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Record W2072942354 · doi:10.1021/ac026158e

A Preconcentration/Matrix Reduction Method for the Analysis of Rare Earth Elements in Seawater and Groundwaters by Isotope Dilution ICPMS

2003· article· en· W2072942354 on OpenAlexfundno aff
Timothy J. Shaw, T. Michael Duncan, Bernhard Schnetger

Bibliographic record

VenueAnalytical Chemistry · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
FundersNational Research Council CanadaNational Science Foundation
KeywordsChemistrySeawaterIsotope dilutionMatrix (chemical analysis)Rare earthDilutionIsotopeIsotope analysisEnvironmental chemistryMass spectrometryMineralogyChromatographyGeologyOceanography

Abstract

fetched live from OpenAlex

A simple method of simultaneous preconcentration and matrix reduction was developed for the analysis of rare earth elements (REEs) in seawater and groundwater by ID ICPMS. The method utilizes partitioning of the REEs with solid hydroxides to separate them from soluble matrix species (e.g., Ba2+, NOM, seawater salts). Acidified samples were spiked and equilibrated with an enriched isotope cocktail (142Ce, 145Nd, 161Dy, 171Yb). Aqueous NH3 was then added to the spiked samples to induce the coprecipitation of the REEs with a small fraction of the natural Mg2+ as Mg(OH)2. The samples were centrifuged and the precipitate was rinsed to remove more than 99.8% of the Ba2+ along with the matrix salts. The precipitate was dissolved in 400 microL of 10% HNO3 for ICPMS analysis. The four spiked elements, determined by isotope dilution, served as internal standards for the remaining REEs. Analysis of NASS-4 and NASS-5 seawater reference materials showed good agreement with published values. Calculated limits of detection for a 1.65-g sample ranged from 0.1 pg/g for the light REEs to 0.02 pg/g for the heavy REEs. The reagent blanks ranged from a high of 0.28 pg/sample for Ce to a low of 0.0036 pg/sample for Tb.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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 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

Citations62
Published2003
Admission routes1
Has abstractyes

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