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Record W2017362010 · doi:10.1021/ac802341y

Separation and Measurement of Pa, Th, and U Isotopes in Marine Sediments by Microwave-Assisted Digestion and Multiple Collector Inductively Coupled Plasma Mass Spectrometry

2009· article· en· W2017362010 on OpenAlexfundno aff
César Negre, Alexander Thomas, J.L. Mas, Jordi García-Orellana, Gideon M. Henderson, Pere Masqué, Rainer Zahn

Bibliographic record

VenueAnalytical Chemistry · 2009
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsnot available
FundersUniversity of British ColumbiaMinisterio de Ciencia e InnovaciónInstitut Polaire Français Paul Emile VictorPolar Knowledge Canada
KeywordsChemistryInductively coupled plasma mass spectrometryAqua regiaMicrowave digestionMass spectrometryInductively coupled plasmaAnalytical Chemistry (journal)ChromatographyDigestion (alchemy)MicrowaveMicrowave ovenDetection limitPlasmaMetal

Abstract

fetched live from OpenAlex

This manuscript describes a new protocol for determination of Pa/Th/U in marine sediments. It is based on microwave-assisted digestion and represents an important reduction of working time over conventional hot-plate digestion methods, and the use of HClO(4) is avoided. Although Th and U are completely dissolved with a first microwave step, around 40% of (231)Pa remains undissolved, and a short hot-plate step with reverse aqua regia is required to achieve total digestion and spike equilibration. Next, the method involves a separation of these elements and a further purification of the Pa fraction using Dowex AG1-X8 resin. Separation with Bio-Rad and Sigma-Aldrich resins was compared; although both perform similarly for Th and U, Pa yields are higher with Bio-Rad. Finally, samples are measured using a Nu instruments multiple collector inductively coupled plasma mass spectrometer (MC-ICPMS). Overall chemical yields range around 50% for Pa, 60% for Th, and 70% for U.

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 categoriesMeta-epidemiology (narrow)
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 score0.030
Threshold uncertainty score1.000

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 head0.016
GPT teacher head0.252
Teacher spread0.235 · 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.

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

Citations27
Published2009
Admission routes1
Has abstractyes

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