Multi-sensor Platform Research Articles

Nitrogen-doped carbonized polymer dots activated by alkalinity of arginine for multicolor multi-platform sensor

Among carbon-based materials, carbon dots (CDs) are popular because of their stable optical properties and good biocompatibility. Their fluorescence properties often are tailored by size, surface modification, or heteroatom doping with multiple precursors. In this paper, the alkalinity of Arginine (Arg) acts as an activator to promote carbonation and increase luminescence efficiency. Meanwhile, Arg acts as a tetrahedron and plays another three roles in preparing amino-modified nitrogen-doped carbonized polymer dots (SA-NCPDs), in which Arg serves as carbon source, nitrogen-doped nitrogen source, and surface modifier. The NCPDs based only on arginine display narrow full width at half maximum (FWHM) of 54 nm. The SA-NCPDs present dual emission distribution in the UV and visible blue regions, respectively. The SA-NCPDs present multicolor emission, especially crimson emission in water under 77 K in different solvents. Besides, SA-NCPDs in different solvents and the prepared polyvinyl alcohol (PVA) composite film have different phosphorescence and long afterglow at 77 K. The excellent biocompatibility and stability of the SA-NCPDs imply it is a potential material in biomedicine. In addition, a multiplatform multicolor ratiometric sensor and visualized colorimetric sensor with high selectivity were constructed successfully for detecting Cu2+, H2O2, and OPD based on self-absorption.

Polarimetric Radar Quantitative Precipitation Estimation

Radar quantitative precipitation estimation (QPE) is one of the primary tasks of weather radars. The QPE quality was substantially improved after polarimetric upgrade of the radars. This study provides an overview of existing polarimetric methodologies for rain and snow estimation and their operational implementation. The variability of drop size distributions (DSDs) is a primary factor affecting the quality of rainfall estimation and its impact on the performance of various radar rainfall relations at S, C, and X microwave frequency bands is one of the focuses of this review. The radar rainfall estimation algorithms based on the use of specific attenuation A and specific differential phase KDP are the most efficient. Their brief description is presented and possible ways for their further optimization are discussed. Polarimetric techniques for the vertical profile of reflectivity (VPR) correction at longer distances from the radar are also summarized. Radar quantification of snow is particularly challenging and it is demonstrated that polarimetric methods for snow measurements show good promise. Finally, the article presents a summary of the latest operational radar QPE products available in the US by integration of the information from the WSR-88D radars via the Multi-Radar Multi-Sensor (MRMS) platform.

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).

Assessment of Various Process Parameters for Optimized Sterilization Conditions Using a Multi-Sensing Platform.

In this study, an online multi-sensing platform was engineered to simultaneously evaluate various process parameters of food package sterilization using gaseous hydrogen peroxide (H2O2). The platform enabled the validation of critical aseptic parameters. In parallel, one series of microbiological count reduction tests was performed using highly resistant spores of B. atrophaeus DSM 675 to act as the reference method for sterility validation. By means of the multi-sensing platform together with microbiological tests, we examined sterilization process parameters to define the most effective conditions with regards to the highest spore kill rate necessary for aseptic packaging. As these parameters are mutually associated, a correlation between different factors was elaborated. The resulting correlation indicated the need for specific conditions regarding the applied H2O2 gas temperature, the gas flow and concentration, the relative humidity and the exposure time. Finally, the novel multi-sensing platform together with the mobile electronic readout setup allowed for the online and on-site monitoring of the sterilization process, selecting the best conditions for sterility and, at the same time, reducing the use of the time-consuming and costly microbiological tests that are currently used in the food package industry.

Design and Development of a Multisensory Real-Time Monitoring Platform for Ultrafast Laser Engraving Process

Design and Development of a Multisensory Real-Time Monitoring Platform for Ultrafast Laser Engraving Process

Multi-sensing signals diagnosis and CNN-based detection of porosity defect during Al alloys laser welding

