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Record W4383217340 · doi:10.6000/1929-5995.2023.12.05

Hemicellulose-g-PAAc/TiO2 Nanocomposite Hydrogel for Dye Removal

2023· article· en· W4383217340 on OpenAlexvenueno aff
Yongdan Li, Sun Xiao, Jiayi Chen, Le Sun

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2023
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsnot available
FundersNorthwestern Polytechnical UniversityNorthwestern University
KeywordsHemicelluloseAdsorptionMaterials scienceFreundlich equationMethylene blueLangmuir adsorption modelChemical engineeringComposite numberPhotodegradationLangmuirPhotocatalysisNanocompositeComposite materialChemistryCelluloseOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Dyes pollution on urban environment is of great concern because of the human health hazards associated with this kind of contaminants, and the use of low-cost photocatalytic composite material is an efficient treatment method to minimize the environmental impact. A novel hemicellulose-g-PAAc/TiO2 composite hydrogel was prepared as a promising alternative material for dye removal. Wheat straw hemicellulose and TiO2 nanoparticles were first modified and then incorporated into hydrogel via covalent bonds. Effects of gel dosage, pH, initial concentration and contact time on the adsorption amount of methylene blue were systematically studied using the prepared hydrogel. The equilibrium adsorption data was fitted well to the Freundlich isotherm model, and Langmuir isotherm analysis indicated that the adsorption capacity of the hemicellulose-g-PAAc/TiO2 composite hydrogel was 389.1 mg/g, and adsorption kinetic study showed that the adsorption process can be described by the pseudo second-order kinetic model. The prepared composite hydrogel exhibited high photodegradation ability for methylene blue under alkaline conditions, and all results indicated that the hemicellulose-g-PAAc/TiO2 composite hydrogel had excellent photocatalytic degradability for dyes, which can be used in practical process.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.049
GPT teacher head0.388
Teacher spread0.339 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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