Precise Sensor Research Articles

Facile Construction of Soft Plasmonic Sensors with Exceptional Optical Activity for Quantitative Chiral Detection.

The facile construction of transmissive films with ultrabroad optical activity, spanning from deep-ultraviolet to short-wave infrared and offering convenient tunability across a wide range, is highly desirable for applications in sensing, imaging, and communication. However, achieving this remains challenging. Here, an easily applied wet-stretching method is introduced that simultaneously orients polymeric substrates and surface-coated plasmonic nanorods. Stacking two such hybrid films at an angle produces ultrastrong (ellipticity≈104 mdeg, gabs≈1) and broadband (200-2500 nm) circular dichroism (CD). The polymer's excellent strength and flexibility allow for broad-range tuning of the CD spectra by applying external force. The optical activity is sensitive to intervening medium, facilitating chiral detection of various inserted analytes in the forms of films, salt pellets, or solutions. This cost-effective and scalable fabrication strategy not only pioneers an expandable method for inducing chirality across diverse materials, but also offers a universal approach for constructing precise, non-destructive, non-contact, and reusable chiral sensors.

Verification of Numerical Models of Steel Bar Coverings Using Experimental Tests—Preliminary Study

In the design of metal bar coverings, the key problem is to correctly determine the numerical model of the analyzed structure. The description of numerical models may differ from the actual, real behavior of the structure. Therefore, there is a need to verify and calibrate them using experimental studies. The aim of this research will be to verify and assess the accuracy of the numerical model of a metal bar roof by conducting experimental studies. A series of repeatable experimental tests will be conducted on the structure model to determine the path of static equilibrium and the form of stability loss of the steel covering. During the test, as the load increases, data will be collected on the displacements of nodes. The displacements of the nodes will be verified using precise triangulation laser sensors and electronic sensors. Based on the results of the tests, conclusions will be drawn regarding the accuracy of the numerical models. Comparison of the results obtained from the numerical models with the experimental data will allow for the identification of possible discrepancies and understanding how the numerical models can be improved. This in turn will contribute to the development of more advanced and more accurate methods for the analysis of metal bar roof structures in the future.

A novel high-dimensional sensor calibration framework integrating thermodynamic laws in complex HVAC systems

Accurate calibration of sensors is critical for ensuring energy efficient operation of Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings. Due to the high dimensionality of sensor data and the complexity of multiple-fault scenarios, calibrating sensors in large and complex HVAC systems presents significant challenges. To address this issue, this study introduces a novel sensor calibration framework that integrates thermodynamic laws for high-dimensional sensor calibration in complex HVAC systems. The traditional calibration method heavily relies on accurate data, making it difficult to apply in practical engineering projects. The innovative aspect of our method lies in its integration of thermodynamic laws, such as mass balance and energy conservation, with sensor calibration framework. This approach enables the framework to handle high-dimensional sensor measurements effectively without any training data. We compared five optimization algorithms and applied them to a central cooling system in Hong Kong. The results demonstrated that the simulated annealing (SA) is the most robust for solving the calibration problem, even in scenarios with up to 21 faulty sensors, with the calibrated sensor accuracy meeting the standards for conventional chiller plant operations. This novel framework provides a robust and reliable solution for high-dimensional sensor calibration in large and complex HVAC systems, addressing the growing need for precise sensor calibration as the number of installed sensors increases.

