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Record W4411080121 · doi:10.1016/j.cattod.2025.115418

Novel insight into the influence of supports on the performance of α-MnO2 catalysts for ozone-assisted VOC oxidation: A comparative study of inert and semiconductor supports, impact of operational parameters, and stability assessments

2025· article· en· W4411080121 on OpenAlex
Farid Jafarihaghighi, Amir Payan, Jafar Soltan

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCatalysis Today · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsCatalysisInertOzoneHeterogeneous catalysisChemistryChemical engineeringNanotechnologyMaterials scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

This study investigates the impact of different supports on the performance of α-MnO₂ in ozone-assisted catalytic oxidation of volatile organic compounds (VOCs). Two supports, ZSM-5 and SiO 2 , were compared for effectiveness in removing polar (acetone) and nonpolar (toluene) VOCs. Catalysts were characterized by TGA, BET, TEM, XRD, HRTEM, XPS, EDX, and SEM analyses. Results showed that α-MnO 2 /ZSM-5 achieved superior removal efficiencies (93% acetone, 96% toluene), attributed to the higher density of oxygen vacancies and lower Mn oxidation states facilitated by ZSM-5. Operational parameters such as relative humidity and temperature were also evaluated, demonstrating enhanced catalyst stability and performance in humid conditions. α-MnO 2 /ZSM-5 exhibited minimal deactivation (<5%) after 10 cycles, highlighting its potential for sustainable air purification applications.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.120
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.336
Teacher spread0.303 · 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