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Record W4413421714 · doi:10.1016/j.xcrp.2025.102789

Figures of merit to quantify betavoltaic device performance

2025· article· en· W4413421714 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCell Reports Physical Science · 2025
Typearticle
Languageen
FieldEnergy
TopicAdvanced Energy Technologies and Civil Engineering Innovations
Canadian institutionsCanadian Nuclear LaboratoriesUniversity of Ottawa
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationFonds de recherche du Québec – Nature et technologiesCMC Microsystems
KeywordsFigure of meritMaterials scienceEnvironmental scienceOptoelectronics

Abstract

fetched live from OpenAlex

Betavoltaic energy conversion is a specialized energy harvesting technology using a semiconductor to convert beta radiation from a radiation source into continuous electricity. Many semiconductor materials and radioactive isotopes are viable, making it challenging to benchmark technologies. To facilitate comparison, modeling, and characterization, we introduce three figures of merit: capture efficiency, gain, and gain efficiency. We showcase these metrics by numerically modeling the performance of GaAs, SiC, and GaN p-n and p-i-n junctions under H 3 and Ni 63 radioisotope irradiation. The capture efficiency describes the energy fraction that can be absorbed by the cell. The gain and gain efficiency quantify carrier multiplier effects and carrier collection effectiveness. The figures of merit study indicates that (1) absorber materials with low atomic numbers improve energy capture in the cell and (2) single-junction betavoltaics do not require complete radiation absorption to obtain maximum operating efficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.264
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it