Evidence map›Paper›PMID 40914892›Full record

ArticleAnalytical chemistry2025

Rapid Copolymer Analysis of Unresolved Mass Spectra by Artificial Intelligence.

Gergő Róth, Ákos Kuki, Aron Kipyegon Rop, Lajos Nagy, Zuura Kyzy Kaldybek, Máté Benedek, Miklós Zsuga, Tibor Nagy, Sándor Kéki

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In one paragraph

Article in Analytical chemistry, 2025. 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

9 authors.

Gergő RóthDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.
Ákos KukiDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.
Aron Kipyegon RopDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.ORCID 0000-0003-0169-7412
Lajos NagyDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.
Zuura Kyzy KaldybekDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.
Máté BenedekDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.
Miklós ZsugaDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.
Tibor NagyDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.ORCID 0000-0001-8568-914X
Sándor KékiDepartment of Applied Chemistry, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary.ORCID 0000-0002-5274-6117

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this Article, we present a novel data analysis method for the determination of copolymer composition from low-resolution mass spectra, such as those recorded in the linear mode of time-of-flight (TOF) mass analyzers. Our approach significantly extends the accessible molecular weight range, enabling reliable copolymer composition analysis even in the higher mass regions. At low resolution, the overlapping mass peaks in the higher mass range hinder a comprehensive characterization of the copolymers. Our approach, however, extracts the hidden information on the unique macromolecular chains embedded in these unresolved peaks. Regression relationships were developed between easily computable mass spectral parameters derived from the measured data and key characteristics of the copolymer structure including the average mole fraction, the number-average repeat unit count, and the monomer distribution. Two regression models were constructed: one using conventional statistical methods and the other employing a machine learning approach based on an Artificial Neural Network (ANN). The spectrum evaluation process is demonstrated for the analysis of various poly(

Identifiers

PMID40914892
PMCPMC12444754

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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.