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Record W2094743154 · doi:10.1063/1.2472645

Implantation profile of Na22 continuous energy spectrum positrons in silicon

2007· article· en· W2094743154 on OpenAlexafffund
Phil Foster, Peter Mascher, Andrew P. Knights, P. G. Coleman

Bibliographic record

VenueJournal of Applied Physics · 2007
Typearticle
Languageen
FieldEngineering
TopicMuon and positron interactions and applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsPositronSiliconMaterials scienceRadioactive sourceIrradiationAtomic physicsPhysicsElectronNuclear physicsOptoelectronicsOpticsDetector

Abstract

fetched live from OpenAlex

The implantation profile of positrons emitted from a continuous energy spectrum source of Na22 in close proximity to a silicon target is modeled. The primary motivation is the use of positron lifetime spectroscopy to characterize layers of defects such as those created by ion irradiation, usually deemed accessible only to techniques which utilize slow positrons. The model combines the Makhov profile, used with considerable success to describe the profile of low energy (<30keV) monoenergetic positrons, with the well-established, universal β-decay energy spectrum. The success of this approach is verified by measuring the fractions of positrons absorbed in thinned silicon samples. This verification utilizes lifetime measurements performed on silicon in a bilayer sandwich configuration with copper as a backing. The model accounts for the uncertainty in the positron backscattering at the silicon∕copper interface. An optimal fit of the model to the experimental data requires that the parameter defining the mean depth of the Makhov profile (usually denoted r) is energy dependent. An example of application is provided in the form of a positron lifetime measurement of defects in silicon introduced by 1.5MeV proton irradiation. Excellent agreement is found between the lifetime data and those obtained using a slow positron technique.

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 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 score0.164
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.224
Teacher spread0.219 · 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 teacher head, 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

Citations5
Published2007
Admission routes2
Has abstractyes

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