Design and Sensitivity Analysis for Biosensors: Latest Advances and Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 1643

Special Issue Editors


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Guest Editor
Device Modelling Group, School of Engineering, University of Glasgow, Glasgow G12 8LT, UK
Interests: semiconductor device physics; microelectronics and semiconductor engineering; nanoelectronics; electrical characterization; optoelectronics device physics; semiconductor fabrication; nanostructure; semiconductor device modeling; material characterization; I-VSemiconductor; MOS; GaN; nanowires; nanotubes; sensors; ISFET; biosensors; circuits; graphene; quantum dots

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Guest Editor
Materials Research and Technology Department, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
Interests: biotechnology; cell biology; biophysics; solid state physics; materials science; materials chemistry; nanotechnology; spectroscopy

Special Issue Information

Dear Colleagues,

Biosensors are becoming a more prominent part of our lives. The requirements of high sensitivity, better reliability, accuracy, and repeatability of biosensors are a few major deal breakers for a biosensor to be commercialized. Most biosensors, either selective or label free, are based on the interaction of the receptor and target molecules but it is always hard to predict the behavior of the sensor system. Even with the advancements, recent research still focuses on the design and optimization of novel biosensor structures. Therefore, this Special Issue focuses on the latest advancements and prospects of novel biosensor designs. We encourage authors to submit manuscripts on FET-based biosensors and biosensing signal amplification circuits are encouraged. Experimental or simulation studies of biosensors to explain the quantitative results are also welcome.

Dr. Naveen Kumar
Dr. César Pascual García
Guest Editors

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Published Papers (2 papers)

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Research

9 pages, 5915 KiB  
Article
Simulation of a Radio-Frequency Wave Based Bacterial Biofilm Detection Method in Dairy Processing Facilities
by Ranajoy Bhattacharya, Ken Cornell and Jim Browning
Appl. Sci. 2024, 14(11), 4342; https://doi.org/10.3390/app14114342 - 21 May 2024
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Abstract
This paper describes the principles behind the radio-frequency (RF) sensing of bacterial biofilms in pipes and heat exchangers in a dairy processing plant using an electromagnetic simulation. Biofilm formation in dairy processing plants is a common issue where the absence of timely detection [...] Read more.
This paper describes the principles behind the radio-frequency (RF) sensing of bacterial biofilms in pipes and heat exchangers in a dairy processing plant using an electromagnetic simulation. Biofilm formation in dairy processing plants is a common issue where the absence of timely detection and subsequent cleaning can cause serious illness. Biofilms are known for causing health issues and cleaning requires a large volume of water and harsh chemicals. In this work, milk transportation pipes are considered circular waveguides, and pasteurizers/heat exchangers are considered resonant cavities. Simulations were carried out using the CST studio suite high-frequency solver to determine the effectiveness of the real-time RF sensing. The respective dielectric constants and loss tangents were applied to milk and biofilm. In our simulation, it was observed that a 1 µm thick layer of biofilm in a milk-filled pipe shifted the reflection coefficient of a 10.16 cm diameter stainless steel circular waveguide from 0.229 GHz to 0.19 GHz. Further sensitivity analysis revealed a shift in frequency from 0.8 GHz to 1.2 GHz for a film thickness of 5 µm to 10 µm with the highest wave reflection (S11) peak of ≈−120 dB for a 6 µm thick biofilm. A dielectric patch antenna to launch the waves into the waveguide through a dielectric window was also designed and simulated. Simulation using the antenna demonstrated a similar S11 response, where a shift in reflection coefficient from 0.229 GHz to 0.19 GHz was observed for a 1 µm thick biofilm. For the case of the resonant cavity, the same antenna approach was used to excite the modes in a 0.751 m × 0.321 m × 170 m rectangular cavity with heat exchange fins and filled with milk and biofilm. The simulated resonance frequency shifted from 1.52 GHz to 1.54 GHz, for a film thickness varying from 1 µm to 10 µm. This result demonstrated the sensitivity of the microwave detection method. Overall, these results suggest that microwave sensing has promise in the rapid, non-invasive, and real-time detection of biofilm formation in dairy processing plants. Full article
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16 pages, 1508 KiB  
Article
Open-Source Wearable Sensors for Behavioral Analysis of Sheep Undergoing Heat Stress
by Barbara Roqueto dos Reis, Tien Nguyen, Sathya Sujani and Robin R. White
Appl. Sci. 2023, 13(16), 9281; https://doi.org/10.3390/app13169281 - 16 Aug 2023
Viewed by 1011
Abstract
Heat stress (HS) negatively affects animal productivity and welfare. The usage of wearable sensors to detect behavioral changes in ruminants undergoing HS has not been well studied. This study aimed to investigate changes in sheep’s behavior using a wearable sensor and explore how [...] Read more.
Heat stress (HS) negatively affects animal productivity and welfare. The usage of wearable sensors to detect behavioral changes in ruminants undergoing HS has not been well studied. This study aimed to investigate changes in sheep’s behavior using a wearable sensor and explore how ambient temperature influenced the algorithm’s capacity to classify behaviors. Six sheep (Suffolk, Dorset, or Suffolk × Dorset) were assigned to 1 of 2 groups in a cross-over experimental design. Groups were assigned to one of two rooms where they were housed for 20d prior to switching rooms. The thermal environment within the rooms was altered five times per period. In the first room, the temperature began at a thermoneutral level and gradually increased before decreasing. Simultaneously, in the second room, the temperature began at hot temperatures and gradually decreased before increasing again. Physiological responses (respiratory rate, heart rate, and rectal temperature) were analyzed using a linear mixed-effects model. A random forest algorithm was developed to classify lying, standing, eating, and ruminating (while lying and standing). Thermal stress shifted daily animal behavior budgets, increasing total time spent standing in hot conditions (p = 0.036). Although models had a similar capacity to classify behaviors within a temperature range, their accuracy decreased when applied outside that range. Although wearable sensors may help classify behavioral shifts indicative of thermal stress, algorithms must be robustly derived across environments. Full article
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