Integrated photonic biosensors provide compact, highly sensitive, and label-free platforms for biochemical detection, making them attractive for on-chip and real-time sensing applications. However, their design remains challenging due to complex resonance behaviour, strong coupling effects, and the computational cost associated with repeated full-wave electromagnetic simulations. In particular, inverse design of microring resonator-based sensors requires accurate modelling of geometry-spectrum relationships while satisfying physical constraints such as resonance conditions and spectral sensitivity requirements. In this work, we propose a physics-informed graph neural network (PI-GNN) for the inverse design of a microring resonator biosensor operating in the 1550 nm band. By representing the photonic structure as a graph and embedding resonance-based physical constraints directly into the learning objective, the model captures both structural connectivity and underlying electromagnetic principles. The proposed approach enables efficient prediction of device geometries that achieve target spectral characteristics, reducing reliance on costly simulations while maintaining physical con
Quantum biosensors offer a promising route to overcome the sensitivity and specificity limitations of conventional biosensing technologies. Their ability to detect biochemical signals at extremely low concentrations makes them strong candidates for next-generation sensing systems. This paper reviews the current state of quantum biosensors and dis-cusses their future implementation in chip-scale platforms that combine microelectronic and photonic technologies. It covers key quantum biosensing approaches including quantum dots, and nitrogen-vacancy (NV) centers. This paper also considers their po-tential compatibility with electronic integrated circuits (EICs), photonic integrated circuits (PICs) and integrated quantum photonic (IQP) systems for future biosensing applications. To our knowledge, this is the first review to systematically connect quantum biosensing technologies with the development of microelectronic and photonic chip-based devices. The goal is to clarify the technological trajectory toward compact, scalable, and high-performance quantum biosensing systems.
Over the past years, rapid progress has been made on soft-matter electronics for wearable and implantable devices, for bioelectronics and optogenetics. Liquid Metal (LM) based electronics were especially popular, due to their long-term durability, when subject to repetitive strain cycles. However, one major limitation has been the need for tethering bioelectronics circuits to external power, or the use of rigid bulky batteries. This has motivated a growing interest in wireless energy transfer, which demands circuit miniaturization. However, miniaturization of LM circuits is challenging due to low LM-substrate adhesion, LM smearing, and challenges on microchip-interfacing. In this article, we address these challenges by high-resolution laser-assisted micropatterning of biphasic LM composites and vapor-assisted LM microchip soldering. Through development of a search algorithm for optimization of the biphasic ink coil performance, we designed and implemented micro coils with trace spacing of 50 μm that can harvest a significant amount of energy (178 mW/cm2) through near field inductive coupling. We show miniaturized soft-matter circuits with integrated SMD chips such as NFC chips, cap
Organoids offer a promising alternative in biomedical research and clinical medicine, with better feature recapitulation than 2D cultures. They also have more consistent responses with clinical results when compared to animal models. However, major challenges exist in the longevity of culture, the reproducibility of organoid properties, and the development of non-disruptive monitoring methods. Recent advances in materials and microfabrication methods, such as 3D printing and compressive buckling, have enabled three-dimensional (3D) interfaces of microfluidics and bioelectronics to manipulate and monitor these biological models in exciting ways. These advanced systems have great potential for applications in drug delivery, personalized medicine, and disease modelling. We conclude with important future considerations to generate longevity using further technological development in organoid and spheroid models.