Novel AlN/ScAlN composite film SAW for achieving highly sensitive temperature sensors

Traditional SAW devices, typically made from piezoelectric materials like quartz and lithium niobate (LiNbO3), face significant challenges, such as incompatibility with CMOS processes and a decline in piezoelectric performance at high temperatures. Recently, aluminum nitride (AlN) and scandium-doped AlN (ScAlN) have gained attention as promising materials for high-performance SAW devices due to their high acoustic velocity, thermal stability, and CMOS compatibility. However, the low piezoelectric coefficient of AlN and Sc precipitation in ScAlN films limit their broader application. This study investigates the fabrication and optimization of SAW resonators using AlN/ScAlN composite films to enhance piezoelectric performance while mitigating Sc precipitation. A one-port SAW sensor device was designed based on the composite piezoelectric film, and structural optimization was performed by introducing groove structures to further reduce acoustic energy leakage and improve the quality factor (Q). Temperature sensing experiments were conducted using a peripheral oscillator circuit system. The experimental results demonstrated that the developed composite film SAW resonator exhibited excellent phase noise performance and thermal stability within the oscillator circuit, achieving a phase noise of −135.18 dBc/Hz@1 MHz and a frequency temperature coefficient of −31.07 ppm/°C. These findings confirm the potential of the AlN/ScAlN composite film as a reliable and precise temperature sensor.

Thermal properties of fluoride fiber Bragg gratings at high to cryogenic temperatures.

The thermal sensitivity of fiber Bragg gratings (FBGs) is extensively employed in diverse industrial and scientific applications. FBGs lie at the core of flexible, low-cost, and highly precise sensors, featuring stability in harsh environments and distributed sensing capability. This study assesses the thermal properties of FBGs in fluoride fibers within a temperature range of 4-373 K. Despite having higher thermal expansion coefficients, FBGs in the near-IR wavelength range do not exhibit high sensitivity at room or higher temperatures. However, the pronounced enhancement of their thermal sensitivity at longer Bragg wavelengths shows the potential for sensing applications in the light of the fluoride glass extended transmission range up to 4-5.5 µm. Most importantly, employing FBGs inscribed in fluoride fibers enables the further expansion of fiber-based sensors to cryogenic environments, as they exhibit a detectable sensitivity of 0.5-1.7 pm/K below 50 K. Overall, the exposure to low temperatures provides valuable information on glass stability and physical parameters, which is beneficial for the further development of photonic systems based on fluoride fibers.

Spectral discrimination of crops and weeds using deep learning assisted by wavelet transform and statistical preprocessing

Abstract Automatic detection and removal of weeds is a challenging task that requires precise sensors. While crops and weeds possess similar features in terms of appearance, they can be discriminated based on spectral information. This can be done because any object has its own specific spectral signature based on its physical structure and chemical contents. This study examined the use of wavelet transform and deep learning for discrimination of weeds from crops. A total of 626 spectral reflectances in the range of 380 to 1,000 nm were obtained for three crops (cucumber [Cucumis sativus L.], tomato [Solanum lycopersicum L.], and bell pepper [Capsicum annuum L.]) and five different weeds (bindweed [Convolvulus spp.], purple nutsedge [Cyperus rotundus L.], narrowleaf plantain [Plantago lanceolata L.], common cinquefoil [Potentilla simplex Michx.], and garden sorrel [Rumex acetosa L.]). Morse wavelet was employed to decompose the spectra and extract the scalograms, which are the RGB representations of the spectral data. Two deep convolutional neural networks (i.e., GoogLeNet and SqueezNet) were employed for the recognition of crops and weeds. In addition, six common classifiers, including linear discriminant analysis, quadratic discriminant analysis, linear support vector machine, quadratic support vector machine, artificial neural networks, and k-nearest neighbors classifier (KNN), were used for the task of crop/weed discrimination to build the comparison with the proposed method. The error of prediction gradually decreased, and a 100% correct classification was achieved after 258 iterations. Analysis showed that SqueezNet provided classification of 100% accuracy, while GoogLeNet’s accuracy was 97.8% for the test set. Among the common classifiers, KNN provided the highest accuracy (i.e., 100%). This study showed that the proposed method can be successfully utilized for classification of crops and weeds.

Engineering DNA Nanocube SAM Scaffolds for FRET-Based Biosensing: Interfacial Characterization and Sensor Demonstration.

