Sensor Layer Research Articles

1D Topological Photonic Crystal based Nanosensor for Tuberculosis Detection.

In this study, we present a nanosized biosensor based on the photobiological properties of one-dimensional (1D) topological photonic crystals (PCs). A topological structure had been designed by combining two photonic crystal structures (PC 1 and PC 2) comprised of functional material layers, Si and SiO2. These two, PC 1 and PC 2, differ in terms of the thickness and arrangement of these dielectric materials. We carried out a comparison between two distinct topological photonic crystals: one using random photonic crystals, and the other featuring a mirror heterostructure. Tuberculosis may be diagnosed by inserting a sensor layer into 1D topological photonic crystals. The sensing process is based on the refractive indexes of the analytes in the sensor layer. When the 1D-topological heterostructure-based PC and its mirror-image structures are stacked together, the sensor becomes more efficient for analyte detection than the conventional PCs. The random-based topological photonic crystal outperformed the heterostructure-based topological photonic crystal in analyte sensing. Photonic media witness notable blue shifts due to the analytes' variations in refractive index. The numerical results of the sensor are computed using the transfer matrix approach. Effective results are achieved by optimizing the thicknesses of the sensor layer and dielectric layers; number of periods and incident angle. In normal incident light, the developed sensor shows a high sensitivity of 1500 nm/RIU with a very low limit of detection in the order of 2.24E-06 RIU and a high-quality factor of 30659.54.

Fluorinated polyaniline-based sensors with enhanced NH3 sensitivity

Poly(2-fluoroaniline) films were prepared by electrochemical oxidation of 2-fluoroaniline in perchloric acid solutions. These films had a characteristic globular structure, but low thickness and conductivity. For these reasons, alternative polymer films were prepared from electrolytic solutions with different aniline:2-fluoroaniline ratios. The physicochemical properties of these polymer films strongly evolved with the ratio since they were thicker and more conductive when the proportion of aniline increased while their morphology also varied significantly. The electrodeposited polymer films were then used as sensitive layers of gas sensors for ammonia detection. The addition of 2-fluoroaniline improved the performance characteristics of gas sensors for the detection of ammonia at room temperature and the poly(aniline/2-fluoroaniline)-based sensors responded to ammonia gas with good repeatability, good sensitivity (0.44 %/ppb), a very low detection limit (4 ppb), and a low dependence on humidity variations.

Chemiresistive NH3 and H2S sensors based on thin films of vitamin B12 derivatives

Despite the large number of materials, e.g. semiconducting oxides, carbon nanomaterials, conducting polymers and others, that are used as active layers of chemiresistive sensors to ammonia and hydrogen sulfide, the search for new materials remains an urgent task. Special attention is paid to the search for environmentally friendly materials. Corrinoids are naturally occurring coordination complexes consisting of a central cobalt ion and a tetrapyrrole ligand called a corrin ring. Cobalamins and their derivatives are widely used as active layers of optical and electrochemical sensors. At the same time, vitamin B12 derivatives have not been studied as active layers of chemiresistive gas sensors. In this work, films of two vitamin B12 derivatives, viz. dicyanocobyrinic acid heptabutyl ester (DC) and its aquacyano form, aquacyanocobyrinic acid heptabutyl ester (AC), deposited by spin coating and drop casting techniques onto interdigitated electrodes, were tested for the first time as active layers of chemiresistive sensors for the detection of low concentrations of ammonia and hydrogen sulfide (1–50 ppm) in air. It was shown that films of both vitamin B12 derivatives demonstrated reversible sensor response to NH3 and H2S at room temperature. The limit of detection (LOD) of H2S was 0.1 ppm for both vitamin B12 derivatives, while LOD of ammonia was noticeably lower (0.06 ppm) in the case of AC films than in the case of DC films (0.4 ppm). Both sensors demonstrated a fairly low regeneration time, which did not exceed 180 s for 5 ppm of NH3 and 185 s for 5 ppm of H2S.