Continuous long-term monitoring and diagnosis of biomedical signals, such as electrocardiograms (ECGs), can help mitigate an increasing threat to public health. Artificial Intelligence (AI) models, such as Convolutional Neural Networks (CNNs), provide accurate monitoring and classification for relevant diseases; however, they require more computational resources than conventional AI hardware can typically afford, especially for a resource-constrained environment on the edge. In this work, we present BenDi, an energy-efficient quasi-stochastic systolic architecture for bioelectronic systems on the edge. BenDi leverages multiple levels of energy and power optimization, ranging from circuits to software quantization, including low supply voltage, the \underline{Ben}t-Pyramid data format for quasi-stochastic multiplication, the \underline{Di}P systolic dataflow, and hardware-aware quantization, to handle CNNs with high accuracy on the edge within limited hardware budgets. The hardware implementation results, using a commercial 22nm technology, show that BenDi architecture, at 0.5 Voltage and 100 MHz, offers 3.35x smaller area and 5x higher energy efficiency, compared to state-of-the-ar
Affinity-based biosensors have become indispensable in modern diagnostics and health monitoring. While considerable research has focused on optimizing analyte transport and binding kinetics, a fundamental parameter - sample volume - remains largely underexplored in biosensor design. This is critical because biosensor performance depends on the absolute number of target molecules present, not solely their concentration, making volume a key consideration where sample availability is limited. To address this gap, we developed a mathematical two-compartment model integrating simplified mass transport, Langmuir binding kinetics, and mass conservation under finite volume constraints. The model accurately simulates biosensor binding kinetics and predicts equilibration time and required volume compared to finite-element simulations, whilst achieving more than 100-fold reduction in computational time. From the framework, we derived analytical expressions for biosensor equilibration time and required volume as a function of the Damköhler number, ranging from reaction-limited to transport-limited systems. These analytical solutions predict equilibration time and required volume for a biosenso
Autonomous implantable bioelectronics rely on wireless connectivity, necessitating highly efficient electromagnetic (EM) radiation systems. However, limitations in power, safety, and data transmission currently impede the advancement of innovative wireless medical devices, such as tetherless neural interfaces, electroceuticals, and surgical microrobots. To overcome these challenges and ensure sufficient link and power budgets for wireless implantable systems, this study explores the mechanisms behind EM radiation and losses, offering strategies to enhance radiation efficiency in wireless implantable bioelectronics. Using analytical modeling, the EM waves emitted by the implant are expanded as a series of spherical harmonics, enabling a detailed analysis of the radiation mechanisms. This framework is then extended to approximate absorption losses caused by the lossy and dispersive properties of tissues through derived analytical expressions. The radiation efficiency and in-body path loss are quantified and compared in terms of three primary loss mechanisms. The impact of various parameters on the EM efficiency of implantable devices is analyzed and quantified, including operating fr
Symmetry lies at the heart of 2D bioelectronics, determining material properties at the fundamental level. Breaking the symmetry allows emergent functionalities and effects. However, symmetry modulation in 2D bioelectronics and the resultant applications have been largely overlooked. Here we devise an oxidized architectural MXene, referred as OXene, that couples orbit symmetric breaking with inverse symmetric breaking to entitle the optimized interfacial impedance and Schottky-induced piezoelectric effects. The resulting OXene validates applications ranging from microelectrode arrays, gait analysis, active transistor matrix, and wireless signaling transmission, which enables highly-fidelity signal transmission and reconfigurable logic gates. Further OXene interfaces are investigated in both rodent and porcine myocardium, featuring high-quality and spatiotemporally resolved physiological recordings, while accurate differentiated predictions, enabled via various machine learning pipelines.
Stretchable electronics capable of conforming to nonplanar and dynamic human body surfaces are central for creating implantable and on-skin devices for high-fidelity monitoring of diverse physiological signals. While various strategies have been developed to produce stretchable devices, the signals collected from such devices are often highly sensitive to local strain, resulting in inevitable convolution with surface strain-induced motion artifacts that are difficult to distinguish from intrinsic physiological signals. Here we report all-printed super stretchable strain-insensitive bioelectronics using a unique universal gradient interface (UGI) to bridge the gap between soft biomaterials and stiff electronic materials. Leveraging a versatile aerosol-based multi-materials printing technique that allows precise spatial control over the local stiffnesses with submicron resolution, the UGI enables strain-insensitive electronic devices with negligible resistivity changes under a 180% stretch ratio. We demonstrate various stretchable devices directly printed on the UGI for on-skin health monitoring with high signal quality and near perfect immunity to motion artifacts, including semicon
High refractive index semiconductor nanowires have recently been demonstrated experimentally as an efficient platform for enhancing the signal in fluorescence-based biosensors. Here, we study through modelling how a vertical GaP nanowire (i) enhances the excitation intensity at the position of the fluorophore attached to the nanowire sidewall, (ii) enhances the probability to collect photons emitted from the fluorophore by directing them preferentially into the numerical aperture of the collection objective, and (iii) through the Purcell effect increases the quantum yield of the fluorophore. With appropriate choice for the geometry of the nanowire, we can reach a larger than $10^2$ enhancement in signal compared to a corresponding conventional planar biosensor platform. We model also imaging-based detection. There, we find that thanks to waveguiding in the nanowire, we can beat the limitations set by the depth of view in conventional microscopy, enabling the use of a long nanowire to enhance the binding-area for fluorophores. As an example, we can focus to the top of a 4000 nm long nanowire and reach a 25 times sharper image from a fluorophore at the bottom of the nanowire, as comp
In the 21st century, biosensors have gathered much wider attention than ever before, irrespective of the technology that promises to bring them forward. With the recent COVID-19 outbreak, the concern and efforts to restore global health and well-being are rising at an unprecedented rate. A requirement to develop precise, fast, point-of-care, reliable, easily disposable/reproducible and low-cost diagnostic tools have ascended. Biosensors form a primary element of hand-held medical kits, tools, products, and/or instruments. They have a very wide range of applications such as nearby environmental checks, detecting the onset of a disease, food quality, drug discovery, medicine dose control, and many more. Thischapter explains how Nano/Micro-Electro-Mechanical Systems (N/MEMS) can be enabling technology towards a sustainable, scalable, ultra-miniaturized, easy-to-use, energy efficient, and integrated bio/chemical sensing system. This study provides a deeper insight into the fundamentals, recent advances, and potential end applications of N/MEMS sensors and integrated systems to detect and measure the concentration of biological and/or chemical analytes. Transduction principle/s, materia
Integrated nanophotonic biosensors offer a promising route toward future biomedical detection applications that may enable inexpensive, portable, and sensitive diagnosis of diseases with a small amount of biological samples for convenient early-stage screening of fatal diseases. However, the current photonic biosensor designs are not suitable for highly integrated and multiplexing device architectures that can achieve the detection of complex combinations of many biomarkers. Here, we propose a topological scheme for the integration of miniature biosensors in photonic crystal chips that can meet the above requirement. Using photonic topological edge states as robust one-dimensional waveguides that connect many photonic biosensors, we propose here the topologically integrated photonic biosensor circuits. We demonstrate that the performance of the topologically integrated photonic biosensors is much more robust against disorders than that of the photonic biosensors connected by the normal photonic waveguides, due to the robust transport of photons along the edge channel. Since disorders arising from the fabrication imperfection and the random distribution of the biomarkers are inevita
Wireless bioelectronic interfaces are increasingly used to control tissue-engineered biohybrid robotic systems. However, a unifying engineering framework linking device design to system-level control remains underdeveloped. Here, we propose that wireless control in biohybrid robotics can be formulated as a coupled co-design problem of integrating signal delivery, spatial selectivity, scalability, and interface stability. We analyze three representative control strategies, wireless electrical stimulation, wireless optoelectronic stimulation, and neuromuscular integration, which operates within a distinct regime with characteristic trade-offs. Across these modalities, the tissue-device interface emerges as a key constraint, governing the interplay between electromagnetic coupling, circuit performance, and biomechanical response. Based on this framework, we outline practical design principles spanning electromagnetic field distribution, circuit architecture, and actuator mechanics. We further propose a transition from open-loop stimulation to closed-loop biohybrid autonomy enabled by organoid-integrated bioelectronics and bidirectional microelectrode interfaces. This work establishes
Scalable production of all-electronic DNA biosensors with high sensitivity and selectivity is a critical enabling step for research and applications associated with detection of DNA hybridization. We have developed a scalable and very reproducible (> 90% yield) fabrication process for label-free DNA biosensors based upon graphene field effect transistors (GFETs) functionalized with single-stranded probe DNA. The shift of the GFET sensor Dirac point voltage varied systematically with the concentration of target DNA. The biosensors demonstrated a broad analytical range and limit of detection of 1 fM for 60-mer DNA oligonucleotide. In control experiments with mismatched DNA oligomers, the impact of the mismatch position on the DNA hybridization strength was confirmed. This class of highly sensitive DNA biosensors offers the prospect of detection of DNA hybridization and sequencing in a rapid, inexpensive, and accurate way.
Physical limit of molecular sensing has been extensively studied in biological systems. Biosensors are engineered equivalents of molecular sensors in living systems and play critical role in disease diagnosis and management. Investigation into the physical limits of biosensors could have major beneficial impact on early disease diagnosis. Here, we present an extension of the classical works on molecular sensing limits of living systems to the realm of biosensors. Two approaches are proposed to estimate concentration with noisy biosensors. We find a trade-off between precision and accuracy.
