Evidence map›Paper›PMID 39178555›Full record

ArticleUltrasonics sonochemistry2024

Machine learning model to predict rate constants for sonochemical degradation of organic pollutants.

Iseul Na, Taeho Kim, Pengpeng Qiu, Younggyu Son

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ultrasonics sonochemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Iseul NaDepartment of Environmental Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea; Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Taeho KimAI Lab, ROSIS IT, Seoul 07547, Republic of Korea.
Pengpeng QiuState Key Laboratory for Modification of Chemical Fibers and Polymer Materials, Institute of Functional Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, PR China.
Younggyu SonDepartment of Environmental Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea; Department of Energy Engineering Convergence, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea. Electronic address: yson@kumoh.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, machine learning (ML) algorithms were employed to predict the pseudo-1st-order reaction rate constants for the sonochemical degradation of aqueous organic pollutants under various conditions. A total of 618 sets of data, including ultrasonic, solution, and pollutant characteristics, were collected from 89 previous studies. Considering the difference between the electrical power (P

Indexed as

CavitationMachine learningReaction ratesSonochemical degradationUltrasound

Identifiers

PMID39178555
PMCPMC11386492

What OpenQuestion holds

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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.