Cristiano Palego

The dielectric property of biological cells exhibits a rich frequency-dependent behavior that is related to cellular structure, function, and molecular makeup. Assessing the dielectric properties of single cells offers a label-free and noninvasive method for disease diagnosis, cell identification, and monitoring of cellular structure and function. This article reviews the fundamentals and applications of microwave biosensors for single-cell analysis, with a focus on how electromagnetic interactions across the $\mathit{\alpha} {-} {,}\mathit{\beta} {-} $ and $\mathit{\gamma} {-} $dispersion regimes can be leveraged to extract cellular parameters such as membrane capacitance, cytoplasmic conductivity, and intracellular hydration. We begin by discussing the physical principles of dielectric dispersion and the equivalent circuit models in biological cells that underpin microwave-based sensing. We then examine major classes of sensing platforms, including transmission-line sensors, capacitive electrode arrays, and resonant structures, highlighting their design strategies, strength and weakness, and applications in single-cell sensing. The role of dielectrophoresis (DEP) as both a manipulation tool and sensing mechanism is also discussed, particularly in the context of hybrid systems that combine low-frequency trapping with high-frequency dielectric readout. Together, these technologies represent a convergence of microwave engineering, biophysics, and microsystem design to enable high-resolution, real-time interrogation of single cells. After describing the theoretical foundations with recent experimental advances, we provide a perspective on the design of next-generation dielectric biosensors tailored to the demands of single-cell diagnostics, drug screening, and functional phenotyping.

In this chapter, we delve into the therapeutic potential of 30.5 GHz millimeter waves on 3D glioblastoma organoids. We specifically investigated mild thermal radiation effects in the context of new emerging focused energy delivery and bioelectromagnetic approaches. Our in-house developed exposure system, coupled with a rigorous dosimetry protocol and extensive multi-physics modeling, supported biological endpoint evaluation in terms of transcriptional profiling, cell morphological changes, and cell phenotypic characterization. Crucially, the induced thermal effect was minimal, aligning closely with our simulation models and indicating the precise control of energy delivery. Notably, a 0.1 W power level enhanced the efficacy of Temozolomide, significantly increasing cell apoptosis while not affecting the differentiation status of glioblastoma organoid cells. This combination suggests a new avenue for glioblastoma treatment, leveraging millimeter wave-induced mechanisms that warrant further investigation. Our findings underscore the promise of this minimally invasive technique, offering a glimpse into future glioblastoma therapies.

Individual cells have many unique properties that can be quantified to develop a holistic understanding of a population. This can include understanding population characteristics, identifying subpopulations, or elucidating outlier characteristics that may be indicators of disease. Electrical impedance measurements are rapid and label-free for the monitoring of single cells and generate large datasets of many cells at single or multiple frequencies. To increase the accuracy and sensitivity of measurements and define the relationships between impedance and biological features, many electrical measurement systems have incorporated machine learning (ML) paradigms for control and analysis. Considering the difficulty capturing complex relationships using traditional modelling and statistical methods due to population heterogeneity, ML offers an exciting approach to the systemic collection and analysis of electrical properties in a data-driven way. In this work, we discuss incorporation of ML to improve the field of electrical single cell analysis by addressing the design challenges to manipulate single cells and sophisticated analysis of electrical properties that distinguish cellular changes. Looking forward, we emphasize the opportunity to build on integrated systems to address common challenges in data quality and generalizability to save time and resources at every step in electrical measurement of single cells.

Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data.

A 5.8-GHz continuous-wave (CW) radar was developed and integrated on a compact printed circuit board (PCB) for near-hive monitoring of honeybees. It supported noninvasive detection of free-flying honeybees at 2 m and micro-Doppler extraction of bee wingbeat signatures at a closer range using both a double-balanced mixer and an in-phase/quadrature (IQ) mixer. An original technique combining full-wave simulations and Doppler-radar monitoring of pendulum motion was used for: radar calibration through spherical targets of different material and size; precise extraction of bee radar cross section (RCS) at 5.8 GHz; and estimation of detection range enhancement through partial silver coating of targets. Finally, the CW radar was integrated with machine learning (ML) to allow automated classification of incoming, outgoing, and hovering honeybees. Different ML approaches were tested, where the highest accuracy of 93.37% was found in ternary classification via support vector machine acting on line spectral frequencies.

All-dielectric superlens made from micro and nano particles has emerged as a simple yet effective solution to label-free, super-resolution imaging. High-index BaTiO₃ Glass (BTG) microspheres are among the most widely used dielectric superlenses today but could potentially be replaced by a new class of TiO₂ metamaterial (meta-TiO₂) superlens made of TiO₂ nanoparticles. In this work, we designed and fabricated TiO₂ metamaterial superlens in full-sphere shape for the first time, which resembles BTG microsphere in terms of the physical shape, size, and effective refractive index. Super-resolution imaging performances were compared using the same sample, lighting, and imaging settings. The results show that TiO₂ meta-superlens performs consistently better over BTG superlens in terms of imaging contrast, clarity, field of view, and resolution, which was further supported by theoretical simulation. This opens new possibilities in developing more powerful, robust, and reliable super-resolution lens and imaging systems.

A 5.8GHz Doppler radar was used to monitor free flying honeybees entering and leaving their hive at a 2m distance. Free falling metal spheres of different size and materials were first used, along with radar cross section (RCS) simulations, for calibration of an in house continuous-wave (CW) radar system. The system was then applied to extract the RCS of free flying honeybees (n=164) at 5.8GHz, which fills a gap in the literature and was found to be in the range of −55 to −60dBsm ± 3dBsm. The Doppler radar was hence integrated with machine learning (ML) techniques to autonomously discriminate the incoming and outgoing flights of honeybees. A neural network built through a random forest algorithm and processing of the data as Line Spectral Pairs (LSPs) achieved a maximum accuracy of 87.83% with a Binary Cross Entropy loss of 0.4274 when interpreting hive departure/entrance events.

A battery-less 5.8-GHz transmitter (TX), compact and lightweight enough to enable flight of ~90 mg honeybees and free roaming of bumblebees within their nest, was developed. The TX was coupled to a compact phased-array antenna receiver to achieve angle of arrival (AOA) estimation and bee localization through a received signal strength indicator approach (RSSI). The receiver was integrated into a commercial unmanned aerial vehicle (UAV) to support autonomous position updates based on the AOA estimates and a software development kit. A simple model predicting the system detection range was finally developed and is herein discussed. Our experimental results provide proof of concept toward autonomous tracking of tagged bees both in open air and in polytunnel settings.