Evidence map›Paper›PMID 38684953›Full record

ReviewLangmuir : the ACS journal of surfaces and colloids2024

Localized Surface Plasmon Resonance Sensing and its Interplay with Fluidics.

Nikhil Bhalla, Amy Q Shen

Abstract readReview
In one paragraph

Review in Langmuir : the ACS journal of surfaces and colloids, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Conundrums of Localized Surface Plasmon Resonance Biosensors.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. 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.

Nikhil BhallaNanotechnology and Integrated Bioengineering Centre (NIBEC), School of Engineering, Ulster University, Belfast BT15 1AP, United Kingdom.ORCID 0000-0002-4720-3679
Amy Q ShenMicro/Bio/Nanofluidics Unit, Okinawa Institute of Science and Technology Graduate Univerisity, Onna-son, Okinawa 904-0495, Japan.ORCID 0000-0002-1222-6264

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this Feature Article, we discuss the interplay between fluidics and the localized surface plasmon resonance (LSPR) sensing technique, primarily focusing on its applications in the realm of bio/chemical sensing within fluidic environments. Commencing with a foundational overview of LSPR principles from a sensing perspective, we subsequently showcase the development of a streamlined LSPR chip integrated with microfluidic structures. This integration opens the doors to advanced experiments involving fluid dynamics, greatly expanding the scope of LSPR-based research. Our discussions then turn to the practical implementation of LSPR and microfluidics in real-time biosensing, with a specific emphasis on monitoring DNA polymerase activity. Additionally, we illustrate the direct sensing of biological fluids, exemplified by the analysis of urine, while also shedding light on a unique particle assembly process that occurs on LSPR chips. We not only discuss the significance of LSPR sensing but also explore its potential to investigate a plethora of phenomena at liquid-liquid and solid-liquid interfaces. This is particularly noteworthy, as existing transduction methods and sensors fall short in fully comprehending these interfacial phenomena. Concluding our discussion, we present a futuristic perspective that provides insights into potential opportunities emerging at the intersection of fluidics and LSPR sensing.

Identifiers

PMID38684953
PMCPMC11100005

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.