Poly(vinyl pyridine) coatings cross-linked with transition metal complexes as active layers for biosensors sensitive to protein adsorption and cell adhesion

The possibility of application of poly(4-vinyl pyridine) layers cross-linked with transition metal complexes as active layers in biomedical sensors was tested. The successful modification of the P4VP coating with CuBr2 or ZnBr2 was verified using time of flight - secondary ion mass spectrometry and X-ray photoelectron spectroscopy. The topography and wettability of the coatings were examined by using atomic force microscopy and water contact angles measurements, respectively. Tests of biological activity of coatings indicated strong protein adsorption, good biocompatibility, and no antimicrobial activity. The potential of the coatings to be used as active layers of biosensors was verified, by systematic impedance-based measurements, which showed the sensitivity of the P4VP:CuBr2 coatings to the presence of proteins and cells in different concentrations. The high selectivity of the coatings toward the defined analyte was confirmed by the specific antigen–antibody immunoreaction, and the possibility of in situ monitoring of protein adsorption and cell adhesion also for individual cells was presented. Finally, the conductive response of a bilayer system that mimics Organic Field Effect Transistor was shown. These results point to a great potential for both coatings to serve as active layers of sensitive and highly selective biosensors.

The Application of Commercial Surface Acoustic Wave Radio Communication Filters as Transducers for DMMP Sensors.

In the present study, we used two popular radio communication SAW resonators as a base for gas sensors and tested their performance. Taking into account issues related to sensor sensitivity, the possibility of applying a sensor layer, the availability of devices, and other related issues, we selected two popular single-port resonators with center frequencies of 315 and 433 MHz (models R315 and R433, respectively) for testing purposes. Both resonators were equipped with a sensitive film of hexafluoroisopropanol-substituted polydimethylsiloxane, a material that selectively absorbs molecules with a high ability to form basic hydrogen bonds. Fabricated sensors were used to detect trace amounts of dimethyl methylphosphonate (DMMP) vapor, which has often been used in similar studies as a nerve chemical warfare agent simulant. Sensors using both devices loaded with sensor layers of an optimal thickness rapidly reacted to a gas containing DMMP at a concentration of 3 mg/m3, generating a stable analytical signal ranging from several to several dozen kilohertz. In the case of R433, the frequency signal was 20.5 kHz at 1 min from the beginning of exposure to DMMP. The obtained results showed that the used transducers exhibited good performance as a base for gas sensors. Finally, their suitability for sensing applications was confirmed by a comparison with the results obtained in previous similar studies.

Low-Cost Flexible Graphene Oxide Humidity Sensor Fabricated Using Inkjet Printing and Aerosol Deposition

Flexible humidity sensors play a critical role in medical diagnostics and industrial control systems. In this study, a low-cost flexible humidity sensor is presented. The humidity sensor is developed by printing silver interdigitated electrodes (IDE) on a polyethylene terephthalate (PET) substrate using an Epson Stylus C88+ inkjet printer. The sensing layer of the humidity sensor was fabricated using graphene oxide (GO) ink, which is deposited onto the electrodes using an aerosol deposition technique. The GO humidity sensor achieves excellent sensing performance over a wide range of humidity levels from 11% to 97% RH range, with a fast response time of 2 s and recovery time of 17 s. The sensor also exhibits ultra-high sensitivity (243 kΩ/%RH), low hysteresis (2.16%), excellent repeatability, long-term stability, and high flexibility (tested at bending radiuses of 4 cm, 3.5 cm, 3 cm, and 2.5 cm). The humidity sensing mechanism of the proposed GO humidity sensor was also discussed. Furthermore, the sensor exhibited excellent capabilities in monitoring human respiration, distinguishing between nose and mouth breathing, detecting finger movements without physical contact, and even recognising basic spoken words. These features of the sensor possess significant potential for various applications in human healthcare.

