Evidence map›Paper›PMID 34677314›Full record

ArticleBiosensors2021

Millimeter-Wave-Based Spoof Localized Surface Plasmonic Resonator for Sensing Glucose Concentration.

Yelim Kim, Ahmed Salim, Sungjoon Lim

Abstract read
In one paragraph

Article in Biosensors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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.

Yelim KimSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea.
Ahmed SalimSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea.ORCID 0000-0001-5374-0465
Sungjoon LimSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea.ORCID 0000-0002-5049-9300

Funding

National Research Foundation of Korea 2020M3H4A3081832National Research Foundation of Korea 2021R1A4A2001316
6 · The paper itself

Abstract

Glucose-monitoring sensors are necessary and have been extensively studied to prevent and control health problems caused by diabetes. Spoof localized surface plasmon (LSP) resonance sensors have been investigated for chemical sensing and biosensing. A spoof LSP has similar characteristics to an LSP in the microwave or terahertz frequency range but with certain advantages, such as a high-quality factor and improved sensitivity. In general, microwave spoof LSP resonator-based glucose sensors have been studied. In this study, a millimeter-wave-based spoof surface plasmonic resonator sensor is designed to measure glucose concentrations. The millimeter-wave-based sensor has a smaller chip size and higher sensitivity than microwave-frequency sensors. Therefore, the microfluidic channel was designed to be reusable and able to operate with a small sample volume. For alignment, a polydimethylsiloxane channel was simultaneously fabricated using a multilayer bonding film to attach the upper side of the pattern, which is concentrated in the electromagnetic field. This real-time sensor detects the glucose concentration via changes in the S11 parameter and operates at 28 GHz with an average sensitivity of 0.015669 dB/(mg/dL) within the 0-300 mg/dL range. The minimum detectable concentration and the distinguishable signal are 1 mg/dL and 0.015669 dB, respectively, from a 3.4 μL sample. The reusability and reproducibility were assessed through replicates.

Indexed as

Biosensing TechniquesBlood GlucoseEquipment DesignMicrofluidicsMicrowavesReproducibility of ResultsSurface Plasmon ResonanceBlood Glucoseglucose solutionmetamaterialmillimeter wavesensorspoof localized surface plasmon polariton

Identifiers

PMID34677314
PMCPMC8533816

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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.