Electrochemical biosensors and the related concept of redox detection at nanogap electrodes are increasingly explored for ultra-sensitive detection of biomolecules. While experimental demonstrations have been encouraging, the associated design and optimization of electrode geometry, beyond the simple one-dimensional architectures, is inherently challenging from multiple aspects related to numerical complexity. Here we develop a facile simulation scheme to address this challenge using well established electronic circuit analysis techniques that are available as open source-ware. Based on this approach, we show that electrode geometry, especially nano-structured redox electrodes on a planar surface, has interesting implications on the detection limits and settling time of electrochemical biosensors. The methodology we developed and the insights obtained could be useful for electrode optimization for a wide variety of problems ranging from biosensors to electrochemical storage.
Developing rapid methods for pathogen detection and growth monitoring at low cell and analyte concentrations is an important goal, which numerous technologies are working towards solving. Rapid biosensors have already made a dramatic impact on improving patient outcomes and with continued development, these technologies may also help limit the emergence of antimicrobial resistance and reduce the ever expanding risk of foodborne illnesses. One technology that is being developed with these goals in mind is asynchronous magnetic bead rotation (AMBR) biosensors. Self-assembled AMBR biosensors have been demonstrated at water/air and water/oil interfaces, and here, for the first time, we report on self-assembled AMBR biosensors used at a solid interface. The solid interface configuration was used to measure the growth of Escherichia coli with two distinct phenomena at low cell concentrations: firstly, the AMBR rotational period decreased and secondly, the rotational period increased after several division times. Taking advantage of this low cell concentration behavior, a 20% signal change from the growth of E. coli O157:H7 was detected in 91 \pm 4 minutes, with a starting concentration o
DNA surface-hybridization biosensors utilize the selective hybridization of target sequences in solution to surface-immobilized probes. In this process, the target is usually assumed to be in excess, so that its concentration does not significantly vary while hybridizing to the surface-bound probes. If the target is initially at low concentrations and/or if the number of probes is very large and have high affinity for the target, the DNA in solution may get depleted. In this paper we analyze the equilibrium and kinetics of hybridization of DNA biosensors in the case of strong target depletion, by extending the Langmuir adsorption model. We focus, in particular, on the detection of a small amount of a single-nucleotide "mutant" sequence (concentration $c_2$) in a solution, which differs by one or more nucleotides from an abundant "wild-type" sequence (concentration $c_1 \gg c_2$). We show that depletion can give rise to a strongly-enhanced sensitivity of the biosensors. Using representative values of rate constants and hybridization free energies, we find that in the depletion regime one could detect relative concentrations $c_2/c_1$ that are up to three orders of magnitude smaller
The ultimate detection limit of optical biosensors is often limited by various noise sources, including those introduced by the optical measurement setup. While sophisticated modifications to instrumentation may reduce noise, a simpler approach that can benefit all sensor platforms is the application of signal processing to minimize the deleterious effects of noise. In this work, we show that applying complex Morlet wavelet convolution to Fabry-Pérot interference fringes characteristic of thin film reflectometric biosensors effectively filters out white noise and low frequency reflectance variations. Subsequent calculation of an average difference in phase between the filtered analyte and reference signals enables a significant reduction in the limit of detection (LOD) enabling closer competition with current state-of-the-art techniques. This method is applied on experimental data sets of thin film porous silicon sensors (PSi) in buffered solution and complex media obtained from two different laboratories. The demonstrated improvement in LOD achieved using wavelet convolution and average phase difference paves the way for PSi optical biosensors to operate with clinically relevant d
Wearable electronics hold great potential in defining new paradigms of modern healthcare, including personalized health management, precision medicine, and athletic performance optimization. This stems from their ability in enabling continuous, real-time health monitoring. To enable molecular-level analysis, biofluids rich in molecular analytes have become one of the most important target samples for wearable sensors. Among them, sweat stands out as an ideal candidate for next-generation wearable health monitoring platforms due to its completely noninvasive nature and ease of acquisition. In recent years, several studies have demonstrated feasible prototype designs for sweat-based wearable sensors. However, one of the major gaps toward large-scale commercialization is the development of clinically validated standards for sweat analysis. One key requirement is to establish the relationship between sweat analytes and those of blood, the latter serving as the gold standard in modern diagnostics. This review provides an overview of sweat biomarkers, with a particular focus on their partitioning mechanisms, which reveal the underlying connections between sweat analytes and their counter