Participating Lab: Air Force Research Laboratory’s Materials and Manufacturing Directorate (AFRL RX)

Microelectronics cleanrooms produce the world’s supply of computer chips, displays, memory, and more. At the Foundational Technologies directory, AFRL scientists and engineers are developing the next generation of cutting-edge electronic and photonic materials and devices to serve national interests. The cleanroom microelectronics engineering intern will have the opportunity to use advanced lithography, etch, and deposition tools to contribute to device process development. Representative example projects include qualification of plasma etch recipes, development of data analysis software, and atomic layer deposition recipe development.

The ability to synthetically tailor the molecular structure and chemical architecture of functional materials provides an opportunity to meet many Air Force application needs. Agile synthetic platforms are desirable for research tasks related to organic sensors, adhesives, and stimuli-responsive materials. Furthermore, incorporation of these systems into flexible devices remains vital to their long term success on the battlefield. Research efforts will leverage AFRL expertise and capabilities in materials science, chemistry, engineering, and biochemistry to target human performance monitoring, wearable devices, and responsive structures. Specific areas of interest include polymer synthesis, surface functionalization and on-demand actuation for the generation of dynamic materials. Candidates will have the opportunity to partner with experimental and computational scientists with the hopes of gaining a deeper understanding of material properties and identifying soft matter materials applications that assist the Air Force. Specific areas of interest include polymer synthesis, sequence specific materials, adaptable materials and organic material synthesis. Furthermore, there remains interest in incorporating machine learning methods into synthesis and material properties studies, as well as using continuous flow chemistry to synthesize materials and macromolecules.

Synthesis of Atomically Precise Polymers with Exact Chain Lengths

The next generation of optical materials will achieve novel performance by utilizing wavelength-scale geometric features to manipulate light in ways beyond the capability of traditional bulk optical materials. However, advanced optimization strategies coupled with computational electromagnetic simulation must be employed to accelerate the design process due to the near-infinite design space for such optical metamaterials. Machine learning approaches have demonstrated the potential to overcome the limitations of ansatz and/or iterative approaches by inverting the design process, delivering optical metamaterial designs with desired properties. This opportunity will allow for the investigation and creation of machine learning models capable of designing optical metasurfaces to achieve the desired performance.

Materials designed for space environments must be lightweight, mechanically robust, thermally stable, and inherently multifunctional. Boron nitride (BN) and carbon nanomaterials are promising candidates that intrinsically meet these criteria. While both material classes possess excellent thermal conductivity and mechanical strength, BN nanomaterials offer superior neutron shielding, whereas carbon nanomaterials provide high electrical conductivity. Hybridizing these materials—via layered architectures or continuous structures—yields advanced

Modern manufacturing environments increasingly use robots equipped with advanced cameras and force/torque sensors to adapt to changes in the environment. While these sensors generate massive amounts of raw, “low-level” data when observing robotic tasks, this unorganized data is not separated into reusable and human-interpretable parts. To help robots apply actions across different manufacturing tasks, we need to translate raw data into higher-level descriptions known as abstractions. For example, a sudden spike in force data isn’t directly actionable on its own. However, if we can map that spike to an abstraction such as “obstruction-encountered”, the robot can select an action like “move-around-obstruction” that worked in previous, similar states. Selecting the appropriate abstractions for robot planning is a complex challenge. In this opportunity, you will investigate which abstractions are useful for task and motion planning in multi-modal manufacturing tasks, such as drone assembly. You will explore, compare, and implement AI methods to generate these abstractions. Thus, this is a great chance to get experience at the intersection of robotics and artificial intelligence.

We seek to develop scalable cell-based technologies based on a fundamental understanding of the mechanisms involved in color generation by Cellulophaga lytica biofilms. We are seeking motivated students to contribute to research projects in biomaterials development and characterization. The successful applicant will join a dynamic laboratory with a broad range of expertise in biomaterials, microbiology, biochemistry, molecular biology, biophysics and biochemistry. Selected participants will design and execute experiments using techniques from these disciplines. Duties may include but are not limited to: bacterial cell culture and manipulation, purification and analysis of cellular constituents, protein expression assays, biomaterials characterization, and/or bioinformatics.

Nondestructive Evaluation (NDE) uses physics and math to provide information about the external and internal structure and properties of materials, including the presence of defects and damage. In this project, material samples will be prepared for evaluation with x-ray computed tomography (XCT). Images from XCT data and optical microscopy will be collected and analyzed to look for indications of defects. Some experience with python coding would be helpful, but not required, for analysis of data and comparison with simulation results.