MétaCan
Menu
Back to cohort
Record W4285591721 · doi:10.1142/s1793604722510389

Facile temperature-controlled preparation of nanowire OMS-2 and its high performance in peroxymonosulfate-activated catalytic degradation of organic pollutants

2022· article· en· W4285591721 on OpenAlexaff
Qingwei Nan, Ying Liu, Lijiao Liang, Xinyu Li, Joe R. Zhao, Chuanbo Hu, Meiying Huang, Jianting Tang

Bibliographic record

VenueFunctional Materials Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsTri Y Environmental Research Institute (Canada)
FundersNatural Science Foundation of Chongqing
KeywordsRhodamine BCatalysisNanowireDegradation (telecommunications)Molecular sieveMethylene blueHydrothermal circulationMaterials scienceOctahedronChemical engineeringPollutantNanotechnologyNuclear chemistryChemistryCrystallographyOrganic chemistryPhotocatalysisCrystal structureComputer science

Abstract

fetched live from OpenAlex

Currently, nanowires present a type of unique one-dimensional materials showing promising prospects in many fields. The octahedron molecular sieve (OMS-2, with chemical compositions of K[Formula: see text][Formula: see text]Mn 8 O 16 ) materials are utilized extensively as efficient catalysts for numerous critical reactions. In this work, it was found that the evolution from [Formula: see text]-MnOOH microrod to OMS-2 nanowire can be induced facilely by altering the temperature of hydrothermal treatment. The obtained OMS-2 nanowire exhibited high catalytic activity and high durability in peroxymonosulfate (PMS) activated degradation of rhodamine B (RhB) or methylene blue (MB). Under optimum conditions (OMS-2 catalyst: 0.1 g⋅L[Formula: see text]; RhB or MB: 8 mg⋅L[Formula: see text], 100 mL), the degradation can be completed within 10 min. The possible reason for OMS-2 nanowire’s high performance was also proposed.

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 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 categoriesInsufficient payload (model declined to judge)
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 score0.160
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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)0.0020.000

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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 teacher head, not a consensus.

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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueFunctional Materials LettersSame topicAdvanced oxidation water treatmentFrench-language works237,207