Catalytic Iridium-Based Janus Micromotors Powered by Ultralow Levels of Chemical Fuels

We describe catalytic micromotors powered by remarkably low concentrations of chemical fuel, down to the 0.0000001% level. These Janus micromotors rely on an iridium hemispheric layer for the catalytic decomposition of hydrazine in connection to SiO_2 spherical particles. The micromotors are self-propelled at a very high speed (of ∼20 body lengths s^(–1)) in a 0.001% hydrazine solution due to osmotic effects. Such a low fuel concentration represents a 10 000-fold decrease in the level required for common catalytic nanomotors. The attractive propulsion performance, efficient catalytic energy-harvesting, environmentally triggered swarming behavior, and magnetic control of the new Janus micromotors hold considerable promise for diverse practical applications

Analysis of the electric power outage data and prediction of electric power outage for major metropolitan areas in Texas using Machine Learning and Time Series Methods

With growing energy usage, power outages affect millions of households. This case study focuses on gathering power outage historical data, modifying the data to attach weather attributes, and gathering ERCOT energy market conditions for Dallas-Fort Worth and Houston metropolitan areas of Texas. The transformed data is then analyzed using machine learning algorithms including, but not limited to, Regression, Random Forests and XGBoost to consider current weather and ERCOT features and predict power outage percentage for locations. The transformed data is also trained using time series models and serially correlated models including Autoregression and Vector Autoregression. This study also focuses on traditional machine learning models that assume sample independence when compared to those that assume serial correlation. The results show machine learning models that utilize both weather features and ERCOT data yield a lower RMSE and higher prediction accuracy than using one feature-set exclusively. In addition, multivariate Vector Autoregressive models have lower RMSE compared to univariate Auto-Regressive, univariate Random Forest and univariate neural network models when weather and ERCOT data are included to predict power outages. Top performing traditional machine learning models are packaged into an external facing web application for public use in determining current power outage risk

Suppress vibration on robotic polishing with impedance matching

Installing force-controlled end-effectors on the end of industrial robots has become the mainstream method for robot force control. Additionally, during the polishing process, contact force stability has an important impact on polishing quality. However, due to the difference between the robot structure and the force-controlled end-effector, in the polishing operation, direct force control will have impact during the transition from noncontact to contact between the tool and the workpiece. Although impedance control can solve this problem, industrial robots still produce vibrations with high inertia and low stiffness. Therefore, this research proposes an impedance matching control strategy based on traditional direct force control and impedance control methods to improve this problem. This method's primary purpose is to avoid force vibration in the contact phase and maintain force-tracking performance during the dynamic tracking phase. Simulation and experimental results show that this method can smoothly track the contact force and reduce vibration compared with traditional force control and impedance control

Artificial Micromotors in the Mouse’s Stomach: A Step toward in Vivo Use of Synthetic Motors

Artificial micromotors, operating on locally supplied fuels and performing complex tasks, offer great potential for diverse biomedical applications, including autonomous delivery and release of therapeutic payloads and cell manipulation. Various types of synthetic motors, utilizing different propulsion mechanisms, have been fabricated to operate in biological matrices. However, the performance of these man-made motors has been tested exclusively under in vitro conditions (outside the body); their behavior and functionalities in an in vivo environment (inside the body) remain unknown. Herein, we report an in vivo study of artificial micromotors in a living organism using a mouse model. Such in vivo evaluation examines the distribution, retention, cargo delivery, and acute toxicity profile of synthetic motors in mouse stomach via oral administration. Using zinc-based micromotors as a model, we demonstrate that the acid-driven propulsion in the stomach effectively enhances the binding and retention of the motors as well as of cargo payloads on the stomach wall. The body of the motors gradually dissolves in the gastric acid, autonomously releasing their carried payloads, leaving nothing toxic behind. This work is anticipated to significantly advance the emerging field of nano/micromotors and to open the door to in vivo evaluation and clinical applications of these synthetic motors

Glucose-fueled Micromotors with Highly Efficient Visible Light Photocatalytic Propulsion

