Vivek Chaturvedi

The studies explore advancements in wireless sensor technologies for medical applications, particularly for wireless detection of skin hydration, non-invasive glucose monitoring, and microbial detection. The first academic paper describes a capacitive humidity sensor for assessing relative humidity and skin hydration, utilizing the sensitive dielectric properties of the monoolein–water system. Tested across various humidity levels and temperatures, these novel double-functional sensors feature interdigitated electrodes covered with monoolein and show a promising potential for wireless detection of skin hydration. The second and third academic papers focus on a wireless potentiometric sensor designed to detect bacteria in urine and monitor microbial biofilm formation in wounds, respectively, using Bluetooth-enabled sensors to detect bacterial growth in vitro and ex vivo. The fourth paper assesses the development of fluid-less, non-invasive blood glucose monitoring devices, comparing their accuracy and challenges with traditional invasive methods. Despite some improvements, the study reveals that current devices like the BG20 glucometer need significant enhancements to meet clinical accuracy standards. Overall, the thesis emphasizes the potential of innovative wireless sensor technologies in improving medical diagnostics, while also addressing the limitations that must be overcome to ensure clinical reliability.

Capacitive humidity sensors typically consist of interdigitated electrodes coated with a dielectric layer sensitive to varying relative humidity levels. Previous studies have investigated different polymeric materials that exhibit changes in conductivity in response to water vapor to design capacitive humidity sensors. However, lipid films like monoolein have not yet been integrated with humidity sensors, nor has the potential use of capacitive sensors for skin hydration measurements been fully explored. This study explores the application of monoolein-coated wireless capacitive sensors for assessing relative humidity and skin hydration, utilizing the sensitive dielectric properties of the monoolein–water system. This sensitivity hinges on the water absorption and release from the surrounding environment. Tested across various humidity levels and temperatures, these novel double functional sensors feature interdigitated electrodes covered with monoolein and show promising potential for wireless detection of skin hydration. The water uptake and rheological behavior of monoolein in response to humidity were evaluated using a quartz crystal microbalance with dissipation monitoring. The findings from these experiments suggest that the capacitance of the system is primarily influenced by the amount of water in the monoolein system, with the lyotropic or physical state of monoolein playing a secondary role. A proof-of-principle demonstration compared the sensor’s performance under varying conditions to that of other commercially available skin hydration meters, affirming its effectiveness, reliability, and commercial viability.

Bacterial infections can affect the skin, lungs, blood, and brain, and are among the leading causes of mortality globally. Early infection detection is critical in diagnosis and treatment but is a time- and work-consuming process taking several days, creating a hitherto unmet need to develop simple, rapid, and accurate methods for bacterial detection at the point of care. The most frequent type of bacterial infection is infection of the urinary tract. Here, we present a wireless-enabled, portable, potentiometric sensor for E. coli. E. coli was chosen as a model bacterium since it is the most common cause of urinary tract infections. The sensing principle is based on reduction of Prussian blue by the metabolic activity of the bacteria, detected by monitoring the potential of the sensor, transferring the sensor signal via Bluetooth, and recording the output on a laptop or a mobile phone. In sensing of bacteria in an artificial urine medium, E. coli was detected in ~4 h (237 ± 19 min; n = 4) and in less than 0.5 h (21 ± 7 min, n = 3) using initial E. coli concentrations of ~10³ and 10⁵ cells mL⁻¹, respectively, which is under or on the limit for classification of a urinary tract infection. Detection of E. coli was also demonstrated in authentic urine samples with bacteria concentration as low as 10⁴ cells mL⁻¹, with a similar response recorded between urine samples collected from different volunteers as well as from morning and afternoon urine samples.