Emulsions stabilized by nanofibers from bacterial cellulose: New potential food-grade Pickering emulsions

In the present work, we investigated the formation and stability of Pickering emulsions stabilized by nanoparticles generated from bacterial cellulose (BC) by hydrochloric acid hydrolysis. The resulting particles, called nanofibers, presented a ribbonlike shape with diameters of 30–80 nm and range in length from 100 nm to several micrometers. The obtained nanofibers showed good hydrophilic and lipophilic properties and had significant ability to reduce the surface tension of oil/water droplets from 48.55 ± 0.03 to 34.52 ± 0.05 mN/m. The oil-in-water Pickering emulsions with a peanut oil concentration of 15% (v/v) were stabilized by only 0.05% (w/v) nanofibers and displayed a narrow droplet size distribution and high intensity with an average droplet size of 15.00 ± 0.82 nm. The morphological studies confirmed the nano-scaled droplets of emulsions. The effects of pH values and temperatures on the creaming ability and physical stability were also evaluated by zeta-potential and droplet sizes. Results showed that emulsions displayed relatively lower creaming ability at pH < 7, while displayed optimal physical stability and dispersibility at pH ≥ 7. The temperature (20–100 °C) and time-dependent test (0–4 weeks) indicated that the Pickering emulsions stabilized by only 0.05% (w/v) nanofibers displayed excellent stability. Due to the sustainability and good bio-compatibility of nanofibers from BC, the developed emulsions stabilized by low concentration of nanofibers can be used as new food-grade Pickering emulsions and have great potential to deliver lipophilic bioactive substances in food industry

Design and Performance Evaluation of a Wearable Sensing System for Lower-Limb Exoskeleton

Because the target users of the assistive-type lower extremity exoskeletons (ASLEEs) are those who suffer from lower limb disabilities, customized gait is adopted for the control of ASLEEs. However, the customized gait is unable to provide stable motion for variable terrain, for example, flat, uphill, downhill, and soft ground. The purpose of this paper is to realize gait detection and environment feature recognition for AIDER by developing a novel wearable sensing system. The wearable sensing system employs 7 force sensors as a sensing matrix to achieve high accuracy of ground reaction force detection. There is one more IMU sensor that is integrated into the structure to detect the angular velocity. By fusing force and angular velocity data, four typical terrain features can be recognized successfully, and the recognition rate can reach up to 93%

Phenothiazine derivatives for efficient organic dye-sensitized solar cells

Novel org. dyes based on the phenothiazine (PTZ) chromophore were designed and synthesized for dye-sensitized solar cells, which give solar energy-to-electricity conversion efficiency (η)of up to 5.5% in comparison with the ref. Ru-complex (N3 dye) with an η value of 6.2% under similar exptl. conditions

A novel analysis method to determine the surface recombination velocities on unequally passivated surfaces of a silicon wafer by the short wavelength spectrum excited quasi-steady-state photoconductance measurement

In this work, we propose an analysis approach to determine the individual surface recombination velocities (S1 and S2) on each surface of an unequally passivated wafer, which precludes the crude assumption of S1=S2 in conventional methods. Taking advantage of the surface distributed excess charge carriers relatively sensitive to the surface recombination, we probe the sample using quasi-steady-state illumination of the xenon flash lamp equipped with a short pass filter (FSP1). A set of samples passivated by SiO2 and SiNx, as well as bare silicon wafers, are prepared in the experiment. On the basis of fitting the measured time-dependent-excess charge carriers, S1 and S2 are determined based on our analysis approach. The spatial and the temporal distributions of excess charge carrier density are presented. The dependence of τeff on the wavelength, S and τbulk is also discussed in detail. The reliability of this method is finally verified with a long pass filter (FLP2)

Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm

The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton