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Record W4388024701 · doi:10.1016/j.jcis.2023.10.110

Rare earth elements interaction with iron-organic matter colloids as a control of the REE environmental dissemination

2023· article· en· W4388024701 on OpenAlexaff
Yasaman Tadayon, Delphine Vantelon, Julien Gigault, Aline Dia, Maxime Pattier, Lionel Dutruch, Mélanie Davranche

Bibliographic record

VenueJournal of Colloid and Interface Science · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversité Laval
FundersHorizon 2020Université de Rennes 1European Commission
KeywordsColloidOrganic matterChemistryEnvironmental chemistryGenetic algorithmSedimentRare earthMineralogyGeologyOrganic chemistryEcology

Abstract

fetched live from OpenAlex

Rare earth elements (REE) are highly sought after for advanced technology, in response concerns about their environmental impact have arisen. The mobility and transport of REEs are influenced by their binding to solid surfaces, particularly colloids. With the widespread occurrence of REEs and their potential increase due to climate change, there is growing interest in understanding colloids composed of organic matter (OM) and iron (Fe). The reactivity of these colloids depends on their structural organization and the availability of Fe phase and OM binding sites. The effect of pH on the binding and mobility of REEs in these colloids in response to structural modification of Fe-OM colloids was investigated. REEs are primarily bind to the OM component of Fe-OM colloids, and their mobility is controlled by the response of OM colloids and molecules to pH conditions. At pH 6, the solubilization of small organic colloids (<3 kDa) control the REE pattern and subsequent speciation and mobility. In contrast, at pH 4, Fe-OM colloids bind less amount of REE but aggregate to form a large network. While most REEs remain soluble, those bound to Fe-OM colloids are expected to be immobilized through settlement or trapping in soil and sediment pores. This study supports the idea that colloids control the REE speciation and subsequent dissemination. The findings are particularly relevant for assessing the fate and ecotoxicology of REE in response to changing environmental conditions and increasing REE concentration in natural systems.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
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 head0.003
GPT teacher head0.206
Teacher spread0.203 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

Explore more

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