It is difficult to achieve high efficiency production of hydrophobic graphene by liquid phase exfoliation due to its poor dispersibility and the tendency of graphene sheets to undergo π−π stacking. Here, we report a water-phase, non-dispersion exfoliation method to produce highly crystalline graphene flakes, which can be stored in the form of a concentrated slurry (50 mg mL−1) or filter cake for months without the risk of re-stacking. The as-exfoliated graphene slurry can be directly used for 3D printing, as well as fabricating conductive graphene aerogels and graphene−polymer composites, thus avoiding the use of copious quantities of organic solvents and lowering the manufacturing cost. This non-dispersion strategy paves the way for the cost-effective and environmentally friendly production of graphene-based materials.

Constructing flexible, high-sensitivity strain sensors with large working ranges is an urgent task in view of their widespread applications, including human health monitoring. Herein, we propose a self-compensated two-order structure strategy to significantly enhance the sensitivity and workable range of strain sensors. Three-dimensional printing was employed to construct highly stretchable, conductive polymer composite open meshes, in which the percolation network of graphene sheets constitutes a deformable conductive path. Meanwhile, the graphene layer coated on the open mesh provides an additional conductive path that can compensate spontaneously for the conductivity loss of the percolation network at large strains, through new conductive paths formed by the graphene sheets in the coating layer and the inner networks. At strains lower than 20%, the sliding and disconnection of graphene sheets coated on the mesh surface largely enhance the sensitivity of the sensor, a 20 times increase as opposed to that of the non-two-order structure sensor. The resulting sensor reveals high gauge factors (from 18.5 to 88 443) in a strain range of 0–350% and the exceptional capability to monitor a wide range of human motions, from the subtle pulse, acoustic vibration to breathing and arm bending.

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For infants admitted at neonatal intensive care unit, the continuous monitoring of health parameters is critical for their optimal treatment and outcomes. So it's crucial to provide proper treatment, accurate and comfortable monitoring conditions for newborn infants. In this paper, we propose wearable sensor systems integrated with flexible material based non-invasive sensors for neonatal monitoring. The system aims at providing reliable vital signs monitoring as well as comfortable clinical environments for neonatal care. The system consists of a smart vest and a cloud platform. In the smart vest, a novel stretching sensor based on Polydimethylsiloxane-Graphene (PDMS-Graphene) compound is created to detect newborns' respiration signal; textile-based dry electrodes are developed to measure Electrocardiograph (ECG) signals; inertial measurement units (IMUs) are embedded to obtain movement information including accelerated speed and angular velocity of newborn wrists. Experiments were conducted to systematically test the sensing related characteristics of the aforementioned flexible materials and the performance of the proposed multi-sensor platform. The results show that the proposed system can achieve high quality signals. The wearable sensor platform is promising for continuous long term monitoring of neonates. The multi-modal physiological and behavioral signals measured by the platform can be further processed for clinical decision support on the neonatal health status.

State estimation suffers some new challenging problems when wireless sensor networks (WSNs) are used in mobile target tracking systems. An important problem among them is low observability, which makes it necessary to further study performance of tracking estimators from an observable degree point of view. This paper studies observable degree analysis (ODA) to formulate the estimator performance for a kind of wireless tracking filter. For a kind of time-varying Kalman estimator, an improved ODA method is proposed by using weighted least square, Cauchy Schwarz inequality and normalization processing. The main contributions are that the relation is firstly analytically established between the observable degree and the estimation performance, and finally the observable degree is normalized in [0,1]. Meanwhile, observable degrees are both given for local state components and the global state. Thereby, we have the conclusion that higher observable degree corresponds to better estimation performance of the Kalman filter. Accordingly, it is potentially hopeful to achieve an effective function to study estimation performance of tracking estimators by directly using the observable degree. Simulation is provided to verify the results on real-time and generality, completeness and consistency/matching of the proposed observable degree analysis.