Detected Research Articles

Advances in Air Quality Monitoring: A Comprehensive Review of Algorithms for Imaging and Sensing Technologies

AbstractAir pollution is a major global concern, leading to serious health problems and environmental damage. This article provides a comprehensive review of historical and current methods used to monitor and predict air quality. It emphasizes the ongoing need for better monitoring techniques. 47 studies are critically analyzed, and computational advancements in air quality monitoring are categorized into sensor‐based and image‐based techniques. This review reveals that sensor‐based algorithmic methods, representing 62% of the reviewed literature, are reliable but often lack flexibility and real‐time monitoring capabilities. On the other hand, image‐based techniques, while innovative, are limited by the size and diversity of datasets, primarily functioning only during daylight hours. To address these limitations, a hybrid approach that integrates both sensor and image‐based methods is proposed. This aims to enhance monitoring by allowing users to visualize pollution levels through an augmented reality layer. The proposed model seeks to provide mobile users with the ability to accurately monitor surrounding air quality by establishing a comprehensive image‐based dataset that includes various features not previously considered in existing datasets.

A portable multi-taxa phenotyping device to retrieve physiological performance traits

Organismal phenotyping to identify fitness traits is transforming our understanding of adaptive responses and ecological interactions of species within changing environments. Here we present a portable Multi-Taxa Phenotyping (MTP) system that can retrieve a suite of metabolic and photophysiological parameter across light, temperature, and/or chemical gradients, using real time bio-optical (oxygen and chlorophyll a fluorescence) measurements. The MTP system integrates three well-established technologies for the first time: an imaging Pulse Amplitude Modulated (PAM) chlorophyll a fluorometer, custom-designed well plates equipped with optical oxygen sensors, and a thermocycler. We demonstrate the ability of the MTP system to distinguish phenotypic performance characteristics of diverse aquatic taxa spanning corals, mangroves and algae based on metabolic parameters and Photosystem II dynamics, in a high-throughput capacity and accounting for interactions of different environmental gradients on performance. Extracted metrics from the MTP system can not only provide information on the performance of aquatic taxa exposed to differing environmental gradients, but also provide predicted phenotypic responses of key aquatic organisms to environmental change. Further work validating how rapid phenotyping tools such as the MTP system predict phenotypic responses to long term environmental changes in situ are urgently required to best inform how these tools can support management efforts.

AI-Enhanced Smart Cooking Pot: A Culinary Companion with Intelligent Sensing

This research introduces SmartCook, an avant-garde kitchen appliance that amalgamates sensor technologies, artificial intelligence (AI), and machine learning to redefine the art of cooking. The device employs an array of sensors, including temperature, moisture, infrared, proximity, electronic tongue, and electronic nose, ingeniously positioned in the lid with foodgrade silicon transparent sealing. This design ensures a non-intrusive manner of sensing, preserving the integrity of both the food and the sensors. SmartCook operates in two modes: Cooking Mode, where experienced users store their personalized cooking patterns, and Guide Mode, designed for beginners seeking expert guidance. The AI processing unit, analysing vapours produced during cooking, intricately captures the nuances of smell, texture, timing, temperature, and moisture to create and store unique recipe patterns for each prepared meal. The addition of a texture-sensing camera placed in the lid, also sealed with foodgrade silicon, enhances the system’s capability to analyze and replicate food textures. This innovative approach transforms SmartCook into a comprehensive, guided, and customizable culinary assistant. The system’s safety mechanisms, including automatic shut-off, prioritize user safety throughout the cooking process. SmartCook revolutionizes home cooking, transforming it into an interactive, guided, and enjoyable journey. With its emphasis on replicating the unique patterns of individual cooks, SmartCook introduces a novel approach to the culinary arts, empowering users to virtually learn and recreate meals from their favourite chefs. This research presents a ground-breaking contribution to the evolving landscape of smart kitchen appliances, offering users an innovative and enjoyable way to explore the world of gastronomy.

A comparative evaluation of maize silage quality under diverse pre-ensiling strategies.

