Evidence map›Paper›PMID 41179153›Full record

ArticleACS omega2025

Quantifying Charge Density in Complex Biopolymer Systems via Conductometric Titration.

Gijs Y Kleine, Philipp K Wilfert, Stephen J Picken

Abstract read
In one paragraph

Article in ACS omega, 2025. 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

3 authors.

Gijs Y KleineAdvanced Soft Matter, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, van der Maasweg 9, Delft 2629 HZ, The Netherlands.ORCID https://orcid.org/0009-0009-0578-4397
Philipp K WilfertEnvironmental Biotechnology, Department of Biotechnology, Faculty of Applied Sciences, Delft University of Technology, van der Maasweg 9, Delft 2629 HZ, The Netherlands.
Stephen J PickenAdvanced Soft Matter, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, van der Maasweg 9, Delft 2629 HZ, The Netherlands.ORCID https://orcid.org/0000-0002-6003-518X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this research, a method has been developed to measure the charge density of complex biopolymer systems. Extracellular polymeric substances (EPS), obtained from wastewater treatment plants (WWTPs), are such a complex mixture of biopolymers and have shown to be difficult to characterize. One important characteristic for the development of applications such as green batteries, heavy metal sorption, and use as a bio flocculant is the charge density. Alginate was used as a model compound because of its well-characterized structure and known charge density. The method was optimized by varying the sample and titrant concentrations, and the titration speed. A sample concentration of 0.01 M was shown to provide the most accurate results, and the titrant concentration and titration speed had less effect on the measured charge density. With the optimal measurement settings, the method was validated and used to determine differences in charge density among EPS extracts from various wastewater treatment sludges. The different EPS samples have varying charge densities between 1.18 and 3.57 mmol/g, and the first derivative of the pH during titrations showed significant differences between samples. The resulting protocol provides a robust, reproducible, and precise approach for analyzing the EPS charge density. Furthermore, it expands the potential of conductometric methods, as systematic differences in the pH derivative curves were observed.

Identifiers

PMID41179153
PMCPMC12573012

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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.