Sensor Elements Research Articles

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH−, CO32−, PO43−, NH3, ClO−, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001–1.0 mol L−1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864–0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

Colorimetric Silver Ion Detection Based on Silver Metallization of Gold Nanorods

AbstractDespite the use of silver in various industries, this heavy metal can pose serious risks to living organisms and the environment. Therefore, the development of simple measurement strategies for detection of silver ions (Ag+) with high sensitivity is of great importance. In this research, gold nanorods (AuNRs) have been used as indicator element in colorimetric sensor, which provides the possibility of facile and visual detection of silver by forming gold@silver core‐shell nanostructures (Au@Ag NRs). The basis of this sensor is the metallization of silver on AuNRs through ascorbic acid (AA) as a reducing agent, which forms Au@Ag NRs. During this mechanism, Ag+ is converted to silver atom (Ago) in the presence of AA and the surface of AuNRs is coated by inducing metallization. Any changes in composition and dimensions of AuNRs will cause color changes in the detection solution and subsequently spectral changes. The obtained results showed a good correlation between silver concentration and probe response (λo–λ) in the range of 2.5 to 30.0 μmol L−1 with a detection limit of 1.3 μmol L−1. In addition, the evaluation of the sensor in water and soil samples showed that it has acceptable accuracy in detecting silver ions in real conditions.

Emulsion template – based porous silicones with piezocapacitive response

Porous silicones were prepared by thiol-ene photoaddition or hydrolysis - condensation cross-linking in emulsion template in the presence of a pyridyl-modified siloxane oligomer as surfactant, at room temperature. Hexamethyldisiloxane (HMDS) was tested as alternative, more eco-friendly solvent in competition with toluene. Variations in the composition of silicone precursors, thus in cross-linking density and method, allowed tuning of mechanical properties. The piezocapacitive response of the porous silicones was investigated, focusing on the dielectric material as potential sensing element in capacitive pressure sensors. The capacitance variation showed linearity on wide pressure domains, roughly between 2 and 300 kPa, depending on the mechanical properties of the porous monoliths. A direct correlation between sensitivity and Young's modulus was found, which allows tuning of the sensing behavior. The softest material, prepared with a binary mixture of silicone precursors, exhibited a sensitivity of 8.7 × 10−3 kPa−1 on 9–29 kPa pressure range. A porous silicone – MWCNT composite showed spectacular increase in sensitivity, with maximum value of 0.747 kPa−1 and 0–7.5 kPa sensing range when compressed at 30%. Gauge factors of 29.2 in the compressive strain range of 13–30% and as high as 88.8 in the compressive strain range of 25–50% were found.

Automation proposal applied to Trolleybus current collector

Automation proposal applied to Trolleybus current collector

State Observer-Based Conditioned Reverse-Path Method for Nonlinear System Identification

In light of the complex behavior of vibrating structures, their reliable modeling plays a crucial role in the analysis and system design for vibration control. In this paper, the reverse-path (RP) method is revisited, further developed, and applied to modeling a nonlinear system, particularly with respect to the identification of the frequency response function for a nominal underlying linear system and the determination of the structural nonlinearities. The present approach aims to overcome the requirement for measuring all nonlinear system states all the time during operation. Especially in large-scale systems, this might be a tedious task and often practically infeasible since it would require having individual sensors assigned for each state involved in the design process. In addition, the proper placement and simultaneous operation of a large number of transducers would represent further difficulty. To overcome those issues, we have proposed state estimation in light of the observability criteria, which significantly reduces the number of required sensor elements. To this end, relying on the optimal sensor placement problem, the state estimation process reduces to the solution of Kalman filtering. On this ground, the problem of nonlinear system identification for large-scale systems can be addressed using the observer-based conditioned RP method (OBCRP) proposed in this paper. In contrast to the classical RP method, the current one can potentially handle local and distributed nonlinearities. Moreover, in addition to the state estimation and in comparison to the orthogonal RP method, a new frequency-dependent weighting is introduced in this paper, which results in superior nonlinear system identification performances. Implementation of the method is demonstrated on a multi-degree-of-freedom discretized lumped mass system, representing a substitute model of a physical counterpart used for the identification of the model parameters.

Prediction of the effects of process informatics parameters on platinum, palladium, and gold-loaded tin oxide sensors with an artificial neural network

In this study, we investigated the effect of the preparation process i.e., the process informatics (PI) parameters of sensor elements on the responses of Pt-, Pd-, and Au-loaded SnO2 sensors. These responses were predicted by an artificial neural network (ANN) using a dataset comprising 441 data points that had been fabricated and evaluated under many parameters in our previous studies. We reported an optimal data preprocessing method based on the relational expression between the sensor response of a semiconductor sensor and the concentration of a target gas and the effect of each PI parameter based on predicted sensor responses under untested conditions.