Predictive-Cognitive Maintenance for Advanced Integrated railway Management

Railway systems play a vital role in modern transportation and Predictive-Cognitive Maintenance (PCM) has emerged as a transformative approach in the context of Advanced Integrated Railway Management for ensuring the safety, reliability, and efficiency of these systems. PCM leverages data analytics and machine learning to optimize railway system maintenance. This requires effective structural health monitoring (SHM) using low-cost sensor devices. This paper presents a prototype solar-powered wireless sensor node with a 3-axis MEMS accelerometer and energy-harvesting features for monitoring rail track vibrations. The node contains a microcontroller that runs embedded machine-learning models to preprocess the vibration data after train crossing. Abnormal vibrations, indicative of defects, were detected in real time using the TinyML inference at the edge. Instead of raw data, only the model results were wirelessly transmitted to a digital twin in the cloud. The digital twin aggregates data across the rail network for system-level assessment of the RUL and maintenance planning. This edge computing approach minimizes wireless transmission and cloud storage compared to raw sensor streaming. Embedded ML enables real-time damage detection, whereas the cloud digital twin enables system-level prognosis insights. The solar-powered platform enables long-term remote monitoring at low cost without wiring or battery changes. A full-scale physical model was used to validate the edge node prototypes against calculation models and wired accelerometers for impulse loads. The results demonstrate that these nodes can provide a sensor layer for cost-effective PCM in railway systems. In summary, this work proposes an edge computing and embedded ML approach for SHM that integrates cloud-based digital twins to enable the predictive-cognitive maintenance of railway infrastructure. Wireless nodes demonstrate potential for low-cost, convenient, and automated rail health monitoring.

Research on Intelligent Heating for Urban Systems Based on Digital Twin

Abstract This article focuses on the intelligent heating platform driven by digital twins, analyzing the overall framework of the system according to the sensor layer, application layer, and network layer, and building an information service platform for intelligent heating networks. The data of the entire heating system, including heat sources, primary networks, heating stations, secondary networks, and heat users, is remotely collected, analyzed, and diagnosed. Subsequently, statistical analysis is conducted on the energy consumption of each heat source and heating station, achieving data sharing and data mining among various business systems. This article conducts research on several key technologies for optimizing decision-making methods of heating system operation scheduling based on simulation models and develops software systems to support the intelligent upgrading of heating systems. The results show that intelligent heating system based on digital twins can better meet the balance between supply and demand in urban heating systems, and optimize the overall operating costs and environmental benefits of the system under multiple constraints.

Determining the influence of relative humidity on the working parameters of a gas sensor based on zinc oxide

This paper investigates the dependence of operating parameters of a gas sensor based on zinc oxide obtained by the method of direct current magnetron sputtering. The production of gas sensor films was carried out using a VUP-5M vacuum unit with an original material-saving magnetron. The study of the dependence of the working parameters of the gas sensor was carried out under standard conditions. It was found that with increasing humidity, the electrical resistance of the gas sensor decreases and, accordingly, the sensitivity to the target gas decreases. A significant reaction of the gas sensor to an increase in humidity was observed in the range of 65–80 % relative humidity. The mechanism of influence of relative humidity on the sensitivity of a gas sensor based on ZnO was investigated. The change in resistance of the gas sensor is caused by trapped electrons on adsorbed oxygen molecules on the surface of the sensitive layer. The capture of electrons from the conduction zone leads to bending of the conduction zone and an increase in the space charge zone, respectively, to a change in the resistance of the sensitive layer of the gas sensor. In the atmosphere, when O2 molecules are adsorbed on the ZnO surface, they remove an electron from the conduction band. The reaction of oxygen adsorbed on the ZnO surface with reducing gases and the replacement of adsorbed oxygen with other molecules changes the bending of the conduction band and reduces the area of space charge. Adsorption of water on the surface of zinc oxide occurs according to the dissociation mechanism, which consists in the adsorption of steam molecules or hydroxyl groups with the subsequent displacement of previously adsorbed oxygen and free electrons and, accordingly, leads to a decrease in the sensitivity of the gas sensor. In addition, the adsorption of water vapor (H2O) molecules leads to less chemisorption of oxygen species on the ZnO surface due to the reduction of the surface area, which is responsible for the sensor response. Approaches to reduce the influence of relative humidity on the sensitivity of a gas sensor based on zinc oxide have been proposed

