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Record W2329545376 · doi:10.1166/jnn.2012.5160

Solid-State NMR and TGA Studies of Silver Reduction in Chabazite

2012· article· en· W2329545376 on OpenAlexaff
Fu Chen, Yan Liu, Roderick E. Wasylishen, Zhenghe Xu, Steven M. Kuznicki

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2012
Typearticle
Languageen
FieldMaterials Science
TopicNuclear materials and radiation effects
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsChabaziteMaterials scienceHydrogenArgonMolecular sieveSelective reductionOxideAdsorptionZeolitePhysical chemistryChemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Silver-exchanged molecular sieves have shown great promise in applications ranging from antimicrobial materials to the adsorption of xenon and iodide, two key contaminants emitted from nuclear reactors. In this work, solid-state 27Al and 29Si MAS NMR and TGA were used to study silver reduction in silver-exchanged chabazite under various thermal conditions. The solid-state NMR results for both 27Al and 29Si show that there are no major changes in the chabazite during silver reduction in an argon stream; however a progressive structural change does take place in the hydrogen stream. The structural change likely involves breaking the silicon oxygen bond of the Si-O-AI fragment of chabazite, leading to the formation of extra-framework aluminum oxide. The TGA results at temperatures up to 600 degrees C indicate that silver reduction is less complete in an argon stream than in a hydrogen stream. In this paper we propose that silver reduction occurs via the following reactions: 2(Ag + ZO-)+H2O --> 1/2O2+2Ag0 + 2ZOH and nAg + mAg = Ag(m+n)n+ (in an argon stream); and Ag(+) + ZO(-) + 1/2H2 = Ag0 + ZOH and 2ZOH = ZO(-) + Z(+) + H2O (in a hydrogen stream).

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 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 score0.015
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.310
Teacher spread0.293 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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