Available research projects

The KU particle astrophysics group specializes in radiowave detection of neutrinos, specifically ultra-high energy neutrinos interacting in cold (Antarctic or Greenlandic) polar ice. Depending on their software background, students would work on either data analysis from existing experiments (ARA, ANITA, PUEO in Antarctica and/or RNO in Greenland) or perform design, simulation, construction and measurement of the radio-wave antennas and hardware used to perform measurements.

In this project, the student will work with real and/or simulated data from either the Deep Underground Neutrino Experiment (DUNE) or the Fermilab Short Baseline Neutrino (SBN) Program, two current and next-generation projects that aim at studying neutrino oscillation, that is the change in type or “flavor” as the elusive neutrinos travel. DUNE and SBND will study the behaviour of neutrinos over a broad range of energies and sources, from accelerator neutrinos to atmospheric and space neutrinos. The project will have both a physics analysis and an event reconstruction components. The first step will consist in performing an analysis task for one of the physics goals at DUNE or SBN, and identifying key metrics that affect the overall physics sensitivity. These result will then be used to investigate what areas of event reconstruction, the crucial step that interprets the DUNE and SBND liquid argon detector signals in terms of the particles emerging from neutrino interactions, directly influence these metrics. Depending on the student’s software skills, some targeted reconstruction algorithm development to boost the analysis physics reach is possible, including the use of machine learning techniques. Good proficiency with Python and/or C++ will be strongly preferred. This project has the potential to impact a future science mega-projects such as DUNE. The student will work as part of a team including a postdoctoral researcher and a couple of graduate students, and will likely have the opportunity to present their work to the broader experimental collaboration(s).

Beliefs about how physics knowledge is developed and assimilated naturally impact a student's learning experience in a physics course.  While interviews and surveys are typically used to assess this epistemology, these tools provide an incomplete assessment as the beliefs expressed in a survey or interview do not always align with actual practices.  We are addressing this limitation by examining student epistemology in introductory physics courses through analysis of self-prepared test aids (formula sheets, e.g.) in these courses.  Since a test aid is the product of an active and deliberate process of organizing knowledge, it provides evidence of knowledge structuring that is a more direct assessment of underlying epistemology than a response in an interview or survey.  An additional goal of this project is to determine how changes in epistemology correlate with changes in self-efficacy and/or mindset, as this will empower the development and evaluation of curriculum reforms and interventions targeting simultaneous improvement of these attributes.  Students participating in this project will use generative coding to characterize the test aids and subsequently determine the correlations between the epistemology determined from these test aids with the results of separate surveys of student self-efficacy and mindset in the same courses.  This analysis will enable a clearer understanding of the relationship between these attributes while also guiding the development and implementation of pedagogical reforms that target improvements in them.

TBA

Lithium-ion batteries, with their high-energy density, high-discharge voltage, and relatively low cost, have been the battery of choice for a wide variety of applications, including portable consumer electronics, hybrid- and all-electric cars, and grid-scale energy storage. However, these batteries also come with drawbacks: potential safety issues and growing concerns regarding the availability of lithium and of the cathode materials. Replacing lithium ions with earth-abundant, non-toxic, and non-flammable ions would alleviate these concerns. We will use computational methods that start from only the atomic structure (so called first-principles methods), based on density functional theory, to find and investigate new cathode materials without having to rely on time-consuming experiments.

Outcomes: During this project students will learn the basics of solid state physics and first-principles methods. Programming skills and basic linux shell, including experience with high-performance computing platforms, will be acquired or further extended.

Bio: Dr. Peelaers is a computational condensed matter physicist who uses and develops first-principles methods, based on density functional theory to improve our understanding of the physics of nanostructured, electronic, and energy materials, with a focus on wide-bandgap oxides. His work aims to design and improve the next generation of power electronics, sensors, memristors, and batteries.

Phase change materials (PCMs) is one of the key materials platforms for next-generation high-density storage, in-memory and neuromorphic computing. These technologies are essential for meeting the growing demand for computing and storage at significantly reduced energy consumption. The performance of PCMs can be optimized by tuning and controlling the structure and properties at the interfaces. In this project, the student will engage in computational research on PCMs using quantum-mechanical first-principles calculations and machine learning techniques. The goal is to explore and design novel interface structures and study their properties. Students with Python programming skills will be preferred.

The KU particle astrophysics group specializes in radiowave detection of neutrinos, specifically ultra-high energy neutrinos interacting in cold (Antarctic or Greenlandic) polar ice. Depending on their software background, students would work on either data analysis from existing experiments (ARA, ANITA, PUEO in Antarctica and/or RNO in Greenland) or perform design, simulation, construction and measurement of the radio-wave antennas and hardware used to perform measurements.

In this project, students will help to develop and run quantum mechanical model and Monte Carlo simulation to understand the photo-to-electrical energy conversion process in organic semiconductors. Organic semiconductors have been used in various flexible electronic devices, and they can be used to make environmentally friendly solar cells and photocatalysts. Understanding the energy conversion mechanism in these materials is critical for designing nanoscale structures suitable for solar energy harvesting.

Prof. Ian Crossfield's ExoLab group is looking for a motivated student researcher to work with us on new studies of exoplanets and/or their host stars.  The project may focus on detecting new planets, modeling atmospheric chemistry of new worlds, observational characterization including transits and/or radial velocities, and determining physical properties of the planets and of their host stars. 

