Lithium-ion batteries (LIBs) are vital energy storage devices for electric vehicles (EVs). Conventionally, LIBs have planar electrodes that present trade-offs between energy and power (charge/discharge speed) due to ion diffusion limitations. EVs require a high energy battery to enable long mileage ranges while also being able to charge quickly (< 15 minutes). 3D battery electrodes can potentially overcome this trade-off, achieving both high energy and power by leveraging 3D structures that create fast ion transport pathways. However, a scalable manufacturing process for 3D electrodes is needed. We are investigating processes for this, and we need a method to characterize our 3D electrodes. There is no method to automatically quantify the features within these 3D structures, which is required for rapid, high quality analysis. By accurately measuring 3D electrode feature sizes, correlations between features and optimal battery performance can be determined. We hypothesize that fabricating fine 3D features (order of 10s of microns) will improve battery performance. To address this need, I have developed an image processing script that characterizes 3D electrode samples. I investigate how threshold values improve accuracy in comparison to manual measurements and am able to achieve < 10% error. I also connect the code’s feature size measurements to our manufacturing process operating conditions to inform how manufacturing conditions can be altered to precisely control feature sizes, which impact battery performance. We expect that higher operating frequencies for our manufacturing process will result in our target fine feature 3D electrodes, achieving high-performance Lithium-ion batteries. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office (AMO) Award Number DE-EE0010226. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.
 

As the world’s reliance on Lithium-ion batteries increases for technologies like electric vehicles, we need to improve battery performance. Traditional Lithium-ion batteries are composed of planar electrodes whose thickness can be optimized for energy capacity or charge rate (power). Thinner electrodes have a faster charge/discharge rate but low energy capacity while thicker electrodes have slow charge rates but higher energy capacity. Three-dimensional (3D) electrode structures that deviate from traditional planar electrodes can mitigate these trade-offs by allowing for fast ion transport while still maintaining a high ion quantity. One structure of interest due to its theoretical performance improvements shown in literature is a line patterned electrode. Line patterned electrodes have material and structural design features that greatly impact battery performance; it is therefore important to have methods to characterize and quantify features prior to battery testing. To rapidly and accurately analyze 3D line electrode feature sizes, we have developed an image processing code that analyzes cross-sectioned images of 3D line electrodes made from battery materials, enabling automated quantification of features such as line width, spacing and height. Cross-sectioned images are converted to black and white, which can then be processed by a function to detect the line edges and calculate the feature sizes. The results of our image processing code were compared to manual measurements to quantify accuracy. We draw connections between 3D line patterned electrode features and Lithium-ion battery performance to demonstrate how 3D electrode structures can be tuned to improve performance. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office (AMO) Award Number DE-EE0009112. The views expressed herein do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

The antibiotic penicillin is highly effective at treating the STI syphilis, caused by the bacterium T. pallidum. However, the United States has seen increases in syphilis cases every year for the past 20 years; congenital syphilis cases have risen more than 219% from 2017 to 2021 and overall syphilis cases have risen 32% from 2020 to 2021. This situation demonstrates the need for an effective vaccine as current approaches are not working. The aim of this project is to utilize phage immunoprecipitation sequencing (PhIP-Seq) techniques to assist in the development of an effective vaccine in rabbits and eventually humans. To this end I have been using PhIP-Seq techniques to systematically profile the immune responses to vaccine candidates and T. pallidum infections in rabbits. When rabbits are immunized with a cocktail of three strains of the protein TprC we saw a protective immune response against treponemes (resulting in no viable treponemes) whereas an immunization with TprD saw reduced immune protection. I used PhIP-Seq methods - informed by next-generation sequencing (NGS) and differential expression analysis - to determine the epitope-specificity of antibodies in polyclonal serum samples from rabbits immunized with these vaccine candidates. Epitope-specificity comparisons between the resulting antibodies of the two immunogens can shed light on regions of these proteins critical for protection against treponemes. In the next few months I plan to integrate alanine scanning mutagenesis into the project to assess amino acid binding specificity and accurately identify crucial residues for antibody-binding. The fusion of scanning mutagenesis with PhIP-Seq will allow me and the other research scientists assisting with the project to refine of the effectiveness of our existing vaccine candidates.