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Record W4386424842 · doi:10.1002/macp.202300223

Polyacrylamide Grafted Activated Carbon by Surface‐Initiated AGET ATRP for the Flocculation of MFT

2023· article· en· W4386424842 on OpenAlexaff
Sarah Bégin, Kevin M. Scotland, Paul R. Pede, Andrew J. Vreugdenhil

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicCoagulation and Flocculation Studies
Canadian institutionsNorthern Ontario Academic Medicine AssociationTrent University
Fundersnot available
KeywordsPolyacrylamideAtom-transfer radical-polymerizationGraftingFlocculationPolymerPolymer chemistryChemical engineeringChemistrySurface modificationActivated carbonPolymerizationMaterials scienceAdsorptionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Polyacrylamide (PAM) is grafted from the surface of activated carbon (AC) by surface‐initiated activators generated by electron transfer atom transfer radical polymerization (SI‐AGET ATRP). This is accomplished by pre‐functionalizing the surface of activated carbon by oxidation, followed by the attachment of an ATRP initiator. From this surface, SI‐AGET ATRP of acrylamide monomers is performed. The resulting AC‐PAM is characterized by FTIR, XPS, TGA, and BET analysis. Additionally, the grafted polymer is cleaved from the surface of AC and its molecular weight distribution is measured by SEC. This material is designed to explore the effect that grafting a polymer flocculant onto AC will have on the polymer's flocculating abilities. This is evaluated by measuring the flocculation and dewatering of mature fine tailings (MFT) when dosed with the AC‐PAM compared to PAM. In all, this work demonstrates the successful grafting of PAM onto AC, as well as potential wastewater applications for this composite material.

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 score0.003

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)0.0010.000

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 head0.017
GPT teacher head0.246
Teacher spread0.230 · 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
Published2023
Admission routes1
Has abstractyes

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