Rolling circle amplification (RCA) is an effective isothermal amplification technique that can generate long single-stranded DNA (ssDNA) with a high yield and sequence programmability. In this study, we studied the production of the DNA nanoparticles by self-assembly RCA with ionic condensation. The padlock DNA was converted into a dumbbell-shaped circular DNA using the mediate RNA and SplintR ligase, followed by amplification with phi29 DNA polymerase at 30℃. The resulting ssDNA, containing repetitive sequences, was condensed into well-defined DNA nanoparticles through the addition of magnesium (Mg²⁺) and pyrophosphate (ppi) ions. Morphologies and surface electrostatic potentials were observed using optical microscopy, atomic force microscopy (AFM), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and surface potential mapping via Kelvin probe force microscopy (KPFM) and electrostatic force microscopy (EFM). Molecular dynamics (MD) simulations were used to investigate a hierarchical self-assembly pathway involving initial Mg²⁺ and ppi cluster formation, DNA-Mg²⁺ coordination, and subsequent DNA incorporation. These findings provide mechanistic insight into RCA-derived DNA nanoparticle formation and highlight the critical role of ionic interactions in directing nanoscale self-assembly. This paper provides insights into the padlock DNA design and scalable production of functional DNA based nanomaterials for applications in drug delivery, biosensing, and nanotechnology.

Exosomes are small extracellular vesicles secreted by parent cells into the extracellular environment. The biomolecules they carry-such as proteins, lipids, and nucleic acids-reflect the molecular profile of their cells of origin. Due to their stability in body fluids and close molecular resemblance to parent cells, exosomes have gained considerable attention as non-invasive biomarkers and diagnostic tools for early and accurate disease detection. This review presents a comprehensive overview of exosome biology, including their biogenesis, molecular composition, and related diagnostic biomarkers. We also summarize current exosome isolation methods and advanced exosome-based diagnostic technologies-such as CRISPR/Cas systems, digital polymerase chain reaction, and surface-enhanced Raman scattering (SERS)-emphasizing their potential for early, sensitive, and non-invasive disease diagnosis.

Self-assembled nanostructures have attracted significant attention as a promising strategy for the fabrication of high-performance sensors. By enabling precise control over morphology, periodicity, and surface functionality at the nanoscale, self-assembly offers a bottom-up approach to producing sensor architecture without requiring complex lithographic techniques. In this review, we present recent advances in gas, optical, biological, and strain sensors that utilize self-assembled structures based on block copolymers, colloidal particles, and hybrid nanomaterials. These systems show enhanced sensitivity and selectivity by increasing surface area, exposing active sites, and improving interactions with target analytes. In particular, hierarchical or porous morphologies derived from self-assembly allow efficient diffusion and signal transduction, and their compatibility with flexible and transparent substrates enables their application in wearable and miniaturized sensor devices. Through selected examples, we highlight how nanostructures formed via self-assembly can be effectively integrated into various sensing platforms, demonstrating their potential for scalable and practical sensor technologies.

The COVID-19 pandemic underscored the urgent need for rapid identification and isolation of infected individuals, thereby accelerating the demand for on-site virus detection technologies. Although polymerase chain reaction (PCR) remains the "gold standard" for viral diagnostics, its dependence on bulky instrumentation, trained personnel, and lengthy processing times limits its applicability in on-site and point-of-care testing (POCT) settings, where speed and simplicity are paramount. To overcome these challenges, loop-mediated isothermal amplification (LAMP) has emerged as a promising alternative, enabling rapid nucleic acid amplification under isothermal conditions. However, conventional tube-based LAMP still involves manual operations such as reagent mixing and sample handling, which restrict its usability outside of laboratory environments. Consequently, the development of integrated LAMP platforms capable of performing sample preparation, amplification, and detection in a streamlined and automated manner, delivering rapid results, has become a critical goal for on-site virus detection. In this review, we highlight recent advances in on-site LAMP-based virus detection platforms employing paper, microfluidics, and hydrogels. Furthermore, we discuss current technical limitations, perspectives, and future directions, including automation, multiplexing, and machine learning (ML)-assisted readouts.