Maize silage serves as a significant source of energy and fibre for the diets of dairy and beef cattle. However, the quality of maize silage is contingent upon several crucial considerations, including dry matter loss, fermentative profile, pH level, ammonia content, and aerobic stability. These aspects are influenced by a multitude of factors and their interactions, with seasonality playing a crucial role in shaping silage quality. In this study an open-source database was utilised to assess the impact of various pre-ensiling circumstances, including the diversity of the chemical composition of the freshly harvested maize, on the silage quality. The findings revealed that seasonality exerts a profound influence on maize silage quality. Predictive models derived from the composition of freshly harvested maize demonstrated that metrics were only appropriate for screening purposes when utilizing in-field sensor technology. Moreover, this study suggests that a more comprehensive approach, incorporating additional factors and variability, is necessary to better elucidate the determinants of maize silage quality. To address this, combining data from diverse databases is highly recommended to enable the application of more robust algorithms, such as those from machine learning or deep learning, which benefit from large data sets.

Flexible Thermistors based on Laser-Induced Graphene from Polyetherimide

Abstract The importance of temperature sensors has notably increased in recent years, paralleling the surge in demand for flexible and stretchable sensor technologies. In this study, we fabricate and characterize a flexible resistive temperature sensor based on Laser-Induced Graphene (LIG) on Polyetherimide (PEI). The structure and morphology of LIG are thoughtfully characterized proving the formation of a graphitic-based structure. Subsequently, the sensors were evaluated in terms of their temperature response, impact of relative humidity (RH), and bending cycles. The results reveal that the PEI-LIG flexible thermistor exhibits a high sensitivity of -0.159%/°C, with minimal influence from humidity. These findings contribute to advancing the research in inexpensive flexible temperature sensing technology, providing insights into material selection and sensor design for diverse applications.

Development of a bionic hexapod robot with adaptive gait and clearance for enhanced agricultural field scouting

High agility, maneuverability, and payload capacity, combined with small footprints, make legged robots well-suited for precision agriculture applications. In this study, we introduce a novel bionic hexapod robot designed for agricultural applications to address the limitations of traditional wheeled and aerial robots. The robot features a terrain-adaptive gait and adjustable clearance to ensure stability and robustness over various terrains and obstacles. Equipped with a high-precision Inertial Measurement Unit (IMU), the robot is able to monitor its attitude in real time to maintain balance. To enhance obstacle detection and self-navigation capabilities, we have designed an advanced version of the robot equipped with an optional advanced sensing system. This advanced version includes LiDAR, stereo cameras, and distance sensors to enable obstacle detection and self-navigation capabilities. We have tested the standard version of the robot under different ground conditions, including hard concrete floors, rugged grass, slopes, and uneven field with obstacles. The robot maintains good stability with pitch angle fluctuations ranging from −11.5° to 8.6° in all conditions and can walk on slopes with gradients up to 17°. These trials demonstrated the robot’s adaptability to complex field environments and validated its ability to maintain stability and efficiency. In addition, the terrain-adaptive algorithm is more energy efficient than traditional obstacle avoidance algorithms, reducing energy consumption by 14.4% for each obstacle crossed. Combined with its flexible and lightweight design, our robot shows significant potential in improving agricultural practices by increasing efficiency, lowering labor costs, and enhancing sustainability. In our future work, we will further develop the robot’s energy efficiency, durability in various environmental conditions, and compatibility with different crops and farming methods.

Exploring mixture estimators in stratified random sampling.

Advancements in sensor technology have brought a revolution in data generation. Therefore, the study variable and several linearly related auxiliary variables are recorded due to cost-effectiveness and ease of recording. These auxiliary variables are commonly observed as quantitative and qualitative (attributes) variables and are jointly used to estimate the study variable's population mean using a mixture estimator. For this purpose, this work proposes a family of generalized mixture estimators under stratified sampling to increase efficiency under symmetrical and asymmetrical distributions and study the estimator's behavior for different sample sizes for its convergence to the Normal distribution. It is found that the proposed estimator estimates the population mean of the study variable with more precision than the competitor estimators under Normal, Uniform, Weibull, and Gamma distributions. It is also revealed that the proposed estimator follows the Cauchy distribution when the sample size is less than 35; otherwise, it converges to normality. Furthermore, the implementation of two real-life datasets related to the health and finance sectors is also presented to support the proposed estimator's significance.

Laser-Induced Periodic Surface Structures and Their Application for Gas Sensing

Semiconducting metal oxides are widely used for solar cells, photo-catalysis, bio-active materials and gas sensors. Besides the material properties of the semiconductor being used, the specific surface topology of the sensors determines device performance. This study presents different approaches for increasing the sensing area of semiconducting metal oxide gas sensors. Micro- and nanopatterned laser-induced periodic surface structures (LIPSSs) are generated on silicon, Si/SiO2 and glass substrates. The surface morphologies of the fabricated samples are examined by FE SEM. We selected the nanostructuring and characterization of nanostructured source Ni/Au and Ti/Au films prepared on glass using laser ablation as the most suitable of the investigated approaches. Surface structures produced on glass by backside ablation provide 100 nm features with a high surface area; they are also transparent and have high resistivity. The value of the hydrogen sensitivity in the range concentrations from 100 to 500 ppm was recorded using transmittance measurements to be twice as great for the nanostructured target TiO2/Au as compared to the NiO/Au. It was found that such transparent materials present additional possibilities for producing optical gas sensors.

