Development Of Sensors Research Articles

Target localization and communication for remote sensing with use case of self-supervised learning in plant disease detection

ABSTRACT With the evolution of internet-of-things, there is an unprecedented development of intelligent sensors owing to their real-time data gathering and transmitting capabilities. A number of these sensors are deployed in the remote or inaccessible environment to harness information about environmental monitoring, disaster management, climate change, wildlife conservation, marine pollution, natural resource management and precision agriculture. These sensors are required to be wirelessly connected to provide comprehensive situational details and share the abundant data. Thus, these sensors are needed to be integrated with communication systems to enable efficient decision making and resource control. This paper presents an integrated sensing and communication (ISAC) system aided with intelligent reflecting surfaces (IRSs) for remote sensing systems. A transmission protocol is framed to assist the sensing and communication. To enable efficient resource control with reduced overhead, a beam training algorithm is proposed that aims to associate beams to user nodes based on maximum received signal. Further, the user location sensing is performed utilizing the effective angles-of-arrivals information. The impact of transmit power P t , IRS-user distance d I 2 , k , number of passive elements N 1 and sensing elements count N 2 on the average achievable rate and localization error is evaluated. It is observed that the proposed method achieves maximum rate of 13 bits/s/Hz at transmit power of 45 dBm with N 1 = 100. The maximum achievable rate improves by 8% in the proposed scheme when the passive elements count in sub-IRS 1 is doubled. Also, the localization error of 10 − 3 is obtained at transmit power of 10 dBm with N 2 = 64 and d I 2 , k = 10 m. The performance comparison with conventional random beam training is also investigated. In the end, the integration of IRS-aided ISAC with self-supervised learning in analysing the remote sensing data for plant disease detection is also discussed as a use case scenario.

Flexible Tactile Sensors with Self-Assembled Cilia Based on Magnetoelectric Composites.

Traditional tactile sensors are single-function, and it is difficult to meet the needs of applications in complex environments. This paper describes the development and applications of flexible tactile sensors with cilia based on magnetoelectric composites made of neodymium iron boron (NdFeB) microparticles with a silver (Ag) nanoshell in polydimethylsiloxane (PDMS). These sensors adopt the inherent magnetism of NdFeB microparticles and the excellent conductivity of the Ag coating. Self-assembly of the composites of NdFeB@Ag/PDMS under the combined effect of intrinsic magnetism and external magnetic field yields cilia that are sensitive to forces with a high conductivity of 36479.33 S/m. The resulting sensors can measure forces in the range of 0.02-0.05 N and recognize surrounding magnetic fields whose magnitudes are larger than 10 mT. These sensors have been used to perform texture recognition, Braille recognition, and object recognition to yield accuracies of 98%, 100%, and 94.58%, respectively. This research based on NdFeB@Ag magnetoelectric microparticles presents a convenient approach to construct tactile sensors with randomly distributed surficial microstructures, leading to the prevalence of low-cost but highly sensitive tactile sensors for humanoid, human machine interface, and healthcare.

Thermoelectric porous laser-induced graphene-based strain-temperature decoupling and self-powered sensing

Despite rapid developments of wearable self-powered sensors, it is still elusive to decouple the simultaneously applied multiple input signals. Herein, we report the design and demonstration of stretchable thermoelectric porous graphene foam-based materials via facile laser scribing for self-powered decoupled strain and temperature sensing. The resulting sensor can accurately detect temperature with a resolution of 0.5°C and strain with a gauge factor of 1401.5. The design of the nanocomposites also explores the synergistic effect between the porous graphene and thermoelectric components to greatly enhance the Seebeck coefficient by almost four times (from 9.703 to 37.33 μV/°C). Combined with the stretchability of 45%, the self-powered sensor platform allows for early fire detection in remote settings and accurate and decoupled monitoring of temperature and strain during the wound healing process in situ. The design concepts from this study could also be leveraged to prepare multimodal sensors with decoupled sensing capability for accurate multi-parameter detection towards health monitoring.

