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Record W2577266387 · doi:10.1186/s40538-016-0084-2

Water-mediated NOE: a promising tool for interrogating interfacial clay–xenobiotic interactions

2017· article· en· W2577266387 on OpenAlexafffund
Ronald Soong, Adolfo Botana, Jasmine Wang, Hashim Farooq, Denis Courtier‐Murias, André J. Simpson

Bibliographic record

VenueChemical and Biological Technologies in Agriculture · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaKrembil FoundationGovernment of Ontario
KeywordsXenobioticChemistryEnvironmental chemistryClay mineralsSorptionAqueous solutionChemical physicsChemical engineeringBiophysicsOrganic chemistryMineralogyAdsorptionBiology

Abstract

fetched live from OpenAlex

The sorption of anthropogenic compounds on clay minerals is a complex molecular process with important implications for the fate of agrochemicals and organic pollutants in the environment. The present study illustrates the use of a water-mediated NOE approach to study clay binding interactions. This method exploits the interfacial water layer on clay surfaces as a hydrogen reservoir for magnetization transfer. The interactions of four different xenobiotics with clay suspension were investigated through this method to demonstrate its capability to screen for the clay–xenobiotic molecular affinity. Further, based on the NOE build-up rates, epitope map of clay–xenobiotic interactions can be generated, explaining the orientation and mechanism of the interactions. The water-mediated NOE approach has the potential to reveal key insights into the role that interfacial water plays in the binding process, providing a better understanding of the partitioning of anthropogenic compounds from bulk water into aqueous clay suspensions.

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)0.001
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 score0.046
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.022
GPT teacher head0.251
Teacher spread0.229 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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