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Adsorption and Co-precipitation of Metoprolol with Struvite Recovered from Synthetic Source Separated Black Wastewater

2019· article· en· W2981464330 on OpenAlexaff
Jiangjiang Wang, Qixing Zhou, Yang Liu

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStruviteAdsorptionWastewaterFertilizerFreundlich equationPrecipitationChemistryLangmuirPhosphateLangmuir adsorption modelMagnesiumSewage treatmentNuclear chemistryEnvironmental engineeringEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Due to the residues of metoprolol (MET) in black wastewater, recovering phosphate as struvite (MgNH 4 PO 4 · 6H 2 O) from black wastewater may pose threats to the utilization of struvite in the agricultural planting and human health. In this study, the adsorption and co-precipitation experiments of MET in the formation of struvite from synthetic black wastewater will be performed and some factors like initial MET concentration, pH values, temperatures were investigated. Results revealed that the adsorption process can be divided into rapid adsorption, fluctuation stage and steady stage, and the maximum MET adsorption quantity was 0.05029mg/g under 80ppb, 20 °C. Chemical adsorption and pseudo second-order kinetics were evident at the low concentration level. Neither Langmuir nor Freundlich isotherms fitted for the adsorption. The presence of Mg[H 2 O] 6 2+ (Hexahydrate and magnesium ions) interfered with MET adsorption through recombining with MET, which was also determined by pH variation. The existence of MET in black wastewater would not obstruct the formation of struvite in alkaline condition generally and serious terrible influences would not be generated when using struvite as fertilizer. A vast majority of MET will stay in the solution after 3 hours’ reaction.

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 score0.022
Threshold uncertainty score0.541

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 communication0.0000.001
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 head0.006
GPT teacher head0.184
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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