The goal of this project is to determine how students navigate an introductory physics course implementing standards-based grading and how student responses to that grading system are mediated by their mindset, self-efficacy, and other dimensions of their identity.  Students will pursue this goal by jointly analyzing data collected from separate assessments of self-efficacy, mindset, course performance, and attitude.  Of particular interest is whether students take advantage of the opportunities afforded to them by this grading system to learn from their mistakes, the resources they employ if they do so, and how aspects of their identity correlate with these decisions and outcomes.  

Projects:

1. Voyager Interstellar Mission Data Analysis work
2. Cassini Mission Data Analysis work

The Voyager mission are two NASA space probes launched in 1977 to explore the gas giant planets and the outer reaches of the solar system. Voyager 1 became the first ever spacecraft to reach interstellar space in 2012. We are seeking an undergraduate to work alongside data scientists to package Voyager data into more modern formats, in order to increase accessibility to the data. The work is critically important at this time as we are expecting the Voyager 2 spacecraft to turn off the LECP instrument (which is our instrument) this spring. LECP is designed to detect electrons and ions and was one of two instruments to detect when the spacecraft reached the Sun's heliopause. The work is important as we are attempting to finish and finalize the Voyager LECP holdings and to submit them to the Planetary Data Systems Plasma Particle Interactions node and the National Space Science Data Center data holdings.

The Cassini mission, launched in 1997, was a NASA/ESA orbiter and probe sent to observe Saturn and its moon Titan. We are seeking a student to work alongside data scientists to package our Cassini data into more modern formats. Our data comes from the MIMI instrument, which is the first instrument ever designed to produce a picture of a planet's magnetosphere. The work is important as we are attempting to finish and finalize the Cassini MIMI data holdings and to submit them to the Planetary Data Systems Plasma Particle Interactions node.

Note: Part of this work will be off-campus (but in Lawrence, KS), so only students that are planning to drive to KU should apply

A student working on this project will use data from ACES, an ALMA large survey of the center of the Milky Way Galaxy.  The student will use a Principal Components Analysis (PCA) to analyze the distribution and properties of Galactic center gas. The PCA technique allows us to efficiently analyze this very rich dataset, which consists of a large number of different molecular transitions, each of which is imaged with unprecedented spatial resolution across the central degrees of our Galaxy. Our ultimate goal is to disentangle the complex distribution of gas in this region, which is complicated by our edge-on view of this region, making the line of sight or true 3D distribution of the gas uncertain. The student will interpret the resulting clustering properties of the data and use these to identify and organize Galactic center gas structures into distinct groups based on common properties, which will help validate theories of the 3D structure as well as the overall gas chemistry of this region. Both of these properties can then be compared to other nearby galaxies, in order to better understand how  all of these galaxy centers evolve with time.

Lithium-ion batteries, with their high-energy density, high-discharge voltage, and relatively low cost, have been the battery of choice for a wide variety of applications, including portable consumer electronics, hybrid- and all-electric cars, and grid-scale energy storage. However, these batteries also come with drawbacks: potential safety issues and growing concerns regarding the availability of lithium and of the cathode materials. Replacing lithium ions with earth-abundant, non-toxic, and non-flammable ions would alleviate these concerns. We will use computational methods that start from only the atomic structure (so called first-principles methods), based on density functional theory, to find and investigate new cathode materials without having to rely on time-consuming experiments.

Outcomes: During this project students will learn the basics of solid state physics and first-principles methods. Programming skills and basic linux shell, including experience with high-performance computing platforms, will be acquired or further extended.

Bio: Dr. Peelaers is a computational condensed matter physicist who uses and develops first-principles methods, based on density functional theory to improve our understanding of the physics of nanostructured, electronic, and energy materials, with a focus on wide-bandgap oxides. His work aims to design and improve the next generation of power electronics, sensors, memristors, and batteries.

The student will design, construct, and test a laser heating apparatus that will be integrated with an inkjet printer for printing high-performance semiconductor quantum dots/graphene photodetectors. The goal is to demonstrate on-chip programmable printing of high-performance electronic and optoelectronic devices with minimum thermal budget using focused laser heating coordinated with printing. The result is directly related to future electronics integrating nanostructured devices with CMOS. The student(s) will determine what the best approach is and implement it accordingly. The student(s) is expected to design and integrate a variety of mechanical, optical and electronic to make the apparatus functional and user friendly.

Memristors can be used as artificial neurons and synapses in neuromorphic circuits for artificial intelligence, computing, etc with high speed and energy efficiency. In this project, the student will team up with graduate students to investigate dynamic properties of memristive switches using scanning tunneling spectroscopy (STS). This involves developing methods to process and visualize STS data, identifying features related to memristor behavior, and correlating them with electrical switching events. The student will explore approaches to optimize data analysis workflows and refine protocols for high-resolution spectroscopy of memristor dynamics. The goal is to understand how nanoscale electronic changes influence device performance and endurance. The results are directly related to advancing the design of next-generation electronic memory and neuromorphic computing devices.

Phase change materials (PCMs) is one of the key materials platforms for next-generation high-density storage, in-memory and neuromorphic computing. These technologies are essential for meeting the growing demand for computing and storage at significantly reduced energy consumption. The performance of PCMs can be optimized by tuning and controlling the structure and properties at the interfaces. In this project, the student will engage in computational research on PCMs using quantum-mechanical first-principles calculations and machine learning techniques. The goal is to explore and design novel interface structures and study their properties. Students with Python programming skills will be preferred.