Evidence map›Paper›PMID 36820151›Full record

ArticleEnvironmental science and ecotechnology2023

A tailored and rapid approach for ozonation catalyst design.

Min Li, Liya Fu, Liyan Deng, Yingming Hu, Yue Yuan, Changyong Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental science and ecotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Min LiState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environment Sciences, Beijing, 100012, China.
Liya FuState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environment Sciences, Beijing, 100012, China.
Liyan DengState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environment Sciences, Beijing, 100012, China.
Yingming HuState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environment Sciences, Beijing, 100012, China.
Yue YuanState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environment Sciences, Beijing, 100012, China.
Changyong WuState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environment Sciences, Beijing, 100012, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Catalytic ozonation is widely employed in advanced wastewater treatment owing to its high mineralization of refractory organics. The key to high mineralization is the compatibility between catalyst formulation and wastewater quality. Machine learning can greatly improve experimental efficiency, while fluorescence data can provide additional wastewater quality information on the composition and concentration of organics, which is conducive to optimizing catalyst formulation. In this study, machine learning combined with fluorescence spectroscopy was applied to develop ozonation catalysts (Mn/γ-Al

Indexed as

Artificial neural networkCatalytic ozonationFluorescence spectroscopyMachine learningOzonation catalyst

Identifiers

PMID36820151
PMCPMC9938169

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.