Evidence map›Paper›PMID 34163855›Full record

ArticleChemical science2020

Microfluidic synthesis of high-valence programmable atom-like nanoparticles for reliable sensing.

Jing Li, Huayi Shi, Runzhi Chen, Xiaofeng Wu, Jiayi Cheng, Fenglin Dong, Houyu Wang, Yao He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chemical science, 2020. 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

8 authors.

Jing LiLaboratory of Nanoscale Biochemical Analysis, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University Suzhou 215123 China houyuwang@suda.edu.cn yaohe@suda.edu.cn.
Huayi ShiLaboratory of Nanoscale Biochemical Analysis, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University Suzhou 215123 China houyuwang@suda.edu.cn yaohe@suda.edu.cn.
Runzhi ChenLaboratory of Nanoscale Biochemical Analysis, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University Suzhou 215123 China houyuwang@suda.edu.cn yaohe@suda.edu.cn.
Xiaofeng WuDepartment of Ultrasound, The First Affiliated Hospital of Soochow University Suzhou 215006 China fldong@suda.edu.cn.
Jiayi ChengLaboratory of Nanoscale Biochemical Analysis, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University Suzhou 215123 China houyuwang@suda.edu.cn yaohe@suda.edu.cn.
Fenglin DongDepartment of Ultrasound, The First Affiliated Hospital of Soochow University Suzhou 215006 China fldong@suda.edu.cn.
Houyu WangLaboratory of Nanoscale Biochemical Analysis, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University Suzhou 215123 China houyuwang@suda.edu.cn yaohe@suda.edu.cn.ORCID https://orcid.org/0000-0002-5134-9881
Yao HeLaboratory of Nanoscale Biochemical Analysis, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University Suzhou 215123 China houyuwang@suda.edu.cn yaohe@suda.edu.cn.ORCID https://orcid.org/0000-0003-1672-4057

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synthesis of programmable atom-like nanoparticles (PANs) with high valences and high yields remains a grand challenge. Here, a novel synthetic strategy of microfluidic galvanic displacement (μ-GD) coupled with microfluidic DNA nanoassembly is advanced for synthesis of single-stranded DNA encoder (SSE)-encoded PANs for reliable surface-enhanced Raman scattering (SERS) sensing. Notably, PANs with high valences (

Identifiers

PMID34163855
PMCPMC8179029

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.