Porosity is one of the most common defects in laser welding of aluminum (Al) alloys, which seriously affects the welding quality. Online monitoring of porosity defect can provide guidance for the adjustment of process parameters to reduce the occurrence of porosity, which has attracted increasing attention. This paper presents a novel method for recognizing and detecting porosity defect during Al alloys laser welding based on multi-sensing signals diagnosis and deep learning. A multi-sensor platform, including a high-speed camera and a coherent light measurement system, is established to measure the keyhole 3D morphological characteristics. The obtained keyhole depth signal is processed by ensemble empirical mode decomposition (EEMD), and it is found that the change of keyhole depth can be used to recognize the regions where the pores appear. Besides, the sliding window algorithm is used to scan the keyhole opening morphological characteristic signals, followed by the wavelet packet transform (WPT) processing on a small segment of the signals in each window to obtain the time-frequency spectrum graphs. The messy spectrum graphs show that the keyhole opening fluctuates violently, indicating the formation of porosity in the corresponding location of the weld seam. A convolutional neural network (CNN) is constructed to identify the spectrum graphs, realizing the online detection of porosity. The results show that the proposed method has high accuracy and good reliability.

A multi-platform sensor for selective and sensitive H2S monitoring: Three-dimensional macroporous ZnO encapsulated by MOFs with small Pt nanoparticles

The high-selectivity and high-sensitivity determination of trace concentrations of toxic gases is a major challenge when using semiconductor metal oxide (SMO) gas sensors in complicated real-world environments. In this study, by strategically combining a three-dimensional inverse opal (3DIO) macroporous ZnO substrate and a ZIF-8 outer filter membrane, two series of sensors with Pt NPs loaded at different locations are developed. In the optimal 3DIO ZnO@ZIF-8/Pt sensor, the existence of small Pt NPs in ZIF-8 cavities can effectively accelerate the absorption of H2S, capture electrons from the N site of ZIF-8, and donate the electron to the S site of H2S, as indicated by density functional theory simulations, leading to a significantly increased response to H2S. Together with the molecular-sieving effect that ZIF-8 exerts on gas molecules with larger kinetic diameters, the 3DIO ZnO@ZIF-8/Pt sensor exhibits a high response to H2S (118–5.5 ppm), a detection limit of 40 ppb, and importantly, a 59-fold higher selectivity to H2S against typical interference gases. In addition, the 3DIO ZnO@ZIF-8/Pt sensor is developed as a multi-platform sensor to evaluate trace concentrations of H2S in meat quality assessment, halitosis diagnosis, and automobile exhaust assessment.

Chemiresistors Based on Li-Doped CuO–TiO2 Films

Chemiresistors based on thin films of the Li-doped CuO–TiO2 heterojunctions were synthesized by a 2-step method: (i) repeated ion beam sputtering of the building elements (on the Si substrates and multisensor platforms); and (ii) thermal annealing in flowing air. The structure and composition of the films were analyzed by several methods: Rutherford Backscattering (RBS), Neutron Depth Profiling (NDP), Secondary Ion Mass Spectrometry (SIMS), and Atomic Force Microscopy (AFM), and their sensitivity to gaseous analytes was evaluated using a specific lab-made device operating in a continuous gas flow mode. The obtained results showed that the Li doping significantly increased the sensitivity of the sensors to oxidizing gases, such as NO2, O3, and Cl2, but not to reducing H2. The sensing response of the CuO–TiO2–Li chemiresistors improved with increasing Li content. For the best sensors with about 15% Li atoms, the detection limits were as follows: NO2 → 0.5 ppm, O3 → 10 ppb, and Cl2 → 0.1 ppm. The Li-doped sensors showed excellent sensing performance at a lower operating temperature (200 °C); however, even though their response time was only a few minutes, their recovery was slow (up to a few hours) and incomplete.

Direct prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping

Liming agricultural fields is necessary for counteracting soil acidity and is one of the oldest operations in soil fertility management. However, the best management practice for liming in Germany only insufficiently considers within-field soil variability. Thus, a site-specific variable rate liming strategy was developed and tested on nine agricultural fields in a quaternary landscape of north-east Germany. It is based on the use of a proximal soil sensing module using potentiometric, geoelectric and optical sensors that have been found to be proxies for soil pH, texture and soil organic matter (SOM), which are the most relevant lime requirement (LR) affecting soil parameters. These were compared to laboratory LR analysis of reference soil samples using the soil’s base neutralizing capacity (BNC). Sensor data fusion utilizing stepwise multi-variate linear regression (MLR) analysis was used to predict BNC-based LR (LRBNC) for each field. The MLR models achieved high adjusted R2 values between 0.70 and 0.91 and low RMSE values from 65 to 204 kg CaCO3 ha−1. In comparison to univariate modeling, MLR models improved prediction by 3 to 27% with 9% improvement on average. The relative importance of covariates in the field-specific prediction models were quantified by computing standardized regression coefficients (SRC). The importance of covariates varied between fields, which emphasizes the necessity of a field-specific calibration of proximal sensor data. However, soil pH was the most important parameter for LR determination of the soils studied. Geostatistical semivariance analysis revealed differences between fields in the spatial variability of LRBNC. The sill-to-range ratio (SRR) was used to quantify and compare spatial LRBNC variability of the nine test fields. Finally, high resolution LR maps were generated. The BNC-based LR method also produces negative LR values for soil samples with pH values above which lime is required. Hence, the LR maps additionally provide an estimate on the quantity of chemically acidifying fertilizers that can be applied to obtain an optimal soil pH value.