A wearable microneedle patch incorporating reversible FRET-based hydrogel sensors for continuous glucose monitoring

Continuous glucose monitors are crucial for diabetes management, but invasive sampling, signal drift and frequent calibrations restrict their widespread usage. Microneedle sensors are emerging as a minimally-invasive platform for real-time monitoring of clinical parameters in interstitial fluid. Herein, a painless and flexible microneedle sensing patch is constructed by a mechanically-strong microneedle base and a thin layer of fluorescent hydrogel sensor for on-site, accurate, and continuous glucose monitoring. The Förster resonance energy transfer (FRET)-based hydrogel sensors are fabricated by facile photopolymerizations of acryloylated FRET pairs and glucose-specific phenylboronic acid. The optimized hydrogel sensor enables quantification of glucose with reversibility, high selectivity, and signal stability against photobleaching. Poly (ethylene glycol diacrylate)-co-polyacrylamide hydrogel is utilized as the microneedle base, facilitating effective skin piercing and biofluid extraction. The integrated microneedle sensor patch displays a sensitivity of 0.029 mM−1 in the (patho)physiological range, a low detection limit of 0.193 mM, and a response time of 7.7 min in human serum. Hypoglycemia, euglycemia and hyperglycemia are continuously monitored over 6 h simulated meal and rest activities in a porcine skin model. This microneedle sensor with high transdermal analytical performance offers a powerful tool for continuous diabetes monitoring at point-of-care settings.

Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control.

The integration of artificial intelligence (AI) and the Internet of Things (IoT) in agriculture has significantly transformed rural farming. However, the adoption of these technologies has also introduced privacy and security concerns, particularly unauthorized breaches and cyber-attacks on data collected from IoT devices and sensitive information. The present study addresses these concerns by developing a comprehensive framework that provides practical, privacy-centric AI and IoT solutions for monitoring smart rural farms. This is performed by designing a framework that includes a three-phase protocol that secures data exchange between the User, the IoT Sensor Layer, and the Central Server. In the proposed protocol, the Central Server is responsible for establishing a secure communication channel by verifying the legitimacy of the IoT Sensor devices and the User and securing the data using rigorous cryptographic techniques. The proposed protocol is also validated using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The formal security analysis confirms the robustness of the protocol and its suitability for real-time applications in AI and IoT-enabled smart rural farms, demonstrating resistance against various attacks and enhanced performance metrics, including a computation time of 0.04 s for 11 messages and a detailed search where 119 nodes were visited at a depth of 12 plies in a mere search time of 0.28 s.

Measurement of Si pixel sensor alignment for the ALICE ITS detector

Abstract A Large Ion Collider Experiment (ALICE) experiment is one of the four experiments at the Large Hadron Collider (LHC) designed to investigate the status of matter under very high energy densities produced during heavy-ion collisions. The ALICE inner tracking system (ITS) consists of seven concentric cylindrical layers of monolithic silicon pixel sensors known as ALICE pixel detector (ALPIDE). The sensors are used to reconstruct the paths of charged particles generated in the collisions. The sensor alignment of the detector must be adjusted to a high precision standard. The adjustment objective is to obtain a detector that can undertake high-resolution measurements. This paper introduces a method for measuring the reference markers utilized in sensor alignment determination. Markers engraved at the chip corners have been detected using the Hough transform, Canny edge detection, and template matching techniques. The distances between two markers are measured to determine the accuracy of the pixel sensor alignment before and after assembly. The proposed methods exhibit an accuracy exceeding 99% and demonstrate high speed analysis. The average processing times for detecting the circle and cross markers are 105.9 ms/image and 113.8 ms/image, respectively. The sensor alignment of the detector must be adjusted to a high precision standard. However, recent studies have shown deviations of up to 5 μ m above the desired value in the measured sensor position. Such deviations do not represent a major issue, nevertheless it is important to measure them in order to speed-up and make more accurate the recursive track-based alignment procedure used to reconstruct the position of each pixel sensor in the tracking detector. The proposed method offers a promising solution to deliver precise and rapid measurements for a large number of examined objects.

