Evidence map›Paper›PMID 29430272›Full record

ReviewBiomicrofluidics2018

Surface enhanced Raman scattering (SERS) based biomicrofluidics systems for trace protein analysis.

Chun-Wei Lee, Fan-Gang Tseng

Abstract readReview
In one paragraph

Review in Biomicrofluidics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Hydrated Ionic Liquids Boost the Trace Detection Capacity of Proteins on TiOLangmuir : the ACS journal of surfaces and colloids · 2021
    Article
  6. Review
  7. Interactions of Whey Proteins with Metal Ions.International journal of molecular sciences · 2020
    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.

Chun-Wei LeeDepartment of Engineering and System, National Tsing Hua University, No. 101, Sec. 2, Kuang-Fu Rd., Hsinchu 30013, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, Surface Enhanced Raman Scattering (SERS) has been widely applied to many different areas, including chemical analysis, biomolecule detection, bioagent diagnostics, DNA sequence, and environmental monitor, due to its capabilities of unlabeled fingerprint identification, high sensitivity, and rapid detection. In biomicrofluidic systems, it is also very powerful to integrate SERS based devices with specified micro-fluid flow fields to further focusing/enhancing/multiplexing SERS signals through molecule registration, concentration/accumulation, and allocation. In this review, after a brief introduction of the mechanism of SERS detection on proteins, we will first focus on the effectiveness of different nanostructures for SERS enhancement and light-to-heat conversion in trace protein analysis. Various protein molecule accumulation schemes by either (bio-)chemical or physical ways, such as immuno, electrochemical, Tip-enhanced Raman spectroscopy, and magnetic, will then be reviewed for further SERS signal amplification. The analytical and repeatability/stability issues of SERS detection on proteins will also be brought up for possible solutions. Then, the comparison about various ways employing microfluidic systems to register, concentrate, and enhance the signals of SERS and reduce the background noise by active or passive means to manipulate SERS nanostructures and protein molecules will be elaborated. Finally, we will carry on the discussion on the challenges and opportunities by introducing SERS into biomicrofluidic systems and their potential solutions.

Identifiers

PMID29430272
PMCPMC5780278

What OpenQuestion holds

Textmetadata
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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.