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High-efficiency recovery of <sup>82</sup>Se from enriched Zn<sup>82</sup>Se scintillating bolometer crystals

2023· article· en· W4367056835 on OpenAlexaff
Stanislav Balabanov, B. Broerman, I. Dafinei, Sergey Filofeev, M. Laubenstein, S.S. Nagorny, S. Nisi, L. Pagnanini, S. Pirro

Bibliographic record

VenueJournal of Instrumentation · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsArthur B. McDonald-Canadian Astroparticle Physics Research InstituteQueen's University
Fundersnot available
KeywordsYield (engineering)IsotopeBolometerNatural abundancePhysicsDouble beta decayExtraction (chemistry)Analytical Chemistry (journal)RadiochemistryNuclear physicsCrystal (programming language)Materials scienceChemistryDetectorOpticsChromatography

Abstract

fetched live from OpenAlex

Abstract As experiments searching for neutrinoless double beta decay push into the inverted hierarchy, enriched isotope target masses of hundreds of kilograms are required. Due to unavoidable losses throughout the entire production chain, the recovery of expensive enriched material used in crystal-based experiments should be given special attention. The CUPID-0 experiment using Zn82Se scintillating bolomoters provides a unique opportunity at the 10-kg-scale to test a recovery process for enriched 82Se. We present a multi-stage, high-yield method consisting of wet chemistry and vacuum distillation. The chemical purity, isotopic abundance, and radiopurity is demonstrated to be preserved after the 82Se extraction with recovery efficiency no less than 86.4% (that potentially can be higher than 94.7%) and chemical purity of 99.999%.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.029
GPT teacher head0.309
Teacher spread0.281 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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