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Record W7096038870

H2 Gas Charging of Zero-Valent Iron and TCE Degradation

2016· article· en· W7096038870 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogenZerovalent ironDegradation (telecommunications)MetalGroundwater remediationEnvironmental remediationAnaerobic corrosionGroundwater
DOInot available

Abstract

fetched live from OpenAlex

Granular zero-valent iron (ZVI) has been widely used to construct permeable reactive barriers (PRB) for the in situ remediation of groundwater contaminated with halogenated hydrocarbons. In the anaerobic condition of most ground-water flow systems, iron undergoes corrosion by water and results in hydrogen gas generation. Several studies have shown that some of the hydrogen gas generated at the iron/water interface can diffuse into the iron lattice. Hydrogen gas also can be an electron donor for dechlorination of chlorinated compounds. In this study, the possibility of hydrogen gas bound in the lattice of ZVI playing a role in dehalogenation and improving the degradation efficiency of ZVI was evaluated. Two different granular irons were tested: one obtained from Quebec Metal Powders Ltd (QMP) and the other from Connelly-GPM. Ltd. For each type of iron, two samples were mixed with water and sealed in testing cells. Since the rate of hydrogen entry varies directly with the square root of the hydrogen pressure, one sample was maintained for several weeks under near-vacuum conditions to minimize the amount of hydrogen entering the iron lattice. The other sample was maintained for the same period at a hydrogen pressure of over 400 kPa to maximize the amount of hydro-gen entering the iron lattice. The degradation abilities of the reacted ironsand the original iron materials were tested by running several sets of batch tests. The results of this study show little to no improvement of inorganic TCE degradation reactions due to the presence of lattice-stored hydrogen in iron material. This is probably due to the high energiesre-

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 score0.030
Threshold uncertainty score0.179

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 studies0.0000.000
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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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