Decorating a gold surface with molecular-level control over the positioning of DNA probes was demonstrated using a self-assembled monolayer (SAM) of wireframe DNA nanocube structures. The DNA nanocubes were specifically adsorbed and oriented using thiol-modified DNA on one face of the cube. The DNA nanocube SAM had a uniform coverage over the gold single crystal bead electrode with a separation of 20-30 nm measured by AFM. The face of the nanocube furthest from the gold surface was designed to hybridize with two different sequences of a 50 base single-stranded DNA probe that was modified with a fluorophore. The first 20 bases were hybridized with the DNA nanocube. One of a pair of FRET fluorophores was used for each probe strand. The dimensions of the nanocube controlled the relative spacing between these fluorophores. When the DNA probes were single-stranded, a FRET signal was observed. FRET decreased to background levels when a complementary DNA target was hybridized to either probe, resulting in a turn-off sensor with little cross-talk between the individual hybridization events. Hybridization isotherms for one target gave KA = 170 pM and a detection limit <50 pM. In addition, the DNA nanocube SAM was configured to be used as a turn-on NeutrAvidin sensor using biotinylated DNA targets hybridized to each probe resulting in an increase in FRET. We show that the wireframe DNA nanocube can be an effective scaffold for preparing biosensors with controlled separation between surface-bound probes facilitating precise sensor surface design and enabling a wide range of sensing modalities with more than one signal available for correlative confirmation of the target binding.

Anchor carbon dots inside NH2-MIL-88B via ship-in-a-bottle strategy for dual signal enhancement in colorimetric-fluorescent sensors

The increase in oxytetracycline (OTC) pollution has become a significant risk to ecological stability and human health because of excessive use. Therefore, developing a precise and reliable sensor for trace OTC detection is critical. In this work, a dual-mode sensor of colorimetric-fluorescent (CL-FL) with dual signal-on was developed for OTC detection. Firstly, a novel carbon dots encapsulating in cavities of MOFs composite (CDs@NH2-MIL-88B) was constructed by the ship-in-a-bottle approach, which enhanced peroxidase-like activity and fluorescent intensity. Further, combining with surface molecularly imprinted polymer (MIP), the dual-mode sensor was constructed to develop selectivity (CDs@NH2-MIL-88B@rMIP). Finally, the dual signal enhancement mechanism of the sensor comes from the interaction between OTC and identifying binding site involving strong hydrogen bonding and metal complexation from imprinted cavity and CDs@NH2-MIL-88B, respectively. The prepared sensor has a linear detection range of 1.00 × 10−9 - 1.00 × 10−5 M with the limit of detection (LOD) of 1.47 × 10−10 M for the CL mode and a detection range of 1.00 × 10−11 −1.00 × 10−7 M with the LOD of 1.84 × 10−12 M for the FL mode. Meanwhile, the sensor shows high sensitivity, accuracy and reliability in OTC detection. Overall, this work provides a promising CL-FL dual-mode sensing systems with signal-on in the detection of antibiotics and other hazardous pollutants.

MEMS Precision Sensors Based on Process Innovation Technology

This article introduces a novel process for achieving micro-scale thermal isolation through solid porous 3D microstructures. It proposes a method to improve existing thermal MEMS devices by utilizing thin-film membrane structures, develops a model for estimating the thermal conductivity of porous microstructures, and constructs porous 3D microstructures made of three different powder materials, studying their applicability for thermal isolation. Breakthroughs have been achieved in process preparation technology, enabling the production of precision MEMS sensors. These sensors are expected to find wide applications in smart cars, intelligent healthcare, electronic resonance sensors, and other fields.

