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Record W4289688205 · doi:10.1088/1361-6528/ac8682

Photocatalytic nanocomposite membranes for environmental remediation

2022· article· en· W4289688205 on OpenAlexaff
Mahsa Golmohammadi, Ali Asghar Sabbagh Alvani, H. Sameie, Bastian Mei, R. Salimi, Dirk Poelman, Federico Rosei

Bibliographic record

VenueNanotechnology · 2022
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMaterials sciencePolyvinylidene fluoridePhotocatalysisNanocompositeMembraneChemical engineeringGraphenePolyethylene glycolPEG ratioDegradation (telecommunications)PolyimideNanotechnologyComposite materialLayer (electronics)PolymerOrganic chemistryCatalysisChemistry

Abstract

fetched live from OpenAlex

Abstract We report the design and one-pot synthesis of Ag-doped BiVO 4 embedded in reduced graphene oxide (BiVO 4 :Ag/rGO) nanocomposites via a hydrothermal processing route. The binary heterojunction photocatalysts exhibited high efficiency for visible light degradation of model dyes and were correspondingly used for the preparation of photocatalytic membranes using polyvinylidene fluoride (PVDF) or polyethylene glycol (PEG)-modified polyimide (PI), respectively. The surface and cross-section images combined with elemental mapping illustrated the effective distribution of the nanocomposites within the polymeric membranes. Photocatalytic degradation efficiencies of 61% and 70% were achieved after 5 h of visible light irradiation using BiVO 4 :Ag/rGO@PVDF and BiVO 4 :Ag/rGO@PI (PEG-modified) systems, respectively. The beneficial photocatalytic performance of the BiVO 4 :Ag/rGO@PI (PEG-modified) membrane is explained by the higher hydrophilicity due to the PEG modification of the PI membrane. This work may provide a rational and effective strategy to fabricate highly efficient photocatalytic nanocomposite membranes with well-contacted interfaces for environmental purification.

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 score0.369
Threshold uncertainty score0.854

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)0.0000.000

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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