Evidence map›Paper›PMID 26916167›Full record

ReviewPharmaceutical research2016

Recent Advances on Luminescent Enhancement-Based Porous Silicon Biosensors.

S N Aisyiyah Jenie, Sally E Plush, Nicolas H Voelcker

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pharmaceutical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Rapid Fluorescence Quenching Detection ofSensors (Basel, Switzerland) · 2021
    Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. 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

3 authors.

S N Aisyiyah JenieARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Future Industries Institute, University of South Australia, Mawson Lakes, SA, 5095, Australia.
Sally E PlushSchool of Pharmacy and Medical Sciences, University of South Australia, Adelaide, SA, 5000, Australia.
Nicolas H VoelckerARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Future Industries Institute, University of South Australia, Mawson Lakes, SA, 5095, Australia. nico.voelcker@unisa.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Luminescence-based detection paradigms have key advantages over other optical platforms such as absorbance, reflectance or interferometric based detection. However, autofluorescence, low quantum yield and lack of photostability of the fluorophore or emitting molecule are still performance-limiting factors. Recent research has shown the need for enhanced luminescence-based detection to overcome these drawbacks while at the same time improving the sensitivity, selectivity and reducing the detection limits of optical sensors and biosensors. Nanostructures have been reported to significantly improve the spectral properties of the emitting molecules. These structures offer unique electrical, optic and magnetic properties which may be used to tailor the surrounding electrical field of the emitter. Here, the main principles behind luminescence and luminescence enhancement-based detections are reviewed, with an emphasis on europium complexes as the emitting molecule. An overview of the optical porous silicon microcavity (pSiMC) as a biosensing platform and recent proof-of-concept examples on enhanced luminescence-based detection using pSiMCs are provided and discussed.

Indexed as

LuminescenceBiosensing TechniquesMagneticsPorositySiliconSiliconbiosensorseuropiumluminescence enhancementmicrocavityporous silicon

Identifiers

What OpenQuestion holds

Textmetadata
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.