Smart integration in agriculture: an Arduino-driven rice grain dryer for optimal post-harvest management

In the field of agriculture, the post-harvest stage often presents a multitude of obstacles that have the potential to undermine the quality and longevity of the crop. One of the primary obstacles in rice production is the effective drying of rice grains, which is crucial for preventing problems such as mold growth, discoloration, and unintentional germination. To tackle this issue, we propose an innovative solution: a rice grain drying system driven by an Arduino microcontroller. This device effectively combines technology accuracy with agricultural needs. The system is equipped with a very precise temperature sensor to maintain a uniform drying environment, fluctuating within the range of 50–60 °C. The use of a nichrome heater, selected for its consistent and dependable heat production, is complemented by an air blower to ensure the even distribution of heat throughout the drying chamber. One notable characteristic of this innovation is its significant drying capacity of 50 kg in each operation. The effectiveness of the product was verified by a rigorous testing process. A batch of glutinous rice weighing 25 kg, initially containing 25.5% moisture, was successfully reduced to 13.5% moisture content within a time frame of 125 min. In a similar vein, a batch of grains weighing 40 kg, which had just undergone threshing, had a moisture content of 22% initially. Remarkably, this batch achieved the desirable moisture level of 14% within a mere two-hour timeframe. This innovative technology not only provides a resolution to the difficulties faced in post-harvest processes but also signifies a fundamental change in thinking, introducing a fresh era where intelligent technology seamlessly integrates with traditional agricultural methods.

Concept of a Cyber–Physical System for Control of a Self-Cleaning Aquaponic Unit

The article aims to present a cyber–physical system (CPS) to support the cultivation of aquaculture in a closed aquaponic system using the deep-water culture (DWC) method. The CPS uses precision sensors as TriOxmatic 700 IQ (for dissolved oxygen and water temperature), AmmoLyt Plus 700 IQ (for ammonium), NiCaVis 701 IQ NI (for nitrites and nitrates), SensoLyt® 700 IQ (for pH), and SL-M5 (for water level). It is built with a Raspberry Pi 4, 8 GB as a server, OpenHAB 3.0 software, and other specialized software for measuring water parameters. Some of the parameters are maintained completely autonomously, while others are indirectly controlled. Basic knowledge of hydroponics and aquaculture is required to set up the system, but day-to-day maintenance can be carried out by employees who receive instructions from the CPS. A method for the physical modification of the fish tank surface by using laser processing is proposed. This results in a change in surface topography (creating diverse microstructure patterns) and its roughness, which is of crucial importance for the bacterial adhesion mechanism.

Greenness assessment of a molecularly imprinted polymeric sensor based on a bio-inspired polymer

Methyldopa, a synthesized dopamine substitute with phenolic, amine, and carboxylic groups, was used to create a selective molecular imprinted polymer (MIP) for detecting formoterol fumarate dihydrate (FFD), a long-acting beta2-agonist for asthma and COPD. The bio-inspired polymer (MD) was electro-grafted onto a pencil graphite electrode (PGE) using cyclic voltammetry in a phosphate buffer (pH 6.5). An indirect method involving a redox probe (ferrocyanide/ferricyanide) and differential pulse voltammetry measured FFD binding to the MIP’s 3D cavities. The sensor showed a linear response range from 1 × 10⁻⁹ M to 2 × 10⁻¹⁰ M, with a detection limit of 1.7 × 10⁻¹¹ M. The polymethyldopa (PMD) and FFD interaction was assessed by UV spectroscopy, and the method was validated per ICH guidelines. Green analytical approaches, including RGB and GAPI, were also implemented. The goal was to use advances in molecularly imprinted polymers to develop a more precise and selective electrochemical sensor for FFD quantification.

