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Record W2114088737 · doi:10.1080/10934520802597648

Degradation of RDX using granular iron and nickel-plated granular iron

2009· article· en· W2114088737 on OpenAlexaff
Lai Gui, Heather L. R. Fenton, Robert W. Gillham

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2009
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsGolder Associates (Canada)University of Waterloo
Fundersnot available
KeywordsNickelDegradation (telecommunications)Formic acidMass transferEffluentChemical engineeringNitrogenFormaldehydeChemistryCarbon fibersCatalysisMaterials scienceEnvironmental chemistryMetallurgyOrganic chemistryChromatographyWaste managementComposite materialComposite number

Abstract

fetched live from OpenAlex

Using granular iron (Fe) and nickel-plated iron (Ni/Fe), this paper examines the effectiveness of these two types of reactive materials for the treatment of hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), a common groundwater and soil contaminant at military facilities. RDX degraded very rapidly in the presence of both Fe and Ni/Fe in column and batch experiments. Enhancement by Ni/Fe did not prove to be effective as the half-lives of RDX ranged from 3 to 24 seconds and 3 to 11 seconds in the Fe and Ni/Fe columns, respectively. Reaction vessel experiments and estimation of the mass transfer coefficient in the column indicated that reaction kinetics was mass transfer limited. Detailed analyses of reaction intermediates and products suggest that RDX degradation proceeds through direct electron transfer processes and following to the same pathways in the presence of Fe and Ni/Fe. The formation of carbon-containing products, including formaldehyde (up to 60%), CO2 (up to 45%) and formic acid (1%) and the nitrogen containing products of ammonium (up to 48%) and N2O (up to 13%), provides convincing evidence that RDX was completely decomposed to non-toxic end products. CO2, previously reported to form only in biological or Fe-microbial combined systems, was detected as one of the main C-bearing end product. Therefore, this study shows that Fe is an effective material for remediating groundwater and industrial effluents containing RDX; and the use of additional enhancement, either biological or with Ni catalyst, does not provide additional advantages.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.422

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 communication0.0000.001
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 head0.263
Teacher spread0.243 · 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
Published2009
Admission routes1
Has abstractyes

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