Sensor For Detection Research Articles

High selectivity and sensitivity through nanoparticle sensors for cleanroom CO2 detection

Clean room facilities are becoming more popular in both academic and industry settings, including low-and middle-income countries. This has led to an increased demand for cost-effective gas sensors to monitor air quality. Here we have developed a gas sensor using CoNiO2 nanoparticles through combustion method. The sensitivity and selectivity of the sensor towards CO2 were influenced by the structure of the nanoparticles, which were affected by the reducing agent (biofuels) used during synthesis. Among all reducing agents, urea found to yield highly crystalline and uniformly distributed CoNiO2 nanoparticles, which when developed into sensors showed high sensitivity and selectivity for the detection of CO2 gas in the presence of common interfering volatile organic compounds observed in cleanroom facilities including ammonia, formaldehyde, acetone, toluene, ethanol, isopropanol and methanol. In addition, the urea-mediated nanoparticle-based sensors exhibited room temperature operation, high stability, prompt response and recovery rates, and excellent reproducibility. Consequently, the synthesis approach to nanoparticle-based, energy efficient and affordable sensors represent a benchmark for CO2 sensing in cleanroom settings.

A recyclable luminescent MOF sensor for on-site detection of insecticide dinotefuran and anti-Parkinson's drug entacapone in various environmental and biological specimens.

The monitoring and precise determination of pesticides and pharmaceutical drugs and their residues have become increasingly important in the field of food safety and water contamination issues. Herein, a fluorescent aluminium MOF sensor (1) was developed for the selective recognition of neonicotinoid insecticide dinotefuran and anti-Parkinson's drug entacapone. Guest-free MOF 1' exhibited ultra-fast response (< 5 s) and ultra-low detection limits of 2.3 and 7.6 nM for dinotefuran and entacapone, which are lower than the previously reported MOF sensors. In the presence of other competitive analytes, great selectivity was achieved towards both analytes. The probe was recyclable up to five cycles. The sensing ability was explored towards entacapone in human serum, urine and dinotefuran in real soil, rice, honey samples, different fruits, vegetables, real water specimens and a wide range of pH media. A low-cost, handy MOF-based polymer thin-film composite (1'@PVDF-PVP) was developed for the on-site detection of dinotefuran and entacapone. Mechanistic studies involving analytical techniques and theoretical calculations suggested that FRET and PET are the probable reasons for entacapone sensing whereas IFE is responsible for dinotefuran detection. The entire work presents a low cost, multi-use photoluminescent sensor of entacapone and dinotefuran to address the environmental pollution.

Nanopore Blockade Sensors for Quantitative Analysis Using an Optical Nanopore Assay.

Nanopore sensing is a popular biosensing strategy that is being explored for the quantitative analysis of biomarkers. With low concentrations of analytes, nanopore sensors face challenges related to slow response times and selectivity. Here, we demonstrate an approach to rapidly detect species at ultralow concentrations using an optical nanopore blockade sensor for quantitative detection of the protein vascular endothelial growth factor (VEGF). This sensor relies on monitoring fluorescent polystyrene nanoparticles blocking nanopores in a nanopore array of 676 nanopores. The fluorescent signal is read out using a wide-field fluorescence microscope. Nonspecific blockade events are then distinguished from specific blockade events based on the ability to pull the particles out of the pore using an applied electric field. This allows the detection of VEGF at sub-picomolar concentration in less than 15 min.

A Highly Sensitive and Selective Fluorescent Sensor for Folic Acid Detection Based on D-penicillamine Stabilized Ag/Cu Alloy Nanoclusters.

In this work, a highly sensitive and selective method for detecting folic acid (FA) was developed using D-penicillamine (DPA) stabilized Ag/Cu alloy nanoclusters (DPA@Ag/Cu NCs). The yellow emission of DPA@Ag/Cu NCs was found to be quenched upon the addition of FA to the system. The fluorescence intensity quenching value demonstrated a linear relationship with FA concentrations ranging from 0.01 to 1200 μM,with a limit of detection(LOD) of 5.3 nM. Furthermore, the detection mechanism was investigated through various characterization analyses, including high resolution transmission electron microscopy, fluorescence spectra, ultraviolet-visible absorption spectra, and fluorescence lifetime. The results indicated that the fluorescence quenching induced by FA was a result of electron transfer from FA to the ligands of DPA@Ag/Cu NCs.The selectivity of the FA sensor was also evaluated, showing that common amino acids and inorganic ions had minimal impact on the detection of FA. Moreover, the standard addition method was successfully applied to detect FA in human serum, chewable tablets and FA tablets with promising results. The use of DPA@Ag/Cu NCs demonstrates significant potential for detecting FA in complex biological samples.

