Soft Robotic Deployable Origami Actuators for Neurosurgical Brain Retraction

Metallic tools such as graspers, forceps, spatulas, and clamps have been used in proximity to delicate neurological tissue and the risk of damage to this tissue is a primary concern for neurosurgeons. Novel soft robotic technologies have the opportunity to shift the design paradigm for these tools towards safer and more compliant, minimally invasive methods. Here, we present a pneumatically actuated, origami-inspired deployable brain retractor aimed at atraumatic surgical workspace generation inside the cranial cavity. We discuss clinical requirements, design, fabrication, analytical modeling, experimental characterization, and in-vitro validation of the proposed device on a brain model.

Advanced Applications of Industrial Robotics: New Trends and Possibilities

This review is dedicated to the advanced applications of robotic technologies in the industrial field. Robotic solutions in areas with non-intensive applications are presented, and their implementations are analysed. We also provide an overview of survey publications and technical reports, classified by application criteria, and the development of the structure of existing solutions, and identify recent research gaps. The analysis results reveal the background to the existing obstacles and problems. These issues relate to the areas of psychology, human nature, special artificial intelligence (AI) implementation, and the robot-oriented object design paradigm. Analysis of robot applications shows that the existing emerging applications in robotics face technical and psychological obstacles. The results of this review revealed four directions of required advancement in robotics: development of intelligent companions; improved implementation of AI-based solutions; robot-oriented design of objects; and psychological solutions for robot–human collaboration.

A Change of Paradigm for the Design and Reliability Testing of Touch-Based Cabin Controls on the Seats of Self-Driving Cars

The current design paradigm of car cabin components assumes seats aligned with the driving direction. All passengers are aligned with the driver that, until recently, was the only element in charge of controlling the vehicle. The new paradigm of self-driving cars eliminates several of those requirements, releasing the driver from control duties and creating new opportunities for entertaining the passengers during the trip. This creates the need for controlling functionalities that must be closer to each user, namely on the seat. This work proposes the use of low-cost capacitive touch sensors for controlling car functions, multimedia controls, seat orientation, door windows, and others. In the current work, we have reached a proof of concept that is functional, as shown for several cabin functionalities. The proposed concept can be adopted by current car manufacturers without changing the automobile construction pipeline. It is flexible and can adopt a variety of new functionalities, mostly software-based, added by the manufacturer, or customized by the end-user. Moreover, the newly proposed technology uses a smaller number of plastic parts for producing the component, which implies savings in terms of production cost and energy, while increasing the life cycle of the component.

Robustly printable freeform thermal metamaterials

Thermal metamaterials have exhibited great potential on manipulating, controlling and processing the flow of heat, and enabled many promising thermal metadevices, including thermal concentrator, rotator, cloak, etc. However, three long-standing challenges remain formidable, i.e., transformation optics-induced anisotropic material parameters, the limited shape adaptability of experimental thermal metadevices, and a priori knowledge of background temperatures and thermal functionalities. Here, we present robustly printable freeform thermal metamaterials to address these long-standing difficulties. This recipe, taking the local thermal conductivity tensors as the input, resorts to topology optimization for the freeform designs of topological functional cells (TFCs), and then directly assembles and prints them. Three freeform thermal metadevices (concentrator, rotator, and cloak) are specifically designed and 3D-printed, and their omnidirectional concentrating, rotating, and cloaking functionalities are demonstrated both numerically and experimentally. Our study paves a powerful and flexible design paradigm toward advanced thermal metamaterials with complex shapes, omnidirectional functionality, background temperature independence, and fast-prototyping capability.

Docked ions: vertical-aligned tubular arrays for highly efficient capacitive deionization

Capacitive deionization (CDI) is an effective method for desalination of brackish water to alleviate the global freshwater crisis. Obtaining high desalination capacity is the primary focus of this field. Based on electrical double layer (EDL) theory, current research is mainly devoted to increasing the specific surface area of electrode materials, however, the NaCl adsorption capacity is typically limited to the range of 10 - 20 mg g−1. In this work, we propose a new design paradigm of using a vertical-aligned nanotubular structure for CDI. This design allows ions to be temporarily held inside the electrodes like ships docked in a harbor (ion-docking effect, IDE) due to the greatly diminished water flow inside the tubes, thus enhancing the desalination capacity. As a result, the obtained CDI device based on vertical-aligned nanotubular P-TiO2 arrays shows an ultra-high NaCl adsorption capacity of ~60 mg g−1 within 30 minutes in 0.01 mol L−1 NaCl solution under 1.2 V, corresponding to a rapid average adsorption rate of 2 mg g−1 min−1. Moreover, the adsorption capacity could be further increased up to 121 and 136 mg g−1 under 1.2 and 1.5 V for 2.5 hours adsorption, respectively, but still far from its equilibrium value. Finally, experiments and theoretical simulations are combined to further understand the IDE in CDI. This work highlights the discovery and the utilization of IDE in CDI, and provides new guidance for the design of CDI electrodes and can facilitate the development of CDI technology.

