Foundry Undergraduate Summer Experience (FUSE)

Principal Investigators: Archana Raja, Edward Barnard

The Raja group uses photons and electrons to study transport of energy, charge, and information on ultrasmall length scales and ultrafast timescales. We are part of the Imaging and Manipulation of Nanostructures facility within the Molecular Foundry where we fabricate atomically thin two-dimensional crystals that serve as the perfect canvas to paint arbitrary potential landscapes for charge carriers and spins.

Project Description:

Atomically thin two-dimensional materials like graphene were discovered over a decade ago and have garnered immense interest both from fundamental science and technological application points of view. These materials are less than 1 nanometer thick and exhibit extraordinary electronic and optical properties due to the unique physics at these nanoscale dimensions. In particular, the enhanced optical absorption and fast transport of excitations in the 2D plane make them promising materials for next generation energy and electronics applications.
A crucial aspect of enabling these applications is layer by layer assembly of 2D materials to tailor their properties. Our research team prepares these structures by deterministically placing materials under a microscope using hardware controlled in a Python environment. The focus of this internship will be automating this stacking process for high reproducibility of material properties. The student can extend the scope of the hardware automation to include aspects of computer vision and machine learning to find and manipulate samples under microscope. The student will work closely with a team of graduate students, postdoctoral scholars, staff scientists and staff engineers at the Imaging and Manipulation of Nanostructures Facility within the Molecular Foundry.

Intern’s role:

• Fabrication of two-dimensional materials
• Write code to control hardware to automate aspects of fabrication
• Collaborate and interact with scientists in the Raja group for feedback on automation
• Deliver oral, written and poster presentation
• Write a paper for peer review or report

What can the intern expect to learn?

• How to plan and execute experimental research in a safe manner
• How to simulate experimental outcomes when possible, with coding and analysis
• How to interpret results and critically assess applied methods
• How to conduct research and ask questions in a collaborative and multidisciplinary environment
• Scientific communication and presentation

Principal Investigator: David Prendergast
Other Mentor: Fabrice Roncoroni

The Prendergast Group focuses on understanding details of electronic and atomic structure and dynamics at functional interfaces. We use electronic structure theory, molecular dynamics, and simulated X-ray spectroscopy to explore chemical dynamics, photochemistry, electrochemistry, catalysis, etc. – processes relevant to energy conversion and storage.

Project Description:

Electrification can render many energy-relevant processes as sustainable. The chemical work done at electrode-electrolyte interfaces is often hindered by unknown details of their molecular and electronic properties. Using molecular dynamics we can explore model interfaces and how they are modified by electrical bias, but the large volumes of data produces often defy thorough classification and analysis. To that end we have been leveraging advances in the machine learning community to cluster similar behavior into physically meaningful groups or arrangements that can then be interrogated using advanced spectroscopic techniques. This project aims to continue this development in the application of clustering to complex molecular dynamics sampling at electrified interfaces of relevance to emergent battery technologies and catalytic conversion.

Main project goal or research question to be addressed by the intern: 

How to quickly and automatically (unsupervised learning) classify large datasets from molecular dynamics runs.

Intern’s role:

• Learning about modeling materials using electronic structure theory and/or molecular dynamics.
• Analyzing the output of common codes.
• Python programming.
• Machine learning methods.

What can the intern expect to learn?

In this project you will learn to apply data mining (clustering algorithms) to reveal hidden patterns in complex data sets that inform physical insight on functional interfaces. You will learn some (more) python and how to visually display your results to inform new research directions.

Principal Investigator: Yi Liu
Other mentor: He (Henry) Li

The Liu group is focused on 1) organic-inorganic hetero-material framework and self-assembly of porous 2D and 3D framework materials with controlled structure and engineered function, 2) design and synthesis of new organic semiconductors for organic electronics, and fundamental understanding of the associated electronic processes, and 3) synthesis and characterization of dielectric polymers and nanostructured polymer composites with tunable dielectric properties for electrostatic energy storage applications.

