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Record W7146108996

Nuclear magnetic resonance of Li8 ions implanted in ZnO

2022· article· W7146108996 on OpenAlexfundno aff
Jonah R. Adelman, Derek Fujimoto, Martin H. Dehn, S. R. Dunsiger, Victoria L. Karner, C. D. Philip Levy, Ruohong Li, Iain McKenzie, Ryan M. L. McFadden, Gerald D. Morris, Matthew R. Pearson, Monika Stachura, Edward Thoeng, John O. Ticknor, Naoki Ohashi, K M Kojima, W. Andrew MacFarlane

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2022
Typearticle
Language
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIonFerromagnetic resonanceIon implantationAnalytical Chemistry (journal)Resonance (particle physics)
DOInot available

Abstract

fetched live from OpenAlex

We report on the stability and magnetic state of ion implanted 8Li in single crystals of the semiconductor ZnO using β-detected nuclear magnetic resonance. At ultradilute concentrations, the spectra reveal distinct Li sites from 7.6 to 400 K. Ionized shallow donor interstitial Li is stable across the entire temperature range, confirmingits ability to self-compensate the acceptor character of its (Zn) substitutional counterpart. Above 300 K, spinlattice relaxation indicates the onset of correlated local motion of interacting defects, and the spectra show a site change transition from disordered configurations to substitutional. Like the interstitial, the substitutional shows no resolved hyperfine splitting, indicating it is also fully ionized above 210 K. The electric field gradient at the interstitial 8Li exhibits substantial temperature dependence with a power law typical of noncubic metals.

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 score0.002
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)0.0010.000

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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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