Search Results (214)

Rapid, point-of-care detection of Escherichia coli O157:H7 remains an unmet clinical need, as culture and molecular methods are slow and poorly suited to decentralized or emergency settings. A label-free, monolithic aptasensor biochip was fabricated in a standard 65 nm CMOS process, featuring three aptamer-functionalized gold sensing pads with matched reference pads for differential readout. A 37-mer DNA aptamer targeting the E. coli O157:H7 lipopolysaccharide was immobilized via thiol–gold self-assembled monolayer chemistry. Binding events were transduced into surface-potential shifts, amplified by an on-chip analog front-end (~100 V/V gain, 101.5 µW), and evaluated using calibration standards, patient specimens, and hospital environmental samples, with fluorescence microscopy for validation. The sensor achieved 47.42 mV/decade sensitivity across 1–10,000 CFU/mL, an IUPAC detection limit near 3.74 CFU/mL, and an empirical LOD of about 11 CFU/mL, with outputs tracking bacterial load and ~5.7% matrix-related deviation. Hospital samples were detectable to 28 CFU/mL. Because the patient-derived and hospital-acquired cohorts (n = 10 and n = 6, respectively) were assembled for pilot analytical and matrix-tolerance characterization rather than for diagnostic-accuracy determination, these results establish detectability and matrix robustness in real clinical and environmental specimens rather than clinical diagnostic sensitivity or specificity, which will require a larger, prospectively enrolled cohort in future work. Sensor kinetics followed Langmuir-type adsorption, saturating within 16–25 min for target pathogens versus slower responses for non-target strains. Selectivity tests against six bacterial species showed discrimination, with cross-reactivity decreasing from related E. coli pathotypes to Enterobacteriaceae to Gram-positive species. Inter-pad variability stayed below 1.5 mV, supporting this compact, low-power platform for scalable, enrichment-free point-of-care pathogen detection. Full article

During operation, several parts of the thermal reactor burners sustain heavy damage and need to be replaced. Different solutions for parts manufacturing are investigated: thermal spraying, Selective Laser Melting (SLM), and hardfacing by Directed Energy Deposition plasma arc (DED-arc). Based on the comparison to original material and the duration of usage, application of those three methods for replacement is studied in order to determine the most suitable one, with Additive Manufacturing (AM) being proposed for targeting the problem. Thermal-sprayed items have a zirconium-oxide-based outer layer. SLM produces a monolithic item, while with the help of DED-arc, a composite structure with a sound metallurgical bond between the base and the added material is produced. The microstructures with the interface zones are observed. Samples are machined and ground, and their friction characteristics are taken with the help of acoustic emission (AE) and Electrical Contact Resistances (ECR) sensors during scratching. As a result, overlaying of the base stainless steel by DED-arc is proposed due to the better metallurgical stability of the added mixture in a hot environment above 800 °C and its hardness characteristics. Full article

This work presents the design evolution, fabrication, and characterisation of an additively manufactured ring-type thermal airflow sensor for in-pipe flow monitoring, developed employing exclusively Fused Deposition Modelling (FDM) additive manufacturing technology and a commercially available Carbon Nanotube (CNT)-enriched Biopolymer Polylactic Acid (PLA) composite filament. The design evolution proceeds through three progressive stages. In the first stage, a flat heater element is characterised through Constant-Current (CC) Joule heating experiments in order to derive the corresponding Temperature Coefficient of Resistance (TCR) and Thermal Resistance from the obtained experimental data. Consequently, a Finite Element Method (FEM) model implemented in COMSOL Multiphysics^® and calibrated with the extracted material parameters validates the experimental temperature–power relationship and predicts the convective cooling behaviour at various airflow velocities. In the second stage, the geometry is optimised by introducing a conductive trace with a reduced-cross-section central region; as a result, an equivalent thermal localisation is achieved at approximately 26% lower supplied power with respect to the initial heating element, enabled by the design freedom inherent in the FDM process. We should note that the specific sensing geometry can also be directly embedded into any 3D-printed structural component (e.g., a bracket or housing), enabling simultaneous local thermal heating and/or thermal monitoring together with structural functionality within a single printed part. In the third and final stage—the target device—a fully monolithic ring-type airflow sensor is directly integrated into a 3D-printed pipe segment during the printing process. Under constant-current excitation at 40 mA, the device exhibits a monotonically decreasing resistance with increasing airflow (ΔR ≈ 117 Ω over 0–4 m/s) due to convective cooling, while in a single flow-interruption cycle, approximately 79% of the flow-induced resistance change was recovered upon flow removal, with a residual offset of approximately 3% of the heated baseline. A coupled electrothermal FEM model of the device further supports the experimental response by comparing the simulated temperature rise with the values inferred from resistance measurements, while also clarifying the role of the effective internal convective cooling conditions imposed by the pipe geometry. Key features of the proposed device are low raw-consumables cost, fast on-site manufacturing employing a commercially available desktop 3D printer, monolithic construction free of wire-bonded interconnections, and simplicity, indicating its potential for flow monitoring and condition-based maintenance systems aboard vessels as well as in a wide range of industrial sectors. We should note that the present characterisation was performed under laboratory conditions employing a single prototype per design stage; the effects of humidity, salt exposure, vibration, temperature cycling, and material-batch variability remain to be assessed prior to shipboard deployment. Full article

