Slick Sheet: Project
The University of Delaware (UD) is developing permanent magnets that contain less rare earth material and produce twice the energy of the strongest rare earth magnets currently available. UD is creating these magnets by mixing existing permanent magnet materials with those that are more abundant, like iron. Both materials are first prepared in the form of nanoparticles via techniques ranging from wet chemistry to ball milling. After that, the nanoparticles must be assembled in a 3-D array and consolidated at low temperatures to form a magnet.

Slick Sheet: Project
Michigan State University (MSU) is developing a new engine for use in hybrid automobiles that could significantly reduce fuel waste and improve engine efficiency. In a traditional internal combustion engine, air and fuel are ignited, creating high-temperature and high-pressure gases that expand rapidly. This expansion of gases forces the engine's pistons to pump and powers the car. MSU's engine has no pistons.

Slick Sheet: Project
General Electric (GE) Global Research is using nanomaterials technology to develop advanced magnets that contain fewer rare earth materials than their predecessors. Nanomaterials technology involves manipulating matter at the atomic or molecular scale, which can represent a stumbling block for magnets because it is difficult to create a finely grained magnet at that scale. GE is developing bulk magnets with finely tuned structures using iron-based mixtures that contain 80% less rare earth materials than traditional magnets, which will reduce their overall cost.

Slick Sheet: Project
The University of Delaware (UD) is developing a new fuel cell membrane for vehicles that relies on cheaper and more abundant materials than those used in current fuel cells. Conventional fuel cells are very acidic, so they require acid-resistant metals like platinum to generate electricity. UD is developing an alkaline fuel cell membrane that can operate in a non-acidic environment where cheaper materials like nickel and silver, instead of platinum, can be used.

Slick Sheet: Project
Tetramer Technologies will develop an anion exchange membrane (AEM) as an alternative to proton exchange membranes (PEM) for use in fuel cells and electrolyzers. The team will test a newly developed AEM for stability in alkaline conditions at a temperature of 80°C, enhanced ion conductivity, controlled membrane swelling, and other required properties. Industry has not yet achieved a cost-effective, commercially viable AEM with long-term chemical and physical stability.

Slick Sheet: Project
Lawrence Berkeley National Laboratory (LBNL) will develop a high power density, rapid-start, metal-supported solid oxide fuel cell (MS-SOFC), as part of a fuel cell hybrid vehicle system that would use liquid bio-ethanol fuel. In this concept, the SOFC would accept hydrogen fuel derived from on-board processing of the bio-ethanol and air, producing electricity to charge an on-board battery and operate the motor.

Slick Sheet: Project
Princeton Optronics will develop a low-cost, high-temperature capable laser ignition system which can be mounted directly on the engine heads of stationary natural gas engines, just like regular spark plugs are today. This will be done using a newly developed high-temperature Vertical Cavity Surface Emitting Laser (VCSEL) pump combined with a solid-state laser gain material that can operate at temperatures typically experienced on a stationary natural gas engine.

Slick Sheet: Project
Carnegie Mellon University will use deep reinforcement learning and atomistic machine learning potentials to predict catalyst surface stability under reaction conditions. Current methods for determining the metastability of bifunctional and complex surfaces undergoing reaction are difficult and expensive. Carnegie Mellon’s technology will enable stability analysis in both traditional catalysts and new classes of materials, including those used in tribology (friction), corrosion-resistant alloys, additive manufacturing, and battery materials.

Slick Sheet: Project
The University of Texas at Austin proposes to create efficient, accurate, and scalable deep neural network (DNN) representations of design optimization problem solutions. The inputs to these DNN representations will be the vector of design requirement parameters, the outputs will be the optimal design variables, and the goal is to learn the map from inputs to outputs (i.e., inverse design). The team will focus on the problem of the optimal shape design of aerodynamic lifting surfaces—in particular aircraft wings—using Reynolds-Average Navier Stokes models for minimal drag and energy savings.

Slick Sheet: Project
The University of Maryland (UMD) will create inverse design tools for the development of enhanced heat transfer surfaces at reduced computational cost. Heat transfer surfaces are used to increase the efficiency of many energy conversion systems, but they are currently designed in a slow, iterative fashion. UMD will use a direct inverse design method map from given environments and performance metrics to design variables or materials.