Direct growth of plasmonic nanomaterials with controlled morphology, size, and spatial arrangement on the surface is an effective route to achieve robust surface-enhanced Raman scattering (SERS) sensor performance. Traditional fabrication strategies including seed-mediated and template-guided synthesis provide structural tunability but often require multistep processing and show limited substrate compatibility. This review focuses on direct surface growth methods that form plasmonic nanoarchitectures in situ on functional substrates. These approaches utilize substrate-adaptive chemical reactions to integrate synthesis and deposition into a single step, promoting strong adhesion, uniform coverage, and consistent SERS hotspot formation. By supporting branched, hierarchical, or interconnected plasmonic domains, it improves analyte access and electromagnetic field confinement for enhanced Raman signal amplification. Compatibility with flexible or porous surfaces and the removal of postsynthetic transfer steps improve reproducibility and scalability of SERS substrates. In addition, clean surface conditions achieved by modifier-free protocols reduce spectral interference and improve signal fidelity. These attributes collectively enable the development of reliable, quantitative SERS sensing platforms. The integration of such optimized substrates with advanced data processing and AI-assisted interpretation systems further supports standardized plasmonic sensing workflows across diverse analytical environments.

Wireless physiological monitoring has emerged as a transformative solution for continuous and non-invasive assessment of vital signs in both clinical and non-clinical environments. Conventional wired monitoring systems often suffer from the bulkiness, usability, and restrictive applications only in hospital. Recent advances in flexible electronics, low-power wireless communication, and integrated multimodal sensing have significantly improved the functionality, wearability, and usability of wireless physiological monitoring devices. This review outlines recent technical advances in the wireless monitoring into five groups based on sensing mechanisms: a) thermoelectric sensors for core body temperature, b) biopotential sensors for electrophysiology, c) optical sensors for photoplethysmography, d) electrochemical sensors for sweat, blood, and interstitial fluid monitoring, and e) mechanical and acoustic sensors for unconventional physiological monitoring.

The rising demand for personalized healthcare has accelerated the development of skin-interfaced sensors that are capable of real-time physiological monitoring, early disease detection, and individualized health management. These devices enable the continuous and high-fidelity tracking of diverse physiological signals, such as cardiac activity, muscle contractions, respiration, and temperature, by closely conforming to the complex and dynamic surface of the human skin. However, conventional sensors fabricated from rigid and brittle materials have poor conformability, mechanical mismatch, and reduced user comfort, thus leading to unreliable signal acquisition and limited long-term usability. Soft conductive nanocomposites have emerged as a promising alternative to conventional sensors. The nanocomposites provide tunable electrical conductivity, mechanical compliance, and biocompatibility by integrating conductive nanofillers into stretchable elastomeric matrices. The filler composition, dispersion, and microstructure of these materials can be optimized to closely match the mechanical properties of the skin while maintaining stable electrical performance under deformation. This review provides a comprehensive overview of recent advances in soft conductive nanocomposites for skin-interfaced physiological monitoring, organized by widely used nanoscale and microscale fillers including metallic, carbon-based, liquid metal, and ionic materials. Furthermore, this review examines how conductive fillers affect the percolation behavior and electromechanical performance of conductive nanocomposites. We highlight representative applications in monitoring the electrical and mechanical form of physiological signals. Finally, we present future research directions aimed at advancing these materials toward next-generation wearable and biointegrated electronics for personalized healthcare.

Ultrasound-to-electric signal conversion based on the triboelectric effect has emerged as a promising strategy for overcoming the inherent limitations of conventional piezoelectric ultrasound sensors, including structural complexity, high fabrication costs, and material constraints. In this review, we comprehensively examine the fundamental mechanisms and recent advances in ultrasound detection and energy harvesting, with a focus on triboelectric nanogenerators (TENGs). Particular focus is placed on ultrasound-driven TENGs (US-TENG), addressing key developments in device architecture, material engineering, and packaging techniques that facilitate effective transduction of ultrasonic excitation into mechanical deformation and subsequent electrical output via electrostatic induction and contact electrification. In addition, we discuss the potential of US-TENGs for integration into self-powered sensors and energy modules that are applicable to implantable medical devices and wearable electronics, highlighting their promise for overcoming the drawbacks of existing sensor platforms. This review offers a current and comprehensive overview of TENG-based ultrasound sensing technologies, highlighting their technological significance, current challenges, and prospects in the fields of sensing and energy conversion.