Exploration of linear and interpretable models for quantification of cell parameters via contactless short-wave infrared hyperspectral sensing

The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing. Replacing optical probes with contactless short-wave infrared (SWIR) hyperspectral cameras allows efficient collection of thousands of absorption signals in a handful of images. This high repetition allows for effective denoising of each spectrum, so interpretable linear models can quantify metabolites. To illustrate, an interpretable linear model called L-SLR is trained using small datasets obtained with a SWIR HSI camera to quantify fructose, viable cell density (VCD), glucose, and lactate. The performance of this model is also compared to other existing linear models, namely Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF). Using only 50% of the dataset for training, reasonable test performance of mean absolute error (MAE) and correlations (r2) are achieved for glucose (r2 = 0.88, MAE = 37 mg/dL), lactate (r2 = 0.93, MAE = 15.08 mg/dL), and VCD (r2 = 0.81, MAE = 8.6 × 105 cells/mL). Further, these models are also able to handle quantification of a metabolite like fructose in the presence of high background concentration of similar metabolite with almost identical chemical interactions in water like glucose. The model achieves reasonable quantification performance for large fructose level (100–1000 mg/dL) quantification (r2 = 0.92, MAE = 25.1 mg/dL) and small fructose level (< 60 mg/dL) concentrations (r2 = 0.85, MAE = 4.97 mg/dL) in complex media like Fetal Bovine Serum (FBS). Finally, the model provides sparse interpretable weight matrices that hint at the underlying solution changes that correlate to each cell parameter prediction.

Non-Enzymatic Electrochemical Glucose Sensors Based on Metal Oxides and Sulfides: Recent Progress and Perspectives

With the sudden advancement of glucose biosensors for monitoring blood glucose levels for the prevention and diagnosis of diabetes, non-enzymatic glucose sensors have aroused great interest owing to their sensitivity, stability, and economy. Recently, researchers have dedicated themselves to developing non-enzymatic electrochemical glucose sensors for the rapid, convenient, and sensitive determination of glucose. However, it is desirable to explore economic and effective nanomaterials with a high non-enzymatic catalysis performance toward glucose to modify electrodes. Metal oxides (MOs) and metal sulfides (MSs) have attracted extensive interest among scholars owing to their excellent catalytic activity, good biocompatibility, low cost, simple synthesis process, and controllable morphology and structure. Nonetheless, the exploitation of MOs and MSs in non-enzymatic electrochemical glucose sensors still suffers from relatively low conductivity and biocompatibility. Therefore, it is of significance to integrate MOs and MSs with metal/carbon/conducive polymers to modify electrodes for compensating the aforementioned deficiency. This review introduces the recent developments in non-enzymatic electrochemical glucose sensors based on MOs and MSs, focusing on their preparation methods and how their structural composition influences sensing performance. Finally, this review discusses the prospects and challenges of non-enzymatic electrochemical glucose sensors.

Development of a Non-Invasive Co2 Breath Sensor with Iot Integration

Development of a Non-Invasive Co2 Breath Sensor with Iot Integration

Green Synthesis of Eosin-Y Coated Silver Nanoparticles for Sensitive and Selective Fluorometric Detection of L-Dopa.

