Bio-inspired Systems for Carbon Dioxide Capture, Sequestration and Utilization

This chapter reviews the study and development of biological, enzymatic and bio-molecular systems for carbon dioxide capture and further sequestration or even utilization. Regardless of the interest on the use of the captured CO2 as C1 synthon on the manufacture of added-value compounds, there is a tremendous unbalance between the requirements of the contemporary society (leading to a massive production of carbon dioxide) and the framework of commercialization of the products from CO2 utilization. In this context, viable options are storage as a solid in the form of calcium or magnesium carbonate and conversion into other energetic frameworks. In addition, it is important to highlight that the conventional energy resources are progressively being replaced by renewable resources. While the change in energetic paradigm is not accomplished, systems that capture and convert carbon dioxide are highly sought. To this end, bio-inspired systems will be presented, starting from the use of compounds from the chiral pool, such as amino acids, saccharides and related bio-polymers, involved in the physical and chemical capture, sequestration and/or utilization of CO2. Additionally, enzymatic systems are presented in the context of sequestration of CO2 in the form of solid carbonates or even utilization of this C1 synthon in the preparation of fuels and commodity chemicals. Carbonic anhydrase is by far the most studied enzyme, as it catalyses the inter-conversion between CO2 and hydrogencarbonate in an effective mode. The biological option comprises the utilization of methanogens, acetogens and other organisms leading to the formation of added-value compounds. Most of the described systems are based on microbial electro-synthesis model and microbial carbon-capture cell prototypes

Complex biological and bio-inspired systems

The understanding and characterization ofthe fundamental processes of the function of biological systems underpins many of the important challenges facing American society, from the pathology of infectious disease and the efficacy ofvaccines, to the development of materials that mimic biological functionality and deliver exceptional and novel structural and dynamic properties. These problems are fundamentally complex, involving many interacting components and poorly understood bio-chemical kinetics. We use the basic science of statistical physics, kinetic theory, cellular bio-chemistry, soft-matter physics, and information science to develop cell level models and explore the use ofbiomimetic materials. This project seeks to determine how cell level processes, such as response to mechanical stresses, chemical constituents and related gradients, and other cell signaling mechanisms, integrate and combine to create a functioning organism. The research focuses on the basic physical processes that take place at different levels ofthe biological organism: the basic role of molecular and chemical interactions are investigated, the dynamics of the DNA-molecule and its phylogenetic role are examined and the regulatory networks of complex biochemical processes are modeled. These efforts may lead to early warning algorithms ofpathogen outbreaks, new bio-sensors to detect hazards from pathomic viruses to chemical contaminants. Other potential applications include the development of efficient bio-fuel alternative-energy processes and the exploration ofnovel materials for energy usages. Finally, we use the notion of 'coarse-graining,' which is a method for averaging over less important degrees of freedom to develop computational models to predict cell function and systems-level response to disease, chemical stress, or biological pathomic agents. This project supports Energy Security, Threat Reduction, and the missions of the DOE Office of Science through its efforts to accurately model biological systems at the molecular and cellular level. The project's impact encompasses applications to biofuels, to novel sensors and to materials with broad use for energy or threat reduction. The broad, interdisciplinary approach of CNLS offers the unparalleled strength of combining science backgrounds and expertise -a unique and important asset in attacking the complex science of biological organisms. This approach also allows crossfertilization, with concepts and techniques transferring across field boundaries

Bio-inspired systems: an exciting Vision for future autonomous biochemical sensing platforms

Through developments in 3D fabrication technologies in recent years, it is now possible to build and characterize much more sophisticated 3D platforms than was formerly the case. Regions of differing polarity, binding behaviour, flexibility/rigidity, can be incorporated into these fluidic systems. Furthermore, materials that can switch these characteristics can be incorporated, enabling the creation of microfluidic building blocks that exhibit switchable characteristics such as programmed microvehicle movement (chemotaxis), switchable binding and release, switchable soft polymer actuation (e.g. valving), and selective uptake and release of molecular targets. These building blocks can be in turn integrated into microfluidic systems with hitherto unsurpassed functionalities that can contribute to bridging the gap between what is required and what science can currently deliver for many challenging applications. The emerging transition from existing engineering-inspired 2D to bioinspired 3D fluidic concepts appears to represent a major turning point in the evolution of microfluidics. Implementation of these disruptive concepts may open the way to realising biochemical sensing systems with performance characteristics far beyond those of current devices. A key development will be the integration of biomimetic functions like self-awareness of condition and self-repair capabilities to extend their useful lifetime. In this lecture, I will present ideas and demonstrations of practical ways to begin building a bio-inspired functional toolbox that could form the basis of these futuristic biomimetic systems

