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Record W2114958512 · doi:10.1139/cjc-2015-0247

Continuous online determination of <sup>226</sup>Ra in liquid effluents using automated column chromatography-ICP-MS

2015· article· en· W2114958512 on OpenAlexafffundvenue
R. Douglas Evans, Andrei Izmer, Karima Benkhedda, Andrew Toms, Angelo Fernando, Wei Wang

Bibliographic record

VenueCanadian Journal of Chemistry · 2015
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsCameco (Canada)Bruker (Canada)Trent University
FundersTrent University
KeywordsChemistryChromatographyDetection limitElutionAnalyteAnalytical Chemistry (journal)Matrix (chemical analysis)EffluentFlow injection analysisVolumetric flow rateSample preparationWaste management

Abstract

fetched live from OpenAlex

A measurement system capable of continuous on-line matrix removal, pre-concentration and analysis of 226 Ra using pre-packed columns coupled to a flow injection system and an ICP-MS was developed. Full instrumental control of both the ICP-MS and the flow injection system provided automatic integration of the transient signals. The flow injection system was programmed to control column conditioning, sample loading, column rinsing, analyte elution and column cleaning operations employing appropriate solutions. The application of this system to the 226 Ra analysis of an industrial liquid effluent was demonstrated. Using this particular instrument together with pre-concentration and matrix removal procedures, a limit of detection of 5.4 fg L −1 (2 mBq L −1 ) and a method detection limit of 16.2 fg L −1 (6 mBq L −1 ) were achieved for the measurement of 226 Ra using a 25 mL sample volume. Total time for sample handling and analysis is approximately 10 minutes. The concentration of 226 Ra in a discharged effluent sample was 0.73 pg L −1 (27 mBq L −1 ), which is in good agreement with the value of 0.81 pg L −1 (30 mBq L −1 ) measured using conventional alpha counting techniques.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.066
GPT teacher head0.357
Teacher spread0.291 · 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 designObservational
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

Citations9
Published2015
Admission routes3
Has abstractyes

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