Kevin Henderson

Development of a fast position-sensitive laser beam detector

We report the development of a fast position-sensitive laser beam detector. The detector uses a fiber-optic bundle that spatially splits the incident beam, followed by a fast balanced photodetector. The detector is applied to the study of Brownian motion of particles on fast time scales with 1 A spatial resolution. Future applications include the study of molecule motors, protein folding, as well as cellular processes.

Experimental Study of the Role of Atomic Interactions on Quantum Transport

We report an experimental study of quantum transport for atoms confined in a periodic potential and compare between thermal and Bose-Einstein condensation (BEC) initial conditions. We observe ballistic transport for all values of well depth and initial conditions, and the measured expansion velocity for thermal atoms is in excellent agreement with a single-particle model. For weak wells, the expansion of the BEC is also in excellent agreement with single-particle theory, using an effective temperature. We observe a crossover to a new regime for the BEC case as the well depth is increased, indicating the importance of interactions on quantum transport.

A Bose-Einstein condensate driven by a kicked rotor in a finite box

We study the effect of different heating rates on a dilute Bose gas confined in a quasi-1D finite, leaky box. An optical kicked rotor is used to transfer energy to the atoms while two repulsive optical beams are used to confine the atoms. The average energy of the atoms is localized after a large number of kicks and the system reaches a nonequilibrium steady state. A numerical simulation of the experimental data suggests that the localization is due to energetic atoms leaking over the barrier. Our data also indicates a correlation between collisions and the destruction of the Bose-Einstein condensate fraction.

A Route to Integrating Dynamic 4D X-ray Computed Tomography and Machine Learning to Model Material Performance

Machine learning has recently been implemented in materials science where it has opened new pathways to modeling and predicting material performance [1]. A material property that is difficult to model and predict (as well as difficult to tailor during material fabrication) is the compressive performance of polymeric foams. These materials are often used in applications where high strength, light weight, low density and/or low cost components are desired and their compressive properties are often essential to the intended application. However, a population of polymer foams samples with analogous polymeric material properties may exhibit extremely different compressive stress-strain curves which can be attributed to the various micrometer-scale void morphologies present in the population. By capturing these void microstructures at various stress-strain states via dynamic 4D (i.e., 3D + time) X-ray computed tomography (X-ray CT), quantitative void microstructure descriptors can used as inputs into machine learning algorithms for the purposes of developing a polymeric foam compressive performance model. This will allow a deeper understanding of exactly how the void microstructure affects a polymer foam’s compressive response in a mathematical framework as well as establish a methodology for the study of other systems undergoing a physical dynamic external stimulus. The work presented here is a proof-of-concept study combining 4D X-ray CT data with a traditional multivariate regression technique, partial least squares regression (PLS), and a machine learning technique, Artificial Neural Network (ANN), for the purposes of modeling the stress-strain response using void microstructural information. Results of these analyses will be shown as well as a path forward for providing a robust study of this material property.

Ultraviolet stimulated electron source for use with low energy plasma instrument calibration.

We have developed and demonstrated a versatile, compact electron source that can produce a mono-energetic electron beam up to 50 mm in diameter from 0.1 to 30 keV with an energy spread of <10 eV. By illuminating a metal cathode plate with a single near ultraviolet light emitting diode, a spatially uniform electron beam with 15% variation over 1 cm(2) can be generated. A uniform electric field in front of the cathode surface accelerates the electrons into a beam with an angular divergence of <1° at 1 keV. The beam intensity can be controlled from 10 to 10(9) electrons cm(-2) s(-1).

Data Challenges of In Situ X-Ray Tomography for Materials Discovery and Characterization

Since its development in the 1970s (Hounsfield, Br J Radiol 46(552):1016–1022, 1973) [1], X-ray tomography has been used to study the three dimensional (3D) structure of nearly every type of material of interest to science, both in the laboratory (Elliott and Dover, J Microsc 126(2):211–213, 1982) [2] and at synchrotron facilities (Thompson et al., Nucl Instrum Methods Phys Res 222(1):319–323, 1984) [3]. The ability to nondestructively image internal structures is useful in the medical community for patient diagnosis. For this same reason, it is critical for understanding material structural morphology. X-ray tomography of static materials can generate a true 3D structure to map out content and distribution within materials including voids, cracks, inclusions, microstructure, and interfacial quality. This technology is even more useful when applying a time component and studying the changes in materials as they are subjected to non-equilibrium stimulations. For example, testing mechanical properties (e.g., compressive or tensile loading), thermal properties (e.g., melting or solidification), corrosion, or electrostatic responses, while simultaneously imaging the material in situ, can replicate real world conditions leading to an increase in the fundamental understanding of how materials react to these stimuli. Mechanical buckling in foams, migration of cracks in composite materials, progression of a solidification front during metal solidification, and the formation of sub-surface corrosion pits are just a few of the many applications of this technology. This chapter will outline the challenges of taking a series of radiographs while simultaneously stressing a material, and processing it to answer questions about material properties. The path is complex, highly user interactive, and the resulting quality of the processing at each step can greatly affect the accuracy and usefulness of the derived information. Understanding the current state-of-the-art is critical to informing the audience of what capabilities are available for materials studies, what the challenges are in processing these large data sets, and which developments can guide future experiments. For example, one particular challenge in this type of measurement is the need for a carefully designed experiment so that the requirements of 3D imaging are also met. Additionally, the rapid collection of many terabytes of data in just a few days leads to the required development of automated reconstruction, filtering, segmentation, visualization, and animation techniques. Finally, taking these qualitative images and acquiring quantitative metrics (e.g., morphological statistics), converting the high quality 3D images to meshes suitable for modeling, and coordinating the images to secondary measures (e.g., temperature, force response) has proven to be a significant challenge when a materials scientist ‘simply’ needs an understanding of how material processing affects its response to stimuli. This chapter will outline the types of in situ experiments and the large data challenges in extracting materials properties information.

From Alloy Processing to Performance: An In Situ Experimental and Modeling Effort

Solidification is present in almost all materials. It is influenced by grain size and shape, chemical homogeneity, defect type and density, and mechanical properties. During micro-mechanical testing, the following occur: 1) Micro-CT (as processed) - Map Initial 3D Microstructure 2) Nano-Radiography (In situ under Tension) - Observe of Damage Initiation/Propagation 3) Micro-CT (Post Mortem) - Global Fracture Study 4) Nano-CT (Post Mortem) - High-Resolution Fracture Study.