Optimal vortex formation as a unifying principle in biological propulsion

I review the concept of optimal vortex formation and examine its relevance to propulsion in biological and bio-inspired systems, ranging from the human heart to underwater vehicles. By using examples from the existing literature and new analyses, I show that optimal vortex formation can potentially serve as a unifying principle to understand the diversity of solutions used to achieve propulsion in nature. Additionally, optimal vortex formation can provide a framework in which to design engineered propulsions systems that are constrained by pressures unrelated to biology. Finally, I analyze the relationship between optimal vortex formation and previously observed constraints on Strouhal frequency during animal locomotion in air and water. It is proposed that the Strouhal frequency constraint is but one consequence of the process of optimal vortex formation and that others remain to be discovered

Foundations of the Pupper Quadruped Robot with Preliminary Work on an End Effector

Quadruped robots serve as bio-inspired systems that present design and control challenges. This Independent Study explored the basics of building, controlling, and simulating a quadruped robot and was an effort in collaboration with Hands-On Robotics. Following the Hands-on Robotics and Stanford Robotics Independent Study online curriculum, we assembled a Pupper quadruped robot. This report also discusses preliminary work designing and building a robotic arm end effector

Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach

Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or power demand, recent interest for neural networks and bio-inspired systems has motivated the implementation of new techniques. Among them, a combination of spiking neural networks and neuromorphic auditory sensors offer an alternative to carry out the human-like speech processing task. In this approach, a spiking convolutional neural network model was implemented, in which the weights of connections were calculated by training a convolutional neural network with specific activation functions, using firing rate-based static images with the spiking information obtained from a neuromorphic cochlea. The system was trained and tested with a large dataset that contains ”left” and ”right” speech commands, achieving 89.90% accuracy. A novel spiking neural network model has been proposed to adapt the network that has been trained with static images to a non-static processing approach, making it possible to classify audio signals and time series in real time.Ministerio de Economía y Competitividad TEC2016-77785-

Fluid-structure interaction study of spider's hair flow-sensing system

In the present work we study the spider's hair flow-sensing system by using fluid-structure interaction (FSI) numerical simulations. We observe experimentally the morphology of Theraphosa stirmi's hairs and characterize their mechanical properties through nanotensile tests. We then use the obtained information as input for the computational model. We study the effect of a varying air velocity and a varying hair spacing on the mechanical stresses and displacements. Our results can be of interest for the design of novel bio-inspired systems and structures for smart sensors and robotics

Bio-Inspired Stereo Vision Calibration for Dynamic Vision Sensors

Many advances have been made in the eld of computer vision. Several recent research trends have focused on mimicking human vision by using a stereo vision system. In multi-camera systems, a calibration process is usually implemented to improve the results accuracy. However, these systems generate a large amount of data to be processed; therefore, a powerful computer is required and, in many cases, this cannot be done in real time. Neuromorphic Engineering attempts to create bio-inspired systems that mimic the information processing that takes place in the human brain. This information is encoded using pulses (or spikes) and the generated systems are much simpler (in computational operations and resources), which allows them to perform similar tasks with much lower power consumption, thus these processes can be developed over specialized hardware with real-time processing. In this work, a bio-inspired stereovision system is presented, where a calibration mechanism for this system is implemented and evaluated using several tests. The result is a novel calibration technique for a neuromorphic stereo vision system, implemented over specialized hardware (FPGA - Field-Programmable Gate Array), which allows obtaining reduced latencies on hardware implementation for stand-alone systems, and working in real time.Ministerio de Economía y Competitividad TEC2016-77785-PMinisterio de Economía y Competitividad TIN2016-80644-