Highly Stretchable and Robust Electrochemical Sensor Based on 3D Graphene Oxide-CNT Composite for Detecting Ammonium in Sweat.

Wearable electrochemical sensors have attracted tremendous attention and have been experiencing rapid growth in recent years. Sweat, one of the most suitable biological fluids for non-invasive monitoring, contains various chemical elements relating abundant information about human health conditions. In this work, a new type of non-invasive and highly stretchable potentiometric sweat sensor was developed based on all-solid-state ion-selective electrode (ISE) coupled with poly(dimethylsiloxane; PDMS) and polyurethane (PU). This highly stretchable composite of PDMS-PU allows the sensor to be robust, with the PDMS providing a flexible backbone and the PU enhancing the adhesion between the electrodes and the substrate. In addition, graphene-carbon nanotube (CNT) network 3D nanomaterials were introduced to modify the ion selective membrane (ISM) in order to increase the charge transfer activity of the ISEs, which also could minimize the formation of water layers on the electrode surface, as such nanomaterials are highly hydrophobic. As a result, the sensor demonstrated a wide detection range of NH4+ from 10-6 M to 10-1 M with high stability and sensitivity-showing a high sensitivity of 59.6 ± 1.5 mV/log [NH4+] and an LOD lower than 10-6 M. Under a strain of 40%, the sensor still showed a sensitivity of 42.7 ± 3.1 mV/log [NH4+]. The proposed highly stretchable and robust electrochemical sweat sensor provides a new choice for wearable-device-based personal daily healthcare management beyond hospital-centric healthcare monitoring.

[Research on spectrum feature of speech processing strategy for cochlear implant].

Cochlear implant (CI) in present Chinese environment will lose pitch information and result in low speech recognition. In order to research Chinese feature-based speech processing strategy for cochlear implant contrapuntally and to improve the speech recognition for CI recipients, we improve the CI front-end signal acquisition platform and research the signal features. Our search includes the waveform, spectrogram, energy intensity, pitch and formant parameters for different speech processing strategies of cochlear implant. Features in two kinds of speech processing strategies are analyzed and extracted for the study of parameter characteristics. Therefore, the proposed aim of this paper is to extend the research on Chinese-based CI speech processing strategy.