Investigation of the Spatio-Temporal Behaviour of Submarine Groundwater Discharge Using a Low-Cost Multi-Sensor-Platform

Submarine groundwater discharge (SGD) is an important pathway of nutrients into coastal areas. During the last decades, interest of researchers in SGDs has grown continuously. However, methods applied for SGD research usually focus on the aquifer or on the mixing processes on larger scales. The distribution of discharged water within the water column is not well investigated. Small remotely operated vehicles (ROV) equipped with environmental sensors can be used to investigate the spatial distribution of environmental parameters in the water column. Herein, a low-cost multi-sensor platform designed to investigate the spatial distribution of water quality properties is presented. The platform is based on an off-the-shelf underwater vehicle carrying various environmental sensors and a short-baseline localisation system. This contribution presents the results of SGD investigations in the area of Woodman Point (Western Australia). Various potential SGD plumes were detected using a skiff equipped with a recreational echo sounder. It was demonstrated that this inexpensive equipment could be used to detect and investigate SGDs in coastal areas. In addition, the low-cost multi-sensor platform was deployed to investigate the spatial distribution of environmental parameters including temperature (T), electric conductivity (EC), dissolved oxygen (DO), oxidation-reduction potential (ORP), pH, and dissolved organic matter fluorescence (FDOM). Three ROV surveys were conducted from different skiff locations. Analyses of the spatial distribution of the environmental parameters allowed the identification of nine potential SGD plumes. At the same locations, plumes were identified during the sonar surveys. In addition, fuzzy logic was used for the fusion of salinity, DO, and FDOM readings in order to enhance SGD detection capability of the designed multi-sensor system. The fuzzy logic approach identified 293 data points as potential within a SGD plume. Average minimum-distance between these points and the identified SGD plumes was 0.5 m and 0.42 m smaller than the minimum-distance average of the remaining data points of survey one and three respectively. It was shown that low-cost ROVs, equipped with environmental sensors, could be an important tool for the investigation of the spatio-temporal behaviour of SGD sites. This method allows continuous mapping of environmental parameters with a high spatial and temporal resolution. However, to obtain deeper insights into the influence of SGDs on the nearshore areas, this method should be combined with other well-established methods for SGD investigation, such as pore water sampling, remote sensing, or groundwater monitoring.

COOPERATIVE LOCALISATION USING IMAGE SENSORS IN A DYNAMIC TRAFFIC SCENARIO

Abstract. Localisation is one of the key elements in navigation. Especially due to the development in automated driving, precise and reliable localisation becomes essential. In this paper, we report on different cooperation approaches in visual localisation with two vehicles driving in a convoy formation. Each vehicle is equipped with a multi-sensor platform consisting of front-facing stereo cameras and a global navigation satellite system (GNSS) receiver. In the first approach, the GNSS signals are used as excentric observations for the projection centres of the cameras in a bundle adjustment, whereas the second approach uses markers on the front vehicle as dynamic ground control points (GCPs). As the platforms are moving and data acquisition is not synchronised, we use time dependent platform poses. These time dependent poses are represented by trajectories consisting of multiple 6 Degree of Freedom (DoF) anchor points between which linear interpolation takes place. In order to investigate the developed approach experimentally, in particular the potential of dynamic GCPs, we captured data using two platforms driving on a public road at normal speed. As a baseline, we determine the localisation parameters of one platform using only data of that platform. We then compute a solution based on image and GNSS data from both platforms. In a third scenario, the front platform is used as a dynamic GCP which can be related to the trailing platform by markers observed in the images acquired by the latter. We show that both cooperative approaches lead to significant improvements in the precision of the poses of the anchor points after bundle adjustment compared to the baseline. The improvement achieved due to the inclusion of dynamic GCPs is somewhat smaller than the one due to relating the platforms by tie points. Finally, we show that for an individual vehicle, the use of dynamic GCPs can compensate for the lack of GNSS data.

