A Dielectric Elastomer-Based Multimodal Capacitive Sensor

Dielectric elastomer (DE) sensors have been widely used in a wide variety of applications, such as in robotic hands, wearable sensors, rehabilitation devices, etc. A unique dielectric elastomer-based multimodal capacitive sensor has been developed to quantify the pressure and the location of any touch simultaneously. This multimodal sensor is a soft, flexible, and stretchable dielectric elastomer (DE) capacitive pressure mat that is composed of a multi-layer soft and stretchy DE sensor. The top layer measures the applied pressure, while the underlying sensor array enables location identification. The sensor is placed on a passive elastomeric substrate in order to increase deformation and optimize the sensor’s sensitivity. This DE multimodal capacitive sensor, with pressure and localization capability, paves the way for further development with potential applications in bio-mechatronics technology and other humanoid devices. The sensor design could be useful for robotic and other applications, such as fruit picking or as a bio-instrument for the diabetic insole.

pH-Regulated Carbon Dots Fluorescence Sensor Array Detection of Multiple Heavy Metal Ions Under Stepwise Prediction

As a large amount of heavy metals leaches into water sources from industrial effluents, heavy metal pollution has become an important factor affecting water quality. To enable the detection of multiple heavy metals, we constructed a pH-regulation fluorescence sensor array. Firstly, by adding a metal chelating agent as receptor, metal ions and carbon quantum dots (CDs) were connected to distinguish between Cr6+, Fe3+, Fe2+, and Hg2+ ions. Thus, the lack of affinity between the indicator functional groups and the analyte was solved. Secondly, by adjusting the pH environment of the solution system, an economical and simple array sensing platform is established, which effectively simplified the array construction. In this study, the SX-model was used in the field of fluorescence sensor array detection for metal ion recognition. Based on the strategy of stepwise prediction, combined with the classification and concentration models, the bottleneck of the unified model in previous studies was broken. This sensor array demonstrated sensitive detection of four heavy metal ions within a concentration range from 1 to 50 µM, with an accuracy of 95.45%. Moreover, it displayed the ability to efficiently identify binary mixed samples with an accuracy of 95.45%. Furthermore, metal ions in 15 real samples (lake water) were effectively discriminated with 100% accuracy. A chelating agent was used to improve the sensitivity of heavy metal ion detection and eventually led to high-precision prediction using the SX-model.

Cuffless blood pressure monitoring from a wristband with calibration-free algorithms for sensing location based on bio-impedance sensor array and autoencoder

Continuous monitoring of blood pressure (BP) is essential for the prediction and the prevention of cardiovascular diseases. Cuffless BP methods based on non-invasive sensors integrated into wearable devices can translate blood pulsatile activity into continuous BP data. However, local blood pulsatile sensors from wearable devices suffer from inaccurate pulsatile activity measurement based on superficial capillaries, large form-factor devices and BP variation with sensor location which degrade the accuracy of BP estimation and the device wearability. This study presents a cuffless BP monitoring method based on a novel bio-impedance (Bio-Z) sensor array built in a flexible wristband with small-form factor that provides a robust blood pulsatile sensing and BP estimation without calibration methods for the sensing location. We use a convolutional neural network (CNN) autoencoder that reconstructs an accurate estimate of the arterial pulse signal independent of sensing location from a group of six Bio-Z sensors within the sensor array. We rely on an Adaptive Boosting regression model which maps the features of the estimated arterial pulse signal to systolic and diastolic BP readings. BP was accurately estimated with average error and correlation coefficient of 0.5 ± 5.0 mmHg and 0.80 for diastolic BP, and 0.2 ± 6.5 mmHg and 0.79 for systolic BP, respectively.

Drift Compensation of the Electronic Nose in the Development of Instruments for Out-of-Laboratory Analysis

A technique was developed to evaluate and compensate for the drift of eight mass-sensitive sensors in an open detection cell in order to estimate the influence of external factors (temperature, changes in the chemical composition of the background) on the out-of-laboratory analysis of biosamples. The daily internal standardization of the system is an effective way to compensate for the sensor signal drift when the sorption properties of sensitive coatings change during their long-term, intensive operation. In this study, distilled water was proposed as a standard for water matrix-based biosamples (blood, exhaled breath condensate, urine, etc.). Further, internal standardization was based on daily calculation of the specific sensor signals by dividing the sensor signals for the biosample according to the corresponding averaged values obtained from three to five standard measurements. The stability of the sensor array operation was estimated using the theory of statistical process control (exponentially weighted moving average control charts) based on the specific signal of the sensor array. The control limits for the statistical quantity of the central tendency for each sensor and the whole array, as well as the variations of the sensor signals, were determined. The average times required for signal and run lengths, for the purpose of statistically substantiated monitoring of the electronic nose’s stability, were calculated. Based on an analysis of the tendency and variations in sensor signals during 3 months of operation, a technique was formulated to control the stability of the sensor array for the out-of-laboratory analysis of the biosamples. This approach was successfully verified by classifying the results of the analysis of the blood and water samples obtained for this period. The proposed technique can be introduced into the software algorithm of the electronic nose, which will improve decision-making during the long-term monitoring of health conditions in humans and animals.