Highly sensitive rhodamine B dye-based electrochemical sensor for lactose detection

Lactose (LAC) is the main disaccharide sugar found in milk and dairy products, making it easily accessible as a food source. In addition to its importance for human and animal health, LAC concentrations serve as a biomarker of milk quality. In this study, we reported the detection of LAC using a highly sensitive electrochemical sensor based on rhodamine B (RhB) dye. The prepared RhB-based sensor exhibited excellent characteristics, including a straightforward manufacturing technique, cost-effectiveness, and exceptional sensitivity. We conducted a comparative analysis of the electrochemical response of the novel RhB-based sensor, evaluating its electrochemical performance across varying LAC concentrations and when different analyte materials (glucose, fructose, and maltose) were employed. The proposed analytical method was utilized to determine LAC using an electrochemical RhB-based sensor under optimal experimental conditions. As a result, it was experimentally observed that high sensor sensitivity of RhB based sensor against LAC was inversely proportional to the impedance value and directly proportional to conductance which corolated with the transport mechanism of RhB.

A Fiber Sensor for VOC Gas Detection Based on Modulated Aggregation of Fluorescence Indicator

A Fiber Sensor for VOC Gas Detection Based on Modulated Aggregation of Fluorescence Indicator

Sensitivity-Enhanced Parity-Time Symmetric Fabry–Pérot Sensor for Detection of Gas Concentration

Sensitivity-Enhanced Parity-Time Symmetric Fabry–Pérot Sensor for Detection of Gas Concentration

Wide Linearity Range 3D Magnetic Sensor and Angular Position Detector Based on a Single FePt Spin-Orbit Torque Device.

Three-dimensional (3D) vector magnetic sensors play a significant role in a variety of industries, especially in the automotive industry, which enables the control of precise position, angle, and rotation of motion elements. Traditional 3D magnetic sensors integrate multiple sensors with their sensing orientations along the three coordinate axes, leading to a large size and inevitable nonorthogonal misalignment. Here, we demonstrate a wide linearity range 3D magnetic sensor utilizing a single L10-FePt Hall-bar device, whose sensitivity is 291 VA-1 T-1 in the z-axis and 27 VA-1 T-1 in the in-plane axis. Based on the spin-orbit torque-dominated magnetization reversal, the linear response of anomalous Hall resistance within a large linear range (±200 Oe) for the x, y, and z components of magnetic fields has been obtained, respectively. Typically, it exhibits a relatively lower magnetic noise level of 7.9 nV at 1 Hz than previous results, improving measurement resolution at the low frequency. Furthermore, we provide a straightforward approach for noncontact angular position detection based on a single Hall-bar device, which shows great potential for application in rotational motion control.

Innovative smart colorimetric sensor for nitrite detection in poultry packaging

Detecting nitrite ions is crucial for environmental and health monitoring, given the prevalence of nitrites in water sources and meat. Colorimetric methods provide a cost-effective, rapid, and straightforward means of nitrite ion detection. This study focuses on monitoring nitrite levels in meat to ensure its safe consumption. Three different dyes—Bromophenol blue (B.P.B.), Eriochrome Black T (E.B.T.), and Potassium permanganate (KMnO4)—were employed to identify and quantify nitrite ions under various experimental conditions such as variable pH, temperature, concentration, and selectivity. UV–visible studies revealed low average detection limits of 0.2054 mM for E.B.T. dye, 0.218 mM for B.P.B., and 3.317 mM for KMnO4 dye solution. An Internet of Things (IoT)-based, portable, and cost-effective device for colorimetric nitrite ion detection was also developed. This innovative system integrates a connected detection mechanism within a smart device, enabling swift acquisition of red, green, and blue (RGB) readings for real-time applications in nitrite ion detection. This development has the potential to offer an economical, precise, and easily transportable method for monitoring nitrite ions, presenting a promising approach to creating accessible tools in this field.