Power-saving effect and sensor characteristics of sandwich structure on oxygen sensors using hot spot

A hot spot which is a glowing orange local area appears in REBa2Cu3Oy (RE: rare earth) ceramic rods when a voltage higher than a certain value is applied at room temperature. After the hot spot appears, the oxygen partial pressure in the ambient atmosphere changes the current through the rod, which can be used as an oxygen sensor without the need for a heating system. In this study, the authors investigated the oxygen-sensing characteristics and the power-saving effect of the sandwich structure in which the sensor layer was stacked between insulation layers. The sample rods were prepared by the solid-state reaction at 960 °C. Materials of 5 mol% Nd2BaCuO5-added Nd1.4Ba1.6Cu3Oy and 20 mol% Nd1.4Ba1.6Cu3Oy-added Nd2BaCuO5 were used for the sensor layer and the insulation layer, respectively. The sensitivity to the oxygen increased with increasing voltage applied to the rod. Under the condition where 17.8 V was applied to the rod with the sandwich structure and 6.6 V to the rod with the monolithic structure, the sensitivities of both rods were almost the same. The sandwich structure had a reduced current value and consumed approximately 26 % less power than the monolithic structure. The response time for the sandwich structure and the monolithic structure were comparable, meaning that the insulation layer did not impair the response time.

Structure of plasmonic multi spectral Apta sensor and analyzing of bulk and surface sensitivity

In this work, a multispectral aptasensor structure, including a sub-layer and two side walls, was presented. The cells are positioned at the down and top of the structure, with the down cells oriented perpendicular to the walls and the top cells aligned parallel to the walls. The validity of the findings was verified by the utilization of a numerical simulation technique known as 3D Finite Difference Time Domain (FDTD). The biosensor under consideration exhibits sensitivities of 1093.7 nm/RIU, 754 nm/RIU, and 707.43 nm/RIU in mode III, mode II, and mode I, respectively. In the majority of instances, the quantity of analyte available is insufficient to coat the surface of the sensor thoroughly. Consequently, in this study, the evaluation of surface sensitivity was undertaken alongside bulk sensitivity. The surface sensitivity of the suggested structure for mode II in the sensor layer, with thicknesses of 10, 20, 30, and 70 nm, is measured to be 25, 78, 344, and 717.636 nm/RIU, respectively. Our design incorporates a unique arrangement of sub-layer and side walls, with cells positioned to maximize interaction with the target analyte. This innovative configuration, combined with Ag for its superior plasmonic properties, enables the detection of E. coli O157 with remarkable sensitivity.

Laser-induced 2D/0D graphene-nanoceria freestanding paper-based films for on-site hydrogen peroxide monitoring in no-touch disinfection treatments