Circuit-theoretic phenomenological model of an electrostatic gate-controlled bi-SQUID

Abstract A numerical model based on a lumped circuit element approximation for a bi-superconducting quantum interference device (bi-SQUID) operating in the presence of an external magnetic field is presented in this paper. Included in the model is the novel ability to capture the resultant behaviour of the device when a strong electric field is applied to its Josephson junctions by utilising gate electrodes. The model is used to simulate an all-metallic SNS (Al-Cu-Al) bi-SQUID, where good agreement is observed between the simulated results and the experimental data. The results discussed in this work suggest that the primary consequences of the superconducting field effect induced by the gating of the Josephson junctions are accounted for in our minimal model; namely, the suppression of the junctions super-current. Although based on a simplified semi-empirical model, our results may guide the search for a microscopic origin of this effect by providing a means to model the voltage response of gated SQUIDs. Also, the possible applications of this effect regarding the operation of SQUIDs as ultra-high precision sensors, where the performance of such devices can be improved via careful tuning of the applied gate voltages, are discussed at the end of the paper.

Novel Ceramic Clay Automatic Feeding System and Simulation Analysis

This study aims to verify the feasibility and effectiveness of an automatic feeding system in the ceramic clay-forming process. Through a series of clay-forming experiments, the system’s performance under various process parameters was examined. Precision sensors and data recording devices were used to monitor and record key data during the experimental process in real-time. The results demonstrate that the automatic feeding system can supply clay steadily and continuously under set parameters, ensuring a smooth forming process and significantly improving efficiency. Quantitatively, the system achieved a 30% increase in Vickers hardness, reflecting enhanced mechanical properties of the formed clay bodies. Additionally, there was a notable improvement in axial stress–strain characteristics, indicating better structural integrity and consistency. These improvements reduced human errors and material waste, enhancing production efficiency and product quality. Future research will focus on further optimizing system design and exploring its applications in a broader range of ceramic manufacturing processes.

An SPR-Based In Situ Methane Sensor for the Aqueous and Gas Phase.

This paper presents a fast, low-cost, precise methane sensor based on refractive index changes in a cryptophane A (CryptA)-doped polystyrene membrane. For the realization of this sensor, we built a surface plasmon resonance sensor with a refractive index resolution of 4.31 × 10-6 and investigated the optimal membrane thickness, i.e., a polymer layer of sufficient sensitivity with the lowest response time. For a membrane thickness of 760 nm, a limit of detection of 135 ppm and a response time constant of 45 s were found. Despite a comparable refractive index resolution and a higher CryptA content, the limit of detection is 3 orders of magnitude larger than that of a reported prototype. The sensor is capable of quantifying methane in the gas or aqueous phase. However, temperature, humidity, and ethanol vapor highly influence the signal. Although the principle of CryptA-based methane measurement is a promising, fast, and low-cost method, it will not be able to compete with state-of-the-art sensing in its current state.

Improvement of Capacitive Accelerometers: Integrating Modeling, Electrode Design, Signal Processing, and Material Selection

This work offers a thorough method for integrating advanced modeling, electrode design, signal processing, and material selection techniques to maximize the performance of capacitive accelerometers. Key performance measures, including noise reduction, sensitivity, and range, can be systematically analyzed and simulated to forecast how design decisions would affect the overall behavior of the sensor. Advanced electrode designs greatly increase capacitance, which results in more precise and trustworthy sensor readings. These designs include optimizing the shape and using high-permittivity materials. Furthermore, to guarantee that the accelerometer’s output precisely represents actual motion even in noisy surroundings, noise reduction strategies including filtering and digital signal processing methods are essential. Additionally, calibration is emphasized as a critical step in preserving measurement accuracy over time and accounting for environmental changes and sensor drift. The choice of material, with an emphasis on thermally stable and high-permittivity materials, is crucial in determining the capacitance, sensitivity, and durability of the sensor. The study offers a paradigm for the creation of capacitive accelerometers that perform better across a variety of applications by striking a balance between these variables.

