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Record W2327079723 · doi:10.5383/ijtee.01.01.006

Solar Photocatalytic Degradation of Environmental Pollutants Using ZnO Prepared by Sol-Gel: 2, 4-Dichlorophenol as Case Study

2010· article· en· W2327079723 on OpenAlexvenueno aff
Muneer M. Ba‐Abbad, Abdul Amir, Hassan Kadhum, Abu Bakar Mohamad, Mohd Sobri Takriff, Kamaruzzaman Sopian

Bibliographic record

VenueInternational Journal of Thermal and Environmental Engineering · 2010
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
Keywords2,4-DichlorophenolPhotocatalysisDegradation (telecommunications)AbsorbanceCatalysisNuclear chemistryPollutantMaterials scienceAqueous solutionIrradiationChemical engineeringChemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

The photocatalytic degradation of 2, 4-dichlorophenol (2,4DCP) in aqueous solution was carried out in batch process with ZnO prepared by sol-gel method. The effects of various factors, such as different calcinations temperatures and amount of catalyst were investigated for efficient degradation of 2, 4-dichlorophenol. The UV/Vis Spectrophotometer was used for analysing the concentration of 2, 4-dichlorophenol in solution at different time intervals during the experiment. The photocatalytic degradation of 2, 4-dichlorophenol was investigated by monitoring molecular absorbance at λ = 285 nm. The average solar intensity of ultra violet (UV) was recorded as 23 W/m2. The photocatalytic activity of the ZnO was evaluated by photocatalytic degradation of initial concentration 50 mg/l of 2, 4-dichlorophenol. Optimum value of catalyst dose was found to be 2 g/l. The experimental results showed that the photocatalytic degradation efficiency of 2, 4-dichlorophenol for 1 h irradiation time was up to 98 %.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 head0.006
GPT teacher head0.209
Teacher spread0.203 · 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 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

Citations24
Published2010
Admission routes1
Has abstractyes

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Same venueInternational Journal of Thermal and Environmental EngineeringSame topicTiO2 Photocatalysis and Solar CellsFrench-language works237,207