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Record W3209788333 · doi:10.1021/acsanm.1c02762

Reusable BiFeO<sub>3</sub> Nanofiber-Based Membranes for Photo-activated Organic Pollutant Removal with Negligible Colloidal Release

2021· article· en· W3209788333 on OpenAlexafffund
Paul Fourmont, Riad Nechache, Sylvain G. Cloutier

Bibliographic record

VenueACS Applied Nano Materials · 2021
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsÉcole de Technologie Supérieure
FundersCanada Research Chairs
KeywordsMembranePollutantMaterials scienceChemical engineeringNanofiberColloidNanotechnologyChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

We report on electrospinning-assisted fabrication of highly efficient and reusable BiFeO3 nanofiber-based membranes for photo-activated organic pollutant removal with negligible colloidal release. For validation purposes, we exploit a fluorescent rhodamine B (RhB)-doped solution photo-degraded using visible and infrared illumination (λ ≥ 400 nm) from a solar simulator. As such, pollutant degradation can be directly monitored in real time. Fabrication yields an outstanding control of the fibers’ morphology, and metal-enhanced photocatalytic properties are achieved by coating the nanofiber membranes with few nanometers of platinum using sputtering technique. This chemical-free functionalization of the nanofibers allows rapid and efficient RhB degradation. After optimization, 2.4 mg of photocatalyst achieves 93% removal after 150 min under solar illumination, which is impressively more efficient compared with previous reports. Most importantly, the colloidal release-free photocatalysis activity coupled to a manufacturing-ready fabrication process makes it ideal for large-scale deployment using industrial grade equipment.

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 categoriesMeta-epidemiology (narrow)
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.226
Teacher spread0.217 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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