Development of Machine Learning Models for Ion-Selective Electrode Cation Sensor Design.

Yuankai Huang, Shifa Zhong, Lan Gan, Yongsheng Chen

Abstract read

In one paragraph

Article in ACS ES&T engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Calibration-free ion-selective electrodes: A critical review of design strategies, materials, and applications in sensing.Talanta · 2026
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.

Yuankai HuangSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

Shifa ZhongSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

Lan GanSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

Yongsheng ChenSchool of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID https://orcid.org/0000-0002-9519-2302

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Polyvinyl chloride (PVC) membrane-based ion-selective electrode (ISE) sensors are common tools for water assessments, but their development relies on time-consuming and costly experimental investigations. To address this challenge, this study combines machine learning (ML), Morgan fingerprint, and Bayesian optimization technologies with experimental results to develop high-performance PVC-based ISE cation sensors. By using 1745 data sets collected from 20 years of literature, appropriate ML models are trained to enable accurate prediction and a deep understanding of the relationship between ISE components and sensor performance (

Identifiers

PMID39021402

DOI10.1021/acsestengg.4c00087

PMCPMC11250033

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

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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.