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Record W2593525991 · doi:10.1021/acssuschemeng.7b00150

Collectable and Recyclable Mussel-Inspired Poly(ionic liquid)-Based Sorbents for Ultrafast Water Treatment

2017· article· en· W2593525991 on OpenAlexaff
Yangyang Lu, He Zhu, Wenjun Wang, Bo‐Geng Li, Shiping Zhu

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2017
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsMcMaster University
FundersZhejiang UniversityState Key Laboratory of Chemical EngineeringNational Natural Science Foundation of China
KeywordsAdsorptionPhotopolymerThermogravimetric analysisChemical engineeringIonic liquidCationic polymerizationMaterials scienceDesorptionChemistryOrganic chemistryPolymer chemistryPolymerPolymerization

Abstract

fetched live from OpenAlex

We report a green method to graft directly poly(ionic liquid) onto polydopamine-modified magnetic-responsive Fe 3 O 4 nanoparticles through an organic solvent-free process of self-initiated photografting and photopolymerization. PIL@PDA@Fe 3 O 4 nanocomposite is characterized by transmission electron microscopy, attenuated total reflection-infrared intensity, X-ray photoelectron spectroscopy, and thermal gravimetric analyzer. The nanoparticles can be used for removal of methylene blue (MB) from water. The adsorption is ultrafast, with a maximum MB adsorption capacity of 72.5 mg/g. A selective adsorption property of PIL@PDA@Fe 3 O 4 is also demonstrated by separation of cationic dyes from anionic dyes in water. The particles can be collected by a magnet and be regenerated by washing with salt solution after adsorption. The results show negligible change in the adsorption and desorption efficiency after 5 cycles. This work demonstrates the potential of PDA as an initiator for photografting and photopolymerization of PIL from surfaces separation applications.

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 score0.024
Threshold uncertainty score0.783

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 studies0.0010.000
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 head0.011
GPT teacher head0.235
Teacher spread0.223 · 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

Citations30
Published2017
Admission routes1
Has abstractyes

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