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Record W2320624414 · doi:10.1166/jnn.2012.6437

A Study of Silver Species on Silver-Exchanged ETS-10 and Mordenite by XRD, SEM and Solid-State <sup>109</sup>Ag, <sup>29</sup>Si and <sup>27</sup>Al NMR Spectroscopy

2012· article· en· W2320624414 on OpenAlexaff
Yan Liu, Fu Chen, Roderick E. Wasylishen, Zhenghe Xu, James A. Sawada, Steven M. Kuznicki

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2012
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMordeniteMolecular sieveMaterials scienceSolid-state nuclear magnetic resonanceZeoliteSolid-stateAdsorptionCharacterization (materials science)Analytical Chemistry (journal)Silver nanoparticleNuclear chemistryPhysical chemistryNanotechnologyNuclear magnetic resonanceNanoparticleChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Silver zeolites, especially Ag-ETS-10 and Ag-mordenite, actively bind xenon and iodine, two prime contaminants common to nuclear accidents. The evolution of silver species on silver exchanged ETS-10 (Ag/ETS-10) and mordenite (Ag/Mor) has been investigated by exposing the materials to a series of activation conditions in Ar, air and H2. The samples were characterized by XRD, SEM and solid-state 109Ag, 29Si and 27AI MAS NMR. The silver reduction and structural evolution have been illustrated by those techniques. The effectiveness of one sample of each type of sieve was tested for its ability to trap mercury from a gas stream. However, the results from this study demonstrate that the adsorption characteristics of silver-loaded sieves cannot necessarily be predicted using a full complement of structural characterization techniques, which highlights the importance of understanding the formation and nature of silver species on molecular sieves.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.271
Teacher spread0.256 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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