Advances in artificial intelligence (AI), the Internet of Things (IoT) and its healthcare-focused subset, the Internet of Medical Things (IoMT) are gradually improving the quality of emergency medicine, speeding up the diagnostic process, improving patient tracking and helping doctors make decisions in real time. This study carefully looks at the technological progress made between 2020 and Aug. 2025 in three main areas: telemedicine tools, wearable health monitoring systems, and point-of-care diagnostic devices. It shows how new technologies like AI built into wearables, cloud-connected remote diagnostics, CRISPR-based detection systems, and mobile-enabled biosensors have all made emergency response systems stronger. The research also looks at how deep learning models have changed over time, from traditional architectures to transformer-based networks, and how they might be used in clinical decision support systems. The combination of these systems with edge computing, federated learning, and image-based diagnostics has changed the way data is processed and acted upon in critical care situations. This analysis looks ahead at how AI-IoT convergence will affect emergency medicine in the future, the problems it will face when it is put into practice, and what the future direction will be.

Continuous bladder function monitoring is essential for the diagnosis and management of urological disorders; however, conventional methods such as catheter-based urodynamic studies are invasive, uncomfortable, and unsuitable for long-term use. Recent advances in flexible electronics, soft materials, and wireless communication have enabled the development of wearable and implantable bioelectronics that support real-time, continuous, patient-friendly bladder monitoring. This review summarizes progress across key modalities, namely, ultrasound- and bioimpedance-based wearable platforms, strain gauge-based implantable platforms, and multimodal implantable systems, which combine sensing with electrical stimulation. Their respective advantages, limitations, and potential clinical applications are discussed. In the future, the integration of multi-parameter systems, energy harvesting, and artificial intelligence-driven analytics could transform these devices into intelligent, self-regulating platforms that not only monitor but also actively manage bladder health. Such innovations are promising for advancements in personalized urological care from episodic clinical assessments toward continuous, adaptive interventions.

Nitrogen dioxide (NO₂) is a toxic gas that poses serious health risks even at low concentrations, necessitating the development of highly sensitive gas detectors. In this study, WSe₂ nano flakes were synthesized by selenizing tungsten thin films through a simple and economical annealing process. The synthesis conditions were optimized to produce abundant nano flakes with various structural defects, including tungsten metal residues, step terraces, and mixed 2H/1T phases. Gas detecting tests demonstrated that the WSe₂ nano flakes exhibit p-type semiconductor behavior. The response was stable and reproducible under repetitive gas exposure cycles. These enhanced defect properties are attributed to the presence of structural defects, which provide additional active sites and improve charge transfer. These perspectives highlight the importance of defect engineering in transition metal dichalcogenides-based gas detectors and provide valuable insights into the development of various high-performance gas detectors using WSe₂ or other transition metal dichalcogenides materials.

A tyrosinase-functionalized electrochemical biosensor based on vertically aligned ZnO nanorods (NRs) was developed for the selective and sensitive detection of dopamine. The sensor architecture features an Au working electrode modified with ZnO NRs, followed by sequential surface treatments using tetraethyl orthosilicate, 3-aminopropyl triethoxysilane, and glutaraldehyde to immobilize tyrosinase. Structural and chemical modifications were confirmed via scanning electron microscopy, energy-dispersive X-ray spectroscopy, and Fourier-transform infrared spectroscopy. Chronoamperometric measurements demonstrated a significant enhancement in sensing performance for the ZnO NRs-based sensor (3.6114 nA/μM) compared to a planar ZnO thin-film electrode (0.4318 nA/μM), with a nine-fold increase in sensitivity attributed to the larger enzyme loading area provided by the three-dimensional nanostructure. The sensor exhibited excellent linearity and a pronounced response at a low operating potential of -250 mV. Interference studies with ascorbic acid and norepinephrine confirmed high selectivity toward dopamine. These findings underscore the utility of nanostructured ZnO interfaces in advancing enzyme-based electrochemical biosensing platforms for neurotransmitter detection under physiologically relevant conditions.

