ELECTROPLATING NICKEL ONTO GALLIUM ARSENIDE AND GALLIUM PHOSPHIDE NANOWIRES FOR BETAVOLTAIC APPLICATIONS
Bibliographic record
Abstract
Betavoltaic (BV) devices represent a promising alternative energy technology, offering long-lasting, maintenance-free power for applications in remote, harsh, or inaccessible environments. Their performance is often limited by self-absorption of beta particles and inefficient carrier collection in conventional planar geometries. To address these challenges, this work investigates the conformal electroplating of nickel (Ni), and ultimately radioisotope nickel-63 in the future, onto gallium arsenide (GaAs) and gallium phosphide (GaP) NWs for use in BV devices. A systematic evolution of electroplating cell designs - from a simple beaker configuration to a custom Teflon cell - was carried out to optimize uniformity, reproducibility, and current efficiency. Direct current (DC) and pulsed electroplating methods were evaluated across NW arrays of varying pitch (360 nm, 600 nm, and 1000 nm). Results demonstrate that pulsed electroplating significantly mitigates mass diffusion limitations compared to DC plating, improving conformality along NW sidewalls. Optimal plating conditions were found to depend strongly on the interplay between on-time, off-time, and lateral diffusion times within NW arrays. These findings provide a framework for achieving controlled Ni coatings on III–V NWs, representing a key step toward high-efficiency, nanoscale BV devices.
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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 | 0.000 | 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".