DEVELOPMENT OF A LOW-COST, HAND-HELD MULTI-SENSOR SYSTEM FOR THE MONITORING OF SMALL WATER BODIES

Abstract. For small-scale monitoring of small water bodies, conventional methods such as GNSS or total station measurements are used. The data acquisition is usually carried out in profile form supplemented with extra measurements of break edges, slope edges or bank courses. However, these methods can be used efficiently only on small sections and with low temporal resolution. At the same time, as the length of rivers or creeks to be monitored increases, the cost-effectiveness of the above methods decreases. Further limitations such as very small sections that are difficult to access and also sections that are sometimes heavily overgrown also prevent the use of large measuring platforms. By contrast, with use of a hand-held compact multi-sensor platform it is possible to survey several hundred kilometres of the smallest rivers and creeks. This publication demonstrates the use of such a platform to record micro-watersheds. For this purpose, the Creek4D project, the measurement principle and the sensor technology used are shown. In addition, first measurement data and the calibration strategy are shown.

MRI Compatible Fiber Optic Multi Sensor Platform for Real Time Vital Monitoring

Microsystems sensors are implemented on a fiber optics based platform towards use in magnetic resonance imaging assisted interventions. The presented platform offers real-time and in-situ pressure, temperature, and localization feedback during imaging assisted surgical procedures. This multi-sensor platform fits into an interventional medical device with a diameter of 2.5 mm. A polymer cap with perforation is utilized for circulation of blood in the vessel to allow correct monitoring of the temperature and pressure in real time. Graded Index (GRIN) lenses were utilized at the tip of the fiber cables improved optical signal collection efficiency. Three light sources having different wavelengths were utilized each for one sensor; a 637 nm laser, a 780 nm laser, and a 875 nm LED (50 nm bandwidth) for pressure, localization, and temperature sensors, respectively. Pressure sensing is performed by a released polymer-metal hybrid membrane having a diffraction grating interferometer readout scheme. Temperature sensing is accomplished based on the change of absorption and transmission in semiconductors (i.e. GaAs) due to variations of energy bandgap with temperature. Localization of the medical device is acquired based on magneto-optical Kerr effect where Iron Oxide magnetic material is employed. The sensor chip revealed a temperature precision of ±0.22 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> C, a pressure resolution of 1 mmHg and a localization resolution of 3 mm, where all values are relevant to medical practice. Integration of the sensors developed in this study helps medical devices such as catheters and stents, allow a new horizon in interventional surgery.

Affinity between Bitumen and Aggregate in Hot Mix Asphalt by Hyperspectral Imaging and Digital Picture Analysis

This study investigated the viability of quantifying the affinity between aggregate and bitumen by means of different imaging techniques. Experiments were arranged in accordance with the rolling-bottle test, as indicated in UNI EN 12697-11, “Test methods for hot bituminous conglomerates—Part 11”. Digital image processing (DIP) techniques have only recently been used for such quantification. The data gathered with a multi-sensor optical platform equipped with VIS–NIR and SWIR spectrometers were compared with DIP outcomes. Data were processed using the unsupervised ISODATA and the supervised parallelepiped algorithms. The exposed aggregate index (EAI) and the bitumen index (BIT) were calculated to retrieve the bitumen percentage coverage of different mixtures. The comparison with the results obtained employing the traditional 6, 24, 48 and 72 testing hours reveals the possibility to implement a standardized analysis methodology combining digital and hyperspectral imagery to highlight potential inaccuracies deriving from the visual interpretation.

BeSafe B2.0 Smart Multisensory Platform for Safety in Workplaces.