Enhancing Performance in Top‐Illuminated Shortwave Infrared Organic Photodetectors via Microcavity Resonance

AbstractShortwave infrared (SWIR) image sensors have unique functions in many optical applications, leading to widespread attention in developing next‐generation materials and photodetector technologies. Organic photodetectors (OPDs) are highly promising due to their flexibility in molecular design and processability. However, integrating OPDs with silicon readout integrated circuits (ROICs) poses numerous challenges, often resulting in underestimated device performance and limiting technological progress. To address the requirements of integrating top‐illuminated OPD with ROICs and to enhance the external quantum efficiency (EQE), optical microcavities are introduced into the OPDs. The EQE in the SWIR region can be effectively enhanced by properly adjusting the thicknesses of the photoactive layer (PAL) and interlayers. Simulations of the optical field distribution further support the active functions of the microcavity structure. The spatial variation of the microcavities allows the spectral response to shift from 1000 to 1400 nm, and the optimized device achieves an EQE of 25.8% at 1260 nm. Finally, the OPDs are integrated with a silicon‐based test kit, and the results reveal comparable sensing performance, demonstrating the high potential of microcavity resonance for device integration. This design effectively improves the integration of OPDs with traditional ROICs and advances SWIR‐based organic image sensor technology further toward commercialization.

Low frequency vibration monitoring of wind turbine tower based on optical fiber sensor and its potential for internet of things

Among all renewable energy sources, wind energy is a cost-effective alternative energy source. The majority of wind turbines are built in harsh environments due to their power generation characteristics, which is one of the prime reasons resulting in frequent failures of wind turbine. Among various failures, the vibration of wind turbine tower cannot be ignored because it is a precursor of the failure of the wind turbine. The electrical vibration sensors have the problems of power supply and electromagnetic interference for the condition assessment of wind turbine tower. A vibration sensor based on optical Fabry-Perot (F-P) interference principle with high sensitivity is designed, fabricated and characterized to further meet the requirements of vibration detection of wind turbine tower. The mechanical simulation model of the diaphragm and optical vibration platform is constructed to verify the sensing characteristic of the F-P optical fiber vibration sensor (OFVS). The experiment results indicate a resonant frequency of the F-P OFVS of 223 Hz, an output sensitivity of 122.22 mV/m·s−2 at 10 Hz, and a horizontal output of less than 6 %. In addition, the designed F-P OFVS possesses the superiorities of compact structure, passive and excellent anti-electromagnetic interference, and has a wide application prospect in the vibration detection of the wind turbine tower.

Measuring shrinkage of expansive soils using a novel automated high‐frequency setup

AbstractSoil shrinkage characteristic curves are used to describe the shrinkage behavior and hydraulic properties of unsaturated soils. To construct soil shrinkage characteristic curves, a high‐data‐density measurement method is needed that relates water content to soil volume changes. We present a fully automated soil shrinkage measurement setup, based on the simplified evaporation method, to characterize the shrinkage behavior of undisturbed natural expansive clay soils. The high data density creates the opportunity to produce soil shrinkage characteristic curves without the need for a mathematical model. The technique allows for resaturation of the samples after drying, enabling differentiation between reversible and irreversible shrinkage. The setup consists of the commercialized HYPROP2 apparatus combined with optical distance sensors to measure the horizontal and vertical dimensions of the samples, yielding data on the sample volume, weight, and soil water suction. The measurement frequency is once per 10 min, and the measurement period is up to 4 weeks, providing a detailed time series of the drying and shrinkage characteristics. The setup can capture the different shrinkage phases and offers the opportunity to relate the soil shrinkage characteristic curve to soil water suction. The measurement data acquisition rate and accuracy enable detailed interpretation of soil water retention curves for nonrigid soils and are shown to be essential for understanding and quantifying the shrinkage potential of several types of deposits.

