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.
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 tractable two-compartment mathematical model integrating simplified mass transport, Langmuir binding kinetics, and mass conservation under finite volume constraints. Validated against experimental measurements and numerical simulations, the model accurately predicts critical performance metrics including assay time and minimum required sample volume while achieving more than a 10,000-fold reduction in computational time compared to commercial simulation packages. Through systematic analysis, we derived quantitative design rules for biosensor optimization that explicitly account for measurement time and sample volume as primary decision variables. We validated t
In modern online learning, understanding and predicting student behavior is crucial for enhancing engagement and optimizing educational outcomes. This systematic review explores the integration of biosensors and Multimodal Learning Analytics (MmLA) to analyze and predict student behavior during computer-based learning sessions. We examine key challenges, including emotion and attention detection, behavioral analysis, experimental design, and demographic considerations in data collection. Our study highlights the growing role of physiological signals, such as heart rate, brain activity, and eye-tracking, combined with traditional interaction data and self-reports to gain deeper insights into cognitive states and engagement levels. We synthesize findings from 54 key studies, analyzing commonly used methodologies such as advanced machine learning algorithms and multimodal data pre-processing techniques. The review identifies current research trends, limitations, and emerging directions in the field, emphasizing the transformative potential of biosensor-driven adaptive learning systems. Our findings suggest that integrating multimodal data can facilitate personalized learning experienc
Graphene Field-Effect Transistors (GFETs) are increasingly employed as biochemical sensors due to their exceptional electronic properties, surface sensitivity, and potential for miniaturization. A critical challenge in deploying GFETs is determining the optimal electrical readout strategy. GFETs are typically operated with either of two modalities: one measuring current in real time (amperometric) and the other monitoring the change in voltage for charge neutrality (potential potentiometric). Here, we undertake a systematic study of the two modalities to determine their relative advantages/disadvantages towards guiding the future use of GFETs in sensing. We focus on viral proteins in wastewater, given the matrix's complexity and the growing interest in the field of wastewater surveillance. Our results show that transconductance offers far superior limits of detection (LOD) but suffers from limited reproducibility, a narrower dynamic range, and is ineffective for some viral proteins. In comparison, we find that Dirac point tracking offers higher reproducibility and superior robustness, but at a higher LOD. Interestingly, both techniques exhibit similar sensitivity, highlighting the
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
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.
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
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.
Biosensors are essential tools which have been traditionally used to monitor environmental pollution, detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from the in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single-biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to
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.
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
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
Surface Plasmon Resonance (SPR) offers a powerful tool for label-free and non-invasive characterization of biomolecular interactions. To date, several experimental configurations, based on two fundamental physical phenomena, e.g., attenuated total reflection and diffraction, have been developed to measure the SPR signal generated due to the resonant interactions between incident light and plasma waves on the metal surface. These configurations are divided into three categories: grating-based, prism-based, and waveguide-based coupling. Among such techniques, one of the prism-based SPR coupling schemes, popularly known as Kretschmann configuration, is most widely used due to its high sensitivity, operational simplicity, lower cost, and real-time detection. This chapter explains the basic instrumentation and reviews the recent trends in the development of Kretschmann configuration-based SPR biosensors with its applications.
Artificially engineered biosensors are highly inefficient in accurately measuring the concentration of biomarkers, particularly, during early diagnosis of diseases. On the other hand, single cellular systems such as chemotactic bacteria can sense their environment with extraordinary precision. Therefore, one would expect that implementing the optimal cellular sensing strategies in state-of-the-art artificial sensors can produce optimally precise biosensors. However because of the presence of measurement noise, strategies that are optimal in biological systems may not be optimal in artificial systems. Therefore, mimicking biological strategies may not be the optimal path in case of artificial sensing systems because of the presence of inherent measurement noise.
Polyelectrolyte microcapsules loaded with fluorescent dyes have been proposed as biosensors to monitor local pH and ionic strength for diagnostic purposes. In the case of charged microcapsules, however, the local electric field can cause deviations of ion densities inside the cavities, potentially resulting in misdiagnosis of some diseases. Using nonlinear Poisson-Boltzmann theory, we systematically investigate these deviations induced by charged microcapsules. Our results show that the microcapsule charge density, as well as the capsule and salt concentrations, contribute to deviations of local ion concentrations and pH. Our findings are relevant for applications of polyelectrolyte microcapsules with encapsulated ion-sensitive dyes as biosensors.
The terahertz (THz) spectral regime offers unique opportunities for next-generation biochemical sensing due to its non-destructive, label-free probing capability and strong sensitivity to molecular vibrations. However, conventional THz biosensors remain hampered by intrinsically low-quality factors and limited sensitivity, severely restricting their utility for trace-level biochemical and chemical detection. Here, we report an ultrasensitive THz metasurface biosensor that harnesses quasi-bound states in the continuum (QBICs) with sharp resonances and enhanced light-matter interactions to overcome these limitations. As a proof of concept, the device achieves label-free detection of a sulfur-containing amino acid cysteine, with an ultrahigh sensitivity of 492 GHz/RIU and an ultralow detection limit down to 0.00025 mg/mL. The synergy between QBIC-induced field confinement and meticulous structural optimization of the metasurface underpins this performance, marking a significant advance over conventional THz metasurface biosensing schemes. These results establish QBIC-based metasurfaces as a promising platform for ultrasensitive and high-precision biochemical and chemical sensing, with
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
This paper presents a comprehensive study on a novel multilayer surface plasmon resonance (SPR) biosensor designed for detecting trace-level toxins in liquid samples with exceptional precision and efficiency. Leveraging the Kretschmann configuration, the proposed design integrates advanced two-dimensional materials, including black phosphorus (BP) and transition metal dichalcogenides (TMDs), to significantly enhance the performance metrics of the sensor. Key innovations include the optimization of sensitivity through precise material layering, minimization of full-width at half-maximum (FWHM) to improve signal resolution, and maximization of the figure of merit (FoM) for superior detection accuracy. Numerical simulations are employed to validate the structural and functional enhancements of the biosensor. The results demonstrate improved interaction between the evanescent field and the analyte, enabling detection at trace concentrations with higher specificity. This biosensor is poised to contribute to advancements in biochemical sensing, environmental monitoring, and other critical applications requiring high-sensitivity toxin detection.