MétaCan
Menu
Back to cohort
Record W4405275701 · doi:10.1021/acs.inorgchem.4c03096

Trapping Properties of Iodine, Cesium, and Tellurium in Uranium Dioxide: A DFT+<i>U</i> Study

2024· article· en· W4405275701 on OpenAlexafffund
Mathieu Gascoin, Michel Freyss, I. Cheik Njifon

Bibliographic record

VenueInorganic Chemistry · 2024
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear Laboratories
FundersAtomic Energy of Canada Limited
KeywordsChemistryCaesiumTelluriumIodineTrappingUraniumRadiochemistryUranium dioxideInorganic chemistryOrganic chemistryNuclear physics

Abstract

fetched live from OpenAlex

We investigate the trapping properties of iodine, cesium, and tellurium in uranium dioxide, using the Hubbard-corrected density functional theory (DFT+ U ). In order to avoid the metastable states inherent to this method, we use the occupation matrix control (OMC) scheme, which also allows us to monitor the oxidation states of the different species. The most favorable trapping sites, oxidation states, and solubility of I, Cs, and Te are evaluated in stoichiometric UO 2 . To that end, vacancy-like defects under various charge states, including uranium and oxygen vacancies, U–O divacancy and bound Schottky defects, as well as the interstitial position, are considered as potential trapping sites in UO 2 . Te is found to exhibit a wide range of possible oxidation states, ranging from Te – to Te 4+, depending on the stable trapping site considered. For I and Cs, one predominant oxidation state for each fission product, namely, I – and Cs +, is found. This behavior is mainly accommodated by the charge of the defects. By providing accurate trapping sites and oxidation states of volatile fission products in UO 2, this study is expected to contribute in the development of larger scale simulation methods, enabling a better prediction and mitigation of corrosion issues in nuclear fuel cladding.

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 score0.004
Threshold uncertainty score0.616

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 head0.015
GPT teacher head0.209
Teacher spread0.194 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueInorganic ChemistrySame topicNuclear Materials and PropertiesFrench-language works237,207