Comparison in composite performance after thermooxidative aging of injection molded polyamide 6 with glass fiber,talc, and a sustainable biocarbon filler

Degradation is an unavoidable part of a material's life making it important to both monitor and control the aging behavior of plastics. This study compares thermooxidative degraded composites of a novel bio-based and sustainable filler, Biocarbon (MBc), against that of traditional and commercially available fillers (glass fiber and talc) used in the automotive industry. The influence of thermooxidative degradation on the composites was studied under accelerated heat aging for 1000 h at 140°C. The mechanical properties of the composites were evaluated using notched Izod impact as well as both tensile and flexural tests. Morphological structure of the composites was investigated using a scanning electron microscopy. Dynamic mechanical analysis and differential scanning calorimetry were used to evaluate the physical transitions both before and after aging. The glass-filled composites displayed the best performance; while, both the talc and biocarbon composites possessed similar strength and ductility performances. Advantageously, the biocarbon composites experienced an 11% reduction in density as compared to talc-filled composites with similar weight content. After aging, all composites exhibited reduced tensile and flexural strengths ranging from 5 to 67% partly due to chain scission. Whereas, the modulus of all composites increased with a range of 1–24% due to an annealing effect. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48618.     

Cylindrical lithium-ion structural batteries for drones

The low cost, simplicity, and easy use of battery-powered multirotor aircraft has led to their adoption in commercial, industrial, agricultural, and military applications. These aircraft, however, have limited payloads and shorter endurance and range than fuel-powered conventional aircraft. To extend these key performance metrics, a structural battery is developed that uses commercially available battery cells as load bearing and power source elements for weight critical applications. The cylindrical structural battery is tested in three-point bending and is found to have four times higher stiffness and two times higher yield strength than the structure without battery reinforcement. Simulations of a quadcopter, redesigned with the proposed cylindrical structural batteries, demonstrate 41% longer hover time.     

Bound to help: cooperative manipulation of objects via compliant,unactuated tails

We examine the problem of moving multiple objects to goal locations by a coordinated team of mobile robots. Each robot is equipped with an unactuated, compliant chain attached as an appendage that we call its tail. Each of our robots tow objects by wrapping their tail around an item, securing it by hooking the end of the tail back onto itself, and then dragging. In addition to towing individually, any two robots wishing to operate within a tightly-knit sub-team are able to link the ends of their respective tails. These conjoined pairs can skim a region of space, clustering multiple objects together to transport several at once. Using operators that model both forms of towing, we formulate the planning problem for collecting multiple objects and transporting them to goal locations. We propose a general framework using logical formulas to express complex tasks. This planning problem is NP-hard and so we settle for either an exhaustive enumeration or a sub-optimal plan. The combinatorics of the action choices make the former prohibitive with as few as eight robots and objects, so we explore heuristics that give satisfactory solutions in reasonable time. We analyze the performance of the proposed algorithm to give an understanding of where it expands fewer search nodes than exact search. The results include data from physical robots executing plans produced by our planner with both individuals and coupled pairs towing objects.     

Engineered Fibrillar Fibronectin Networks as Three‐Dimensional Tissue Scaffolds

Extracellular matrix (ECM) proteins, and most prominently, fibronectin (Fn), are routinely used in the form of adsorbed pre‐coatings in an attempt to create a cell‐supporting environment in both two‐ and three‐dimensional cell culture systems. However, these protein coatings are typically deposited in a form which is structurally and functionally distinct from the ECM‐constituting fibrillar protein networks naturally deposited by cells. Here, the cell‐free and scalable synthesis of freely suspended and mechanically robust three‐dimensional (3D) networks of fibrillar fibronectin (fFn) supported by tessellated polymer scaffolds is reported. Hydrodynamically induced Fn fibrillogenesis at the three‐phase contact line between air, an Fn solution, and a tessellated scaffold microstructure yields extended protein networks. Importantly, engineered fFn networks promote cell invasion and proliferation, enable in vitro expansion of primary cancer cells, and induce an epithelial‐to‐mesenchymal transition in cancer cells. Engineered fFn networks support the formation of multicellular cancer structures cells from plural effusions of cancer patients. With further work, engineered fFn networks can have a transformative impact on fundamental cell studies, precision medicine, pharmaceutical testing, and pre‐clinical diagnostics.     

Limpet Tooth-Inspired Painless Microneedles Fabricated by Magnetic Field-Assisted 3D Printing

Microneedle arrays show many advantages in drug delivery applications due to their convenience and reduced risk of infection. Compared to other microscale manufacturing methods, 3D printing easily overcomes challenges in the fabrication of microneedles with complex geometric shapes and multifunctional performance. However, due to material characteristics and limitations on printing capability, there are still bottlenecks to overcome for 3D printed microneedles to achieve the mechanical performance needed for various clinical applications. The hierarchical structures in limpet teeth, which are extraordinarily strong, result from aligned fibers of mineralized tissue and protein-based polymer reinforced frameworks. These structures provide design inspiration for mechanically reinforced biomedical microneedles. Here, a bioinspired microneedle array is fabricated using magnetic field-assisted 3D printing (MF-3DP). Micro-bundles of aligned iron oxide nanoparticles (aIOs) are encapsulated by polymer matrix during the printing process. A bioinspired 3D-printed painless microneedle array is fabricated, and suitability of this microneedle patch for drug delivery during long-term wear is demonstrated. The results reported here provide insights into how the geometrical morphology of microneedles can be optimized for the painless drug delivery in clinical trials.     