This study is to produce biogenic silver nanoparticles (AgNPs) by utilizing aqueous extracts derived from Turnera Sublata (TS) leaves under visible light. Subsequently, these nanoparticles are coated with eosin-yellow (EY) to enhance sensitivity and selectivity in L-3,4-dihydroxyphenylalanine (L-dopa) detection. This method encompasses the deposition of metal onto the Ag NPs, resulting in the formation of EY-AgNPs. The crystalline, spherical nanoparticles, prepared as described, exhibit a particle size of 20nm. Different instruments were used to characterize them, including UV-Vis spectroscopy, fluorescence spectroscopy, FTIR spectroscopy, selected area electron diffraction (SAED), transmission electron microscopy (TEM), and X-ray diffraction (XRD) analysis. The spherical structured morphology and size of the EY-AgNPs has been confirmed through SAED and TEM studies. This study pioneered the integration of characteristic hydroxyl-Ag chemistry and specialized steric interference of organic pigment in luminescent AgNPs to develop a simple method for detecting dopa. The sensor's dynamic range and limit of detection were assessed. Experimental results revealed that green-emitting AgNPs shielded by interference from biogenic AgNPs and the strong affinity of hydroxyl-silver provided a high-sensitivity detection limit of 1.84 nM. Furthermore, a new green approach for sensor development using human serum albumin (HSA) assay demonstrated that organic dye on the surface of nanomaterials further enhances sensing properties.

Smart Luminescent Materials for Emerging Sensors: Fundamentals and Advances.

Smart luminescent materials have drawn a significant attention owing to their unique optical properties and versatility in sensor applications. These materials, encompassing a broad spectrum of organic, inorganic, and hybrid systems including quantum dots, organic dyes, and metal-organic frameworks (MOFs), offer tunable emission characteristics that can be engineered at the molecular or nanoscale level to respond to specific stimuli, such as temperature, pH, and chemical presence. Recent advancements have been driven by the integration of nanotechnology, which enhances the sensitivity and selectivity of luminescent materials in sensor platforms. The development of photoluminescent and electrochemiluminescent sensors, for instance, has enabled real-time detection and quantification of target analytes with high accuracy. Additionally, the incorporation of these materials into portable, user-friendly devices, such as smartphone-based sensors, broadens their applicability and accessibility. Despite their potential, challenges remain in optimizing the stability, efficiency, and biocompatibility of these materials under different conditions. This review provides a comprehensive overview of the fundamental principles of smart luminescent materials, discusses recent innovations in their use for sensor applications, and explores future directions aimed at overcoming current limitations and expanding their capabilities in meeting the growing demand for rapid and cost-effective sensing solutions.

Cu doped ceria nanoparticles-rhodamine B as a novel chemiluminescence system and its application for nitrite detection in water and food samples.

Fe, Ni, and Cu doped ceria nanoparticles (CeNPs) were prepared with a simple and one-pot hydrothermal synthesis method. We investigated the chemiluminescence (CL) interaction between these NPs and rhodamine B (Rh B) and found that the highest CL intensity was related to the Rh B- Cu doped CeNPs. We assigned that to the higher catalytic property of Cu doped NPs compared to the others. Cu doped CeNPs have been applied for the first time as a catalyst in chemiluminescence reactions. Rh B- Cu doped CeNPs reaction was introduced as a novel CL system, and its mechanism was studied. Considering the sensitivity, simplicity, portability, and rapidness of the CL methods, we applied the introduced reaction to the development of a CL sensor for nitrite monitoring. In the presence of nitrite, the CL intensity of the system decreased, and there was a linear relationship between the CL intensity and nitrite concentration in the range 0.5-100 µM. Based on this fact, a sensitive and selective CL sensor with the detection limit of 0.2µM was established for nitrite detection in various food and water samples.

Recent advances in electrochemical sensing and remediation technologies for ciprofloxacin.

