Superhydrophobic Polymers

Superhydrophobic polymers are particularly attractive materials, as they combine low cost, ease of processing, and compatibility with a variety of applications. Surfaces that display the Cassie–Baxter wetting state are particularly attractive for their self-cleaning properties. In this chapter, a brief overview of the wetting principles will be followed by an account of several techniques currently used to impart superhydrophobicity onto polymer surfaces. Surface roughness and surface structure will be the focus of this chapter, with an emphasis on topographies that exhibit microscale or nanoscale features arranged in hierarchical order.

Sustainable life cycles of natural-precursor-derived nanocarbons

Sustainable societal and economic development relies on novel nanotechnologies that offer maximum efficiency at minimal environmental cost. Yet, it is very challenging to apply green chemistry approaches across the entire life cycle of nanotech products, from design and nanomaterial synthesis to utilization and disposal. Recently, novel, efficient methods based on nonequilibrium reactive plasma chemistries that minimize the process steps and dramatically reduce the use of expensive and hazardous reagents have been applied to low-cost natural and waste sources to produce value-added nanomaterials with a wide range of applications. This review discusses the distinctive effects of nonequilibrium reactive chemistries and how these effects can aid and advance the integration of sustainable chemistry into each stage of nanotech product life. Examples of the use of enabling plasma-based technologies in sustainable production and degradation of nanotech products are discussed—from selection of precursors derived from natural resources and their conversion into functional building units, to methods for green synthesis of useful naturally degradable carbon-based nanomaterials, to device operation and eventual disintegration into naturally degradable yet potentially reusable byproducts.

Autonomous UAVs wildlife detection using thermal imaging, predictive navigation and computer vision

There is an increased interest on the use of Unmanned Aerial Vehicles (UAVs) for wildlife and feral animal monitoring around the world. This paper describes a novel system which uses a predictive dynamic application that places the UAV ahead of a user, with a low cost thermal camera, a small onboard computer that identifies heat signatures of a target animal from a predetermined altitude and transmits that target’s GPS coordinates. A map is generated and various data sets and graphs are displayed using a GUI designed for easy use. The paper describes the hardware and software architecture and the probabilistic model for downward facing camera for the detection of an animal. Behavioral dynamics of target movement for the design of a Kalman filter and Markov model based prediction algorithm are used to place the UAV ahead of the user. Geometrical concepts and Haversine formula are applied to the maximum likelihood case in order to make a prediction regarding a future state of the user, thus delivering a new way point for autonomous navigation. Results show that the system is capable of autonomously locating animals from a predetermined height and generate a map showing the location of the animals ahead of the user.

Synthesis and applications of carbon nanomaterials for energy generation and storage

The world is facing an energy crisis due to exponential population growth and limited availability of fossil fuels. Over the last 20 years, carbon, one of the most abundant materials found on earth, and its allotrope forms such as fullerenes, carbon nanotubes and graphene have been proposed as sources of energy generation and storage because of their extraordinary properties and ease of production. Various approaches for the synthesis and incorporation of carbon nanomaterials in organic photovoltaics and supercapacitors have been reviewed and discussed in this work, highlighting their benefits as compared to other materials commonly used in these devices. The use of fullerenes, carbon nanotubes and graphene in organic photovoltaics and supercapacitors is described in detail, explaining how their remarkable properties can enhance the efficiency of solar cells and energy storage in supercapacitors. Fullerenes, carbon nanotubes and graphene have all been included in solar cells with interesting results, although a number of problems are still to be overcome in order to achieve high efficiency and stability. However, the flexibility and the low cost of these materials provide the opportunity for many applications such as wearable and disposable electronics or mobile charging. The application of carbon nanotubes and graphene to supercapacitors is also discussed and reviewed in this work. Carbon nanotubes, in combination with graphene, can create a more porous film with extraordinary capacitive performance, paving the way to many practical applications from mobile phones to electric cars. In conclusion, we show that carbon nanomaterials, developed by inexpensive synthesis and process methods such as printing and roll-to-roll techniques, are ideal for the development of flexible devices for energy generation and storage – the key to the portable electronics of the future.