Simulation of heating of the sensitive element of the heat flux sensors in a high-velocity flow under variable input conditions

Simulation of heating of the sensitive element of the heat flux sensors in a high-velocity flow under variable input conditions

High-Performance Ni3(HHTP)2 Film-Based Flexible Field-Effect Transistor Gas Sensors.

Conductive metal-organic frameworks (MOFs) have received increasing attention in recent years and present high application potential as sensing elements in electronic sensors. In this study, flexible field-effect transistor (FET) sensors based on conductive MOF, i.e., Ni3(HHTP)2, have been constructed. This Ni3(HHTP)2 sensor has high sensitivity (detection limit of 56 ppb) as well as superior selectivity for NO2 detection at room temperature, which is demonstrated by accurate gas detection in a mixed gas atmosphere. Moreover, by employing six flexible substrates, i.e., polyimide (PI), tape (PET), facemask, paper cup, tablecloth, and take-out bag (textile), we successfully demonstrate the universality of the flexible sensor construction with conductive MOF as sensing film on various substrates. This study of conductive MOF-based flexible electronic sensors offers a new opportunity for a wide range of sensing applications with wearable and portable electronic devices.

Facile synthesis of sub-10 nm Cu-MOF nanofibers as electrochemical sensor element for picomolar chloramphenicol detection

Precise detection of antibiotics in aquatic environments is significant for human health and ecological environment development due to the excessive use of antibiotics. However, the accurate detection of ultralow-level antibiotics, such as picomolar level, has always been an extremely challenging task. Herein, we synthesized the sub-10 nm copper-based metal–organic framework nanofibers (Cu-MOF NFs) with a large specific area and high conductivity for achieving the electrochemical detection of picomolar-level chloramphenicol (CAP) in real water samples. As a result, by optimization of synthesis time and detection conditions, Cu-MOF NFs with a diameter of about 5 nm exhibited the ultralow limit of detection with 0.03 pM and wide detection range with 0.1––1.0 × 106 pM for CAP detection, as well as the designed sensor electrode has the excellent specificity, reproducibility, and stability. Importantly, the prepared sensor electrode exhibits reliable analytical results for CAP in real water samples, such as tap water, bottled water, and milk, indicating that designed sensor electrode has great application potential for the precise detection of antibiotics in practical samples.

Aligned and unaligned single-walled carbon nanotube bilayer films for uncooled infrared sensors

Semiconducting single-walled carbon nanotube (SWCNT) film fabrication using the dip-coating method has successfully produced a unique film with two layers of different morphologies, an upper layer of aligned SWCNT film and a lower layer of non-aligned SWCNT network film. In the upper layer, SWCNTs form SWCNT bundles of several SWCNT thicknesses, and the bundles form thin aligned SWCNT films of 2–7 nm in thickness with parallel and equally spaced bundles of SWCNTs. When a bolometer device with this bilayer film was fabricated on electrodes, the upper aligned SWCNT film was the main conductive path with a low resistivity of about 0.2 Ωcm around room temperature. The alignment film also achieved a high temperature coefficient of resistance (TCR) of more than −5%/K due to its thin thickness, which is hardly affected by the slight metallic SWCNT presence. These excellent properties of a high TCR and low resistance are advantageous for the development of innovative sensor elements.

Monolithic integration of superconducting-nanowire single-photon detectors with Josephson junctions for scalable single-photon sensing

We demonstrate superconducting single-photon detectors (SPDs) that integrate signals locally at each pixel. This capability is realized by the monolithic integration of superconducting-nanowire SPDs with Josephson electronics. The motivation is to realize superconducting sensor elements with integrating capabilities similar to their CMOS-sensor counterparts. The pixels can operate in several modes. First, we demonstrate that photons can be counted individually, with each detection event adding an identical amount of supercurrent to an integrating element. Second, we demonstrate an active gain control option, in which the signal added per detection event can be dynamically adjusted to account for variable light conditions. Additionally, the pixels can either retain signal indefinitely to record all counts incurred over an integration period, or the pixels can record a fading signal of detection events within a decay time constant. We describe additional semiconductor readout circuitry that will be used in future work to realize scalable, large-format sensor arrays of superconducting SPDs compatible with CMOS array readout architectures.

A Spin Valve-Based Rhombus-Shaped Micro-Object Implementing a Full Wheatstone Bridge.