In-Mold Electronics (IMEs) present a highly promising monolithic integration strategy for manufacturing miniaturized 3D MEMS optical tweezers, offering exceptional environmental adaptability and structural compactness. However, the precision of such optical systems is heavily constrained by the injection molding process. During the molding phase, high-pressure melt scouring and severe thermo-mechanical coupling frequently induce geometric misalignment, manifesting as node displacement, localized warpage, and residual stress accumulation in the embedded circuits. This displacement critically alters the cross-sectional area of conductive traces, leading to resistance fluctuations that can destabilize the driving current. According to American Wire Gauge (AWG) standards, ensuring the geometric fidelity of this sensor-CPU interconnect pathway is fundamental to maintaining signal integrity. To address these manufacturing bottlenecks, this study systematically investigates the process stability of IME circuits Cyclic Olefin Copolymer (COC) is strategically selected as the substrate material over Polycarbonate (PC) and Liquid Silicone Rubber (LSR) due to its ultra-high light transmittance, extremely low water absorption, and superior thermomechanical stability. Based on finite element simulation, a data-driven intelligent optimization framework is developed. Latin Hypercube Sampling (LHS) is first utilized to efficiently sample the multi-dimensional process space, comprising melt temperature, packing pressure, and packing time. To handle the non-stationary nature of process feedback signals, wavelet analysis is introduced to decouple high-frequency noise, extracting Wavelet Energy Entropy (WEE) as a highly robust dynamic metric for process stability. Subsequently, a hybrid NSGA-II-MOPSO multi-objective algorithm is deployed to cooperatively optimize the injection parameters. The simulation-based optimization results demonstrate a substantial enhancement in manufacturing precision. Under the optimal parameter configuration, the average node displacement of the embedded circuits decreases significantly from 0.034 mm to 0.014 mm, achieving a 58.82% reduction. Simultaneously, volumetric shrinkage drops from 5.755% to 4.832% (a 16.04% reduction), while residual stress is maintained well within the structural safety threshold of optical-grade polymers. By clarifying the deformation control mechanism during the manufacturing phase, this study provides a highly reliable, data-driven methodological framework for the precision mass production of micro-nano optical systems. Full article

While double-layer insulation structures are widely adopted, their thermal performance is critically dependent on the thermophysical behavior of the interstitial air cavity, a variable often oversimplified in current design practices. This article moves beyond generic material descriptions to investigate the specific mechanism of heat transfer transition within sealed air gaps sandwiched between graphite polystyrene boards. The innovation of this experiment lies in the rigorous isolation of air gap thickness as the primary independent variable within a 1 × 1 × 1 m closed building model, instrumented with high-precision GPRS temperature and humidity sensors to capture real-time thermal gradients under the authentic climate conditions of Anyang, Henan. The results demonstrate a non-monotonic relationship between gap thickness and effective thermal resistance, governed by the competition between molecular conduction and buoyancy-driven natural convection. Specifically, the data validates that a 20 mm air gap represents the statistically significant optimum, thereby maximizing insulation efficiency while minimizing radiative heat loss. Using this optimized structure reduces steady-state heat flux compared to monolithic equivalents and aligns with the energy conservation target. Unlike previous studies limited by simulation assumptions or short-term testing, this research provides empirically verified, long-term field data that bridges the gap between theoretical fluid dynamics and practical building envelope engineering. These findings offer a robust, physics-based reference for optimizing double-layer insulation systems in the Central Plains, directly supporting the low-carbon retrofitting of existing building stocks. Full article

