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Record W3011344368 · doi:10.1021/acs.iecr.0c00533

Understanding the Interaction Mechanism between Elemental Selenium and Ferric Hydroxide in Wastewater Treatment

2020· article· en· W3011344368 on OpenAlexafffund
Jingyi Wang, Junmeng Li, Lei Xie, Qingxia Liu, Hongbo Zeng

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2020
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsFerricChemistryHydroxideSolubilityInorganic chemistryChlorideSettlingParticle (ecology)Particle sizeAdsorptionPrecipitationChemical engineeringNuclear chemistryOrganic chemistryPhysical chemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

The conversion of selenium oxyanions to elemental selenium (Se0) of low solubility and bioavailability is an effective industrial approach for selenium management in wastewater treatment. The generated Se0 particles require further treatment with coagulants such as ferric ions to facilitate the precipitation of Se0 particles. In this work, the settling of Se0 particles in simulated wastewater was investigated with amorphous ferric hydroxide (Fe(OH)3) as a coagulant prepared from hydrolysis of ferric chloride. The effects of ferric ion dosage, water composition, and pH on the Se0 removal percentage were investigated. The surface properties, i.e., ζ potential and morphology, were characterized, which influenced the interactions between Se0 and Fe(OH)3. The forces acting between Se0 and Fe(OH)3 surfaces were directly measured, for the first time, using atomic force microscopy (AFM). The water composition and pH had a significant effect on the adhesion force. In simulated wastewater, the adhesion force generally increased with pH, suggesting that the adsorption of Ca2+ and Mg2+ on Fe(OH)3 surface increased with pH, which enhanced the adhesion. Interestingly, long-range pull-off forces and sawtooth patterns were observed on the retraction force–separation curves, which were attributed to the stretching of Fe(OH)3 particle aggregates or chains during separation. Bulk settling tests showed that the best Se0 removal performance of Fe(OH)3 was found to be around pH 8, which was because the largest amount of Fe(OH)3 precipitates was found around this pH. The results indicate that the Fe(OH)3 solubility as well as the related intermolecular and surface forces play the predominant role in determining the Se0 removal performance of the ferric coagulant. This work, for the first time, revealed the interaction mechanisms of Se0 particle and amorphous Fe(OH)3, providing useful insights on the performance of ferric coagulants on Se0 particle removal from wastewater under various solution conditions. The experimental approach used in this work can be readily extended to other water treatment systems and processes such as polymer flocculation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.574

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.359
GPT teacher head0.360
Teacher spread0.001 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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