Amplifying Fibre Optic Sensor Signals with Graphene Oxide-Silver Nanostars

This work presents a straightforward yet impactful method to enhance optical fibre sensor signals by integrating silver nanostars (AgNS) with graphene oxide (GO-AgNS) as a sensing material. AgNS were synthesized successfully via a one-step chemical reduction method using hydroxylamine as a reducing agent. The concentration of hydroxylamine was varied (7.2, 3.6, 1.8, and 0.9) x 10−4 %v/v to optimize AgNS formation, with the longest spike of the nanostar observed at lower hydroxylamine concentrations, as confirmed by transmission electron microscopy (TEM). UV-visible measurements revealed the highest peak absorption for AgNS synthesized using 0.9 x 10−4 %v/v hydroxylamine, 0.05 M sodium hydroxide, 0.8 mM silver nitrate, and 0.045 M sodium citrate, with an average length of 352.62 ± 59.72 nm measured from TEM images. GO was synthesized via a modified Hummer’s method and mixed with AgNS at varying ratios (1:5, 1:10, 1:15, and 1:20) to form GO-AgNS coatings for the fibre optic probe. The GO-AgNS coating at a ratio of 1:5 exhibited the highest light absorption intensity, enhancing optical signal by 218% compared to GO-coated and 174% compared to AgNS-coated fibre optic sensors. These findings demonstrate that embedding AgNS onto the GO matrix structure as a sensing material significantly improves sensor performance, suggesting promising applications for super-sensitive, cost-effective, and rapid detection in fibre optic-based sensors.

Research and correction for over-response phenomenon when using inorganic scintillator optical fiber X-ray sensor to measure off-axis ratio (OAR)

Research and correction for over-response phenomenon when using inorganic scintillator optical fiber X-ray sensor to measure off-axis ratio (OAR)

DC current sensor based upon a fused fiber optic Fabry-Perot interferometer (FPI) and copper wire

In order to meet the demand for direct current (DC) current measurements in industrial production, a new type of fiber optic DC current sensor, combining thin copper wire and fiber optic Fabry-Perot interferometer (FPI), is reported. FPI is a sandwich structure formed by splicing a single-mode fiber (SMF) and a quartz capillary, with a cavity length of approximately 200 μm. The copper wire, with a length of 3.2 cm and a diameter of 1 mm, is attached to the FPI to form the sensor. Due to the excellent thermal conductivity of copper, the results showed that the sensor increased the sensitivity from 4.1 pm/°C to 11 pm/°C. After applying direct current to the sensor, a large quantity of heat generated by the copper wire was absorbed by the FPI, resulting in a significant shift in the resonant wavelength of FPI, which can indirectly measure the current. Three current experiments were performed, and parabolic curves have been used in fitting of the Dip wavelength and the square of the current. The results provide high correlation coefficients with values exceeding 0.99. Therefore, this curve may be used to measure the square of the current, further obtaining the current. In summary, the sensor is compact and durable, easy to manufacture, and provides high correlation coefficients, providing an alternative for measuring DC current.

Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.

In Situ Label‐Free Monitoring of Riboflavin Concentration in Shewanella via a Fiber‐Optic Biosensor Modified by Ti3C2‐MXene@ SGNps Composites

AbstractRiboflavin is a common marker of microbial corrosion and can act as an electron shuttle in the solvated state to enhance electron transfer efficiency. In this paper, an in‐situ label‐free fiber‐optic biosensor modified by Ti3C2‐MXene@Square Gold nanoparticles (SGNps) composites and riboflavin kinase is developed, which behaved with high sensitivity for measuring riboflavin. The sensing interface of the sensor is constructed by stepwise assembly of SGNps and riboflavin kinase using Ti3C2‐MXene nanosheets as the supporting substrate. Riboflavin kinase catalyzed the phosphorylation of riboflavin to form flavin mononucleotides in the presence of adenine nucleoside triphosphate and Mg2+. Experimental results showed that the sensitivity of riboflavin measurement is 5.61 nm µm−1 and the limit of detection (LOD) is 115 nmol L−1 (nm) in the concentration range of 0–2.656 µm. In situ label‐free monitoring of riboflavin in Shewanella, the sensitivity of the sensor in bacterial fragmentation fluid is 53.90 nm/OD within the optical density (OD) of 0–0.175, and the LOD is OD = 0.017. The above results verified the feasibility of the sensor for in situ monitoring of riboflavin in microbial corrosive environments, with ultra‐low detection limit and good selectivity, which undoubtedly provided great research value for the application of optical fiber sensors in microbial corrosion monitoring.

