diameter-to-pitch ratios ranging from 0.55 to 0.8, depending on the source. The energy capture efficiency improved when low energy beta (β) emitters were used. For devices utilizing 63Ni source compounds, the β-generated current densities approached 0.95 μA cm−2, representing an improvement by a factor as high as 5.8 compared to planar devices. In the case of 3H source compounds, the generated current density was 3.05 μA cm−2, a factor of 15.5 larger than comparable planar devices. However, NW devices utilizing sources with a higher decay energy, such as 147Pm, did not demonstrate any improvements over planar geometries. Using the results for optimum NW geometries, NW-based or other nanostructured devices could be made to surpass the present commercial BV batteries.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".