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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 OpenAlexaff
Ahmed Tawfik, Mohamed Gar Alalm, Hanem M. Awad, Muhammad Islam, Muhammad Abdul Qyyum, Ala’a H. Al‐Muhtaseb, Ahmed I. Osman, Moonyong Lee

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations93
Published2022
Admission routes1
Has abstractyes

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