Evidence map›Paper›PMID 40428690›Full record

ReviewMicromachines2025

Optical Detection Techniques for Biomedical Sensing: A Review of Printed Circuit Board (PCB)-Based Lab-on-Chip Systems.

Francisco Perdigones, Pablo Giménez-Gómez, Xavier Muñoz-Berbel, Carmen Aracil

Abstract readReview
In one paragraph

Review in Micromachines, 2025. 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. 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

4 authors.

Francisco PerdigonesElectronic Engineering Department, Higher Technical School of Engineering, University of Seville, 41092 Seville, Spain.ORCID 0000-0002-3996-8970
Pablo Giménez-GómezDepartment of Materials and Environmental Chemistry, Stockholm University, 106 91 Stockholm, Sweden.ORCID 0000-0003-3443-802X
Xavier Muñoz-BerbelInstitut de Microelectrònica de Barcelona (IMB-CNM, CSIC), Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain.ORCID 0000-0002-6447-5756
Carmen AracilElectronic Engineering Department, Higher Technical School of Engineering, University of Seville, 41092 Seville, Spain.ORCID 0000-0003-2589-5114

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lab on Printed Circuit Boards (Lab-on-PCB) technology has emerged as a promising platform, offering miniaturization, integration, and cost-effective fabrication for a wide range of sensing applications. This review explores the most common optical detection techniques implemented on printed circuit boards (PCBs), including absorbance, fluorescence, and chemiluminescence, discussing their working principles, advantages, and limitations in the context of PCB-based sensing. Additionally, evanescent wave generation is considered as an alternative optical approach with benefits for specific applications. Elements such as excitation sources, photodetectors, and the distinguishing characteristics of each method are analyzed to provide a comprehensive, but concise, overview of the field. Emphasis is placed on how the PCB platform influences the performance, sensitivity, and feasibility of these detection methods, highlighting relevant design considerations. This work aims to provide a solid foundation for researchers interested in optical sensing within this technology, serving as a reference for future developments and applications in PCB-based optical detection.

Indexed as

absorbancebiomedical applicationschemiluminescencefluorescenceLab-on-PCBprinted circuit boards

Identifiers

PMID40428690
PMCPMC12114130

What OpenQuestion holds

Textmetadata
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.