Open source computer-vision based guidance system for UAVs on-board decision making

The use of UAVs for remote sensing tasks; e.g. agriculture, search and rescue is increasing. The ability for UAVs to autonomously find a target and perform on-board decision making, such as descending to a new altitude or landing next to a target is a desired capability. Computer-vision functionality allows the Unmanned Aerial Vehicle (UAV) to follow a designated flight plan, detect an object of interest, and change its planned path. In this paper we describe a low cost and an open source system where all image processing is achieved on-board the UAV using a Raspberry Pi 2 microprocessor interfaced with a camera. The Raspberry Pi and the autopilot are physically connected through serial and communicate via MAVProxy. The Raspberry Pi continuously monitors the flight path in real time through USB camera module. The algorithm checks whether the target is captured or not. If the target is detected, the position of the object in frame is represented in Cartesian coordinates and converted into estimate GPS coordinates. In parallel, the autopilot receives the target location approximate GPS and makes a decision to guide the UAV to a new location. This system also has potential uses in the field of Precision Agriculture, plant pest detection and disease outbreaks which cause detrimental financial damage to crop yields if not detected early on. Results show the algorithm is accurate to detect 99% of object of interest and the UAV is capable of navigation and doing on-board decision making.

Uncertainty based online planning for UAV target finding in cluttered and GPS-denied environments

There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.

Single layer graphene film by ethanol chemical vapor deposition: Highly efficient growth and clean transfer method

The choice of ethanol (C2H5OH) as carbon source in the Chemical Vapor Deposition (CVD) of graphene on copper foils can be considered as an attractive alternative among the commonly used hydrocarbons, such as methane (CH4) [1]. Ethanol, a safe, low cost and easy handling liquid precursor, offers fast and efficient growth kinetics with the synthesis of fullyformed graphene films in just few seconds [2]. In previous studies of graphene growth from ethanol, various research groups explored temperature ranges lower than 1000 °C, usually reported for methane-assisted CVD. In particular, the 650–850 °C and 900 °C ranges were investigated, respectively for 5 and 30 min growth time [3, 4]. Recently, our group reported the growth of highly-crystalline, few-layer graphene by ethanol-CVD in hydrogen flow (1– 100 sccm) at high temperatures (1000–1070 °C) using growth times typical of CH4-assisted synthesis (10–30 min) [5]. Furthermore, a synthesis time between 20 and 60 s in the same conditions was explored too. In such fast growth we demonstrated that fully-formed graphene films can be grown by exposing copper foils to a low partial pressure of ethanol (up to 2 Pa) in just 20 s [6] and we proposed that the rapid growth is related to an increase of the Cu catalyst efficiency due weak oxidizing nature of ethanol. Thus, the employment of such liquid precursor, in small concentrations, together with a reduced time of growth and very low pressure leads to highly efficient graphene synthesis. By this way, the complete coverage of a copper catalyst surface with high spatial uniformity can be obtained in a considerably lower time than when using methane.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Fish-scale bio-inspired multifunctional ZnO nanostructures

Scales provide optical disguise, low water drag and mechanical protection to fish, enabling them to survive catastrophic environmental disasters, predators and microorganisms. The unique structures and stacking sequences of fish scales inspired the fabrication of artificial nanostructures with salient optical, interfacial and mechanical properties. Herein, we describe fish-scale bio-inspired multifunctional ZnO nanostructures that have similar morphology and structure to the cycloid scales of the Asian Arowana. These nanostructured coatings feature tunable light refraction and reflection, modulated surface wettability and damage-tolerant mechanical properties. The salient properties of these multifunctional nanostructures are promising for applications in: - (i) optical coatings, sensing or lens arrays for use in reflective displays, packing, advertising and solar energy harvesting; - (ii) self-cleaning surfaces, including anti-smudge, anti-fouling and anti-fogging, and self-sterilizing surfaces, and; - (iii) mechanical/chemical barrier coatings. This study provides a low-cost and large-scale production method for the facile fabrication of these bio-inspired nanostructures and provides new insights for the development of novel functional materials for use in 'smart' structures and applications.