Ciprofloxacin (CIP) is an extensively used broad-spectrum, fluoroquinolone antibiotic used for treating diverse bacterial infections. Effluent treatment plants (ETPs) worldwide lack technologies to detect or remediate antibiotics. CIP reaches the aquatic phase primarily due to inappropriate disposal practices, lack of point-of-use sensing, and preloaded activated charcoal filter at ETPs. The co-existence of bacteria and CIP in such aqueous pools has promoted fluoroquinolone resistance in bacteria and should be minimized. The worldwide accepted standard detection methodologies for the detection of CIP are high-performance liquid chromatography and mass spectrometry, which are lab-based, require state-of-the-art equipment, and are expensive. Hence, it is difficult to integrate them for on-site monitoring. Further, the current remediation technologies like conventional sludge-treatment techniques fail to remove antibiotics such as CIP. Several point-of-use technologies for the detection of CIP are being investigated. These typically involve the development of electrochemical sensors where substrates, modifiers, biorecognition elements, and their chemistries are designed and optimized to enable robust, point-of-use detection of CIP. Similarly, remediation techniques like adsorption, membrane filtration, ion exchange, photocatalysis, ozonation, oxidation by Fenton's reagent, and bioremediation are explored, but their onsite use is limited. The use of these sensing and remediation technologies in tandem is possibly the only way the issues related to antimicrobial resistance may be effectively tackled. This article provides a focused critical review on the recent advances in the development of such technologies, laying out the prospects and perspectives of their synergistic use to curb the menace of AMR and preserve antibiotics.

Using low-cost sensors to assess common air pollution sources across multiple residences

The rapid development of low-cost sensors provides the opportunity to greatly advance the scope and extent of monitoring of indoor air pollution. In this study, calibrated particle matter (PM) sensors and a non-negative matrix factorisation (NMF) source apportionment technique are used to investigate PM concentrations and source contributions across three households in an urban residential area. The NMF is applied to combined data from all houses to generate source profiles that can be used to understand how PM source characteristics are similar or differ between different households in the same urban area. PM2.5 and PM10 concentrations in all three houses were greater, more variable, and significantly different to ambient concentrations recorded at a nearby ambient monitoring site. Concentrations were also significantly different between houses, with the World Health Organisation 24-h guideline limits for PM2.5 breached in one household. The applied methodology was highly successful at modelling concentrations for all the houses (R2≥ 0.983), finding that across the houses the I/O (indoor to outdoor sources ratio) was the lowest for PM1 (down to 0.08), and greatest for PM10 (up to 4.93). Whilst the sources could not be clearly distinguished further than being outdoors or indoors, the methodology provides clear insights to source variability within and between the monitored houses. These results highlight the importance of monitoring indoor air pollution to improve pollution exposure estimates, as whilst people may live in areas with acceptable ambient air quality, they can be exposed to unhealthy concentrations in their own homes. This method may be applied in future studies for extended periods to investigate the influence of source seasonality on PM concentrations or scaled up to investigate source variability across larger geographical areas.

Chemical Detection Using Mobile Platforms and AI-Based Data Processing Technologies

The development of reliable gas sensors is very important in many fields such as safety, environment, and agriculture, and is especially essential for industrial waste and air pollution monitoring. As the performance of mobile platforms equipped with sensors such as smartphones and drones and the technologies supporting them (wireless communication, battery performance, data processing technology, etc.) are spreading and improving, a lot of efforts are being made to perform these tasks by using portable systems such as smartphones or installing them on unmanned wireless platforms such as drones. For example, research is continuously being conducted on chemical sensors for field monitoring using smartphones and rapid monitoring of air pollution using unmanned aerial vehicles (UAVs). In this paper, we review the measurement results of various chemical sensors available on mobile platforms including drones and smartphones, and the analysis of detection results using machine learning. This topic covers a wide range of specialized fields such as materials engineering, aerospace engineering, physics, chemistry, environmental engineering, electrical engineering, and machine learning, and it is difficult for experts in one field to grasp the entire content. Therefore, we have explained various concepts with relatively simple pictures so that experts in various fields can comprehensively understand the overall topics.