Deep tomography for the three-dimensional atmospheric turbulence wavefront aberration

Multiconjugate adaptive optics (MCAO) can overcome atmospheric anisoplanatism to achieve high-resolution imaging with a large field of view (FOV). Atmospheric tomography is the key technology for MCAO. The commonly used modal tomography approach reconstructs the three-dimensional atmospheric turbulence wavefront aberration based on the wavefront sensor (WFS) detection information from multiple guide star (GS) directions. However, the atmospheric tomography problem is severely ill-posed. The incomplete GS coverage in the FOV coupled with the WFS detection error significantly affects the reconstruction accuracy of the three-dimensional atmospheric turbulence wavefront aberration, leading to a nonuniform aberration detection precision over the whole FOV. We propose an efficient approach for achieving accurate atmospheric tomography to overcome the limitations of the traditional modal tomography approach. We employed a deep-learning-based approach to the tomographic reconstruction of the three-dimensional atmospheric turbulence wavefront aberration. We propose an atmospheric tomography residual network (AT-ResNet) that is specifically designed for this task, which can directly generate wavefronts of multiple turbulence layers based on the Shack-Hartmann (SH) WFS detection images from multiple GS directions. The AT-ResNet was trained under different turbulence intensity conditions to improve its generalization ability. We verified the performance of the proposed approach under different conditions and compared it with the traditional modal tomography approach. The well-trained AT-ResNet demonstrates a superior performance compared to the traditional modal tomography approach under different atmospheric turbulence intensities, various turbulence layer distributions, higher-order turbulence aberrations, detection noise, and reduced GSs conditions. The proposed approach effectively addresses the limitations of the modal tomography approach, leading to a notable improvement in the accuracy of atmospheric tomography. It achieves a highly uniform and high-precision wavefront reconstruction over the whole FOV. This study holds great significance for the development and application of the MCAO technology.

Temperature-Switch-Controlled Second Harmonic Mode Sensor for Brain-Tissue Detection

Identifying brain-tissue types holds significant research value in the biomedical field of non-contact brain-tissue measurement applications. In this paper, a layered metastructure is proposed, and the second harmonic generation (SHG) in a multilayer metastructure is derived using the transfer matrix method. With the SHG conversion efficiency (CE) as the measurement signal, the refractive index ranges that can be distinguished are 1.23~1.31 refractive index unit (RIU) and 1.38~1.44 RIU, with sensitivities of 0.8597 RIU−1 and 1.2967 RIU−1, respectively. It can distinguish various brain tissues, including gray matter, white matter, and low-grade glioma, achieving the function of a second harmonic mode sensor (SHMS). Furthermore, temperature has a significant impact on the SHG CE, which can be used to define the switch signal indicating whether the SHMS is functioning properly. When the temperature range is 291.4~307.9 Kelvin (K), the temperature switch is in the “open” state, and the optimal SHG CE is higher than 0.298%, indicating that the SHMS is in the working state. For other temperature ranges, the SHG CE will decrease significantly, indicating that the temperature switch is in the “off” state, and the SHMS is not working. By stimulating temperature and using the response of SHG CE, the temperature-switch function is achieved, providing a new approach for temperature-controlled second harmonic detection.

A Novel Low-Frequency Electromagnetic Active Inertial Sensor for Drug Detection

The purpose of this paper is to demonstrate a new discovery regarding the interaction between materials and very low radio frequencies. Specifically, we observed a feedback response on an inertia active sensor when specific frequencies (around 2–4 kHz) are used to irradiate targeted pharmaceutical samples like aspirin or paracetamol drugs. The characteristics of this phenomenon, such as excitation and relaxation time, the relation between deceleration and a material’s quantity, and signal amplitude, are presented and analyzed. Although the underlying physics of this phenomenon is not yet known, we have shown that it has potential applications in remote identification of compounds, detection, and location sensing, as well as identifying substances that exist in plants without the need for any processing. This method is fast, accurate, low-cost, non-destructive, and non-invasive, making it a valuable area for further research that could yield spectacular results in the future.

Synthesis of silica inverse opal and evaluation of its potential as an optical chemical sensor for detection of metal salts

In this paper, SiO2 inverse opal photonic crystals, made by the co-assembly of SiO2 and polymethylmethacrylate (PMMA) microspheres, were used to detect 20 different metal salts with a concentration of 0.002 mol/L. Surprisingly, two different behaviors were observed. Some metal salts caused a redshift in photonic bandgap (PBG) while the others led to a PBG’s blueshift. The observed different behaviors of salts are due to their distinct influence on the lattice constant of the inverse structures which was precisely investigated by analyzing the field emission scanning electron microscopy (FESEM) images and ultraviolet–visible (UV–Vis) spectroscopy. It has been revealed that the competition between the hydrophilicity of SiO2 and the hygroscopicity of metal salts led to a shift of PBG toward blue or red. The maximum shift in the PBG range was observed for mercury (II) nitrate salt (19 nm), which indicates a sensitivity of 9.5 nm per concentration unit (mM). Furthermore, the lowest displacement of the PBG was observed for the sodium nitrate salt (5 nm), which represents a sensitivity of 2.5 nm/mM. The lowest and highest values of the detection limit (LOD) for mercury (II) nitrate salt and sodium nitrate salt equal 0.193 mmol/L and 0.887 mmol/L, respectively. In addition, according to the calibration curve, with a regression coefficient of R2 > 0.95, there is a linear relationship between the salt concentration and the displacement of the PBG of the structure. Based on the results illustrating the sensitivity of the synthesized inverse opal to the type and concentration of the salts, inverse opal can be regarded as a promising structure to create a new perspective in the field of chemical sensing.