3D Printed Graphene-Based 3000 K Probe

High-temperature heating is ubiquitously utilized in material synthesis and manufacturing, which often features a rapid production rate due to the significantly improved kinetics. However, current technologies generally provide overall and steady-state heating, thereby limiting their applications in micro/nano-manufacturing that require selective patterning and swift heating. Herein, significantly improved control over small-scale heating is reported by utilizing 3D printed reduced-graphene-oxide (RGO) probe triggered by electrical Joule heating, which enables precise heating with high spatial (sub-millimeter scale) and temporal (milliseconds) resolutions. The block copolymer-modified aqueous-based RGO ink enabled 3D printing of high-precision structures, and a bio-inspired cellular microstructure is constructed to achieve control of the electrical conductivity and maximize structure robustness (benefit for efficient heating and operability). In particular, a thermal probe featuring a microscale tip with excellent heating capabilities (up to ≈3000 K, ultra-fast ramping rate of ≈10⁵ K s⁻¹, and durations in milliseconds) is fabricated. This thermal probe is ideal for surface patterning, as it is demonstrated for the selective synthesis of patterned metal (i.e., platinum and silver) nanoparticles on nano-carbon substrates, which is not possible by traditional steady-state heating. The material construction and heating strategy can be readily extended to a range of applications requiring precise control on high-temperature heating.     

Effect of dam removal on habitat use by spawning Atlantic salmon

By impeding migration and degrading habitat downstream, dam construction has caused population declines in many migratory fish populations. As part of the landlocked Atlantic salmon (Salmo salar) restoration program in Lake Champlain, the Willsboro Dam was removed from the Boquet River, NY in 2015 providing an opportunity to study the effects of dam removal on spawning habitat quality and availability. Spawning habitat surveys were conducted downstream of the dam site in 2014, 2016 and 2017, and in historical spawning grounds upstream in 2016 and 2017. The habitat used was characterized by measuring depth, water velocity, and substrate size at each redd. Mean habitat use did not differ between upstream and downstream sites for any variables in 2016 and only differed for depth in 2017. However, the variance in depth and substrate used for spawning were lower at the upstream site in 2016, likely due to an abundance of habitat. In the downstream site, the mean and variance in depth at redds decreased after dam removal as did the variance in substrate size, increasing the habitat suitability of redds. When compared to literature data, habitat used upstream of the former dam was of medium quality in both 2016 and 2017, and improved downstream from low to medium quality in both column velocity and substrate size after dam removal. This study illustrates that positive shifts in the quality of habitat used can occur rapidly following dam removal by allowing access to suitable spawning habitat upstream and improving habitat downstream.     

A User‐based Visual Analytics Workflow for Exploratory Model Analysis

Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well‐known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.     

Learning proactive behavior for interactive social robots

Learning human–robot interaction logic from example interaction data has the potential to leverage “big data” to reduce the effort and time spent on designing interaction logic or crafting interaction content. Previous work has demonstrated techniques by which a robot can learn motion and speech behaviors from non-annotated human–human interaction data, but these techniques only enable a robot to respond to human-initiated inputs, and do not enable the robot to proactively initiate interaction. In this work, we propose a method for learning both human-initiated and robot-initiated behavior for a social robot from human–human example interactions, which we demonstrate for a shopkeeper interacting with a customer in a camera shop scenario. This was achieved by extending an existing technique by (1) introducing a concept of a customer yield action, (2) incorporating interaction history, represented by sequences of discretized actions, as inputs for training and generating robot behavior, and (3) using an “attention mechanism” in our learning system for training robot behaviors, that learns which parts of the interaction history are more important for generating robot behaviors. The proposed method trains a robot to generate multimodal actions, consisting of speech and locomotion behaviors. We compared this study with the previous technique in two ways. Cross-validation on the training data showed higher social appropriateness of predicted behaviors using the proposed technique, and a user study of live interaction with a robot showed that participants perceived the proposed technique to produce behaviors that were more proactive, socially-appropriate, and better in overall quality.     

Synthesis of Metal Oxide Nanoparticles by Rapid,High‐Temperature 3D Microwave Heating

Microwave‐assisted fabrication has propelled the recent synthesis and processing approaches of various nanomaterials. However, in most previous studies, the synthesis temperature is limited to below 1100 K, which restricts its application. Here, a rapid, in situ 3D heating method to manufacture well‐dispersed metal oxide nanoparticles on a 3D carbonized wood (denoted as C‐wood) host using microwaves as the driving power is reported. The moderate electronic conductivity of C‐wood contributes to the local Joule heating and the good thermal conductivity guarantees the rapid 3D heating of the overall material. The temperature of the C‐wood increases from room temperature to ≈2200 K in 4 s (≈550 K s^?1), stabilizing to 1400 K, and then cooling back down to room temperature within 2 s. The preloaded precursor salts rapidly decompose and form ultrafine (≈11 nm) metal oxide nanoparticles on the surface of the C‐wood during the rapid quenching. The process takes place in air, which helps prevent the metal oxides from being reduced by the carbon. The 3D heating method offers an effective route to the rapid and scalable synthesis of metal oxide nanoparticles.