Global demand for materials is immense and rapidly growing; extraction and processing of materials accounts for more than one-third of global carbon flows for human-related activities, on the order of 5.5 Gigatons/year. Direct materials production represents approximately 7% of total US energy consumption. This talk will describe the development of analytical and computational tools that consider the economic and environmental impacts of design, systems, and process choices relevant to materials use. The speaker will describe approaches to assessing the environmental and economic impact of materials and processes as early in their development as possible. The work described leverages information along the development trajectory including data mining of literature about laboratory synthesis, creating techno-economic models of protyping and scaled manufacturing as well as assessing macroeconomic implications on materials markets particularly for the case of substitution and shifts in recycling. The presentation will also describe an example on beneficial use of industrial byproducts in the built environment.
While trillions of sensors that will soon connected to the “Internet of Everything” (IoE) promise to transform our lives, they simultaneously pose major obstacles, which we are already encountering today. The massive amount of generated raw data (i.e., the “data deluge”) is quickly exceeding computing capabilities, and cannot be overcome by isolated improvements in sensors, transistors, memories, or architectures alone. Rather, an end-to-end approach is needed, whereby the unique benefits of new emerging nanotechnologies – for sensors, memories, and transistors – are exploited to realize new system architectures that are not possible with today’s technologies. However, emerging nanomaterials and nanodevices suffer from significant imperfections and variations. Thus, realizing working circuits, let alone transformative nanosystems, has been infeasible. In this talk, I present a path towards realizing these future systems in the near-term, and show how based on the progress of several emerging nanotechnologies (carbon nanotubes for logic, non-volatile memories for data storage, and new materials for sensing), we can begin realizing these systems today. As a case-study, I will discuss how by leveraging emerging nanotechnologies, we have realized the first monolithically-integrated three-dimensional (3D) nanosystem architectures with vertically-integrated layers of logic, memory, and sensing circuits. With dense and fine-grained connectivity between millions of on-chip sensors, data storage, and embedded computation, such nanosystems can capture terabytes of data from the outside world every second, and produce “processed information” by performing in-situ classification of the sensor data using on-chip accelerators. As a demonstration, we tailor a demo system for gas classification, for real-time health monitoring from breath.
The utility of carbon nanomaterials is highly dependent upon the precision upon which they can be assembled and functionalized. New methods enable high impact applications in sensing, mechanical, membrane, and energy storage/conversion. Approaches to the formation of functional assemblies of carbon nanotubes will be described that involved the non-covalent immobilization of the materials into functional assemblies. In a non-covalent method, no direct chemical bonds are made to the carbon nanotubes, thereby leaving their electronic properties intact. New covalent connections to the graphene surfaces (sidewalls) of the carbon nanotubes will also be discussed and how these materials can serve to modify their electronic properties for devices as well as hard wire functional assemblies to the carbon nanotubes to provide interactions with chemicals (sensors) or electrocatalysis (energy conversion). Many of these methods are also applicable to the functionalization of graphite to create new forms of graphene. We will also show how high purity graphene can be produced in using new scalable electrochemical methods.
We are in the process of transitioning to a new economy where highly complex, custom products are manufactured on demand by automated manufacturing systems. For example, 3D printers are revolutionizing production of metal parts in aerospace, automotive, and medical industries. Manufacturing electronics on flexible substrates opens the door to a whole new range of products for consumer electronics and medical diagnostics. In this talk, I will show that computation is an integral component of modern design and manufacturing. I will demonstrate how computational tools allow creating digital materials with precisely controlled physical properties and how these digital materials are used to automatically synthesize product designs with desired specifications. I will also show how computational tools enable real-time, closed-feedback loop in additive manufacturing systems to improve their reliability and to fabricate complex products with integrated electronics.
The impact of energy production in our lives stands in stark contrast to the speed, or lack thereof, in solving the most expensive and pervasive issues in energy production. Examples range from the continuing prevalence of fouling, which drains 0.25% of the GDP of developed countries, to the lack of ways to quantify damage to materials. The Mesoscale Nuclear Materials group at MIT (MIT-MNM) focuses on science-based solutions to these "dirty issues," combining branches of physics and engineering to produce industry-ready solutions in years, not decades. We will focus on three issues facing the nuclear industry as well as others: (1) The formation and prevention of CRUD in reactors, (2) rapid qualification of new materials during irradiation, and (3) the stored energy fingerprints of radiation damage as a new way to quantify damage to materials.
