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Record W2789681290 · doi:10.3390/w10020202

Floating Photocatalysts for Passive Solar Degradation of Naphthenic Acids in Oil Sands Process-Affected Water

2018· article· en· W2789681290 on OpenAlexafffundabout
Tim Leshuk, Harish Krishnakumar, Diogo de Oliveira Livera, Frank Gu

Bibliographic record

VenueWater · 2018
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooShell Canada
KeywordsOil sandsNaphthenic acidAsphaltDegradation (telecommunications)Environmental sciencePhotocatalysisWaste managementExtraction (chemistry)Mixing (physics)Water treatmentPulp and paper industryChemistryMaterials scienceEnvironmental engineeringOrganic chemistryCatalysisCorrosion

Abstract

fetched live from OpenAlex

Oil sands process-affected water (OSPW), generated from bitumen extraction in the Canadian oil sands, may require treatment to enable safe discharge to receiving watersheds, as dissolved naphthenic acids (NAs) and other acid extractable organics (AEO), identified as the primary toxic components of OSPW, are environmentally persistent and poorly biodegradable. However, conventional advanced oxidation processes (AOPs) are impractically expensive to treat the volumes of OSPW stockpiled in the Athabasca region. Here we prepared floating photocatalysts (FPCs) by immobilizing TiO2 on glass microbubbles, such that the composite particles float at the air-water interface for passive solar photocatalysis. The FPCs were demonstrated to outperform P25 TiO2 nanoparticles in degrading AEO in raw OSPW under natural sunlight and gentle mixing conditions. The FPCs were also found to be recyclable for multiple uses through simple flotation and skimming. This paper thus demonstrates the concept of a fully passive AOP that may be scalable to oil sands water treatment challenges, achieving efficient NA reduction solely through the energy provided by sunlight and natural mixing processes (wind and waves).

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 score0.008
Threshold uncertainty score0.387

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

Citations38
Published2018
Admission routes3
Has abstractyes

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