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Record W4293065055 · doi:10.1111/wej.12820

Cationic lignin polymers as flocculants for municipal wastewater

2022· article· en· W4293065055 on OpenAlexafffund
Courtney Moore, Weijue Gao, Pedram Fatehi

Bibliographic record

VenueWater and Environment Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCoagulation and Flocculation Studies
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsFlocculationAlumTurbidityLigninChemistryWastewaterChemical oxygen demandPulp and paper industryPolymerCationic polymerizationCoagulationChlorideChemical engineeringEnvironmental engineeringOrganic chemistryEnvironmental science

Abstract

fetched live from OpenAlex

Abstract In this research, two lignin‐[2‐(methacryloyloxy)ethyl]trimethylammonium chloride (METAC) polymers were generated and assessed for their efficiency in treating municipally produced wastewater. The removals of chemical oxygen demand (COD) and turbidity were 47.5% and 71.2%, respectively, for the singular flocculation system at the dosage of 40–70 mg/L. For the dual coagulation/flocculation system, the polymer with a higher charge density (AM2) achieved higher COD and turbidity removals than the polymer with a lower charge density (AM1). To achieve the same organic removal from the wastewater, the alum use could be reduced from 150 mg/L in the single alum use to 35 mg/L in the dual system when used along with 65 mg/L of AM2. In both systems, lignin‐METAC polymers exhibited better performance than the flocculant used at a local wastewater treatment plant. Lignin‐METAC polymers could be used as bio‐based flocculants for the replacement of petroleum‐based flocculants and inorganic coagulants.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2022
Admission routes2
Has abstractyes

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