Nanostructured Substrate-Mediated Bubble Degassing in Microfluidic Systems.

Microfluidic platforms have been widely used in a variety of fields owing to their numerous advantages. The prevention and prompt removal of air bubbles from microchannels are important to ensuring the optimal functioning of microfluidic devices. The entrapment of bubbles in the microchannels can result in flow instability and device performance disruption. Active and passive methods are the primary categories of degassing technologies. Active methods rely on external equipment, and passive methods operate autonomously without any external sources. This study proposed a passive degassing method that employs a nanoscale surface morphology integrated into the substrate of a microfluidic device. Nanostructures exhibit a microchannel geometry and are fabricated based on surface micromachining technology using silver ink and chemical etching. Consequently, the gas permeability is enhanced, resulting in effective degassing through the nanostructure. The performance of this degassing method was characterized under varying substrate permeabilities and input pressure conditions, and it was found that increased permeability facilitates the degassing performance. Furthermore, the applicability of our method was demonstrated by using a serpentine channel design prone to gas entrapment, particularly in the corner regions. The nanostructured substrate exhibited significantly improved degassing performance under the given pressure conditions in comparison to the glass substrate.

Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures.

The quantification of comfort in binding parts, essential human-machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.

Nanoplastics from disposable paper cups and microwavable food containers.

Nanoplastics (NPs, <1 µm) pose greater risks due to their increased absorption rates in biological systems. In this study, we investigated the release of NPs from paper cups and microwavable food containers coated with low-density polyethylene (LDPE) and polylactic acid (PLA). For disposable paper cups, we found that LDPE-coated cups released up to 26-fold more NPs (maximum 1.9 × 107 per cup) than PLA-coated ones. The NPs release from LDPE-coated cups was increased at high temperatures above 80 °C, and further increased by physical agitation. However, negligible NP release was observed when the inner coating thickness exceeded 1 mm. For microwavable food containers, those with PLA coatings were more susceptible to the effects of microwave. Depending on the cooking time, we noticed a significant difference (up to 40000 times) in the number of released NPs between LDPE and PLA coatings. Additionally, higher microwave power level led to an increase of NPs, even with constant total energy input. Considering the release of NP, PLA coatings for disposable paper cups and LDPE coatings for microwavable food containers seem more suitable. Furthermore, our results suggest that multi-use cups significantly reduce NPs release due to their material thickness, making them a safer alternative to disposable ones.

Quantification of Comfort for the Development of Binding Parts in a Standing Rehabilitation Robot.

Human-machine interfaces (HMI) refer to the physical interaction between a user and rehabilitation robots. A persisting excessive load leads to soft tissue damage, such as pressure ulcers. Therefore, it is necessary to define a comfortable binding part for a rehabilitation robot with the subject in a standing posture. The purpose of this study was to quantify the comfort at the binding parts of the standing rehabilitation robot. In Experiment 1, cuff pressures of 10-40 kPa were applied to the thigh, shank, and knee of standing subjects, and the interface pressure and pain scale were obtained. In Experiment 2, cuff pressures of 10-20 kPa were applied to the thigh, and the tissue oxygen saturation and the skin temperature were measured. Questionnaire responses regarding comfort during compression were obtained from the subjects using the visual analog scale and the Likert scale. The greatest pain was perceived in the thigh. The musculoskeletal configuration affected the pressure distribution. The interface pressure distribution by the binding part showed higher pressure at the intermuscular septum. Tissue oxygen saturation (StO2) increased to 111.9 ± 6.7% when a cuff pressure of 10 kPa was applied and decreased to 92.2 ± 16.9% for a cuff pressure of 20 kPa. A skin temperature variation greater than 0.2 °C occurred in the compressed leg. These findings would help evaluate and improve the comfort of rehabilitation robots.

Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites.

Workers at construction sites are prone to fall-from-height (FFH) accidents. The severity of injury can be represented by the acceleration peak value. In the study, a risk prediction against FFH was made using IMU sensor data for accident prevention at construction sites. Fifteen general working movements (NF: non-fall), five low-hazard-fall movements, (LF), and five high-hazard-FFH movements (HF) were performed by twenty male subjects and a dummy. An IMU sensor was attached to the T7 position of the subject to measure the three-axis acceleration and angular velocity. The peak acceleration value, calculated from the IMU data, was 4 g or less in general work movements and 9 g or more in FFHs. Regression analysis was performed by applying various deep learning models, including 1D-CNN, 2D-CNN, LSTM, and Conv-LSTM, to the risk prediction, and then comparing them in terms of their mean absolute error (MAE) and mean squared error (MSE). The FFH risk level was estimated based on the predicted peak acceleration. The Conv-LSTM model trained by MAE showed the smallest error (MAE: 1.36 g), and the classification with the predicted peak acceleration showed the best accuracy (97.6%). This study successfully predicted the FFH risk levels and could be helpful to reduce fatal injuries at construction sites.

sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups.

Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC (r > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly.

The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.

In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, increase in the number of training data, and additional training with external data). Three-axis acceleration and angular velocity data were obtained from 30 healthy male subjects by attaching an IMU to the middle of the left and right anterior superior iliac spines (ASIS). Internal and external tests were performed using our lab dataset and SisFall public dataset, respectively. The results showed that ANN and SVM were suitable for the time-series and discrete data, respectively. The classification performance generally decreased, and thus, specific feature vectors from the raw data were necessary when untrained motions were tested using a public dataset. Normalization made SVM and ANN more and less effective, respectively. Equalization increased the sensitivity, even though it did not improve the overall performance. The increase in the number of training data also improved the classification performance. Machine learning was vulnerable to untrained motions, and data of various movements were needed for the training.

Detection of Pre-Impact Falls from Heights Using an Inertial Measurement Unit Sensor.

Many safety accidents can occur in industrial sites. Among them, falls from heights (FFHs) are the most frequent accidents and have the highest fatality rate. Therefore, some existing studies have developed personal wearable airbags to mitigate the damage caused by FFHs. To utilize these airbags effectively, it is essential to detect FFHs before collision with the floor. In this study, an inertial measurement unit (IMU) sensor attached to the seventh thoracic vertebrae (T7) was used to develop an FFH detection algorithm. The vertical angle and vertical velocity were calculated using the inertial data obtained from the IMU sensor. Forty young and healthy males were recruited to perform non-FFH and FFH motions. In addition, experiments using a human mannequin and dynamics simulations were performed to obtain FFH data at heights above 2 m. The developed algorithm achieved 100% FFH detection accuracy and provided sufficient lead time such that the airbags could be inflated completely before collision with the floor.

Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags.

Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user's body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously.

Simple Synthesis of Nanocrystalline Tin Sulfide/N-Doped Reduced Graphene Oxide Composites as Lithium Ion Battery Anodes.

Composites of nitrogen-doped reduced graphene oxide (NRGO) and nanocrystalline tin sulfides were synthesized, and their performance as lithium ion battery anodes was evaluated. Following the first cycle the composite consisted of Li2S/LixSn/NRGO. The conductive NRGO cushions the stress associated with the expansion of lithiation of Sn, and the noncycling Li2S increases the residual Coulombic capacity of the cycled anode because (a) Sn domains in the composite formed of unsupported SnS2 expand only by 63% while those in the composite formed of unsupported SnS expand by 91% and (b) Li percolates rapidly at the boundary between the Li2S and LixSn nanodomains. The best cycling SnS2/NRGO-derived composite retained a specific capacity of 562 mAh g-1 at the 200th cycle at 0.2 A g-1 rate.