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Record W2329428308 · doi:10.1021/ie502427y

Decontamination of Paraoxon and Parathion on Sensitive Equipment Materials by Catalytic Methanolysis

2014· article· en· W2329428308 on OpenAlexaff
V.A. Blinov, Konstantin Volchek, Wenxing Kuang, Akshay Bhalerao, Carl E. Brown

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsParaoxonHuman decontaminationParathionMethanolChemistryCatalysisReaction rate constantReagentKineticsNuclear chemistryOrganic chemistryWaste managementPesticide

Abstract

fetched live from OpenAlex

Sensitive equipment materials were decontaminated from organophosphorus compounds paraoxon or parathion by immersion into a catalytic reactive solution and by spraying with the same solution. Immersion of contaminated material samples into methanol-based catalytic solutions resulted in an effective decontamination. Greater than 99% decontamination was observed for paraoxon on high-impact polystyrene (HI-PS) over 15 min of reaction time. Under the same process conditions, the decontamination from parathion did not exceed 95%. The catalytic decompositions of paraoxon and parathion followed first-order reaction kinetics with rate constants of 5.4 × 10 –3 and 1.3 × 10 –3 s –1, respectively. These values were by an order of a magnitude lower than the respective rate constants reported for homogeneous reactions in a methanol solution. Decontamination by spraying the catalyst on the contaminated surface revealed that multiple applications would be required to overcome a rapid evaporation of methanol from the surface and an associated loss of catalytic activity.

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)0.001
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 score0.007
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.055
GPT teacher head0.286
Teacher spread0.230 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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