Evidence map›Paper›PMID 40277567›Full record

ArticleBiosensors2025

The Optimization of a T-Cell Resonator: Towards Highly Sensitive Photoacoustic Spectroscopy for Noninvasive Blood Glucose Detection.

Thasin Mohammad Zaman, Md Rejvi Kaysir, Shazzad Rassel, Dayan Ban

Abstract read
In one paragraph

Article in Biosensors, 2025. 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

4 authors.

Thasin Mohammad ZamanDepartment of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.ORCID 0009-0006-7341-5065
Md Rejvi KaysirDepartment of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh.ORCID 0000-0002-3365-150X
Shazzad RasselDepartment of Electrical and Computer Engineering, Tennessee State University, 3500 John A Merritt Bivs, Nashville, TN 37209, USA.ORCID 0000-0002-0917-9209
Dayan BanDepartment of Electrical and Computer Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada.ORCID 0000-0002-5271-3163

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Noninvasive blood glucose monitoring is crucial for diabetes management, and photoacoustic spectroscopy (PAS) offers a promising solution by detecting glucose levels through human skin. However, weak acoustic signals in PAS systems require optimized resonator designs for enhanced detection sensitivity. Designing such resonators physically is complex, requiring the precise identification of critical parameters before practical implementation. This study focused on optimizing a T-shaped photoacoustic resonator using finite element modeling in a COMSOL Multiphysics environment. By systematically varying the geometric design parameters of the T-cell resonator, a maximum increase in the pressure amplitude of 12.76 times with a quality factor (Q-factor) of 47.5 was achieved compared to the previously designed reference acoustic resonator. This study took a significant step forward by identifying key geometric parameters that influence resonator performance, paving the way for more sensitive and reliable noninvasive glucose monitoring systems.

Indexed as

Biosensing TechniquesBlood GlucosePhotoacoustic TechniquesSpectrum AnalysisT-LymphocytesHumansBlood Glucoseacoustic resonatormid-infrared (MIR) spectroscopyphotoacoustic resonator (PAR)photoacoustic spectroscopy (PAS)Q-factorT-cell

Identifiers

PMID40277567
PMCPMC12024988

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.