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Record W2734867617 · doi:10.1109/mnano.2017.2708818

Oily Wastewater Treatment by Nano-TiO<sub>2</sub>-Induced Photocatalysis: Seeking more efficient and feasible solutions

2017· article· en· W2734867617 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Nanotechnology Magazine · 2017
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBTEXWastewaterEthylbenzeneTolueneEnvironmental chemistryOil refineryContaminationBenzeneChemistryEnvironmental sciencePhotocatalysisPetrochemicalWaste managementOrganic chemistryEnvironmental engineeringCatalysis

Abstract

fetched live from OpenAlex

Oil is one our most important energy sources However, the wide use of oil could lead to diverse environmental problems, such as oily wastewater discharge. Oily wastewater is water that has become contaminated through oil and gas production, the refinery process, transportation, or storage. The dispersion and dissolution of oil fractions into water result in its contamination with free oil and grease, aliphatic hydrocarbons, aromatic hydrocarbons (e.g., benzene, toluene, ethylbenzene, and xylenes, known as BTEX), phenols, polycyclic aromatic hydrocarbons (PAHs), and highly soluble organic compounds (e.g., carboxyl acids. Many of these components are toxic, persistent, and bioaccumulative, posing a threat to ecosystems and human beings [6]-[9]. Thus, efficient treatment of oily wastewater is a necessity.

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.

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), Science and technology studies
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.244
Teacher spread0.224 · 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