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Record W4392281913 · doi:10.1080/00295450.2023.2300900

Sorption of U(VI) on MX-80 Bentonite and Granite in Ca-Na-Cl Saline Solutions

2024· article· en· W4392281913 on OpenAlexafffund
Zhiwei Zheng, Fabiola Guido Garcia, Jianan Liu, Shinya Nagasaki, Tammy Yang

Bibliographic record

VenueNuclear Technology · 2024
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsNuclear Waste Management OrganizationMcMaster University
FundersNuclear Waste Management Organization
KeywordsSorptionBentoniteIonic strengthUraniumNuclear chemistryChemistryMineralogyAnalytical Chemistry (journal)Materials scienceEnvironmental chemistryGeologyAdsorptionMetallurgyAqueous solutionPhysical chemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

Uranium has been identified as an element of interest for the safety assessment of a deep geological repository for used nuclear fuel. This paper examines the sorption behavior of U(VI) onto MX-80 bentonite and granite in Ca-Na-Cl solutions of varying ionic strengths [0.05 to 3 mol/kgw (m)] and across a pH range of 4 to 10. U(VI) sorption on MX-80 showed that U(VI) sorption gradually increased with pHm until pHm = 6, where it reached its maximum, and decreased slightly with pHm until pHm = 8, and then became constant. U(VI) sorption on granite increased along with pHm, reached the maximum around pHm = 7 to 8, and then slightly decreased with pHm. Both MX-80 and granite showed essentially no ionic strength dependence for sorption of U(VI). A nonelectrostatic surface complexation model successfully predicted sorption of U(VI) onto MX-80 and granite using the formation of an inner-sphere surface complex. Optimized values of surface complexation reaction constants (log K0) for the formation reactions of these surface species are proposed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2024
Admission routes2
Has abstractyes

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