Evidence map›Paper›PMID 39590001›Full record

ReviewBiosensors2024

Research Trends in the Development of Block Copolymer-Based Biosensing Platforms.

Yong-Ho Chung, Jung Kwon Oh

Abstract readReview
In one paragraph

Review in Biosensors, 2024. 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. Review
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

2 authors.

Yong-Ho ChungDepartment of Chemical Engineering, Hoseo University, Asan-si 31499, Republic of Korea.
Jung Kwon OhDepartment of Chemistry and Biochemistry, Concordia University, Montreal, QC H4B 1R6, Canada.

Funding

Hoseo University Research grant in 2024Natural Science and Engineering Research Council (NSERC) in Canada Discovery Grant
6 · The paper itself

Abstract

Biosensing technology, which aims to measure and control the signals of biological substances, has recently been developed rapidly due to increasing concerns about health and the environment. Top-down technologies have been used mainly with a focus on reducing the size of biomaterials to the nano-level. However, bottom-up technologies such as self-assembly can provide more opportunities to molecular-level arrangements such as directionality and the shape of biomaterials. In particular, block copolymers (BCPs) and their self-assembly have been significantly explored as an effective means of bottom-up technologies to achieve recent advances in molecular-level fine control and imaging technology. BCPs have been widely used in various biosensing research fields because they can artificially control highly complex nano-scale structures in a directionally controlled manner, and future application research based on interactions with biomolecules according to the development and synthesis of new BCP structures is greatly anticipated. Here, we comprehensively discuss the basic principles of BCPs technology, the current status of their applications in biosensing technology, and their limitations and future prospects. Rather than discussing a specific field in depth, this study comprehensively covers the overall content of BCPs as a biosensing platform, and through this, we hope to increase researchers' understanding of adjacent research fields and provide research inspiration, thereby bringing about great advances in the relevant research fields.

Indexed as

Biosensing TechniquesPolymersBiocompatible MaterialsNanostructuresBiocompatible MaterialsPolymersbiosensing platformsbiosensorsblock copolymersnanostructuresself-assembly

Identifiers

PMID39590001
PMCPMC11591610

What OpenQuestion holds

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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 OpenQuestion graph.