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Record W4213000306 · doi:10.1007/s10311-022-01390-4

Solar photo-oxidation of recalcitrant industrial wastewater: a review

2022· review· en· W4213000306 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

VenueEnvironmental Chemistry Letters · 2022
Typereview
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsPolytechnique Montréal
FundersBryden Centre, Queen's University BelfastAcademy of Scientific Research and TechnologyNational Research CentreNational Research FoundationMinistry of Science and ICT, South KoreaEuropean CommissionNational Research Foundation of KoreaDepartment for the Economy
KeywordsWastewaterPhotocatalysisPollutantPhotodegradationIndustrial wastewater treatmentSewage treatmentWaste managementAdvanced oxidation processReusabilityEnvironmental scienceCatalysisEnvironmental chemistryMaterials scienceChemistryPulp and paper industryEnvironmental engineeringComputer scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Conventional methods to clean wastewater actually lead to incomplete treatments, calling for advanced technologies to degrade recalcitrant pollutants. Herein we review solar photo-oxidation to degrade the recalcitrant contaminants in industrial wastewater, with focus on photocatalysts, reactor design and the photo-Fenton process. We discuss limitations due to low visible-light absorption, catalyst collection and reusability, and production of toxic by-products. Photodegradation of refractory organics by solar light is controlled by pH, photocatalyst composition and bandgap, pollutant properties and concentration, irradiation type and intensity, catalyst loading, and the water matrix.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.036
GPT teacher head0.271
Teacher spread0.235 · 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