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Record W2148644502 · doi:10.1155/2014/606534

Degradation of TCE by TEOS Coated nZVI in the Presence of Cu(II) for Groundwater Remediation

2014· article· en· W2148644502 on OpenAlexaff
Amruthur S. Ramamurthy, Mahmoud M. Eglal

Bibliographic record

VenueJournal of Nanomaterials · 2014
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsConcordia University
Fundersnot available
KeywordsEnvironmental remediationDegradation (telecommunications)Materials scienceGroundwaterGroundwater remediationContaminated groundwaterEnvironmental chemistryChemical engineeringContaminationGeotechnical engineeringChemistry

Abstract

fetched live from OpenAlex

The removal of TCE by nanofer zero valent iron (nanofer ZVI) coated with tetraethyl orthosilicate (TEOS) in the presence of Cu(II) at different environmental conditions was studied. The kinetics of TCE degradation by nanofer ZVI was determined. At a dosage of 10 mg of nanofer ZVI, almost 63% of TCE was removed, when Cu(II) and TCE were present. It contrasts with 42% degradation of TCE in the absence of Cu(II). SEM/EDS images indicated that Cu(II) is reduced to form Cu 0 and Cu 2 O. These formations are considered to be responsible for enhancing TCE degradation. Direct reduction involves hydrogenolysis and β ‐elimination in the transformation of TCE, while indirect reduction involves atomic hydrogen and no direct electron transfer from the metal to reactants. The reduction of activation energy was also noted indicating that the rate limiting step for TCE degradation in the presence of Cu(II) is surface chemical reaction rather than diffusion. Most of iron present in nanofer ZVI get dissolved causing the generation of localized positive charge regions and form metal chlorides. Local accumulation of hydrochloric acid inside the pits regenerates new reactive surfaces to serve as sources of continuous electron generation. No significant effect of TCE was noticed for Cu(II) sequestration.

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)0.002
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 score0.005
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.010
GPT teacher head0.220
Teacher spread0.209 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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