Many poor healthcare outcomes and the majority of wasted healthcare spending can be attributed to bad decision making. It is widely accepted that decision support systems are needed to address this issue, and that machine learning has a key role to play in constructing such systems. However, learning to predict the impact of care decisions is made challenging by the need to scale out to complex populations being managed for complex diseases across complex care networks. We will present some recent work that addresses these challenges.
The next generation of energy storage, sensors and neuromorphic computer logics in electronics rely largely on solving fundamental questions of mass and charge transport of ionic carriers and defects in materials and their structures. Here, understanding the defect kinetics in the solid state material building blocks and their interfaces with respect to lattice, charge carrier types and interfacial strains are the prerequisite to design novel energy storage, sensing and computing functions. Through this presentation basic theory and model experiments for solid state oxides their impedances and memristance, electro-chemo-mechanics and lattice strain modulations is being discussed as a new route for engineering material and properties on the examples of solid state batteries, environmental CO2 sensors and memristors for memory and neuromorphic computing chips. Central are the making of new oxide film materials components, and manipulation of the charge carrier transfer and defect chemistry (based on ionic and electronic carriers), which alter directly the device performances and new operation metrics.
In this talk, I will present an overview of my research in the past decade on large scale optimization for machine learning and collective behavior in networked,natural, engineering, and social systems. These collective phenomena include social aggregation phenomena as well as emergence of consensus, swarming, and synchronization in complex network of interacting dynamic systems such as mobile robots and sensors. A common underlying theme in this line of study is to understand how a desired global behavior can emerge from purely local interactions. The evolution of these ideas into social systems has lead to development of a new theory of collective decision making among people and organizations. Examples include participation decisions in uprisings, social cascades, investment decisions in public goods, and decision making in large organizations. I will investigate distributed strategies for information aggregation, social learning and detection problems in networked systems where heterogeneous agents with different observations (with varying quality and precision) coordinate to learn a true state (e.g., finding aggregate statistics or detecting faults and failure modes in spatially distributed wireless sensor networks, or deciding suitability of a political candidate, quality of a product, and forming opinions on social issues of the day in social networks) using a stream of private observations and interaction with neighboring agents. I will end the talk with a a new vision for research and graduate education at the interface of information and decision systems, data science and social sciences.
This lecture will detail the creation of ultrasensitive sensors based on electronically active conjugated polymers (CPs) and carbon nanotubes (CNTs). Conceptually a single nano- or molecular-wire spanning between two electrodes would create an exceptional sensor if binding of a molecule of interest to it would block all electronic transport. Nanowire networks of CNTs modified chemically or in composites with polymers provide for a practical approximation to the single nanowire scheme. Creating chemiresistive and FET based sensors that have selectivity and accuracy requires the development of new methods. I will discuss covalent and non-covalent medication of CNTs with groups that impart selectivity for target analytes. This can involve reactions at the CNT sidewalls and rapping of the CNTs with CPs. Highly specific chemical processes orthogonal responses can be produced for mixtures of analytes through careful integration of chemical functionality. A prevailing problem in all chemiresistive schemes, which is seldom highlighted by researchers, is drift. This is intrinsic for systems that need to interface with their surroundings and changes in the position of ions of small changes in the organization of the CNTs relative to each other, the electrodes, or their surroundings can change the base resistance. I will detail different methods designed to lock the CNT networks in place. These novel compositions are also designed to accommodate functionality and I will demonstrate how we can use a diversity of transition metals to create selective responses to gases. We will also show that this scheme creates CNT networks that are robust enough for solution sensing and demonstrate chemiresistive based glucose sensing. I will also briefly discuss the successful use of CNT based gas sensors for the detection of ethylene and other gases relevant to agricultural and food production/storage/transportation and integrated systems that increase production, manage inventories, and minimize losses.