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Record W2766432939 · doi:10.1039/c7dt03616g

Microwave-assisted activation and modulator removal in zirconium MOFs for buffer-free CWA hydrolysis

2017· article· en· W2766432939 on OpenAlexaff
Yaroslav Kalinovskyy, N. John Cooper, Marcus J. Main, Simon J. Holder, Barry A. Blight

Bibliographic record

VenueDalton Transactions · 2017
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsUniversity of New Brunswick
FundersDefence Science and Technology LaboratoryUniversity of Kent
KeywordsHydrolysisZirconiumCatalysisChemistrySolventDegradation (telecommunications)Metal-organic frameworkAcetic acidInorganic chemistryOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

A novel, facile and efficient method was developed for the activation of acetic acid modulated zirconium MOFs. The protocol involves briefly heating the material in water using microwave irradiation. MOF-808, DUT-84 and UiO-66 were all activated in this manner to remove the modulator and organic solvent from the framework post synthesis, with retention of MOF integrity post activation. The degree of activation was characterised by the use of TGA and NMR. The catalytic activity of the activated MOFs and their non-activated counterparts was investigated for chemical warfare agent (CWA) hydrolysis. Upon activation, an increase in the rate of hydrolysis was observed in the degradation of CWA simulant dimethyl 4-nitrophenyl phosphate (DMNP). MOF-808 and DUT-84 were also screened as catalysts for the hydrolysis of the V-series agent VM, with remarkable half-lives obtained for MOF-808 in the absence of any buffers. Currently employed MOF activation procedures involve the use of additional organic solvents post synthesis; we believe this method to be ideally efficacious for the organic desolvation of zirconium MOFs and removing modulator additives.

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 score0.223
Threshold uncertainty score0.837

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 studies0.0010.000
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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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

Citations37
Published2017
Admission routes1
Has abstractyes

Explore more

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