Evidence map›Paper›PMID 42655620›Full record

ReviewSensors (Basel, Switzerland)2026

Microwave Sensors for Dielectric Characterization: Planar Architectures, Extraction Methods, and Emerging Applications.

Feifei Tan, Changjun Liu

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Feifei TanCollege of Communication Engineering, Chengdu University of Information Technology, Chengdu 610225, China.ORCID 0000-0003-3644-2807
Changjun LiuCollege of Electronics and Information Engineering, Sichuan University, Chengdu 610207, China.ORCID 0000-0003-0079-6793

Funding

National Natural Science Foundation of China U2015A22
6 · The paper itself

Abstract

Microwave dielectric characterization is essential for material evaluation, process monitoring, biomedical sensing, and nondestructive testing. This review critically evaluates planar microwave sensors for dielectric characterization, with the core scope restricted to printed microstrip and coplanar-waveguide structures, SRR/CSRR and DGS configurations, substrate-integrated waveguides, interferometric sensors, and microfluidic platforms. Adjacent non-planar or system-level techniques are included only when they provide transferable lessons in calibration, inversion, or deployment. Unlike earlier surveys that primarily catalog devices or extraction methods, the literature is organized here through a design-decision hierarchy linking architecture, operating principle, readout mechanism, sample interface, and application. Representative approaches are compared not only by frequency, sensitivity, Q-factor, and sample volume, but also by calibration burden, fabrication tolerance, environmental robustness, cost, and scalability. Particular attention is given to uncertainty sources in practical measurement chains, FR-4 and PCB manufacturing variability, long-term drift and sensor aging, and the application-specific limitations of machine-learning-assisted inversion. The resulting synthesis provides design-oriented guidance for selecting and translating planar microwave sensors into reliable industrial, biomedical, and microwave-processing measurement systems.

Indexed as

complex permittivitydielectric characterizationextraction methodsmachine learningmicrofluidic sensingmicrowave sensorsnondestructive evaluationplanar architectures

Identifiers

PMID42655620
PMCPMC13517173

What Socratic 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 Socratic graph.