Optical phased arrays (OPAs) enable solid-state beam steering for light detection and ranging (LiDAR) applications; however, they are limited by inefficiency in the nonlinear optical performance and slow thermal phase modulation. In this study, we present a novel graphene-integrated silicon nitride (SiN) waveguide designed for solid-state LiDAR, which helps in simultaneously enhancing the nonlinear optical performance and thermal phase modulation efficiency within a single device to improve the OPA functionality. By leveraging the high third-order nonlinear susceptibility and superior thermal conductivity of graphene, the waveguide enhances the Kerr nonlinearity and accelerates the phase modulation for precise OPA-based beam steering. These properties are optimized by integrating graphene with SiN waveguides, and simulations are conducted using Lumerical finite-difference time-domain and HEAT Solver tools to validate the design. The simulations demonstrate an improvement of 8.2% in the Kerr nonlinearity and third-harmonic generation at 516.6 nm, along with a faster thermal response and reduced power consumption when compared with titanium nitride (TiN) heaters. This complementary metal-oxide-semiconductor-compatible, scalable platform overcomes the limitations of the conventional silicon-based OPAs and contributes to the development of high-resolution, low-power LiDAR systems. This study paves the way for compact, energy-efficient, and high-performance solid-state LiDAR solutions, with potential applications in telecommunications, sensing, and other photonic systems that require precise OPA-based beam steering and enhanced nonlinear functionalities.

This study proposes a machine learning-based knock localization system for human-to-thing interfaces employing elastic wave signals captured by a piezoelectric array. Eight sensors were symmetrically arranged on a plastic plate, and the relative arrival-time differences of the elastic waves were extracted as the primary features for classification. Unlike conventional approaches, which rely on prior knowledge of elastic wave propagation speed and assume material homogeneity, the proposed method overcomes these limitations and enables real-time applicability through a simplified pipeline. Knock data were collected from nine spatial zones under both intra-subject and inter-subject conditions. In the intra-subject evaluation, where training and testing were conducted on data from the same subject, the ensemble classifier achieved perfect performance across all metrics (accuracy, precision, recall, and F1 score = 1). When evaluated on data from three previously unseen subjects, the model maintained high accuracy (accuracy = 0.957), demonstrating strong generalization to user variability. These findings confirm the robustness and practicality of the proposed system, particularly in application domains where conventional touch sensors are unsuitable, such as in smart windows and automotive panels.

A novel sensor system is presented for the non-invasive measurement of blood glucose concentration by applying an electric field to the skin. Unlike conventional invasive methods, the proposed sensor estimates glucose levels indirectly by analyzing temperature variations caused by dielectric loss, which arises from interactions between the electric field and glucose molecules. Experimental results across a glucose concentration range of 50-400 mg/dL demonstrated optimal performance, with the highest linearity and sensitivity achieved at 50 kHz and 24 V. Additionally, as the glucose concentration increased, the sensor output gradually decreased, exhibiting a strong linear relationship between the glucose level and the thermal response. In experiments simulating physiological conditions with the addition of 0.9% NaCl, the decreasing trend was more pronounced, confirming the sensor's high responsiveness and reliability in electrolyte environments. The proposed sensor system has the potential to overcome the limitations of invasive techniques and improve the quality of life for patients with diabetes mellitus.

This paper introduces an adaptive digital readout integrated circuit (DROIC) equipped with a pixel-level multiple single-slope analog-to-digital converter designed for high-resolution microbolometer infrared focal plane arrays. This DROIC features exceptionally low power consumption, making it well-suited for ultralight portable devices. Because it was engineered to adjust to rapidly changing ambient conditions, the circuit enhances the dynamic range and signal-to-noise ratio across a broad input spectrum and efficiently offers multiple operational modes. The DROIC was designed using a 0.18 ㎛ CMOS process and integrated into a 640 × 512 array of 17 ㎛ × 17 ㎛ pixels. It achieved a maximum integration time of 1.82 ms and dynamic range of 81.9 dB, while its power consumption remained below 20.5 nW per pixel at a frame rate of 30 Hz.

Dry etching of lithium niobate (LiNbO₃), a key material for integrated optics, is hindered by nonvolatile lithium fluoride (LiF) byproducts that degrade pattern quality. This study investigates the effect of proton exchange (PE) pretreatment and mask type (PR/Cr vs. Cr-only) on the CF₄/He plasma etching of z-cut LiNbO₃. PE pretreatment was found to double the etch rate while dramatically suppressing the sidewall redeposition and "fencing" to produce clean, near-vertical profiles. This improvement is attributed to hydrogen ions (H⁺) from the PE process acting as scavengers reacting with fluorine radicals to form volatile hydrogen fluoride, thereby inhibiting LiF formation, which was confirmed by the absence of fluorine residues on PE-treated surfaces. While a Cr-only mask yielded the highest profile quality, a PR/Cr composite mask provided the maximum etch rate. These findings establish PE pretreatment as a highly effective method for enhancing the efficiency and quality of LiNbO₃ etching, offering a foundational technology for fabricating high-performance optical devices.

This study presents a multilayer paper-based biosensor with low power consumption capable of rapidly detecting drought stress in plants under field conditions. Proline, a biomarker whose concentration increases during drought, was used to analyze drought stress in plants, and the proline concentration in the sample was determined using a colorimetric method based on the proline-ninhydrin reaction. A multilayer paper-based structure was fabricated using wax printing and origami techniques. To enable rapid sample analysis, the sensing unit was positioned on a waterproof layer, thereby reducing the power required for sensor operation. Additionally, the sensor was fabricated in a sealed configuration to prevent external contamination of the sample during analysis. Proline samples with concentrations ranging from 100 μM to 30 mM were analyzed using the fabricated sensor. The sensor successfully detected proline at concentrations as low as 500 μM, and the ΔRGB value exhibited a linear increase with rising proline concentration. Therefore, we anticipate that the proposed multilayer paper-based biosensor can be effectively applied for plant drought diagnosis in the field and contribute to improving crop productivity.

This study proposes an underwater 3D object reconstruction method using forward looking sonar (FLS) and profiling sonar (PS) fusion through voxel-based space-carving. Underwater environments commonly rely on sonar sensors because of optical equipment limitations. However, the inherent dimensional ambiguity of conventional sonar systems prevents accurate object localization, necessitating sensor fusion approaches to achieve precise 3D reconstruction. The proposed methodology employs a complementary fusion strategy: FLS assigns probabilistic weights to all potential object regions considering vertical ambiguity, whereas PS complements this by executing conservative carving operations to remove regions where objects are confirmed absent. This approach enables comprehensive background and object mapping followed by object segmentation, unlike conventional region-of-interest-based reconstruction. Validation using ray-tracing simulation with objects (slope box) demonstrated that PS effectively carves away ambiguous vertical regions initially predicted by FLS, resulting in significantly improved 3D reconstruction accuracy. The results confirm that the proposed sensor fusion approach successfully resolves dimensional ambiguity inherent to single-sensor methods, achieving precise object localization through systematic probabilistic space-carving in underwater environments.

Conductive metal-organic frameworks (c-MOFs) have emerged as promising gas-sensing materials due to their intrinsic electrical conductivity at room temperature, high porosity, large surface area, and tunable chemical composition and structure. In particular, the presence of exposed catalytic sites-such as metal nodes and functionalized ligands-enables the binding of airborne chemical species. These interactions, through redox reactions and/or local polarity changes, induce measurable variations in electrical resistance. Common hazardous and environmental gases such as ammonia, hydrogen sulfide, nitrogen dioxide, and nitric oxide, which possess strong polarity and acid-base characteristics, are especially well-suited for detection by c-MOF-based sensors. However, the practical application of c-MOFs remains limited by their relatively low sensitivity and reversibility. These limitations can be addressed through rational compositional engineering to design fully reversible and sensitive c-MOF chemiresistors. In addition, various structural designs including the formation of hollow and hierarchical architectures in both thin films and nanoparticles have been employed to enhance sensitivity. Because many of these approaches are recent and represent first attempts within the c-MOF research field, this review aims to provide a comprehensive overview of the state-of-the-art strategies, offering design guidance for the development of room-temperature-operating gas sensors targeting hazardous and environmental gases.