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Record W3160528265 · doi:10.1515/ract-2020-0068

Experimental study on modeling of Pu sorption onto quartz

2021· article· en· W3160528265 on OpenAlexaff
Ko Hemmi, Andrew Walker, Tetsuji Yamaguchi

Bibliographic record

VenueRadiochimica Acta · 2021
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsMcMaster University
FundersNuclear Regulation Authority
KeywordsSorptionChemistryQuartzCarbonatePartition coefficientIonic strengthMuscoviteAnalytical Chemistry (journal)Inorganic chemistryNuclear chemistryAdsorptionChromatographyPhysical chemistryAqueous solutionOrganic chemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract Plutonium(IV) sorption onto quartz in carbonate solutions was systematically investigated under anaerobic conditions to analyze the sorption behaviors of Pu(IV) with a non-electrostatic model (NEM). Pu(IV) sorption data was obtained from batch sorption experiments as a function of pH and carbonate concentration. The Pu(IV) sorption onto quartz showed similar tendencies to Th(IV), which is considered to be chemically analogous as a tetravalent actinoid. The distribution coefficient, K d , of Pu(IV) onto quartz showed inverse proportionality to the square of the total carbonate concentration under the investigated pH conditions of 8–11. The modeling study, however, revealed a Th(IV) sorption model, which is ≡SOTh(OH) 4 − and ≡SOThOH(CO 3 ) 2 2− , could not be applied to simulate the Pu(IV) sorption onto quartz. It was inferred that the electrostatic repulsion between negatively charged ligands limited the formation of ≡SOM(OH) 4 − and ≡SOMOH(CO 3 ) 2 2− for Pu(IV) with smaller ionic radii than Th(IV). The Pu(IV) sorption model was developed as ≡SOPu(OH) 3 and ≡SOPu(OH) 4 − . In addition, data of Pu(IV) sorption onto muscovite was obtained in order to be compared with data for quartz.

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 score0.031
Threshold uncertainty score0.825

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)0.0010.000

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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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