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Vol – 2, No. 1, July – September 2026

Microstrip Antenna for Sensing Water Purity and Impurities: A Systematic Review

Authors:

Shahad Khalid Khaleel, Yaqeen Sabah Mezaal, Omer Saad Abdulqader Abdulwahab

DOI:

Abstract:

Microstrip and planar microwave sensors offer a compact solution for water quality monitoring, changing the electromagnetic characteristics of a liquid into measurable changes in resonance frequency, reflection or transmission coefficients, phase and quality factor. In this critical review, the representative peer-reviewed studies on salinity, chlorine, ammonia, iron, lead, trace-metal pollution and chloride detection are discussed with focus on sensor topology, operating frequency, liquid-sensor interaction, calibration strategy and evidence quality. The literature demonstrates that patch, split-ring resonator (SRR), complementary SRR (CSRR) and related planar structures can sense controlled changes in aqueous composition with small sample volumes and, in some cases, without direct electrical contact. The electromagnetic response is however generally non-specific as several dissolved species can alter the complex permittivity and conductivity simultaneously. Therefore, a large resonance shift or a low fitted error should not be interpreted in itself as a validated limit of detection or selective chemical identification. A trend away from laboratory salinity measurement toward in-situ monitoring, portable instrumentation and chemically assisted selective detection is seen in recent work. The main remaining issues to be resolved are matrix effects in real water, temperature dependence, packaging and substrate variability, calibration drift, independent validation, uncertainty reporting and multi-analyte discrimination. The review therefore recommends differential or multi-frequency architectures, controlled microfluidic interfaces, selective chemical reactions or recognition layers where appropriate, and portable VNA/LiteVNA readout and statistically rigorous calibration and validation protocols.

Keywords:

microstrip antenna, microwave sensor, water quality, salinity, SRR, CSRR, dielectric permittivity, chlorine, trace metals, calibration, environmental monitoring

Refference:

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2. K. Zhang, R. K. Amineh, Z. Dong, and D. Nadler, “Microwave Sensing of Water Quality,” IEEE Access, vol. 7, pp. 69481–69493, 2019. 10.1109/ACCESS.2019.2918996.
3. J. Muñoz-Enano, P. Vélez, M. Gil, and F. Martín, “Planar Microwave Resonant Sensors: A Review and Recent Developments,” Applied Sciences, vol. 10, no. 7, Art. no. 2615, 2020. 10.3390/app10072615.

4. I. Frau, S. Wylie, P. Byrne, P. Onnis, J. Cullen, A. Mason, and O. Korostynska, “Microwave Sensors for In Situ Monitoring of Trace Metals in Polluted Water,” Sensors, vol. 21, no. 9, Art. no. 3147, 2021. 10.3390/s21093147.
5. C. Liu, C. Liao, Y. Peng, W. Zhang, B. Wu, and P. Yang, “Microwave Sensors and Their Applications in Permittivity Measurement,” Sensors, vol. 24, no. 23, Art. no. 7696, 2024. 10.3390/s24237696.
6. X. Liu, C. Yang, X. Xing, G. Yang, G. Ding, Q. Sun, Z. Song, N. Qin, P. Zhao, and Y. Dong, “Portable and Selective Chloride Ion Detection in High-Salinity Water via a Microwave Sensor and Synergistic Precipitation-Dielectric Strategy,” Talanta, vol. 302, Art. no. 129434, 2026. 10.1016/j.talanta.2026.129434.
7. S. K. Yee, N. T. J. Ong, S. C. J. Lim, N. S. Mohd Zin, S. H. Dahlan, A. Y. I. Ashyap, and C. F. Soon, “Microwave Sensing of Ammonia and Iron Concentration in Water Based on Complementary Double Split-Ring Resonator,” Sensors and Actuators Reports, vol. 3, Art. no. 100044, 2021. 10.1016/j.snr.2021.100044.
8. A. S. Mekki, S. M. Yousif, and M. M. J. Abed, “Testing of Chlorine Dosage in Drinking Water Using Microstrip Patch Sensor,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 21, no. 3, pp. 528–534, 2023. 10.12928/TELKOMNIKA.v21i3.24417.
9. K. H. Yeap, J. L. Lam, S. H. Loh, V. Dakulagi, and A. U. Mazlan, “Microwave Sensor for Sodium Chloride Density Measurement in Aqueous Solutions,” Journal of Engineering Technology and Applied Physics, vol. 6, no. 2, pp. 52–56, 2024. 10.33093/jetap.2024.6.2.8.
10. H. Jasim, A. A. Al-Behadili, and S. Ahmed, “Highly Sensitive Microstrip Patch Sensor for Water Salinity Monitoring,” Progress In Electromagnetics Research C, vol. 157, pp. 247–257, 2025. 10.2528/PIERC25051504.
11. H. Jasim, S. Ahmed, I. A. Mocanu, and A. A. Al-Behadili, “Microstrip Patch Sensor for Characterizing Saline Solution Based on Complimentary Split-Ring Resonators (SC-SRRs),” Sensors, vol. 25, no. 7, Art. no. 2319, 2025. 10.3390/s25072319.
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13. Z. Ul Islam, A. Bermak, and B. Wang, “A Review of Microstrip Patch Antenna-Based Passive Sensors,” Sensors, vol. 24, no. 19, Art. no. 6355, 2024. 10.3390/s24196355.