Parkinson’s Kinetigraph for Wearable Sensor Detection of Clinically Unrecognized Early-Morning Akinesia in Parkinson’s Disease: A Case Report-Based Observation

Early-morning off periods, causing early-morning akinesia, can lead to significant motor and nonmotor morbidity in levodopa-treated fluctuating Parkinson’s disease (PD) cases. Despite validated bedside scales in clinical practice, such early-morning off periods may remain undetected unless specific wearable technologies, such as the Parkinson’s KinetiGraph™ (PKG) watch, are used. We report five PD cases for whom the PKG detected early-morning off periods that were initially clinically undetected and as such, untreated. These five cases serve as exemplars of this clinical gap in care. Post-PKG assessment, clinicians were alerted and targeted therapies helped abolish the early-morning off periods.

Synergistic Effect of ZIF-8 and Pt-Functionalized NiO/In2O3 Hollow Nanofibers for Highly Sensitive Detection of Formaldehyde

A rapid and accurate monitoring of hazardous formaldehyde (HCHO) gas is extremely essential for health protection. However, the high-power consumption and humidity interference still hinder the application of HCHO gas sensors. Hence, zeolitic imidazolate framework-8 (ZIF-8)-loaded Pt-NiO/In2O3 hollow nanofibers (ZPNiIn HNFs) were designed via the electrospinning technique followed by hydrothermal treatment, aiming to enable a synergistic advantage of the surface modification and the construction of a p-n heterostructure to improve the sensing performance of the HCHO gas sensor. The ZPNiIn HNF sensor has a response value of 52.8 to 100 ppm HCHO, a nearly 4-fold enhancement over a pristine In2O3 sensor, at a moderately low temperature of 180 °C, along with rapid response/recovery speed (8/17 s) and excellent humidity tolerance. These enhanced sensing properties can be attributed to the Pt catalysts boosting the catalytic activity, the p-n heterojunctions facilitating the chemical reaction, and the appropriate ZIF-8 loading providing a hydrophobic surface. Our research presents an effective sensing material design strategy for inspiring the development of cost-effective sensors for the accurate detection of indoor HCHO hazardous gas.

The development of sensitive graphene-based surface acoustic wave sensors for NO2 detection at room temperature

Bilayer graphene (Bl-Gr) and sulphur-doped graphene (S-Gr) have been integrated with LiTaO3 surface acustic wave (SAW) sensors to enhance the performance of NO2 detection at room temperature. The sensitivity of the Bl-Gr SAW sensors toward NO2, measured at room temperature, was 0.29º/ppm, with a limit of detection of 0.068 ppm. The S-Gr SAW sensors showed 0.19º/ppm sensitivity and a limit of detection of 0.140 ppm. The origin of these high sensitivities was attributed to the mass loading and elastic effects of the graphene-based sensing materials, with surface changes caused by the absorption of the NO2 molecules on the sensing films. Although there are no significant differences regarding the sensitivity and detection limit of the two types of sensors, the measurements in the presence of interferent gases and various humidity conditions outlined much better selectivity and sensing performances towards NO2 gas for the Bl-Gr SAW sensors.Graphical

Marine Infrastructure Detection with Satellite Data—A Review

A rapid development of marine infrastructures can be observed along the global coasts. Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are being constructed without a proper quantification of these human activities. Therefore, effective monitoring is required to maintain transparency towards environmental standards, marine resource management, inventorying objects, and global security. This study reviews remote sensing-based approaches to offshore infrastructure detection over the past 12 years. We analyzed 89 studies from over 30 scientific journals, highlighting spatial and temporal trends, methodological approaches, and regional and thematic research foci. Our results show a significant increase in research interest, especially since 2019. Asia, and especially China, is the predominant focus region in terms of first authorship, funding, and areas of investigation. Aquaculture is the most studied infrastructure, followed by platforms, offshore wind farms, and artificial islands. Gaofen, Sentinel, and Landsat are the most used satellite sensors for detection. The apparent shift towards automated detection methods, especially Deep Learning algorithms, reflects advances in computer vision. This study highlights the key role of earth observation in the field of off-shore infrastructure detection, which can contribute towards outlining effective monitoring practices for marine activities, as well as highlighting important knowledge gaps.

