MICRO Abstracts

Would you like to learn more about the sort of research MICRO students work on? The abstracts below describe the work MICRO students have performed recently.

Projects '22-'23 Academic Year

Anastacia De Gorostiza

Anastacia De Gorostiza1, Katherine Stoll2, Henry Carter3, Yudong Yang3, Jonathan Sessler3, Zachariah Page3, Samuel Serna-Otálvaro2,4, Anuradha Agarwal2
1 McKetta Department of Chemical Engineering and Texas Materials Institute, The University of Texas at Austin, Austin, TX 78712-1062, USA
2 Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
3 Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712-1224, United States
4 Bridgewater State University, Physics Department, 131 Summer St., Bridgewater, MA 02324, USA

Sensitive and Selective On-Chip Methane Detection

Rising methane emissions due to human industrial activity have increased interest in understanding and preventing such emissions as they pertain to climate change. However, one prevailing challenge in quantifying methane emissions from industrial sources is the development of a device that is both inexpensive and sensitive to concentrations below 0.1 ppm. A potential low-cost and high-efficiency material platform for methane sensing is the use of on-chip photonic sensors incorporated with a methane-sensitive polymer cladding. In this project, we present a sensitive on-chip methane sensor with a polymer cladding layer composed of a blend of styrene-acrylonitrile block copolymer and a methane-selective molecule cryptophane A. The performance of our polymer-cladded chip was compared to a control chip. Furthermore, the selectivity of cryptophane A to methane and other gases was explored.

Dawn Ford

Dawn Ford1, Mads Weile2,3, Frances M. Ross3, Julian Klein3
1Department of Physics, University of Virginia, Charlottesville, VA
2Department of Physics, Technical University of Denmark, Lyngby, Denmark
3Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA

Efficient detection of defects and phases in electron microscopy images of CrSBr using deep learning

Two-dimensional (2D) magnets are an interesting class of materials whose magnetic ordering is intrinsically correlated to its atomic structure. Manipulating the atomic structure of 2D magnets provides an avenue to control spin-related phenomena. Traditionally, such modifications to the lattice are achieved through strain engineering and material machining over tens of nm length scales, however the effect of point defects has not been thoroughly studied in 2D magnets. A particularly exciting material is the air-stable 2D magnetic semiconductor CrSBr that has weak antiferromagnetic interlayer coupling and ferromagnetic ordering in the monolayer limit, with the magnetic moments situated at the Cr atoms. A focused electron beam in a scanning transmission electron microscope (STEM) can move Cr atoms in the CrSBr lattice, with potential to induce tailored magnetic properties in the material by precise defect patterning. The ability to create user defined magnetic ordering in optically active and magnetic materials is useful for a variety of applications in quantum simulation and quantum photonics. We propose a machine learning workflow to effectively track and study structural changes of CrSBr when imaged under the electron beam. In this work we explore the effectiveness of different methods of generating labeled STEM images to train a convolutional neural network (CNN). By automating the image labeling process, we aim to increase the accuracy of defect detection. We further improve the approach by using human knowledge in conjunction with the CNN to precisely study the effect of defects in the CrSBr lattice.

Gabrielle Wood

Advisor: Dr. Cecile Chazot

One of the barriers to the widespread adoption of textile recycling is the need for the improvement of the quality of closed loop recycling through the separation of blended fabrics. Specifically, elastane is a polyurethane-polyurea copolymer widely used for its high elasticity, strength, and chemical resistance. Often blended with polyamide and nylon, elastane fibers are core spun, meaning that the filaments are surrounded by yarn plies of the other materials. While elastane only makes up a small percentage of blended fabrics, this core-spun nature along with its resistance to most organic solvents make its separation difficult, resulting in a complete absence of large-scale recycling methods to process the fabrics. Here, we investigate how selective dissolution can be applied as a viable method for chemical recycling of elastane-containing fiber blends. We combine solubility parameter calculations with experimental validation to assess the efficacy of potential green solvents in isolating elastane from the blended materials. Numerical implementation of group contribution theory is used to calculate Hansen Solubility Parameters (HSPs), enabling the study of the compatibility of spandex and blended materials with potential green recycling solvents. This method has the potential to identify scalable chemical recycling methods based on an environmentally-friendly selective dissolution method.

