Evidence map›Paper›PMID 41826285›Full record

ArticleNature communications2026

Machine learning assisted single-molecule sensing towards standard-free quantification of per- and polyfluoroalkyl carboxylic acids.

Jiaqi Zuo, Hong-Shuang Li, Wen Tang, Xian Zhao, Meng-Yuan Cheng, Zekai Yang, Siyu Tian, Pufeng Li, Xueying Xie, Dan Luo and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Jiaqi Zuo *Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.ORCID http://orcid.org/0000-0001-9170-9688
Hong-Shuang Li *Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.ORCID http://orcid.org/0009-0001-6920-5106
Wen Tang *Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.
Xian ZhaoKey Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.
Meng-Yuan ChengKey Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.
Zekai YangKey Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.
Siyu TianKey Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.
Pufeng LiKey Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.
Xueying XieKey Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.
Dan LuoKey Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China.
Kaipei QiuKey Laboratory of Environmental Risk Assessment and Control on Chemical Process, Ministry of Ecology and Environment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, PR China. kaipeiqiu@gmail.com.ORCID http://orcid.org/0000-0002-9807-9487

Funding

Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology) 2023YFC3008803National Natural Science Foundation of China (National Science Foundation of China) 21972041National Natural Science Foundation of China (National Science Foundation of China) 22006037Natural Science Foundation of Shanghai (Natural Science Foundation of Shanghai Municipality) 23ZR1416300
6 · The paper itself

Abstract

Per- and polyfluoroalkyl carboxylic acids (PFCAs) are of global concern for their ubiquitous presence in the environment. However, precise quantification of PFCAs remains challenging due to the shortage of standards. Herein, with the aid of machine learning, a probe-directed nanopore based single-molecule electrochemical sensor is developed towards standard-free digital quantification of PFCAs. To correctly predict the signal without standards, a strict linear relationship (R

Identifiers

PMID41826285
PMCPMC13128836

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.