Pattern recognition in myoelectric control that relies on the myoelectric activity associated with arm motions is an effective control method applied to myoelectric prostheses. Individuals with transhumeral amputation face significant challenges in effectively controlling their prosthetics, as muscle activation varies with changes in arm positions, leading to a notable decrease in the accuracy of motion pattern recognition and consequently resulting in a high rejection rate of prosthetic devices. Therefore, to achieve high accuracy and arm position stability in upper-arm motion recognition, we propose a Deep Adversarial Inception Domain Adaptation (DAIDA) based on the Inception feature module to enhance the generalization ability of the model. Surface electromyography (sEMG) signals were collected from 10 healthy subjects and two transhumeral amputees while performing hand, wrist, and elbow motions at three arm positions. The recognition performance of different feature modules was compared, and ultimately, accurate recognition of upper-arm motions was achieved using the Inception C module with a recognition accuracy of 90.70% ± 9.27%. Subsequently, validation was performed using data from different arm positions as source and target domains, and the results showed that compared to the direct use of a convolutional neural network (CNN), the recognition accuracy on untrained arm positions increased by 75.71% (p < 0.05), with a recognition accuracy of 91.25% ± 6.59%. Similarly, in testing scenarios involving multiple arm positions, there was a significant improvement in recognition accuracy, with recognition accuracy exceeding 90% for both healthy subjects and transhumeral amputees.

CO2 sequestration in sediments as solid hydrate is considered a potential way to capture and store anthropogenic CO2. When CO2 hydrate is formed in front of CO2 migration, the injection channel will be blocked, and the removal of hydrate blockage becomes the first problem that must be faced. This work proposed an N2 injection method to remove CO2 hydrate blockage. Based on numerical simulation, a study was conducted using TOUGH+MIXHYD v.1.0 to confirm the feasibility of N2 injection and compare it to depressurization. The spatial and temporal distribution characteristics of pressure, temperature, hydrate saturation, and gas saturation were investigated. Under the combined effects of temperature, pressure, and gas composition, secondary CO2-N2 hydrate can form far from the injection point, causing an increase in local temperature and hydrate saturation. The rate of CO2 hydrate dissociation using direct depressurization is significantly slower compared to N2 injection methods. As the pressure of N2 injection increases, the rate of CO2 hydrate dissociation notably accelerates, which does not show a significant increase with increasing injection temperature. This work introduced a novel approach to addressing the issue of CO2 hydrate blockage, which holds prominent significance for the advancement of hydrate-based CO2 geological sequestration.

Ni and I co-doped TiO2 nanoparticles with mixed anatase-brookite phase (Ni/I-TiO2) were synthesized by the hydrothermal-calcination method for photocatalytic CO2 reduction with H2O vapor. The I and Ni co-doping, anatase-brookite phase junction, as well as the presence of Ti(III) and Ni(III) endowed the catalyst with promoted visible light absorption, increased reduction ability of photogenerated electrons, facilitated charge separation and migration along with modulated CO2 adsorption mode and capacity, ultimately resulting in boosted photocatalytic activity and product selectivity as compared with those of pure TiO2. The CO, CH4 and O2 yields of the optimized Ni/I-TiO2 reached 3.49, 158.25 and 246.30 mu mol/gcat after 15 h of light illumination, which decreased slightly after 9 cycles, and the product selectivity was above 98 % for photocatalytic reduction of CO2 to CH4 with Ni and I co-doping. The in-situ FTIR results with the observation of center dot CH3O and center dot CH3 intermediates confirmed CH4 formation, and the possible mechanism of photocatalytic CO2 reduction over Ni/I-TiO2 was elucidated. This work provides new insights to pursue efficient catalysts for the selective photocatalytic CO2 reduction to CH4.