Spin valves with a synthetic antiferromagnet were fabricated via magnetron sputtering. It was shown that the fabricated spin valve layers had a perfect microstructure and smooth interfaces, and therefore, an RKKY interaction dominated in the coupling of the ferromagnetic layers separated by a copper spacer. Rhombus-shaped micro-objects were fabricated from a single spin valve film. The thermomagnetic treatment procedure was found to form unidirectional anisotropy in the micro-object such that the values of the exchange bias fields in the rhombus' nonparallel sides were opposite in sign. For the CoFeNi/Ru/CoFeNi synthetic antiferromagnet, we determined the differences between the ferromagnetic layer thicknesses at which the thermomagnetic treatment formed the same exchange bias all over each rhombus' side. We also fabricated a sensor element in which each side of the rhombus was the shoulder of a Wheatstone bridge. After the thermomagnetic treatment procedure, each shoulder worked as an active magnetosensitive element, enabling the device to operate as a full Wheatstone bridge. The sensor output exhibited a step shape, high sensitivity to field changes, and significant magnetic hysteresis. Such characteristics are suitable for switching devices.

Machine learning-based fluorescence sensor array: Accurate discrimination, quantitative assay of phenothiazine drugs via versatile DNA probes

Phenothiazine drugs are widely used to treat psychotic diseases in humans. The phenothiazine ligand enables unique interaction between the drugs and DNA double helix. We discovered that it was impossible to select aptamer to selectively bind with phenothiazine drug. In this study, an alternative fluorescence sensor array was first developed to accurately discriminate four typical phenothiazine drugs based on machine learning. As the eight sensor elements, versatile DNA probes were designed based on different oligonucleotides, two unique fluorescence dyes, and diverse buffer solutions. The fingerprint data can offer the fluorescence response patterns against each phenothiazine drug. Linear discriminant analysis (LDA) can discriminate single one or mixture samples of phenothiazine drugs with 100% accuracy. Besides, one neural network model showed a good linear relationship between predicted and actual concentrations to quantitatively assay specific phenothiazine drugs. The proposed fluorescence sensor array presents a new way to accurately discriminate phenothiazine drugs in pharmacy or biology field.

Thermal Conductivity Gas Sensors for High-Temperature Applications.

This paper describes a fast and flexible microfabrication method for thermal conductivity gas sensors useful in high-temperature applications. The key parts of the sensor, the microheater and the package, were fabricated from glass-coated platinum wire and the combination of laser micromilling (ablation) of already-sintered monolithic ceramic materials and thick-film screen-printing technologies. The final thermal conductivity gas sensor was fabricated in the form of a complete MEMS device in a metal ceramic package, which could be used as a compact miniaturized surface-mounted device for soldering to standard PCB. Functional test results of the manufactured sensor are presented, demonstrating their full suitability for gas sensing applications and indicating that the obtained parameters are at a level comparable to those of standard industrially produced sensors. The results of the design and optimization principles of applied methods are discussed with regard to possible wider applications in thermal gas sensor prototyping in the future. The advantage of the developed sensors is their ability to operate in air environments under high temperatures of 900 °C and above. The sensor element material and package metallization were insensitive to oxidation compared with classical sensor-solution-based metal-glass packages and silicone MEMS membranes, which exhibit mechanical stress at temperatures above 700 °C.

Migrated coprime array with low mutual coupling for space limited aperture

It is well established that sparse sensor array can estimate more sources than number of sensors exploiting difference coarray. However, the array aperture size is restricted in space limited scenarios and all sensor elements are distributed in limited space. This could raise potential problems due to the mutual coupling effect. To address this issue, a migrated coprime based sparse array configuration is designed considering the following schemes. Enhancing the length of consecutive part in difference coarray while introducing no extra sensor. The mutual coupling level is reduced as low as possible, and the array aperture is also limited to acceptable level to allow possible applications in space limited scenarios. Simulation results elaborate the superiority of proposed strategy from the above aspects.

Numerical modeling of metal-dielectric metasurface as an element of microwave sensors for biomedical applications

This paper reports the results of numerical modeling of wave reflection coefficient behavior of the metal-dielectric metasurface at microwaves that can be used for biomedical applications. The study includes optimization of the working parameters of the metasurface-based structure as a sensitive element of a microwave sensoring systems for determination of proteins concentration in different solutions. In the current research a unit with the geometry, which is similar to the geometry of one well of the standard 96-multiwell laboratory microplate, is used as a resonant metasurface unit cell and liquid-holding structure. Human serum albumin (HSA) is used as a protein specimen in our model study. The present numerical modeling is based on the results of our previous experimental measurements of complex permittivity values of HSA water solutions and its biochemical reaction mixtures using microwave dielectrometry method and the developed setup. The unit cell approach calculations are performed by COMSOL Multiphysics software. The optimization of working parameters of the metal-dielectric metasurface structure with tested solutions allow us to observe the resonance effects of the wave reflection coefficient in the microwave range. We can determine the HSA concentration changes in water solutions and enzymatic reaction mixtures by the resonance frequency shift of the wave reflection coefficient of the metal-dielectric metasurface. Developed metal-dielectric metasurface-based structure demonstrates prospects to be used as a sensitive element of microwave sensors for proteins concentration determination with biomedical purposes.

