Evidence map›Paper›PMID 40256518›Full record

ArticleACS omega2025

Cationic Polymer Brushes Functionalized with Carbon Dots and Boronic Acids for Bacterial Detection and Inactivation.

Qicheng Zhang, Si Chen, Xiaoting Xue, Solmaz Hajizadeh, Tomohiko Yamazaki, Lei Ye

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. 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. Review
  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

6 authors.

Qicheng ZhangDivision of Pure and Applied Biochemistry, Department of Chemistry, Lund University, Lund 22100, Sweden.
Si ChenPolymer & Materials Chemistry, Department of Chemistry, Lund University, Lund 221 00, Sweden.
Xiaoting XuePolymer & Materials Chemistry, Department of Chemistry, Lund University, Lund 221 00, Sweden.
Solmaz HajizadehDivision of Pure and Applied Biochemistry, Department of Chemistry, Lund University, Lund 22100, Sweden.ORCID https://orcid.org/0000-0002-0348-8756
Tomohiko YamazakiResearch Center for Macromolecules and Biomaterials, National Institute for Materials Science (NIMS), Tsukuba 305-0047, Japan.ORCID https://orcid.org/0000-0003-2136-8042
Lei YeDivision of Pure and Applied Biochemistry, Department of Chemistry, Lund University, Lund 22100, Sweden.ORCID https://orcid.org/0000-0002-3646-4072

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drug-resistant bacterial infections are among the most severe physiological challenges facing human health. Therefore, the detection and inactivation of pathogenic bacteria remains a crucial therapeutic goal in modern society. In this study, we design multifunctional nanocomposites aimed at bacterial binding, fluorescence labeling, and synergistic antibacterial treatment. These nanocomposites are prepared by introducing cationic polymers with quaternary ammonium compounds onto silica nanoparticles using surface-initiated atom transfer radical polymerization, followed by incorporation of copper-doped carbon dots and modification of boronic acid. The cationic polymer units and boronic acid end groups enhance the bacterial binding capacity and synergistic bactericidal effects in cooperation with the carbon dots. Due to the stable fluorescent properties of carbon dots, the nanocomposites can generate fluorescence signals around bacteria, enabling bacterial fluorescence imaging. Overall, this study demonstrates a multifunctional nanocomposite-assisted strategy for bacterial labeling, imaging, and deactivation, providing a novel approach for bacterial detection and synergistic treatment.

Identifiers

PMID40256518
PMCPMC12004185

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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.