Multimodal reinforcement learning agents must fuse signals with vastly different noise profiles—yet existing architectures, whether monolithic ($\pi$0, DreamerV3) or modular (MSDP, VTDexManip), allow noise from unreliable modalities to contaminate reliable ones at the point of fusion. We propose filter before mixing: each modality’s representation is independently refined by a per-modality Flow Matching module before spectral-domain fusion via a Fourier Neural Operator (FNO) with a residual gate ensuring that refinement is never harmful. The resulting architecture, FreamerV1 (Filter-before-mixing dreamer), has 93M parameters (0.4M trainable). On MiniGrid, FreamerV1 reaches 87.7 ± 8.2% (3 seeds) at 5000 episodes, while the encoder-only baseline degrades to 78% due to catastrophic forgetting. With OGM-GE (On-the-fly Gradient Modulation) for adaptive per-modality gate control, FreamerV1 achieves an 8.0% relative improvement in success rate over manual tuning with halved seed-to-seed variance (three seeds). On Crafter (no language modality), it achieves an 11.7% relative improvement over DreamerV3 in the official Crafter score (geometric mean of 22 achievement success rates; 10 seeds). On PAMAP2 wearable sensors—where no pretrained encoder exists—the foundation encoder achieves 2.4× higher reward and 16× lower variance than a vanilla MLP, confirming that the filter-before-mixing advantage grows with encoder noise. Full article

Driven by the profound convergence of the Internet of Things (IoT) and ubiquitous computing, wearable multifunctional sensors have emerged as a key technology for high-precision human activity recognition (HAR). Advancements in novel materials and flexible electronics have propelled the evolution of these sensors, enabling advances in decoupling heterogeneous signals, enhancing system robustness, and expanding environmental perception. This review systematically examines the frontier research on wearable multifunctional sensors for HAR. We provide an in-depth analysis of three core architectural design paradigms: architecture-level integration, which relies on physical spatial isolation for hardware-level signal decoupling; monolithic integration, which strives for extreme spatial compactness and spatiotemporal signal consistency; and the emerging intrinsically multifunctional design, which leverages novel stimuli-responsive materials for the intrinsic orthogonal discrimination of multidimensional signals. Furthermore, we delineate the diverse application scenarios of these highly integrated sensing platforms across medical rehabilitation, sports science, human–computer interaction (HCI), and daily behavior perception. Finally, this article discusses the critical challenges currently confronting this technology and outlines its future development prospects. Full article

A compact, low-cost star tracker system tailored for small satellite applications was designed and prototyped. The system was designed with a fast f/1.2 aperture, a 20 × 13° field of view, and a theoretical angular resolution of 10 arcs—sufficient for the determination of attitude and orbit of a satellite. The optical design is based on a monolithic Maksutov–Cassegrain architecture, with lens assemblies fabricated from CR39 or PMMA to eliminate collimation requirements and improve vibration resistance. The lens was machined using Single-Point Diamond Turning to a precision better than λ/14. It was coated with a multilayer antireflective and highly reflective coatings applied via magnetron sputtering to reduce stray reflections and improve light throughput. The housing was produced using electron beam powder-bed fusion with Ti-64 alloy, while the use of commercial imaging sensors minimizes overall cost. Prototype testing confirmed to plate-solve star patterns with precision better than 27 arcs at 100 ms imaging time across all analysed images. Full article

A monolithic digital Silicon Photomultiplier (SiPM) featuring 1024 microcells with a 30-micrometer pitch and a 50% fill factor has been designed in a 110-nanometer CMOS image sensor technology. The device under consideration integrates both SPAD sensors and front-end electronics in the same substrate. It can count up to 1024 photons in less than 22 ns, while assigning timestamps to the first and last detected photons with a time resolution of less than 100 ps. A parallel counter structure combined with a fast adder tree provides photon counting in digital form with low latency, whereas a carefully balanced fast NAND tree ensures a fixed-pattern time uncertainty not exceeding 26 ps. The architecture incorporates in-pixel memory for individual cell disabling and configurable thresholding on the timing signal for noise mitigation. In order to optimize the fill factor, a part of the electronics is placed outside the array, while the most sensitive elements of the timing and counting circuits are laid out close to the sensor, in the SPAD array. A serial readout is employed to provide a single output connection per SiPM, thereby simplifying system integration. Full article