Jon-Edward Stokes

Jon-Edward Stokes1, Richard Church2, Professor A. John Hart3
1Department of Physics and Astronomy, Howard University, Washington, DC
2Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA
3Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA

Finite element analysis of lithium-ion batteries with optimized energy and power density via a 3D honeycomb architecture

Despite significant advances the existing planar lithium-ion battery configuration is approaching its theoretical energy density limit. One strategy to obtain additional increases in energy density is to play with the cell geometry to minimize the mass and volume contributions of inactive materials. Typically, this minimization is achieved by increasing electrode thickness. However, this increased thickness results in increased Li-ion diffusion distances, thereby decreasing the power density. For this reason, planar cells are viewed as having an inherent trade-off between energy and power density. This trade-off may be overcome by further modifying the full cell geometry to incorporate 3D, such as interpenetrating or interdigitated, architectures. 3D architectures enable both high energy and power density due to a higher surface area for the same volume and footprint of cell. As a result, a given volume of active material can be distributed with a lower electrode feature thickness which decrease the Li-ion diffusion distance compared to an equivalent planar cell. In this study, we use perform finite element analysis simulations using COMSOL to predict the performance of 3D batteries with a unique 3D architecture built upon honeycomb-patterned vertically aligned carbon nanotubes (CNTs). Here, CNTs have been chosen for their excellent mechanical strength and electrical conductivity, allowing them to serve as a 3D scaffold for cell fabrication. These discharge simulations will determine the properties required to make these 3D CNT-based cells competitive with existing lithium-ion designs in terms of CNT spacing and height, and will be incorporated into the experimental work trying to produce 3D CNT-based full cells.

Jordan Coney

Advisor: Dr. Juejun Hu/Tushar Sanjay Karnik

Light Loss in Bending Waveguides

I will work for MIT’s MICRO program with Dr. Juejun Hu’s lab under a graduate student named Tushar Sanjay Karnik, where he will minimize the light loss from curved waveguides using the simulation tool Lumerical. The goal is to make integrated photonic circuits more miniature in size. We will look at the different techniques and parameters of carrying this out. A waveguide is a structure that guides waves. For us, they guide electromagnetic waves (think microscopic optical fiber). The electromagnetic waves propagate through the waveguide by a process called total internal reflection. When you bend a waveguide, radiation becomes lost from before the bend to after the curve. In this project, the implemented radiation sources are mid-infrared. The materials that are being used in this project are considered III-V semiconductors. The materials used are essential in integrated photonics because they can be used to make on-chip lasers. We are using an InP cladding and an InGaAs core for the waveguide. I plan on encountering challenges with scheduling as well as software issues. I have had cases where I have difficulty creating the code to carry out our simulation. Last semester I introduced the air trench to our configuration simulation, so I will not continue to expand on the depth of this and explore its physical limitations.

Nolan James Murphy-Genao

Nolan James Murphy-Genao1, Carl Thrasher2, Robert MacFarlane3

1Department of Chemical & Biomolecular Engineering, University of Connecticut, Storrs, CT 06269, USA

2,3Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA

Self-assembly of nanostructures is an innovative methodology where atoms, nanoscale building blocks, or molecules organize themselves in response to stimuli (e.g., thermal, light) into complex ordered structures or patterns with nanometer features without any notable human intervention. This facile mechanism offers surfeit potential as it is currently a promising practical, economical, and proliferative avenue for nanofabrication. Unfortunately, modern self- assembly technologies do not possess spatial control over rudimentary or intricate architectures. This gratuitous obstacle is a significant detriment as successfully obtaining complete regulation of polycrystalline structures could be an avant-garde discovery. However, forming superlattices comprised of DNA nanoparticles that serve as programmable atom equivalents (PAEs) that perform on substrates offers new possibilities for manipulating hierarchical structures by guiding their crystallographic orientation and placement. This work uses substrates patterned via optical lithography to explore the nucleation and growth behavior of PAE crystals on substrates, control the shape of polycrystalline assemblies, and the location and orientation of large single crystals with the addition of computer-guided characterization of external properties to analyze the progress.