Smart Wheel Chair using IOT

The microcontroller based wheelchair using android is a device designed to assist individuals with mobility impairments. The system utilizes an Android application installed on a smartphone to control the movement of the wheelchair. The smartphone communicates with a microcontroller via Bluetooth, which is responsible for processing the received commands and controlling the movement of the wheelchair's motors. The system includes obstacle detection sensors that detect any obstacles in the wheelchair's path and prevent collisions. The microcontroller based wheelchair using android is a device that combines modern technologies to help individuals with mobility impairments. It utilizes an Android application and a microcontroller to control the movement of the wheelchair. Here are some more details about this innovative device: Android Application: The Android application is installed on a smartphone and acts as a remote control for the wheelchair. The user can use the application to move the wheelchair forward, backward, left, and right. Additionally, the application provides the user with real-time feedback on the wheelchair's battery level, speed, and direction. Microcontroller: The microcontroller is responsible for processing the commands received from the smartphone and controlling the movement of the wheelchair's motors. The microcontroller is programmed to interface with the wheelchair's motors and control their speed and direction. Improving the quality of life for the elderly and disabled people and giving them the proper care at the right time is one the most important roles that are to be performed by us being a responsible member of the society. It's not easy for the disabled and elderly people to maneuver a mechanical wheelchair, which many of them normally use for locomotion. Hence there is a need for designing a wheelchair that is intelligent and provides easy maneuverability. In this context, an attempt has been made to propose a thought controlled wheelchair, which uses the captured signals from the brain and eyes and processes it to control the wheelchair. Electroencephalography (EEG) technique deploys an electrode cap that is placed on the user's scalp for the acquisition of the EEG signals which are captured and translated into movement commands by the arduino microcontroller which in turn move the wheelchair

Grooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors

Improving measurement accuracy is the core issue with surface acoustic wave (SAW) micro-force sensors. An electrode transducer can stimulate not only the SAW but also the bulk acoustic wave (BAW). A portion of the BAW can be picked up by the receiving transducer, leading to an unwanted or spurious signal. This can harm the device’s frequency response characteristics, thereby potentially reducing the precision of the micro-force sensor’s measurements. This paper examines the influence of anisotropy on wave propagation, and it also performs a phase-matching analysis between interdigital transducers (IDTs) and bulk waves. Two solutions are shown to reduce the influence of BAW for SAW micro sensors, which are arranged with acoustic absorbers at the ends of the substrate and in grooving in the piezoelectric substrate. Three different types of sensors were manufactured, and the test results showed that the sidelobes of the SAW micro-force sensor could be effectively inhibited (3.32 dB), thereby enhancing the sensitivity and performance of sensor detection. The SAW micro-force sensor manufactured using the new process was tested and the following results were obtained: the center frequency was 59.83 MHz, the fractional bandwidth was 1.33%, the range was 0–1000 mN, the linearity was 1.02%, the hysteresis was 0.59%, the repeatability was 1.11%, and the accuracy was 1.34%.

A novel split PEC sensor based on magneto–optic nanostructure and photocurrent polarity switching strategy

BackgroundPhotoelectrochemical (PEC) sensors have attracted much attention due to their low cost, simple instrumentation and high sensitivity. However, conventional PEC sensors require layer–by–layer modification of the photoelectrode surface, which has the disadvantages of being time–consuming and unstable. In addition, complex interfering substances in real samples may lead to false–positive or false–negative detection results. It was thought that the above drawbacks could be eliminated by the construction of a polarity inversion PEC sensor. In this work, a magnetically separated PEC sensor was constructed for the detection of Carcinoembryonic antigen (CEA). ResultsDuring the experiment, the construction of the sensor was used for sensitive detection of CEA. In the experimental process, Fe3O4@SiO2@CdS, a semiconductor material with magnetic properties, was chosen as the substrate material, and ZnO/CuO was used as the marker on the DNA2 molecule, and a split magnetic separation PEC sensor was constructed, which was used to realize the sensitive detection of CEA. Eventually, the detection range of the sensor for CEA detection is 1–10000 pg/mL, with the detection limit of 0.34 pg/mL. Additionally, the PEC sensor has the advantages of high speed, high efficiency, high sensitivity, good specificity, and high stability. The sensing platform constructed in this work can also be extended to detect other targets, which provides a new idea for PEC sensing platforms. SignificanceIn this experiment, we developed a split PEC immunosensor based on magneto–optic nanostructure and photocurrent polarity switching strategy. Specifically, the proposed magnetic nanostructure Fe3O4@SiO2@CdS–DNA1 exhibits good paramagnetism and dispersion ability. By magnetic separation process, the PEC signals of opposite polarity can be obtained.