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Record W318450921 · doi:10.2166/wst.2015.226

Enhanced coagulation with in situ manganese dioxide on removal of humic acid in micro-polluted water

2015· article· en· W318450921 on OpenAlexaff
Yubin Zeng, Ziyang Zeng, Junlin Wang

Bibliographic record

VenueWater Science & Technology · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumic acidChemistryCoagulationManganesePermanganateIn situSulfateEnvironmental chemistryWater treatmentPotassium permanganateNuclear chemistryInorganic chemistryEnvironmental engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The morphology and surface characteristics of manganese dioxide (MnO₂) formed in situ, which was prepared through the oxidation of MnSO₄using KMnO₄, were studied. The effects of factors including the form of MnO₂, dosage, pH, dosing sequence of in situ MnO₂on the enhanced coagulation were systematically evaluated. The results of analysis by the UV254 and permanganate index CODMn methods indicated that humic acid removal increased from 9.2 and 2.5% to 55.0 and 38.9%, when 10 mg/L of the in situ MnO₂ was added in the presence of 2 mg/L of polyaluminum sulfate. The studies of orthogonal experiment revealed that coagulation was most affected by the pH, whereas the dosage of in situ MnO₂and slow stirring duration exhibited a weaker effect. At a pH value of 4.0, in situ MnO₂dosage of 10 mg/L, slow stir over 40 min, and the total solids content was 20 mg/L, the humic acid removal by UV254 and CODMn methods reached 71.2 and 61.2%. These results indicated that the presence of in situ MnO₂enhanced the coagulation and removal of humic acid from water.

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 score0.047
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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