Rachel Myers

Rachel Myers1,2, Dr. Joelle P. Straehla2,3, Dr. Paula T. Hammond2,3,4

1 Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County; Baltimore, MD
2 Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Cambridge, MA
3 Broad Institute of MIT and Harvard; Cambridge, MA, USA
4Department of Chemical Engineering, Massachusetts Institute of Technology; Cambridge, MA

Techniques for high-throughput validation of the genomic drivers of nanoparticle interactions in cancer cells

Cancer nanomedicine is a promising venture for the delivery of therapeutics to patients with cancer, as nanoparticles (NPs) can be designed to increase tissue specificity and efficacy for a range of therapeutic cargos. Pooled nanoPRISM screening has identified numerous candidate genes that can regulate NP-cancer-cell interactions, but high-throughput approaches are needed to evaluate them simultaneously1. Based on this established work that nominated candidate genes, we sought to validate their role in NP delivery by using a CRISPR/Cas9 pooled screen. In this screen, each cell has a single gene de-activated, and next generation sequencing can be used to determine the relative enrichment of genes in populations defined by their extent of NP association. Here, we discuss screen design and data analysis strategies to determine which genes were associated with enriched or depleted NP interactions after performing a pooled knockout screen on BT245 human glioma cells treated with layer-by-layer nanoparticles. Layer-by-layer nanoparticles allow for the tunable assembly of nanoparticles and allow us to decouple the influences of the NP core or the layered surface chemistry on NP-cell interactions. Here, we dosed the cells with two NP formulations: 1) a bare liposomal NP, and 2) a liposomal NP layered with hylauronic acid. The hylauronic acid layer was of interest because cancer cells commonly express hylauronic acid receptors, also known as CD44 receptors. We found that several genes of varying functionalities were significantly involved in NP uptake upon knockout in the cells. This genomics-based approach to study NP-cell interactions provides an efficient means to interrogate biologic regulators of NP delivery to cancer cells.

Reference: 1Boehnke N, Straehla JP, Safford HC, Kocak M, Rees MG, Ronan M, et al. Massively parallel pooled screening reveals genomic determinants of nanoparticle delivery. Science 2022;377:eabm5551. https://doi.org/10.1126/science.abm5551.

Temiloluwa Akande

Advisors: Rafael Gomez-Bombarelli and Pablo Leon

The effects of different functionalization of standard electrolyte solvents for lithium batteries using computational workflow and molecular dynamics.

A lithium-ion battery is a rechargeable battery that uses the reduction of lithium ions to store, generate and transfer energy. The lithium-ion battery set up consists of the anode typically carbon based such as graphite. The cathode, the positive electrode, is usually a metal oxide. The electrolyte is the electrolyte would consist of the lithium salt and organic solvent. Typical electrolytes used are highly volatile and impractical for many applications. Although a wide array of electrolytes exists, carbonate-based electrolytes have been used in commercial Li-ion batteries for three decades and are a natural and practical choice to replace common type. Common types of electrolytes are carbonate-based electrolyte with cyclic carbonates (e.g., ethylene carbonate (EC), and propylene carbonate (PC)), linear carbonates (e.g., ethyl methyl carbonate (EMC), diethyl carbonate (DEC), and dimethyl carbonate (DMC)).

The aim of this project is to use molecular dynamics to understand the effect of altering the concentration of ions and solvents of the electrolyte of lithium-ion batteries. To do this, an array of boxes each comprising of a varying number of solvents (eg ethylene carbonate (EC)) and a varying number of ions (lithium or hexafluorophosphorus PF6) would be made. The number of ions and solvents for each box created would also vary by a factor of 10 and 2 to consider what happens to boxes made entirely too small. After the boxes are created, they would be equilibrated through a process where the different solvent and ion molecules eventually condensing into a liquid-phase box from a gas-phase box.

