Evidence map›Paper›PMID 40871766›Full record

ArticleSensors (Basel, Switzerland)2025

Enhancing Sensitivity of Commercial Gold Nanoparticle-Based Lateral Flow Assays: A Comparative Study of Colorimetric and Photothermal Approaches.

Jully Blackshare, Hansel A Mina, Amanda J Deering, Bartek Rajwa, J Paul Robinson, Euiwon Bae

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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.

Jully BlackshareApplied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0009-0001-2748-2156
Hansel A MinaDepartment of Food Science, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0001-8315-2517
Amanda J DeeringDepartment of Food Science, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0002-5074-2788
Bartek RajwaBindley Bioscience Center, Discovery Park, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0001-7540-8236
J Paul RobinsonDepartment of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0001-8383-3240
Euiwon BaeApplied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0001-6095-3223

Funding

United States Department of Agriculture 59-8072-6-001
6 · The paper itself

Abstract

Lateral flow assays (LFAs) are extensively utilized in point-of-care diagnostics due to their affordability, simplicity, and rapid time-to-results. However, their low sensitivity remains a significant limitation, particularly for detecting foodborne pathogens at concentrations below regulatory thresholds. This study evaluated two distinct sensing modalities-photothermal speckle imaging and colorimetric line intensity analysis-for their potential to enhance the sensitivity of commercially available LFAs. Photothermal imaging quantified refractive index shifts induced by plasmonic heating of gold nanoparticles, while colorimetric analysis used smartphone-acquired images processed with machine learning. The photothermal method achieved a limit of detection (LOD) of 2.13 × 10

Indexed as

Biosensing TechniquesColorimetryGoldMetal NanoparticlesHumansLimit of DetectionSmartphoneGoldcolorimetric analysisfoodborne pathogenslateral flow assaysmachine learningphotothermal speckle imagingSalmonella detectionsmartphone-based biosensing

Identifiers

PMID40871766
PMCPMC12390516

What OpenQuestion holds

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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.