Advanced ECG Monitoring: Machine Learning And Robotics For Real-Time Health Management

Cardiovascular diseases are becoming increasingly prevalent due to lifestyle factors such as poor diet, lack of exercise, and conditions like diabetes and hypertension. Electrocardiography (ECG) is a widely used diagnostic tool for detecting various heart conditions, including arrhythmias and myocardial infarction. However, manual analysis of ECG signals is often subjective, time-consuming, and prone to variability. To address these challenges, this paper proposes a comprehensive system that integrates an Arduino-based ECG acquisition module with neural networks for automatic analysis and a robotic system for autonomous sensor placement and intervention. The main objective is to create a smart, real-time ECG monitoring and Computer-Aided Diagnosis (CAD) system capable of early detection and management of heart diseases. The system utilizes machine learning and deep learning techniques to enhance diagnostic accuracy, focusing on implementing a neural network model for ECG classification. A robotic arm is also integrated into the system to ensure precise sensor placement and emergency response

Meniscus-Guided 3D Nanoprinting Technology for Quantum Device Assembly

The future we envision is always driven by technological innovation, and among the most promising fields is quantum technology. Ultra-fast computing, highly precise sensors, and unbreakable communication networks, once confined to science fiction, are now becoming reality. At the heart of realizing these advancements is 3D nanoprinting technology. This technique, which meticulously builds structures at the atomic scale, is essential for assembling quantum devices. In particular, meniscus-guided 3D nanoprinting is gaining attention as a revolutionary method, enabling extreme precision that was previously unattainable through traditional approaches, accelerating the commercialization and widespread use of quantum devices. However, the significance of quantum technology extends beyond mere technical achievements. It is also emerging as a key factor in national competitiveness and security. With intense global competition in areas like quantum computing, encryption, and sensing, research and investment to bridge the technological gap with leading nations are more crucial than ever. This article explores the principles and potential applications of meniscus-guided 3D nanoprinting, highlighting why this technology is poised to play a pioneering role in the quantum revolution.

A novel application of laser speckle imaging technique for prediction of hypoxic stress of apples

BackgroundFruit storage methods such as dynamic controlled atmosphere (DCA) technology enable adjusting the level of oxygen in the storage room, according to the physiological state of the product to slow down the ripening process. However, the successful application of DCA requires precise and reliable sensors of the oxidative stress of the fruit. In this study, respiration rate and chlorophyll fluorescence (CF) signals were evaluated after introducing a novel predictors of apples' hypoxic stress based on laser speckle imaging technique (LSI).ResultsBoth chlorophyll fluorescence and LSI signals were equally good for stress detection in principle. However, in an application with automatic detection based on machine learning models, the LSI signal proved to be superior, due to its stability and measurement repeatability. Moreover, the shortcomings of the CF signal appear to be its inability to indicate oxygen stress in tissues with low chlorophyll content but this does not apply to LSI. A comparison of different LSI signal processing methods showed that method based on the dynamics of changes in image content was better indicators of stress than methods based on measurements of changes in pixel brightness (inertia moment or laser speckle contrast analysis). Data obtained using the near-infrared laser provided better prediction capabilities, compared to the laser with red light.ConclusionsThe study showed that the signal from the scattered laser light phenomenon is a good predictor for the oxidative stress of apples. Results showed that effective prediction using LSI was possible and did not require additional signals. The proposed method has great potential as an alternative indicator of fruit oxidative stress, which can be applied in modern storage systems with a dynamically controlled atmosphere.

DeepTool: A deep learning framework for tool wear onset detection and remaining useful life prediction

Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a self-collected dataset of sensor data from milling tests carried out under different cutting settings to extract relevant information from the sensor signals. The main contributions of this study are:•Self-Collected Dataset: Makes use of an extensive, self-collected dataset to record precise sensor signals during milling.•Advanced Predictive Modeling: Employs hybrid autoencoder-LSTM and encoder-decoder LSTM models to estimate tool wear onset and predict its remaining useful life with over 95 % R2 accuracy score.•Comprehensive Feature Extraction: Employs an efficient feature extraction technique from the gathered sensor data, emphasising both time-domain and frequency-domain aspects associated with tool wear.