The photocatalyst film, composed of tetragonal BiOI nanosheets and cubic phase CuI nanoparticles, was synthesized on the FTO substrate by a simple electro-deposition method. The orderly crisscrossed nanosheet structure caused exterior hydrophobic property, resisting the excess H2O 2 O molecules and further inhibiting the competitive H2O 2 O reduction process. The novel BiOI/CuI catalyst exhibited excellent photocatalytic ability of CO2 2 reduction into CO with 100 % selectivity in H2O 2 O vapor. Typically, the optimal 150BiOI/CuI photocatalyst exhibited CO yield of 7237.65 mu mol/cm2 2 after 11 h of simulated sunlight illumination, achieving quantum efficiency of 2.5 % at 380 nm. The excellent performance of the BiOI/CuI composite film in photocatalytic CO2 2 reduction can be attributed to the construction of hydrophobic surface and S-scheme heterojunction with I 3- /I-- redox mediator, as confirmed by the in-situ XPS, hole injection test and cyclic voltammetry results. This study lays the groundwork for employing highly efficient iodide-based photocatalysts in gas-liquid-solid triphase catalytic systems.

Training with “Extended Reality” or X-Reality (XR) systems can undoubtedly enhance the control of the myoelectric prostheses. However, there is no consensus on which factors improve the efficiency of skill transfer from virtual training to actual prosthesis abilities. This review examines the current status and clinical applications of XR in the field of myoelectric prosthesis training and analyses possible influences on skill migration. We have conducted a thorough search on databases in the field of prostheses using keywords such as extended reality, virtual reality and serious gaming. Our scoping review encompassed relevant applications, control methods, performance evaluation and assessment metrics. Our findings indicate that the implementation of XR technology for myoelectric rehabilitative training on prostheses provides considerable benefits. Additionally, there are numerous standardised methods available for evaluating training effectiveness. Recently, there has been a surge in the number of XR-based training tools for myoelectric prostheses, with an emphasis on user engagement and virtual training evaluation. Insufficient attention has been paid to significant limitations in the behaviour, functionality, and usage patterns of XR and myoelectric prostheses, potentially obstructing the transfer of skills and prospects for clinical application. Improvements are recommended in four critical areas: activities of daily living, training strategies, feedback, and the alignment of the virtual environment with the physical devices.

Photo-assisted gas sensors for n-butyl alcohol detection have attracted considerable attention due to their great potential in safety production and human health. However, the sensitivity, selectivity and stability should be further improved for practical application. Herein, Bi2WO6 nanosheet supported on CuBi2O4 nanorod sensor was realized by the two-step hydrothermal method, and it exhibited excellent sensitivity and stability for n-butyl alcohol detection under blue LED light illumination at the operating work temperature of 110 degrees C with gas concentration from 0.5 to 800 ppm. Meanwhile, the gas concentration of n-butyl alcohol obtained from the sensor was comparable to that of GC analysis. Furthermore, the sensor showed lower responses to other 25 kinds of common organic gases including alcohols, aldehydes, benzenes and ketones compared with that of n-butyl alcohol. More attractively, the Bi2WO6/CuBi2O4 sensor maintained its stable photo-assisted sensing activity over 3 months. The excellent gas-sensing property was caused by light illumination and formation of Bi2WO6/CuBi2O4 heterojunction, leading to abundant active electrons and superoxide radicals. The S-scheme mode of charge transfer at the Bi2WO6/CuBi2O4 heterojunction was revealed by the in-situ XPS and in-situ ESR analyses. The results gained herein may be useful for rational design of S-scheme heterojunction in photo-assisted gas sensor for n-butyl alcohol analysis.

Currently, sEMG-based pattern recognition is a crucial and promising control method for prosthetic limbs. A 1D convolutional recurrent neural network classification model for recognizing online finger and wrist movements in real time was proposed to address the issue that the classification recognition rate and time delay cannot be considered simultaneously. This model could effectively combine the advantages of the convolutional neural network and recurrent neural network. Offline experiments were used to verify the recognition performance of 20 movements, and a comparative analysis was conducted with CNN and LSTM classification models. Online experiments via the self-developed sEMG signal pattern recognition system were established to examine real-time recognition performance and time delay. Experiment results demonstrated that the average recognition accuracy of the 1D-CNN-RNN classification model achieved 98.96% in offline recognition, which is significantly higher than that of the CNN and LSTM (85.43% and 96.88%, respectively, p < 0.01). In the online experiments, the average accuracy of the real-time recognition of the 1D-CNN-RNN reaches 91% ± 5%, and the average delay reaches 153 ms. The proposed 1D-CNN-RNN classification model illustrates higher performances in real-time recognition accuracy and shorter time delay with no obvious sense of delay in the human body, which is expected to be an efficient control for dexterous prostheses.