Effect of the Current-Collecting Carbon Nanotubes Layer on the Properties of the Lead Zirconate Titanate Film for Vibration Sensors

One of the challenging problems in the research and development of vibration sensors relates to the formation of Ohmic contacts for the removal of an electrical signal. In some cases, it is proposed to use arrays of carbon nanotubes (CNTs), which can serve as highly elastic electrode materials for vibration sensors. The purpose of this work is to study the effect of a current-collecting layer of CNTs grown over silicon on the properties of a lead zirconate titanate (PZT) film, which is frequently employed in mechanical vibration sensors or energy harvesters. For the experiments, a vibration sensor mock-up was created with the PZT-CNT-Ni-V-SiO2-Si and PZT-CNT-Ni-V-Si structures where an array of vertically oriented CNTs was grown over an oxidized or high-alloyed silicon substrates by plasma chemical deposition from a gas phase. Then, a thin film of PZT was applied to the CNT layer with a high-frequency reactive plasma spraying. For comparison, the PZT film was applied to silicon without a CNT layer (PZT-Si structure). The calculated average value of the piezoelectric module is 112 pm/V for the Ni-PZT-PT-Ni-Si-SiO2 sample, and 35 pm/V for PZT-Ni-SiO2-Si. It can be seen that the contact realized with the help of CNT ensures more than three times the best efficiency in terms of the piezoelectric module. The value of the piezoelectric module of the vibration sensor with the PZT-CNT-Ni-V-Si structure was 186 pm/V, and the value of the residual polarization was 23.2 µC/cm2, which is more than eight and three times, respectively, higher than the values of these properties for the vibration sensor with the PZT-Si structure. It is shown that the vibration sensor can operate in the frequency range of 0.1–10 kHz.

Development of Novel Conductive Inks for Screen-Printed Electrochemical Sensors: Enhancing Rapid and Sensitive Drug Detection

The development of screen-printed electrochemical sensors represents a rapidly expanding research field with great potential for applications in the rapid and sensitive determination of drugs in complex matrices. This work presents a review of the state-of-the-art examples of this technology, focusing on its application in real matrices such as water, pharmaceutical formulations, and biological fluids. We discuss the main materials used in developing conductive inks, highlighting their properties and influence on sensor performance. The characterization of materials and sensors is crucial to ensure the reproducibility and reliability of results. Additionally, we address the challenges associated with the application of these sensors in complex matrices, such as interferences from other components and the need for sample pretreatment. Finally, we present future perspectives for developing screen-printed electrochemical sensors, with an emphasis on new technologies and materials that can improve the sensitivity, selectivity, and stability of these devices.

Photoexcited CuO/TiO2 Heterojunction for Photoelectrochemical Sensors for Nonenzymatic Glucose Detection.

Photoelectrochemical sensors have been studied for glucose detection because of their ability to minimize background noise and unwanted reactions. Titanium dioxide (TiO2), a highly efficient material in converting light into electricity, cannot utilize visible light. In this regard, we developed a nonenzymatic glucose sensor by using a simple one-step electrospinning technique to combine cupric oxide with TiO2 to create a heterojunction. The prepared nanofibers exhibit an extremely high aspect ratio and have a dense structure. These characteristics enhance the quantity of electron-hole pairs generated by light and the speed at which electrons are transferred. They also reduce the distance that charges need to travel and offer reactive sites for the catalytic oxidation of glucose. The sensor has a direct and proportional reaction within glucose concentration ranging from 30 μM to 2 mM under sunlight conditions. It achieves a detection limit of 9.9 μM with a signal-to-noise ratio of 3. The sensors also exhibit excellent stability, reproducibility, and selectivity. This study provides insights for development of photoelectrochemical sensors to detect glucose.

Operando Photoelectrochemical Surface-Enhanced Raman Spectroscopy: Interfacial Mechanistic Insights and Simultaneous Detection of Patulin.

Comprehending the biosensing mechanism of the biosensor interface is crucial for sensor development, yet accurately reflecting interfacial interactions within actual detection environments remains an unsolved challenge. An operando photoelectrochemical surface-enhanced Raman spectroscopy (PEC-SERS) biosensing platform was developed, capable of simultaneously capturing photocurrent and SERS signals, allowing operando characterization of the interfacial biosensing behavior. Porphyrin-based MOFs (Zr-MOF) served as bifunctional nanotags, providing a photocurrent and stable Raman signal output under 532 nm laser irradiation. Aptamer was used to bridge the Zr-MOF and the silver-encased gold nanodumbbells (AuNDs@AgNPs). The simultaneous in situ acquisition of target-induced PEC and SERS signal responses facilitated the correlation of electron transfer information from the photocurrent with the distance information from the SERS signal. It revealed the biosensing mechanism in which target-induced aptamer conformational bending drove the Zr-MOF to approach the electrode. However, the increase in charge transfer observed through conventional electrochemical methods contradicts the conclusions drawn from the operando PEC-SERS analysis. Comprehensive analysis indicated that redox probes introduced during the non-in-situ measurement process became adsorbed within the MOF pores, potentially affecting the judgment of the biosensing mechanism. In addition, the operando PEC-SERS biosensor simultaneously obtained two independent signals, providing self-verification to improve the accuracy and reliability of patulin detection. The linear ranges were 1 pg mL-1-10 ng mL-1 for the PEC method and 1 pg mL-1-100 ng mL-1 for the SERS method, respectively. This work provides a powerful tool for determining the interface characteristics of biosensors.

Development of colorimetric sensor based on N-GOs-CNTs@CeO2 nano-enzyme and its rapid detection of kanamycin residues in food

Development of colorimetric sensor based on N-GOs-CNTs@CeO2 nano-enzyme and its rapid detection of kanamycin residues in food

A Mitochondria-targeted Iridium (Ⅲ) Complex-Based Sensor for endogenous GSH Detection in living Cells

Glutathione (GSH) plays an important role in maintaining REDOX homeostasis in biological systems. Development of reliable glutathione sensors is of great significance to better understand the role of biomolecules in...

Gum arabic-CNT reinforced hydrogels: dual-function materials for strain sensing and energy storage in next-generation supercapacitors

Recent advancements in the field of conductive hydrogels have made these promising candidates for the development of human motion sensors, for energy storage in soft and flexible electronic devices, owing to their excellent mechanical properties.

Nickel oxide decorated popcorn derived biochar as a non-invasive electrochemical sensor for sensitive detection of glucose in saliva

Activated carbon provides an ideal platform for nickel ions, which are stable and sensitive enough to detect glucose in biological liquid samples. This work presents the development of a novel electrochemical sensor based on acid-activated popcorn carbon (PPC-A) loaded with nickel oxide nanocomposites (NiO/PPC-A) for non-invasive glucose sensing in saliva. PPC-A demonstrates outstanding performance in the enrichment of NiO nanoparticles. The conductive porous structure of PPC-A not only promotes electron transmission, but also serves as an excellent carrier for NiO nanoparticles in minimizing their aggregation and supplying more active sites. The synergistic interaction between PPC-A and NiO boosted the electrocatalytic activity for sensitive glucose detection. NiO/PPC-A composite exhibits excellent structural characteristics and electrochemical performance, with a high specific surface area (776m²/g) and uniform pore distribution, enhancing its suitability as electrocatalysis. The prepared NiO/PPC-A/GCE sensor has high sensitivity (228.17 μA mM⁻¹·cm⁻²), wide linear range (1 μM-2 mM), and low detection limit (0.34μM, S/N = 3), making it suitable for detecting glucose levels in human saliva without sample pre-treatments. Consequently, the NiO/PPC-A/SPE based sensor demonstrates a good linear correlation (correlation coefficient of 0.9373) between salivary glucose and blood glucose concentrations, indicating its potential for non-invasive glucose detection. Moreover, the NiO/PPC-A/GCE electrode shows good selectivity, reproducibility, repeatability, and long-term stability. The NiO/PPC-A composite shows great potential in the development of a simple, efficient, and cost-effective non-invasive glucose sensor, offering promising prospects for clinical diagnostics.