BLUE FINGERS STUDENT AWARD

The AES Student Award recognizes the most outstanding student paper submitted to the AES Annual Conference by a graduate student at the time of application.

If you are interested in applying for this award, visit this page.

2024 Recipient

Alaleh Vaghef Koodehi, Rochester Institute of Technology

Title: On the use of nonlinear electrophoresis for altering migration order in electrokinetic separations

Analytical separation techniques play a crucial role in the identification, quantification, and purification of chemical and biological components. Analyte migration order holds significant importance in all migration-based analytical separation methods. This study presents the manipulation of microparticle migration order in insulator-based electrokinetic separations. Three distinct microparticle mixtures were investigated: a binary mixture of particles with similar electrical charge but different sizes, and two tertiary mixtures of particles with different sizes. Each particle mixture underwent two distinct separations processes: one under low electric fields (in the linear electrokinetic regime) and another under high electric fields (in the nonlinear electrokinetic regime). In the linear regime, the separation predominantly occurs based on charge differences, while in the nonlinear regime the separation primarily depends on particle size and shape variations. The results demonstrated that switching from the linear to the nonlinear electrokinetic regime altered particle elution order for all studied mixtures. Furthermore, employing the nonlinear electrokinetic regime consistently yielded superior separation performance in terms of separation resolution (Rs), as it allows nonlinear electrophoresis to act as the discriminatory electrokinetic mechanism. These findings have potential applications in analyzing complex samples containing bioparticles within the micron size range. Notably, this study represents the first report of altering particle elution order in an insulator-based electrokinetic system.

2024 Recipient

Raphael Oladokun, West Virginia University

Title: Dielectrophoretic Characterization of Breast Cancer Immune Cells Using PBMCs from MMTV-PyMT
Models

Peripheral blood mononuclear cells (PBMCs), produced from hematopoietic stem cells (HSC), are crucial in surveilling for signs of infection, foreign invaders, and cells associated with diseases, including cancer cells. These cellular communication and interactions induce alterations in PBMCs' electrophysiological properties, which are detectable using dielectrophoresis. In this study, we explore the dielectric properties of PBMCs from FVB/N MMTV-PyMT+ (stages I-IV breast carcinoma, PyMT-PBMC) and FVB/N (wild-type, WT-PBMC) age-matched mice at 4+ weeks. Our approach uses a DEP-based microfluidic platform, to probe changes in subcellular components like the cytoskeleton, lipid bilayer membrane, cytoplasm, focal adhesion proteins, and extracellular matrix (ECM). We hypothesize that these changes, which occur at the onset of breast carcinoma, regulate the dielectric properties (conductivity, σ, and permittivity, ε) of PBMCs, affecting their bioelectric signals and aiding in detection. ANOVA analysis indicated significant differences in the crossover frequencies of stage IV PyMT-PBMCs at conductivity levels of 0.01 S/m and 0.05 S/m. Post hoc pairwise analysis of WT-PBMCs confirmed distinct crossover frequencies from 0.01 to 0.05 S/m across the conductivity range. PyMT PBMCs exhibited increased crossover frequency, polarizability, higher membrane capacitance, and folding factor.

The dielectric properties obtained are essential for designing a DEP-based sorting microdevice. Distinct cell response under the same electric field gradient and medium conductivity suggests a favorable sorting condition for the two cell types PyMT+ and WT PBMCs at 0.02 S/m, 250 kHz, and 8 Vpp. The overarching goal is to apply this non-invasive technique in clinical settings to enhance early detection of breast cancer, thereby minimizing the limitations of traditional screening methods like mammography.

Past Winners

2023 Negar Farhang Doost, West Virginia University

2022 Md Nazibul Islam, Texas A&M University

2021 James Hagan, University of Rhode Island

2020 Nicole Hill, Rochester Institute of Technology

2019 Anna Nielsen, Brigham Young University

2018 Claire V. Crowther, Arizona State University