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Record W2007968035 · doi:10.1021/es100879w

Manganese Valence in Oxides Formed from in Situ Chemical Oxidation of TCE by KMnO<sub>4</sub>

2010· article· en· W2007968035 on OpenAlexafffund
Diana B. Loomer, Tom A. Al, Vernon J. Banks, Beth L. Parker, K. Ulrich Mayer

Bibliographic record

VenueEnvironmental Science & Technology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversity of British ColumbiaUniversity of GuelphUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsX-ray photoelectron spectroscopyValence (chemistry)ManganesePermanganateChemistryInorganic chemistryPotassium permanganateIn situAnalytical Chemistry (journal)Environmental chemistryChemical engineering

Abstract

fetched live from OpenAlex

Batch and column experiments designed to simulate in situ chemical oxidation (ISCO) in a sand aquifer were conducted to create Mn-oxides (MnOx) by oxidation of trichloroethylene (TCE) with permanganate (MnO4-). Electron energy-loss spectroscopy (EELS) and X-ray photoelectron spectroscopy (XPS) were used to quantify Mn valence in the oxides. The valence of Mn in the MnOx generated in near-source ISCO conditions was 2.2 and 2.3 when formed at low (<3) and neutral (6-7) pH conditions, respectively. There is no significant difference between these values. Valence was found to be sensitive to the preparation method and to aging. When formed in the presence of excess MnO4-, or aged for 3 months, Mn valence ranged from 2.5 to 3.6. Aging in a lower pH environment inhibited Mn oxidation. The EELS and XPS methods provided similar results, but there was a slight bias to higher values for XPS. This work demonstrates that MnO2(s) may not be the main product of MnO4- reaction with chlorinated solvents as is commonly assumed and that the efficiency of ISCO treatment may be greater than previously known.

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 score0.145
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.002
GPT teacher head0.166
Teacher spread0.164 · 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

Citations24
Published2010
Admission routes2
Has abstractyes

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