An affordable optical detection scheme for LSPR sensors

Biosensing technologies are essential for advancing healthcare by enabling rapid point-of-care (POC) testing and diagnosis, potentially saving lives. Biosensors, such as Localized Surface Plasmon Resonance (LSPR) sensors with gold nanoparticles (AuNPs), show promise in early disease diagnosis due to their simple structure and high sensitivity. However, their commercialization is limited by high production costs and the need for precise optoelectronic systems. This article proposes an affordable optical detection scheme for LSPR sensors named BioColor. BioColor uses a color CMOS camera to capture images of light passing through the sensor elements, which are plasmonic papers composed of AuNPs. Variations in the refractive index over the sensor’s surface cause changes in the color of the transmitted light. This color change can be detected through image processing algorithms and the detection results are visualized on the BioColor mobile app, providing instant automated access to sensing outcomes.

Develоpment оf а thermоcаtаlytic sensоr fоr mоnitоring ethyl аlcоhоl vаpоrs in humаn exhаled аir аnd prоcess gаses

In this work, highly sensitive thermocatalytic methods and selective devices for observe the content of ethanol vapor in the air and waste industrial gas mixtures have been developed. The main indicators of the developed sensors for the determination of ethanol were assessed. The possibility of creating highly sensitive, selective sensors using measuring and compensation thermosensitive sensor elements containing catalysts of varying activity in relation to the components of complex gas-air mixtures has been experimentally confirmed. The created sensors allow the selective determination of ethanol vapor in a mixture of various gases. The scope of application of the developed sensors is environmental monitoring of atmospheric air and process gases, pre-trip and pre-shift examination of drivers, analysis in treatment rooms, clinics and clinics to accurately determine the clinical condition of patients, etc. Sensors can also be used as a means of self-monitoring.

A stochastic photo-responsive memristive neuron for an in-sensor visual system based on a restricted Boltzmann machine.

In-sensor computing has gained attention as a solution to overcome the von Neumann computing bottlenecks inherent in conventional sensory systems. This attention is due to the ability of sensor elements to directly extract meaningful information from external signals, thereby simplifying complex data. The advantage of in-sensor computing can be maximized with the sampling principle of a restricted Boltzmann machine (RBM) to extract significant features. In this study, a stochastic photo-responsive neuron is developed using a TiN/In-Ga-Zn-O/TiN optoelectronic memristor and an Ag/HfO2/Pt threshold-switching memristor, which can be configured as an input neuron in an in-sensor RBM. It demonstrates a sigmoidal switching probability depending on light intensity. The stochastic properties allow for the simultaneous exploration of various neuron states within the network, making identifying optimal features in complex images easier. Based on semi-empirical simulations, high recognition accuracies of 90.9% and 95.5% are achieved using handwritten digit and face image datasets, respectively. In addition, the in-sensor RBM effectively reconstructs abnormal face images, indicating that integrating in-sensor computing with probabilistic neural networks can lead to reliable and efficient image recognition under unpredictable real-world conditions.

Nonlinear mathematical model of pressure measurement systems in gas-liquid media

The primary element of the instrumentation for measuring the pressure of a gas-liquid medium is a sensor that supplies data on the pressure of the working medium. It determines the proper functioning of machines, mechanisms, and systems. Increasing the service life, reducing development time, and reducing the cost of sensors is one of the important tasks. Mathematical modeling of pressure measurement systems’ functioning plays an important role at the design stage of such systems. This article examines a nonlinear one-dimensional model of a mechanical system “pipeline – pressure sensor” designed to measure and control the pressure of the working gas-liquid medium in the combustion chambers of engines. In such a system, the sensor is connected to the engine via a pipeline and is located at some distance from it to reduce the impact of vibration accelerations and high temperatures. The purpose of the work is to study the dynamics and stability of joint oscillations of the elastic sensitive element in the pressure sensor and of the working medium in the pipeline for a given law of pressure change in the combustion chamber. The study is provided under the assumption that the working medium is ideal and compressible. To describe the movement of the working medium (gas or liquid), a nonlinear model of fluid and gas mechanics is used. Mathematical description of the process of interest includes an initial boundary value problem, whose formulation contains a nonlinear partial differential equation. To solve it, numerical-analytical method of solution based on the Galerkin method is proposed, which makes it possible to reduce the study of the problem to solving a system of ordinary differential equations. A numerical experiment is carried out and examples of calculating the dynamics of the sensor's sensitive element are presented. The proposed mathematical model makes it possible to determine the law of change in the deviation of the sensor's sensitive element depending on the law of change in pressure in the combustion chamber. The research results are intended for use at the design stage of pressure measurement systems.