The next phase of the project would consider how the boxes created are affected by Temperature, speed, density all as a function of time. The results from such analysis would give insight into designing the boxes for molecular simulation which would in turn give insight into creating carbonate electrolytes for lithium ion batteries.

Projects '21-'22 Academic Year

Anastacia De Gorostiza

In the past two decades, the development of silicon photonics has revolutionized the field of integrated circuits by using silicon to guide light. Furthermore, silicon-on-insulator provides a low-cost and high-efficiency material platform for use in photonic integrated circuits (PICs). However, a common problem in PICs is the size difference between a waveguide and the light source, typically a laser or LED coupled to the chip through an optical fiber. Thus, a main issue often being studied in the field today is how to optimize the coupling of light emitted from a laser or LED to these significantly smaller waveguide structures. In order to achieve higher coupling efficiency between these devices in a PIC, researchers have adopted two main techniques: edge coupling and grating coupling. For the past semester, I have been working on increasing the coupling efficiency from LED sources into PIC chips designed by the Electronic Materials Research Group (EMAT) at MIT.

Eyobel Haile

Metal Additive Manufacturing by selective laser melting (SLM) is a promising technology for large scale manufacturing, but is also a time consuming, expensive, and labor intensive process. From this perspective, it is important to make sure that the printing process goes as smoothly as possible and produces consistent parts to avoid additional processing steps or failed prints. In this project, in-situ quality control techniques were implemented in order to inspect the structural integrity of printed parts during the process. The hardness data of certain materials of interest was collected and used for determining the local quality of printed parts while controlling for all other variables. Linking measured hardness to temperature gradient, porosity and part defects enable identification of localized defects in the print. Such a method opens new opportunities for reducing the occurrence of failed prints and improve printing quality.

Joshua Chaj Ulloa

Deep Neural Networks Hyperparameters Optimization within Photonic Meta Surface Devices Simulations

Joshua Chaj Ulloa, Sensong An, JueJun Hu

Photonic systems and devices are applicable to various biomedical research applications, with the overarching scope of the field being the study of light and its applications to biosensing, metamaterial optimization, and optical engineering. However, for photonic devices to be applicable to their specific field applications, customization and specification with regards to their device geometry and optical response must be studied and troubleshot for their certain optical properties to be tailor-made. Therefore, researchers have applied the field of neural network machine learning algorithms toward the specific development and optimization of various physical, spectral, and performance characteristics of photonic devices to enhance these properties. Our project focuses on the development of neural networks that will allow us to recognize hidden patterns and correlations for the photonic meta surface devices experimental datasets. Where an overall exploration will be conducted of the effects specific hyperparameter tuning will have on the neural network functionality and performance as this tuning allows for the specific optimization of the hidden layer and overall structure of the deep neural network. The analysis will apply specific hyperparameter tuning methods such as Grid Search, Random Search, and Bayesian Optimization toward monitoring the performance of the complex neural networks developed. This unique approach provides promise toward the enhanced simulation and design development process of photonic meta surface devices toward their specific biomedical research applications.

Nicholas Layman

Using Dynamic Time Warping for Identification of Peptides in Affinity Selection Mass Spectrometry

Nicholas Layman, Somesh Mohapatra, Rafael Gómez-Bombarelli

One of the early stages of drug discovery is the identification of a protein's specific binders to aid in localizing medicine absorption and to ensure effective treatment. Identifying specific binders one at a time is straightforward but extremely time-consuming so we use affinity selection - mass spectrometry (AS-MS) to test many potential binders at once and obtain a collection of suitable binders. To make this process more robust we repeat it a few times per protein and combine these trials into one average dataset. These datasets cannot be combined naively due to small temporal variations in a filtering stage of AS-MS. To overcome these challenges, I implemented and optimized dynamic time warping methods to obtain and analyze a typical dataset. The fully functional and accessible code base I developed enables process automatization as the first step towards systematic binder selection from any AS-MS dataset.