Rapid, standardised discrimination of soil taxonomic units remains challenging when relying solely on conventional field descriptions and laboratory analyses, particularly at high sampling densities. This study evaluated whether proximal spectroscopy and hyperspectral imaging can support the classification of Brazilian Soil Classification System (SiBCS) suborders and pedogenetic horizons when surface and subsurface spectra are treated separately. Six intact soil monoliths (0.12 × 1.60 m) were collected in Paraná State, southern Brazil, representing one Organossolo (Ooy), three Latossolos (LVd, LVd1, and LVd2) and two Argissolos (PVAd and PVd). For each monolith, 800 spectra were acquired per sensor with a non-imaging VIS–NIR–SWIR spectroradiometer (350–2500 nm), and 800 spectra per sensor per monolith were extracted from the SWIR hyperspectral images (1200–2450 nm). Principal component analysis (PCA) was used to summarise spectral variability, and supervised classification was performed via k-nearest neighbours, random forest, decision tree and gradient boosting for suborders (10-fold cross-validation), and a neural network was used for within-profile horizon classification. PCA indicated that most of the spectral variance was captured by a dominant axis, with clearer separation among suborders in the SWIR space than in the full VIS–NIR–SWIR range. With respect to suborder classification, subsurface spectra outperformed surface spectra, and SWIR outperformed VIS–NIR–SWIR: the best accuracies were 0.96 for subsurface SWIR (gradient boosting; AUC = 0.99; MCC = 0.95) and 0.89 for surface SWIR (k-nearest neighbours; AUC = 0.98; MCC = 0.87). Within-profile horizon classification via VIS–NIR–SWIR achieved accuracies of 0.84–0.97 with the Neural Network, with most misclassifications occurring between adjacent horizons. Overall, subsurface SWIR information provided the most reliable basis for taxonomic discrimination, whereas horizon classification was feasible but reflected gradual spectral transitions along the profile. Full article

The accurate measurement of dynamic forces is pivotal for advancing manufacturing process monitoring and enhancing equipment intelligence. To address the challenges of contact interface force field nonlinearity in existing PVDF piezoelectric film force sensors and the inability of a monolithic PVDF piezoelectric film to measure multi-dimensional forces, this study designs a uniform-load double-bossed elastic force-transmitting diaphragm to achieve contact interface force field reconstruction between the sensor’s elastic sensing structure and the sensitive element group. Building upon the load-bearing surface domain segmentation technique, the silver ink electrode on the front surface of a complete circular PVDF piezoelectric film is segmented into four independent sector-shaped rings. Each sector ring, together with its underlying PVDF piezoelectric film, constitutes a sensitive element, and these four sensitive elements are integrated to form the sensitive element group. The three-dimensional force measurement method of this sensitive element group in the Cartesian coordinate system is investigated. The measurement of three-dimensional force is realized by leveraging the tensile-compressive piezoelectric effect of each sensitive element in conjunction with a pre-stressed assembly structure. Quasi-static calibration test results indicate that the charge sensitivities of the force sensor in the X-, Y-, and Z-directions are 52.63 pC/N, 55.96 pC/N, and 9.02 pC/N, respectively, with a linearity ≤4.6%. Dynamic calibration test results reveal that the force measurement module exhibits a natural frequency of 4675.5 Hz. Experimental investigations into the response of triaxial cutting forces to variations in cutting speed, feed rate, and cutting depth were conducted, which verified the sensor’s ability to capture dynamic three-dimensional cutting forces. This study provides an effective solution for the structural design and three-dimensional force measurement methodology of PVDF piezoelectric film force sensors. Full article

For next-generation biomedical and biochemical sensor nodes, the analog front-end demands a direct interface with current-output sensors, extreme miniaturization, and nanowatt power consumption to enable energy autonomy. This work directly addresses these needs by presenting a comparative analysis of four minimalist, first-order, current-mode $\Delta \Sigma$ modulator ($\Delta \Sigma$M) architectures. Optimized for ultra-low-voltage operation (supply $\le 0.5$ V), the investigated topologies—including resistive, switched-capacitor, and current-reference-based cores—exploit passive integration and charge-domain feedback, eliminating the need for power-hungry active blocks. Detailed circuit-level simulations confirm that, with ad hoc techniques, it is possible to achieve stable first-order noise shaping in the deep near-threshold region, delivering up to 10-bit resolution while consuming less than 10 nW at a 0.5 V supply voltage achieving a signal bandwidth in the sub-10 hertz range. This study validates that robust $\Delta \Sigma$ conversion is feasible under extreme area and power constraints by leveraging architectural simplicity. The clear performance–complexity trade-offs outlined make these current-mode architectures ideal candidates for monolithic integration within miniaturized, energy-autonomous sensing systems. Full article