A one-shot CO2 laser-based strategy to generate conductive reduced graphene oxide (rGO) decorated with nanoceria (nCe) is proposed. The 2D/0D rGO-nCe films, integrated as catalytic sensing layers in paper-based sensors, were employed for on-site monitoring of indoor fogging treatments against Listeria monocytogenes (Lm), a ubiquitous pathogenic bacterium. The rGO-nCe laser-assisted synthesis was optimized to preserve the rGO film morphological and electron-transfer features and simultaneously integrate catalytic nCe. The films were characterized by microscopical (SEM), spectroscopical (EDX, Raman, and FTIR), and electrochemical techniques. The most performing film was integrated into a nitrocellulose substrate, and the complete sensor was assembled via a combination of xurography and stencil printing. The rGO-nCe sensor's catalytic activity was proved toward the detection of H2O2, obtaining sensitive determination (LOD = 0.3 µM) and an extended linear range (0.5–1500 µM). Eventually, the rGO-nCe sensor was challenged for the real-time continuous monitoring of hydrogen peroxide aerosol during no-touch fogging treatment conducted following the EU’s recommendation for biocidal product use. Treatment effectiveness was proved toward three Lm strains characterized by different origins, i.e., type strain ATCC 7644, clinical strain 338, and food strain 641/6II. The sensor allows for discrimination and quantification treatments at different environmental biocidal amounts and fogging times, and correlates with the microbiological inhibition, promoting the proposed sensor as a useful tool to modulate and monitor no-touch treatments.Graphical

Microsphere-Structured Protein Hydrogel Dielectrics for Capacitive Wearable Sensors.

The desire for healthy living has created a crucial need for portable flexible health-monitoring devices based on biomaterials. Toward this end, we report a microsphere-structured hydrogel that uses bovine serum albumin (BSA) as a dielectric layer for capacitive pressure sensors. We developed a theoretical model that describes how stacking dielectric layers of spheres affects the performance of capacitive sensors. We also prepared a prototype sensor featuring the unique microsphere structure to create capacitive sensors with high sensitivity (360.91 strain sensitivity), excellent cyclical stability, and a long service life (over 5000 stretching-compression cycles). Furthermore, the design of the hydrogel sensor allows for easy integration into fabrics to create devices such as smart wristbands, which can collect a diverse range of health data. Thus, BSA-hydrogel-based sensors not only provide safe wearable devices but also advance the performance of high-sensitivity capacitive sensors.

A blockchain-machine learning ecosystem for IoT-Based remote health monitoring of diabetic patients

Diabetes poses a global health challenge, demanding continuous monitoring and expert care for effective management. Conventional monitoring methods lack real-time insights and secure data-sharing capabilities, necessitating innovative solutions that leverage emerging technologies. Existing centralized monitoring systems often entail risks such as data breaches and single points of failure, emphasizing the necessity for a secure, decentralized approach that integrates the Internet of Things (IoT), blockchain, and machine learning for efficient and secure diabetes management. This paper introduces a decentralized, blockchain-based framework for remote diabetes monitoring, IoT sensors, machine learning models, and decentralized applications (DApps). The proposed framework comprises five layers: the IoT Sensor Layer, which collects real-time health data from patients; the Blockchain Layer, leveraging smart contracts on the Ethereum blockchain for secure data sharing and transactions; the machine learning Layer, analyzing patient data to detect diabetes; and the DApps Layer, facilitating interactions between patients, doctors, and hospitals. For intelligent decision-making regarding diabetes based on data collected from different sensors, nine machine learning algorithms, including logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, Random Forest, AdaBoost, stochastic gradient boosting (SGD), and Naive Bayes, were trained and tested on the PIMA dataset. Based on the performance evaluation parameters such as accuracy, recall, F1-score, and the area under the curve (AUC), it was found that the AdaBoost model achieved the highest predictive accuracy of 92.64%, followed by the Decision Tree with an accuracy of 92.21% in diabetes classification.