14. M. S. Sim, K. Y. You, R. Dewan, F. Esa, M. R. Salim, C. S. Khe, S. Y. N. Kew, and F. Hamid, “Microwave Sensors Loaded with Metamaterial-Inspired Resonators for Dielectric Material Characterization: A Review,” Sensors and Actuators A: Physical, vol. 375, Art. no. 115322, 2024. 10.1016/j.sna.2024.115322.
15. A. C. T. Hui, S. S. Tiang, K. H. Teng, W. H. Lim, and M. Mokayef, “Detection of Lead with IoT Water Monitoring System Using Microstrip Antenna-Based Sensor,” in Advances in Intelligent Manufacturing and Mechatronics, Lecture Notes in Electrical Engineering, vol. 988, pp. 127–137, Springer, 2023. 10.1007/978-981-19-8703-8_11.

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Electromagnetic Biosensing: A Comprehensive Review on Microwave and Terahertz Antennas for Bacterial and Microbial Detection

Authors:

Shahad Khalid Khaleel, Baraa F. Al-Azzawi

DOI:

Abstract:

Clinically, highly sensitive and specific detection of bacterial and other microbial contamination is necessary for diagnostic purposes, as well as for safety control in food processing, water quality monitoring and environmental monitoring and research. Conventional culture and nucleic-acid methods are still required reference techniques, but can be labor intensive including enrichment, sample preparation, use of laboratory equipment, and trained personnel. A complementary approach is represented by microwave and terahertz (THz) electromagnetic biosensors, where the loading, binding and/or biochemical composition of the microbe affects the complex electromagnetic response of a resonant structure. The physical sensing mechanism, resonator architectures, surface-recognition strategies, microfluidic integration, and engineering limitations of microbial detection are reviewed. Special attention is given to experimentally proven microwave resonator platforms for Escherichia coli and to novel THz metasensors, and to the different types of analytical selectivity and intrinsic electromagnetic sensitivity. Other significant translation obstacles (aqueous dielectric loss, non-specific adsorption, matrix effects, temperature drift, calibration transfer, and the necessity for real sample validation) are also highlighted, along with potential differential measurement, phase-based sensing, microfluidic pre-concentration, and data-driven spectral analysis

Keywords:

Electromagnetic biosensors, bacterial detection, microwave resonators, terahertz metasurfaces, split-ring resonators, microfluidics, biofunctionalization, biofunctionalization; label-free sensing, Escherichia coli

Refference:

1. S. Montoya-Villada, E. Reyes-Vera, and J. Orozco, “Microwave technologies for biomedical diagnosis and therapy: advances, challenges, and perspectives,” Microchimica Acta, vol. 193, no. 4, art. 218, 2026. 10.1007/s00604-025-07826-2.
2. S. Mohammadi, R. Narang, M. Mohammadi Ashani, H. Sadabadi, A. Sanati-Nezhad, and M. H. Zarifi, “Real-time monitoring of Escherichia coli concentration with planar microwave resonator sensor,” Microwave and Optical Technology Letters, vol. 61, pp. 2534–2539, 2019. 10.1002/mop.31913.
3. S. Mohammadi, A. V. Nadaraja, K. Luckasavitch, M. C. Jain, D. J. Roberts, and M. H. Zarifi, “A label-free, non-intrusive, and rapid monitoring of bacterial growth on solid medium using microwave biosensor,” IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 1, pp. 2–11, 2020. .1109/TBCAS.2019.2952841.
4. I. Piekarz, S. Górska, S. Odrobina, M. Drab, K. Wincza, A. Gamian, and S. Gruszczynski, “A microwave matrix sensor for multipoint label-free Escherichia coli detection,” Biosensors and Bioelectronics, vol. 147, art. 111784, 2020. 10.1016/j.bios.2019.111784.
5. M. C. Jain, A. V. Nadaraja, S. Mohammadi, B. M. Vizcaino, and M. H. Zarifi, “Passive microwave biosensor for real-time monitoring of subsurface bacterial growth,” IEEE Transactions on Biomedical Circuits and Systems, vol. 15, no. 1, pp. 122–132, 2021. 10.1109/TBCAS.2021.3055227.
6. I. Piekarz, S. Górska, K. Wincza, and S. Gruszczynski, “Planar single and dual-resonant microwave biosensors for label-free bacteria detection,” Sensors and Actuators B: Chemical, vol. 351, art. 130899, 2022. 10.1016/j.snb.2021.130899.
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9. W. Cui, J. Wang, C. Ding, P. Van Cappellen, E. A. Ho, and C. L. Ren, “A functionalized microwave biosensor for rapid, reagent-free detection of E. coli in water samples,” Biosensors and Bioelectronics, vol. 278, art. 117334, 2025. 10.1016/j.bios.2025.117334.
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Design and Performance Analysis of a Planar Microstrip Filter Sensor for Non-Invasive Human Tension and Physiological Stress Detection

Authors:

Nabaa S.M. Abed

DOI:

Abstract:

Psychological stress is accompanied by autonomic changes that can modify sudomotor activity, skin hydration, and the ionic environment at the skin surface. Because tissue water content and conductivity contribute to microwave dielectric behavior, these physiological changes can perturb the electromagnetic loading experienced by a resonant sensor. This study presents a compact planar microstrip band-stop sensor operating near 2.45 GHz for non-invasive monitoring of stress-related physiological changes. The sensor uses a modified split-ring resonator (SRR) coupled to a 50-Ω microstrip line on Rogers RT/duroid 5880 (εr = 2.2, tanδ = 0.0009, h = 1.57 mm) and occupies 30 × 25 mm². HFSS simulations were performed using a three-layer skin model and the dielectric states reported in the original study. Across the modeled tissue range, ε′ increased from 38.0 to 46.5 and conductivity from 1.45 to 1.95 S/m, while the resonance shifted from 2.312 to 2.185 GHz, corresponding to a 127 MHz shift and an average sensitivity of 14.94 MHz per unit change in ε′. The unloaded-to-resting transition produced an additional 138 MHz shift and should not be confused with the tissue-state sensitivity range. Experimental measurements on 12 healthy adults used a two-port VNA and GSR as a reference physiological signal. The manuscript reports phase-level values of 2.14 ± 0.35 μS at baseline, 6.85 ± 0.92 μS during the Stroop task, and 2.80 ± 0.41 μS during recovery, with corresponding measured resonances of 2.315, 2.242, and 2.301 GHz. The previously reported R² = 0.984 is retained only as a reported experimental association because individual-level raw data are not supplied in the manuscript; confidence intervals, subject-level RMSE/MAE, and repeated-measures inference require the original paired measurements. The revised interpretation therefore treats the microwave response as a stress-related physiological proxy rather than a direct or diagnostic measure of psychological stress.

