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Record W4221072977 · doi:10.3389/fchem.2022.840133

Mussel-Inspired Magnetic Dissolving Pulp Fibers Toward the Adsorption and Degradation of Organic Dyes

2022· article· en· W4221072977 on OpenAlexaff
Jiawei Yang, Shengchang Lu, Hui Wu, Hui‐Chao Hu, Qingxian Miao, Liulian Huang, Lihui Chen, Yonghao Ni

Bibliographic record

VenueFrontiers in Chemistry · 2022
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsUniversity of New Brunswick
FundersNational Natural Science Foundation of China
KeywordsAdsorptionCatalysisDissolutionChemical engineeringCoatingDissolving pulpPulp (tooth)Materials scienceMethylene blueDegradation (telecommunications)ChemistryOrganic chemistryNanotechnologyCellulosePhotocatalysis

Abstract

fetched live from OpenAlex

In this work, a simple synthetic method was used to prepare a new type of magnetic dissolving pulp (MDP) @polydopamine (PDA) fibers. The hydroxyl groups of the fibers were converted into carboxyl groups after succinylation. Fe 3 O 4 nanoparticles were grown in situ on the fibers. The prepared MDP@PDA fibers have catalytic reduction efficiency and adsorption performance for methylene blue organic dyes, and it has been thoroughly tested under various pH conditions. Fe 3 O 4 @PDA fibers have high reusability, are easy to separate, and regenerate quickly. The catalytic and adsorption efficiency barely decreases after repeated use. The surface of dissolving pulp fibers with a functionalized multifunctional PDA coating is used to create multifunctional catalysts and adsorbent materials. This study presents a very useful and convenient method for the synthesis and adjustment of MDP@PDA fibers, which have a wide range of potential applications in catalysis and wastewater treatment.

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 categoriesInsufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.008
GPT teacher head0.199
Teacher spread0.192 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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