Wearable technologies are becoming a profitable means of monitoring a person’s health state, such as heart rate and physical activity. The use of the smartwatch is becoming consolidated, not only as a novelty but also as a very useful tool for daily use. In addition, other devices, such as helmets or belts, are beneficial for monitoring workers and the early detection of any anomaly. They can provide valuable information, especially in work environments, where they help reduce the rate of accidents and occupational diseases, which makes them powerful Personal Protective Equipment (PPE). The constant monitoring of the worker’s health can be done in real-time, through temperature, falls, noise, impacts, or heart rate meters, activating an audible and vibrating alarm when an anomaly is detected. The gathered information is transmitted to a server in charge of collecting and processing it. In the first place, this paper provides an exhaustive review of the state of the art on works related to electronics for human activity behavior. After that, a smart multisensory bracelet, combined with other devices, developed a control platform that can improve operators’ security in the working environment. Artificial Intelligence and the Internet of Things (AIoT) bring together the information to improve safety on construction sites, power stations, power lines, etc. Real-time and historic data is used to monitor operators’ health and a hybrid system between Gaussian Mixture Model and Human Activity Classification. That is, our contribution is also founded on the use of two machine learning models, one based on unsupervised learning and the other one supervised. Where the GMM gave us a performance of 80%, 85%, 70%, and 80% for the 4 classes classified in real time, the LSTM obtained a result under the confusion matrix of 0.769, 0.892, and 0.921 for the carrying-displacing, falls, and walking-standing activities, respectively. This information was sent in real time through the platform that has been used to analyze and process the data in an alarm system.

Camera-LIDAR Integration: Probabilistic Sensor Fusion for Semantic Mapping

An automated vehicle operating in an urban environment must be able to perceive and recognise objects and obstacles in a three-dimensional world for navigation and path planning. In order to plan and execute accurate and sophisticated driving maneuvers, a high-level contextual understanding of the surroundings is essential. Due to the recent progress in image processing, it is now possible to obtain high definition semantic information in 2D from monocular cameras, though cameras cannot reliably provide the high accuracy 3D information provided by lasers. The fusion of these two sensor modalities can overcome the shortcomings of each individual sensor, though there are a number of important challenges that need to be addressed in a probabilistic manner. In this paper we address the common, yet challenging, LIDAR/camera/semantic fusion problems which are seldom approached in a wholly probabilistic manner. Our approach is capable of using a multi-sensor platform to build a three-dimensional semantic voxelized map that considers the uncertainty of all of the processes involved. We present a probabilistic pipeline that incorporates uncertainty from the sensor readings (cameras, LIDAR, IMU and wheel encoders), compensation for the motion of the vehicle, and heuristic label probabilities for the semantic images depicted in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fig. 1</xref> . We also present a novel and efficient viewpoint validation algorithm to check for occlusions within the camera frame. A probabilistic projection is performed from the camera images to the LIDAR point cloud. Each labelled LIDAR scan then feeds into an octree map-building algorithm that updates the class probabilities of the map voxels every time a new observation is available. We validate our approach using a set of qualitative and quantitative experiments using the USyd Campus Dataset. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">¹</xref> These tests demonstrate the usefulness of a probabilistic sensor fusion approach by evaluating the performance of the perception system in a typical autonomous vehicle application.

Fractional Gabor Convolutional Network for Multisource Remote Sensing Data Classification

Remote sensing using multisensor platforms has been systematically applied for monitoring and optimizing human activities. Several advanced techniques have been developed to enhance and extract the spatially and spectrally semantic information in the hyperspectral image (HSI) and light detection and ranging (LiDAR) data processing and analysis. However, an abundance of redundant information and sometimes a lack of discriminative features reduce the efficiency and effectiveness of multisource classification methods. This article proposes a fractional Gabor convolutional network (FGCN), focusing on efficient feature fusion and comprehensive feature extraction. First, the proposed FGCN uses Octave convolution layers to perform multisource information fusion and preserve discriminative information. Second, fractional Gabor convolutional (FGC) layers are proposed to extract multiscale, multidirectional, and semantic change features. The completeness and discrimination of the multisource features using different FGC kernels are improved, which yield robust feature extraction against semantic changes. Finally, the fractional Gabor feature and spectral feature are combined with two weighting factors which can be learned during the network training. Experimental results and comparisons with state-of-the-art multisource classification methods indicate the effectiveness of the proposed FGCN. With the FGCN, we can obtain an 89.90% overall accuracy on the challenging Muufl Gulfport (MUUFL) data set, with an improvement of 3% over state-of-the-art methods.