Получение и обработка акустических откликов волн Лэмба в датчиках водных растворов базовых вкусов

In this work, the total acoustic wave losses in 128°Y-LiNbO3 wafers during liquid application due to radiation emission of waves into the liquid medium, as well as its viscosity, conductivity, density and dielectric constant of the liquid analyte are measured. Solutions with 5 basic flavors, widely used for calibration and reference creation in liquid media testing, were selected as test liquids. The measurements were performed in the range of 30...60 MHz. After the measurements, the experimental data were presented in the form of polar histograms, from the comparison of areas and shapes of which the flavor of a particular liquid was determined. Then, a principal component analysis (PCA) method was applied to the same experimental results to describe linear elastic wave deformation processes based on experimental data containing known values of physical wave measurements for different types of liquid media, which was previously used to identify gases and predict the properties of various substances from the linear response of a normal acoustic-electronic wave. In this paper, a comparative analysis of the results obtained using polar histograms and the standard PCA classification method is performed. The advantages of the latter are demonstrated. The results of this study are useful both for the development of acoustic sensors without sensor layers and for the application of machine learning methods to data in other types of sensors.

Characterization of a Food‐Safe Colorimetric Indicator Based on Black Rice Anthocyanin/PET Films for Visual Analysis of Fish Spoilage

ABSTRACTThe safety of food products is of prime importance for consumers and manufacturers. Many means can be used to validate a food product's safety before it is consumed. This study is about the preparation, characterization and evaluation of a generally recognized as safe (GRAS) sensitive colorimetric sensor that detects volatile gases (TVB‐N) resulting from fish spoilage, thus indicating the pH variation of packaged fish products. This is performed by coating a thin layer of ink sensors on the surface of the supporting matrix (corona‐treated PET). Various visual pH indicators were prepared based on black rice anthocyanin as an FDA‐approved dye. Black rice, which contains more than 80% cyanidin‐3‐glucoside, is the most prevalent anthocyanin component. Because of its low toxicity and high concentration, it can be utilized as a natural food colourant. pH indicators based on black rice can show distinct colours in various pH: from red (low pH) to violet (4–6) and deep purple/blue (6–7), blue (7–9) to yellowish/light brown (9–13) throughout the acid–base reaction by the analyte. The ink formulation was prepared by incorporating a binder system (PVOH‐PEG) for higher surface wettability, a crosslinking agent (citric acid) for higher adhesion, an antifoaming agent (natural vanillin) and acetic acid as a pH fixing agent. Corona treatments affected substrate surface chemistry in this study. The samples with thermal treatment passed the ASTM D3330 tape test, the 8000 passages for dry sponge and the 25 passages for wet sponge through the abrasion method. Anthocyanin concentration in formulated ink based on calculation by UV–vis spectra is 0.240 mg/100 g. Sensitivity tests towards TVB‐N gases were carried out at a temperature of 4°C to evaluate the performance of colorimetric films with formulated ink along with thermal treatment (temperature: 165°C, time: 5 min) using the volatile gases emitted by the fish sample inside the package.

Fabrication of half-metallic Co2FeAlxSi1–x thin film with small magneto-crystalline anisotropy constant K1

A tunnel magnetoresistance (TMR) sensor is a highly sensitive magnetic sensor workable at room temperature. To achieve smaller magnetic field detection, both a high TMR ratio and small magnetic anisotropy of the free layer are required. In this study, we developed single crystalline Co-based Heusler alloy Co2FeAlxSi1-x films as the free layer of TMR sensors and systematically investigated their crystalline and magnetic properties. The magnetic tunnel junction (MTJ) with these alloys is a promising device for highly sensitive TMR sensors due to their half-metallicity. We evaluated the B2 and L21 ordering parameters SB2 and SL21. SB2 was above 80 % for all the samples and SL21 was 48 % at a maximum, which indicates that all the samples exhibit half-metallicity. In addition, we evaluated their first magneto-crystalline anisotropy constant K1 from magnetization curves. K1 changed from positive to negative as Al component x increases and K1 was about 1200 erg/cc at x = 0.33. These results indicate that Co2FeAlxSi1-x films around x = 0.33 have a good candidate for the free layer of TMR sensors because of their half-metallicity and small magnetic anisotropy.