Rachel Myers

Determining the genomic drivers of polystyrene nanoparticle interactions in cancer cells

Rachel Myers1, Dr. Joelle P. Straehla2,3,4,5,6, Dr. Paula T. Hammond2,3

1 Department of Chemical, Biochemical and Environmental Engineering, University of Maryland Baltimore County; Baltimore, MD
2 Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Cambridge, MA
3Department of Chemical Engineering, Massachusetts Institute of Technology; Cambridge, MA
4 Broad Institute of MIT and Harvard; Cambridge, MA, USA
5 Department of Pediatric Oncology, Dana-Farber Cancer Institute; Boston, MA
6 Division of Pediatric Hematology/Oncology, Boston Children’s Hospital; Boston, MA

Cancer nanomedicine is a promising venture for the delivery of therapeutics to cancerous tissue, as nanoparticles (NP) have been shown to increase circulation time, reduce early degradation and clearance, improve specificity, and lower off-target effects of the drug. However, the high variability between cancer cell lines presents an issue with understanding which NP surface chemistries result in successful nanoparticle trafficking, penetration, and payload delivery. This project focuses on using machine learning algorithms to determine the genomic drivers of NP-cancer cell interactions observed in a massively parallel pooled screening. Here, we explore techniques to translate the high-throughput cell screening data into informative genomic nanoparticle trafficking networks. The use of random forest algorithms, clustering, and protein-protein interaction databases to identify biomarkers for interactions in polystyrene nanoparticles is studied. This novel paradigm to study NP-cell interactions may provide an efficient, genomics-based approach to design nanocarriers for cancer treatment.

Samuel Figueroa

Refractory Metal Nanocrystalline Alloy Development using Nanophase Separation Sintering

Samuel David Figueroa1, Christian Edward Oliver2, Yannick Naunheim2, Christopher Schuh2
1University of California, San Diego, La Jolla, CA 92093, United States
2Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA

Nanocrystalline alloys are of great interest due to their exceptional physical properties. Recently, a novel nanoscale phase separation sintering (NPSS) technique has shown production of nanocrystalline binary alloys. NPSS studies have demonstrated that nanocrystalline powder particles supersaturated with a secondary element begin to phase separate upon heating to form solid-state necking to promote rapid diffusion of atoms. This project aims to expand the applicability of NPSS to the molybdenum-chromium (Mo-Cr) binary alloy system using a master sintering curve model to predict densification behavior. This work is based on preliminary experimental characterization of the Mo-Cr NPSS system. Attempts to implement a master sintering curve analysis imply this method helps determine average kinetics, but it is not useful for modeling the various mechanisms of NPSS. Future work will focus on the use of more sophisticated analytical models in tandem with experimental investigation to characterize nanocrystalline structures in the Mo-Cr sintering system.

Tobi Majekodunmi

Finite Element Analysis of a Three-Dimensional Solid-State Battery

Tobi Majekodunmi1*, Richard Church2, Professor A. John Hart3

1Department of Mechanical Engineering, University of Maryland, Baltimore County, Baltimore, MD
2Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA
3Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA

Lithium-ion batteries are ubiquitous within today’s energy storage applications, particularly in the emerging field of electric vehicles. To be competitive with existing transportation technologies and overcome the “range anxiety” felt by many consumers, electric vehicles require batteries with higher energy densities than what is currently available. Without exploring other chemistries, the simple solution is to utilize thick electrodes. However, increasing electrode thickness in planar cell designs comes at the expense of power density, which negatively impacts the car’s acceleration. MIT’s Mechanosynthesis Group is developing a solid-state battery for Lamborghini’s pilot electric supercar. This battery utilizes a 3D, interdigitated geometry and highly conductive carbon nanotubes to simultaneously increase energy density and power output. This project performed discharge simulations with COMSOL modeling software to guide experimental design and fabrication by analyzing how the battery’s geometry, ionic conductivity, and electronic conductivity impact performance.