Ultra-high response ethanol sensors from fully-printed co-continuous and mesoporous tin oxide thin films

Printed vapor and moisture sensors are essential components of the multi-sensor platforms that the pharmaceutical and food safety industries require in large quantities; however, fully-printed gas or volatile organic compounds (VOCs) sensors have rarely been reported in the literature. In this regard, we demonstrate the fabrication of high surface-to-volume ratio co-continuous mesoporous tin oxide based fully-printed chemiresistive-type ethanol gas sensors. Herein, a soft templating method that mimic evaporation induced self-assembly (EISA) process has been utilized with amphiphilic triblock co-polymer pluronic F127 (PEO106-PPO70-PEO106) as the templating agent and a low-cost swelling agent ´xylene´ as the micelle expander to obtain pore diameter in the range of 10–25 nm. The tuned pore size at this range is found optimal for high surface area and high pore volumes, at the same time. The fully-printed ethanol sensors fabricated with such mesoporous SnO2 active elements show highly selective sensitivity towards ethanol with an average response of 1050 for 100 ppm and a very short response and recovery time of 9 and 129 s, respectively. The observed high response can be attributed to the high density and easily accessible active sites and simultaneous high mobility electron transport through the well-crystalline and co-connected tin oxide ligaments.

In situ synthesis of fluorescent polydopamine polymer dots based on Fenton reaction for a multi-sensing platform.

The development of fluorescent nanosensors has attracted extensive research interest owing to their superior optoelectronic properties. However, current fluorescent nanoprobes generally involve complicated synthesis processes, background signal disturbance, and limited analyte detection. In this work, a facile and time-saving synthetic strategy for the preparation of green emitting polydopamine polymer dots (PDA-PDs) from dopamine via Fenton reaction at room temperature was proposed for the first time. The obtained PDA-PDs possessed excellent luminescence properties, with a long-wavelength emission of 522 nm, a large Stokes shift of 142 nm, and good photostability against ionic strength and UV irradiation. The formation mechanism of fluorescent PDA-PDs is as follows: in the presence of Fe2+ and H2O2, dopamine could rapidly undergo oxidation to its quinone derivatives and further polymerize to synthesize the fluorescent PDA-PDs with the acceleration of hydroxyl radicals produced from the Fenton reaction. Thus, a versatile turn-on fluorescence sensing method was developed for the detection of multi-analytes (including Fe2+, dopamine, H2O2, and glucose) based on monitoring the intrinsic fluorescence signal of the in situ formation of PDA-PDs. This sensing method could be efficiently applied for the detection of Fe2+, dopamine, and glucose in real human serum samples. Moreover, a three-input AND molecular logic gate based on this sensing platform was designed with the fluorescence signal of PDA-PDs as the gate. Finally, the proposed PDA-PDs could have immense broad prospects in nanomaterials and biosensors.

Fully Inkjet-Printed Mesoporous SnO2-Based Ultrasensitive Gas Sensors for Trace Amount NO2 Detection.

Printed sensors are among the most successful groups of devices within the domain of printed electronics, both in terms of their application versatility and the emerging market share. However, reports on fully printed gas sensors are rare in the literature, even though it can be an important development toward fully printed multisensor platforms for diagnostics, process control, and environmental safety-related applications. In this regard, here, we present the traditional tin oxide-based completely inkjet-printed co-continuous and mesoporous thin films with an extremely large surface-to-volume ratio and then investigate their NO2 sensing properties at low temperatures. A method known as evaporation-induced self-assembly (EISA) has been mimicked in this study using pluronic F127 (PEO106-PPO70-PEO106) as the soft templating agent and xylene as the micelle expander to obtain highly reproducible and spatially homogeneous co-continuous mesoporous crystalline SnO2 with an average pore diameter of the order of 15-20 nm. The fully printed SnO2 gas sensors thus produced show high linearity for NO2 detection, along with extremely high average response of 11,507 at 5 ppm NO2. On the other hand, the sensors show an ultralow detection limit of the order of 20 ppb with an easy to amplify response of 31. While the excellent electronic transport properties along such co-continuous, mesoporous structures are ensured by their well-connected (co-continuous) ligaments and pores (thereby ensuring high surface area and high mobility transport at the same time) and may actually be responsible for the outstanding sensor performance that has been observed, the use of an industrial printing technique ascertains the possibility of high-throughput manufacturing of such sensor units toward inexpensive and wide-range applications.