Evidence map›Paper›PMID 38559741›Full record

ArticleJACS Au2024

Dynamic Interface-Assisted Rapid Self-Assembly of DNA Origami-Framed Anisotropic Nanoparticles.

Yanfei Qu, Fengyun Shen, Hongzhen Peng, Guifang Chen, Lihua Wang, Lele Sun

Abstract read
In one paragraph

Article in JACS Au, 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. 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

6 authors.

Yanfei QuSchool of Life Science, Shanghai University, Shanghai 200444, China.
Fengyun ShenInstitute of Materiobiology, Department of Chemistry, College of Science, Shanghai University, Shanghai 200444, China.
Hongzhen PengInstitute of Materiobiology, Department of Chemistry, College of Science, Shanghai University, Shanghai 200444, China.
Guifang ChenSchool of Life Science, Shanghai University, Shanghai 200444, China.ORCID https://orcid.org/0000-0002-4470-9105
Lihua WangInstitute of Materiobiology, Department of Chemistry, College of Science, Shanghai University, Shanghai 200444, China.
Lele SunInstitute of Materiobiology, Department of Chemistry, College of Science, Shanghai University, Shanghai 200444, China.ORCID https://orcid.org/0000-0002-8052-9451

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The ordered arrangement of nanoparticles can generate unique physicochemical properties, rendering it a pivotal direction in the field of nanotechnology. DNA-based chemical encoding has emerged as an unparalleled strategy for orchestrating precise and controlled nanoparticle assemblies. Nonetheless, it is often time-consuming and has limited assembly efficiency. In this study, we developed a strategy for the rapid and ordered assembly of DNA origami-framed nanoparticles assisted by dynamic interfaces. By assembling Au nanoparticles (AuNPs) onto DNA origami with different sticky ends in various directions, we endowed them with anisotropic specific affinities. After assembling DNA origami-framed AuNPs onto supported lipid bilayers with freely diffusing single-stranded DNA via DNA hybridization, we found that DNA origami-framed AuNPs could form larger ordered assemblies than those in 3D solution within equivalent time frames. Furthermore, we also achieved rapid and ordered assembly of liposome nanoparticles by employing the aforementioned strategy. Our work provides a novel avenue for efficient and rapid assembly of nanoparticles across two-dimensional interfaces, which is expected to promote the application of ordered nanoparticle assemblies in sensor and biomimetic system construction.

Identifiers

PMID38559741
PMCPMC10976600

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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.