Deflection estimation of reinforced concrete beams using long-gauge optic sensors

Deflection is a crucial indicator in structural health monitoring, directly impacting the service life, safety, and economic viability of structures. However, current indirect methods for measuring deflection, such as multi-element FBG sensors or accelerometers, often have limitations. While they may be suitable for flexure-dominated reinforced concrete (RC) specimens in the elastic stage, they neglect the significant influence of nonlinearity and shear deformation. This paper introduces a new approach for estimating the deflection of reinforced concrete (RC) beams using distributed long-gauge optic sensors. The method tackles the limitations of current methods by incorporating the crucial factors of shear deformation, neutral axis position variation and shear stiffness degradation. Experimental studies are first conducted to validate the accuracy and stability of long-gauge optic sensors in small-scale RC members. Algorithms are then utilized the measurement data to estimate the deflection. The results demonstrate the excellent performance of long-gauge optic sensors in measuring shear deformation and vertical deflection of RC beams, as evidenced by comparisons with LVDT measurements. Additionally, a method for decoupling flexural and shear deformation is proposed. This method offers a preliminary assessment of the reinforced concrete (RC) beam type by quantifying the relative significance of shear deformation.

Biomass based nanofiber membrane composite with xylan derived carbon dots for fluorescence detection nitrite in food real samples

In our latest research endeavor, we are proud to present an innovative approach to the synthesis of carbon dots (CDs) derived from the biomass xylan, which we have termed P-CDs. These P-CDs are meticulously integrated with a state-of-the-art biomass nanofiber membrane composed of polycaprolactone (PCL) and polylactic acid (PLA), resulting in the creation of a novel solid-state fluorescent sensor, designated as NFP-CDs. This cutting-edge sensor has been meticulously engineered for the highly sensitive and specific detection of nitrite ions (NO2−), a critical parameter in various fields. The NFP-CDs sensor stands out for its user-friendly design, cost-effective production, and portable nature, making it an ideal choice for rapid and visible nitrite ion detection. It exhibits an extraordinary response time of less than 1 s, which is a testament to its high sensitivity. Furthermore, the sensor demonstrates exceptional selectivity and specificity, with a remarkably low detection threshold of 0.36 μM. This is achieved through a sophisticated dual detection mechanism that synergistically combines colorimetric and spectral analyses, ensuring accurate and reliable results. In addition to its impressive technical specifications, the NFP-CDs sensor has been rigorously tested and validated for its efficacy in detecting nitrite ions in real-world samples. These samples include a diverse range of food products such as rock sugar, preserved mustard, kimchi, and canned fish. The sensor has demonstrated a remarkable recovery rate, which varies from 99 % to 106 %, highlighting its potential for practical application in nitrite ion detection. This research not only offers a robust and effective strategy for the detection of nitrite ions but also carries profound implications for enhancing food safety and bolstering environmental monitoring efforts. The development of the NFP-CDs sensor represents a significant step forward in the field of sensor technology, providing a powerful tool for the detection of nitrite ions and contributing to the broader goals of public health and environmental stewardship.

Self-calibratable absolute modular rotary encoder: a theoretical feasibility study

Abstract This paper introduces a self-calibratable absolute modular rotary encoder based on the Equal Division Average (EDA) method, designed to significantly enhance measurement accuracy and simplify installation. The proposed design integrates multiple optical sensors into the encoder stator Printed Circuit Board (PCB), enabling precise absolute position measurement through the averaging of sensors data. This multi-sensor approach compensates for alignment and installation inaccuracies, which are common issues in conventional modular encoders. To validate the design and predict encoder behaviour prior to manufacturing, a theoretical modelling of virtual optical sensors is performed. Based on experimentally collected cross-calibration data from a real optical encoder, this modelling framework enables to estimate the efficiency of self-calibration algorithm with sufficient accuracy and optimize the design and performance of the encoder. The obtained results confirm that the proposed measurement system significantly reduces error margin, improving the reliability and precision of position feedback. The initial position deviation of several hundreds of arcseconds might be reduced and kept below 8 arcseconds by using two or more additional optical sensors, even with the 0.5 mm misalignment of the encoder stator.

Nanoscale Photonic Barcodes Based on Anodic Alumina Photonic Crystal Heterostructures: Implications for Optical Communications, Data Storage, and Sensing

Nanoscale Photonic Barcodes Based on Anodic Alumina Photonic Crystal Heterostructures: Implications for Optical Communications, Data Storage, and Sensing