Keywords:

microwave sensor, microstrip filter, split-ring resonator, skin permittivity, electrodermal activity, physiological stress, non-invasive sensing, 2.45 GHz

Refference:

1. C. Kirschbaum, K.-M. Pirke, and D. H. Hellhammer, “The ‘Trier Social Stress Test’—a tool for investigating psychobiological stress responses in a laboratory setting,” Neuropsychobiology, vol. 28, no. 1–2, pp. 76–81, 1993. 10.1159/000119004.
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6. A. Klimek, I. Mannheim, G. Schouten, E. J. M. Wouters, and M. W. H. Peeters, “Wearables measuring electrodermal activity to assess perceived stress in care: A scoping review,” Acta Neuropsychiatrica, vol. 37, p. e19, 2023. 10.1017/neu.2023.19.
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mRNA Vaccines and Their Role in Modulating the Immune Response

Authors:

Esraa Ahmed Abdulqadur

DOI:

Abstract:

Messenger RNA (mRNA) vaccines have transitioned from a research concept of decades to a validated therapeutic platform, accelerated by the rapid development of highly effective vaccines during the COVID-19 pandemic. The hallmark of these vectors is the delivery of genetic instructions, rather than a preformed antigen. The delivered transcript is translated by host cells and the resultant protein is presented to the immune system, thus engaging both the innate and adaptive arms in a manner that closely mimics natural infection . This review summarizes the immunological mechanisms that shape the immune response to mRNA vaccines.

We first describe the molecular architecture of the transcript—the 5′ cap, untranslated regions, open reading frame, and poly(A) tail—and the nucleoside modifications and lipid nanoparticle (LNP) delivery systems that govern its behaviour in vivo. We then examine innate immune sensing by endosomal and cytosolic pattern recognition receptors (TLR3/7/8, RIG-I, MDA5, PKR and the OAS/RNase L system), the pivotal type I interferon response, and the intrinsic adjuvant activity of the LNP carrier. We describe how these signals license dendritic cell maturation and antigen presentation, bridging to the adaptive response: germinal centre formation, T follicular helper and B-cell driven antibody affinity maturation, and CD4⁺ and CD8⁺ T-cell programming, culminating in durable immunological memory. Finally, we consider the balance between immunogenicity and reactogenicity, survey applications in infectious disease and personalised cancer immunotherapy, and outline the principal challenges and future directions of the field, including self-amplifying RNA and tolerogenic mRNA design.

Keywords:

mRNA vaccine, lipid nanoparticle, innate immunity, pattern recognition receptors, type I interferon, adaptive immunity, germinal centre, neoantigen, cancer immunotherapy, self-amplifying RNA

Refference:

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A Statistical Framework for Brain MRI Diagnostic Classification and Evaluation in Medical Image Processing

Authors:

Israa Abdulameer Resen

DOI:

Abstract:

The proposed work introduces the statistical framework of quantitative analysis and image processing methods for detecting structural brain changes and classifying the severity of dementia cases using  cross-sectional database. From  cases, only  individuals were included in a filtered analysis sample. Patients were divided into two groups according to CDR score. Cognitively Normal ( ) and Demented ( ). Continuous variables were tested for normal distribution using the Anderson-Darling test, followed by parametric (two-sample t-test) and non-parametric (Mann-Whitney U test) comparison between the groups. Further, a multivariable binary logistic regression model was developed based on structural MRI biomarkers of brain volume changes. Normalised Whole Brain Volume ( ), Estimated Total Intracranial Volume ( ), and Atlas Scaling Factor ( ) were investigated. Other continuous covariates such as  score and  and a dichotomous gender covariate were created. Empirical results showed that  score ( , ) and  ( ) were independent, statistically significant covariates associated with dementia risk. Therefore, progressive cerebral atrophy and cognitive dysfunction are the main predictors of impairment. Other covariates ( , , ) turned out to be non-significant in the multivariable model, since their influence is incorporated in structural brain volume changes and cognitive function test scores. The constructed logistic regression model had good performance in terms of calibration and fit ( , Hosmer-Lemeshow ). Evaluation of model classification performance revealed high accuracy of prediction of dementia presence with Area Under the ROC Curve ( ) of  (  CI: ]) and concordance index of . Overall, a combination of quantitative  biomarkers and standard cognitive tests provides an efficient diagnostic framework for making clinical decisions in Alzheimer’s disease and other forms of dementia.

Keywords:

Applied Medical Statistics, Binary Logistic Regression, Diagnosis, Volumetric Markers, Anderson-Darling Normality Test, ROC/AUC Analysis, OASIS-1 Brain MRI.

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