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Record W2007672279 · doi:10.1021/es001465s

Electrochemical Treatment of 2,4,6-Trinitrotoluene and Related Compounds

2000· article· en· W2007672279 on OpenAlexafffund
James D. Rodgers, Nigel J. Bunce

Bibliographic record

VenueEnvironmental Science & Technology · 2000
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolysisTrinitrotolueneAnodeElectrochemistryChemistryElectrolytic cellCathodeYield (engineering)ElectrolyteExplosive materialPollutantSupporting electrolyteEnvironmental remediationInorganic chemistryEnvironmental chemistryElectrodeMaterials scienceContaminationMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

This work involves electrolysis of nitrotoluene congeners, which are persistent pollutants that enter the environment as a consequence of their manufacture and use as explosives. Reduction to aminotoluenes occurred with high current efficiency at a variety of cathodes, at potentials -0.5 to -1 V vs SCE. The products were formed in high chemical yield and with excellent mass balance. Preliminary experiments were also carried out to find methods of removing the electrolysis products from solution by oxidative oligomerization. The most satisfactory method was partial reoxidation at a Ti/IrO2 anode, suggesting an overall remediation technology in which reduction is followed by reoxidation of the spent catholyte in the anode compartment of the same electrolytic cell.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.003
GPT teacher head0.201
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations87
Published2000
Admission routes2
Has abstractyes

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