Proteolysis Targeting Chimeras With Reduced Off-targets

Proteolysis Targeting Chimeras (PROTACs), a class of heterobifunctional molecules that recruit target proteins to E3 ligases, have gained traction for targeted protein degradation. However, pomalidomide, a widely used E3 ligase recruiter in PROTACs, can independently degrade other targets, such as zinc-finger (ZF) proteins, that hold key functions in normal development and disease progression. This off-target degradation of pomalidomide-based PROTACs raises concerns about their therapeutic applicability and long-term side effects. Therefore, there is a crucial need to develop rules for PROTAC design that minimize off-target degradation. In this study, we developed a high-throughput platform that interrogates the off-target degradation of ZF domains and discovered, using this platform, that PROTACs with the current design paradigm induce significant degradation of several ZF proteins. To identify new rules for PROTAC design, we generated a rationalized library of pomalidomide analogs with distinct exit vector modifications on the C4 and C5 positions of the phthalimide ring and profiled their propensities for ZF protein degradation. We found that modifications on the C5 position with nucleophilic aromatic substitution (SNAr) reduce off-target ZF degradation. We applied our newfound design principles on a previously developed ALK oncoprotein-targeting PROTAC and generated PROTACs with enhanced potency and minimal off-target degradation. We envision the reported off-target profiling platform and pomalidomide analogs will find utility in the design of specific PROTACs.

Multi-Source Hybrid Energy Harvesting Wireless Sensor Network Framework for Addressing Energy Unpredictability Issues

Energy harvesting wireless sensor network (EH-WSN) harvests energy from the environment to supply power to the sensor nodes which apparently enhances their lifetime. However, the unpredictable nature of the resources throws challenges to the sustainability of energy supply for the continuous network operation. This creates a gap between unstable energy harvesting rates & energy requirements of the nodes of the network. The state-of-the-art algorithms proposed so far to address this problem domain are not able to bridge the gap fully to standardize the framework. Hence there is considerable scope of research to create a trade-off between EH techniques and specially designed protocols for in EH-WSN. Current study evaluates the performance and efficiency of some futuristic techniques which incorporate advanced tools and algorithms. The study aims to identify the strength and weaknesses of the proposed techniques which can emerge specific research requirement in this field. Finally, we propose a research direction towards Multi-source Hybrid EH-WSN (MHEHWSN) which is able to maximize energy availability and functional efficiency. The scope of this study is to develop a notion of a framework which eliminates the limitations of very recent techniques of EH-WSN by including multiple energy resources to extract required energy even in presence of unpredictability. However, keeping in mind the ease of use and less complex structure Multi-source hybrid EH technique requires a careful design paradigm.

Inverse Design of 2D Airfoils using Conditional Generative Models and Surrogate Log-Likelihoods

This paper shows how to use conditional generative models in 2D airfoil optimization to probabilistically predict good initialization points within the vicinity of the optima given the input boundary conditions, thus warm starting and accelerating further optimization. We accommodate the possibility of multiple optimal designs corresponding to the same input boundary condition and take this inversion ambiguity into account when designing our prediction framework. To this end, we first employ the conditional formulation of our previous work BezierGAN---Conditional BezierGAN (CBGAN)---as a baseline, then introduce its sibling conditional entropic BezierGAN (CEBGAN), which is based on optimal transport regularized with entropy. Compared with CBGAN, CEBGAN overcomes mode collapse plaguing conventional GANs, improves the average lift-drag (C_l/C_d) efficiency of airfoil predictions from 80.8% of the optimal value to 95.8%, and meanwhile accelerates the training process by 30.7%. Furthermore, we investigate the unique ability of CEBGAN to produce a log-likelihood lower bound that may help select generated samples of higher performance (e.g., aerodynamic performance). In addition, we provide insights into the performance differences between these two models with low-dimensional toy problems and visualizations. These results and the probabilistic formulation of this inverse problem justify the extension of our GAN-based inverse design paradigm to other inverse design problems or broader inverse problems.