Synthetic micro/nanomotors fueled by glucose are highly desired for numerous practical applications because of the biocompatibility of their required fuel. However, currently all of the glucose-fueled micro/nanomotors are based on enzyme-catalytic-driven mechanisms, which usually suffer from strict operation conditions and weak propulsion characteristics that greatly limit their applications. Here, we report a highly efficient glucose-fueled cuprous oxide@N-doped carbon nanotube (Cu_2O@N-CNT) micromotor, which can be activated by environment-friendly visible-light photocatalysis. The speeds of such Cu_2O@N-CNT micromotors can reach up to 18.71 μm/s, which is comparable to conventional Pt-based catalytic Janus micromotors usually fueled by toxic H_2O_2 fuel. In addition, the velocities of such motors can be efficiently regulated by multiple approaches, such as adjusting the N-CNT content within the micromotors, glucose concentrations, or light intensities. Furthermore, the Cu_2O@N-CNT micromotors exhibit a highly controllable negative phototaxis behavior (moving away from light sources). Such motors with outstanding propulsion in biological environments and wireless, repeatable, and light-modulated three-dimensional motion control are extremely attractive for future practical applications

Hairy root transgene expression analysis of a secretory peroxidase (PvPOX1) from common bean infected by Fusarium wilt

Plant peroxidases (POXs) are one of the most important redox enzymes in the defense responses. However, the large number of different plant POX genes makes it necessary to carefully confirm the function of each paralogous POX gene in specific tissues and disease interactions. Fusarium wilt is a devastating disease of common bean caused by Fusarium oxysporum f. sp. phaseoli. In this study, we evaluated a peroxidase gene, PvPOX1, from a resistant common bean genotype, CAAS260205 and provided direct evidence for PvPOX1’s role in resistance by transforming the resistant allele into a susceptible common bean genotype, BRB130, via hairy root transformation using Agrobacterium rhizogenes. Analysis of PvPOX1 gene over-expressing hairy roots showed it increased resistance to Fusarium wilt both in the roots and the rest of transgenic plants. Meanwhile, the PvPOX1 expressive level, the peroxidase activity and hydrogen peroxide (H2O2) accumulation were also enhanced in the interaction. The result showed that the PvPOX1 gene played an essential role in Fusarium wilt resistance through the occurrence of reactive oxygen species (ROS) induced hypersensitive response. Therefore, PvPOX1 expression was proven to be a valuable gene for further analysis which can strengthen host defense response against Fusarium wilt through a ROS activated resistance mechanism

Catalytic Iridium-Based Janus Micromotors Powered by Ultralow Levels of Chemical Fuels

We describe catalytic micromotors powered by remarkably low concentrations of chemical fuel, down to the 0.0000001% level. These Janus micromotors rely on an iridium hemispheric layer for the catalytic decomposition of hydrazine in connection to SiO_2 spherical particles. The micromotors are self-propelled at a very high speed (of ∼20 body lengths s^(–1)) in a 0.001% hydrazine solution due to osmotic effects. Such a low fuel concentration represents a 10 000-fold decrease in the level required for common catalytic nanomotors. The attractive propulsion performance, efficient catalytic energy-harvesting, environmentally triggered swarming behavior, and magnetic control of the new Janus micromotors hold considerable promise for diverse practical applications

Constant real-space fractal dimensionality and structure evolution in Ti62Cu38 metallic glass under high pressure

The structure of binary Ti62Cu38 metallic glass is investigated under pressures up to 33.8 GPa using the pair distribution function analysis based on high-energy x-ray scattering and reverse Monte Carlo (RMC) simulations. At a global scale, its relative volume shows a continuously smooth curve as a function of pressure. The isothermal bulk modulus of Ti62Cu38 metallic glass is estimated as B0=132(3)GPa with B0′=5.8(0.4). At a local scale, the atomic packing structure under compression conditions, which is extracted from RMC simulations, shows that the topological short-range order is dominated by the deformed icosahedron polyhedra and basically maintains stable. From the relationship between the relative volume and changing ratio of the atomic separation distances, the real-space fractal dimensionality of this metallic glass is determined as about 2.5 for all of the first four peaks. This experimental result reveals the consistent nature of the fractal feature on the degree of self-similarity in this sample within the entire experimental pressure range