Bound states in the continuum (BICs) can be transformed into quasi-bound states (quasi-BICs) via intentional symmetry breaking, thereby enabling ultrahigh-Q resonances critical for refractometric sensing applications. To advance detection capabilities for organic analytes, we proposed an all-dielectric metasurface monolithically integrated within a microfluidic channel. Mirror symmetry was intentionally disrupted through a cylindrical perturbation applied to one of two identical elliptical resonators, which excited a quasi-BIC mode at 1.9591 THz with a numerically validated Q-factor of 1959. This resonance manifested an absorption peak approaching unity, featuring a full-width at half-maximum (FWHM) of merely 1 GHz. Multipolar decomposition revealed that the mode originated from a synergistic electric-quadrupole (EQ)–magnetic-dipole (MD) pair, wherein the EQ contribution exceeded the MD counterpart by 20%. Capitalizing on this high-Q resonance, the sensor attained a sensitivity of 240 GHz per refractive-index unit (GHz RIU⁻¹) and a figure of merit (FOM = S/FWHM) of 240, while demonstrating robust performance against fabrication tolerances spanning −4% to +4%. Additionally, we verified that oblique-incidence illumination could activate a quasi-BIC within the identical spectral band, circumventing the need for structural asymmetry and thus expanding operational versatility. Benefiting from its geometric simplicity and competitive performance, this architecture exhibited substantial potential for on-chip sensing of organic compounds. Full article

The integration of complementary metal–oxide–semiconductor (CMOS) and micro-electromechanical systems (MEMSs) technologies for miniaturized biosensor fabrication enables unprecedented spatiotemporal resolution in monitoring the bioelectrical activity of the nervous system. Wafer-level CMOS technology incurs high costs, but multi-project wafer (MPW) runs mitigate this by allowing multiple users to share a single wafer. Still, monolithic CMOS biosensors require specialized surface materials or device geometries incompatible with standard CMOS processes. Performing MEMS post-processing on the few square millimeters available in MPW dies remains a significant challenge. In this paper, we present a MEMS post-processing workflow tailored for CMOS dies that supports both surface material modification and layout shaping for intracortical biosensing applications. To address lithographic limitations on small substrates, we optimized spray-coating photolithography methods that suppress edge effects and enable reliable patterning and lift-off of diverse materials. We fabricated a needle-like, 512-channel simultaneous neural recording active pixel sensor (SiNAPS) technology based neural probe designed for integration with optical fibers for optogenetic studies. To mitigate photoelectric effects induced by light stimulation, we incorporated a photoelectric shield through simple modifications to the photolithography mask. Optical bench testing demonstrated >96% light-shielding effectiveness at 3 mW of light power applied directly to the probe electrodes. In vivo experiments confirmed the probe’s capability for high-resolution electrophysiological measurements. Full article

This paper presents the design and implementation of the Power Stage GAIA (PSG), a high-current digital bias board developed by the Italian National Institute for Astrophysics (INAF) to extend the capabilities of the GAIA bias system. The PSG was developed within the Advanced European THz Receiver Array (AETHRA) project to support next-generation cryogenic receivers for millimeter-wave astronomy. Specifically, the AETHRA Work Package 1 (WP1) W-band downconverter integrates Monolithic Microwave Integrated Circuits (MMICs) requiring currents significantly exceeding the 50 mA limit of standard bias boards. To address these requirements, the PSG introduces a modular extension providing ten independent channels, each capable of delivering up to 500 mA with a programmable output range of 0–5 V. A key feature of the design is the adoption of a fully linear architecture based on LT1970 power amplifiers and INA225 precision sensors managed via an I²C digital interface. This approach ensures the high current capability required by modern power amplifiers while strictly avoiding the spectral noise and Radio Frequency Interference (RFI) typical of switching power supplies. Experimental validation confirms the system’s robustness and precision: the board demonstrated linear operation up to 460 mA and exceptional long-term stability, with a measured RMS voltage deviation below 50 µV. These results establish the PSG as a scalable, low-noise solution suitable for biasing high-power MMICs in future cryogenic receiver arrays. Full article