Project Description:

Polymer-based electrostatic film capacitors are fundamental energy storage elements in advanced electronic and power systems. However, the energy storage performance of polymer materials degrades under thermal stress due to temperature-induced electrical conduction. This project aims to employ wide bandgap inorganic materials as surface coatings to repel electrical conduction across the polymer thin films. First, flexible polymer films will be prepared. Second, different thin oxide/nitride coatings with large bandgap will be fabricated using different cutting-edge deposition methods. Last, capacitor devices will be developed, and their electrical and dielectric properties will be characterized. Moreover, mechanistic studies will be conducted on electrical conduction suppression and energy storage performance improvement of electrostatic film capacitors.

Main project goal or research question to be address by the intern

Tackle the fabrication challenges of novel flexible polymer-based film capacitors and reveal the enhancement mechanisms of surface oxide/nitride coatings with different electronic properties.

Intern’s role:

The intern will carry out organic thin film preparation using solution casting and spin coating methods, will perform inorganic thin coating fabrication using different nano-deposition methods, and will also perform basic electrical and dielectric characterizations including electrical resistivity, dielectric constant, dielectric breakdown strength, capacitive energy storage measurements et al.

What can the intern expect to learn?

The intern will learn the basic knowledge of the chemical and physical properties of polymers, the principle of experiment design, the preparation of organic thin films and inorganic thin coatings, and the measurement of electrical and dielectric properties. Moreover, the intern will learn how to conduct an interdisciplinary study from chemical synthesis, materials preparation and electronic device implementation.

Principal Investigators: Ed Barnard, Carolin Sutter-Fella, Steve Whitelam
Note: this project can accept up to 3 interns

The Foundry Data Group supports building new automated equipment, implementing data sharing and analysis pipelines, and building data support infrastructure. This includes data storage, analysis, and access, as well as working with Foundry scientists and users to develop and improve data workflows for domain specific problems.

In the Inorganic Facility of the Molecular Foundry, we develop methods for synthesis of compound semiconductors resulting in well controlled morphology and electronic structure. The Sutter-Fella group focuses on chemical solution synthesis of functional materials, understanding and manipulating synthesis-property relationships including nucleation and growth as well as evolution of functional properties. To do so we use correlative in situ techniques that we develop in our lab or at the synchrotron. Materials of interest include hybrid organic-inorganic perovskites, halide double perovskites and oxides.

An outstanding problem of materials science is to develop predictive, microscopic rules for self-assembly: given a nanoscale building block, such as a protein or small molecule, how will it self-assemble? As time evolves, what phases and structures will it form, and what will be the yield of the `target’ structure when (and if) it assembles? The Whitelam group uses the tools and techniques of statistical mechanics to address these questions.

Project Description:

We have recently demonstrated the ability of neural networks to learn time-dependent protocols for materials self-assembly and synthesis. We are now starting to apply these ML techniques to robotic materials synthesis to produce optimized materials such as perovskite photovoltaics.

Most current protocols for materials synthesis and self-assembly are simple, informed by experience or intuition: keep the temperature fixed, cool the substrate slowly, gradually increase the rate of spin. However, the optimal protocol for a particular task might be arbitrarily complicated, involving the complicated variation with time of multiple parameters. Finding such a protocol by systematic search would be infeasible for a human experimenter. However, machine-learning algorithms recently used to play board games and computer games can be adapted to do exactly this.

The focus of this internship will be testing these algorithms with our SpinBot One automated robot platform for perovskite PV synthesis. This will involve preparing samples and operating the robot to produce and characterize these PV materials. Data analysis and programming will be an important part of this project, so we can access the quality of the materials and the algorithms. The student(s) will work closely with a team of graduate students, postdoctoral scholars, staff scientists and staff engineers in multiple facilities within the Molecular Foundry. We have opportunities in this for interns with different backgrounds and interests from chemistry, materials science, automation, computer science and machine learning.

Intern’s role:

• Fabrication of Perovskite photovoltaic materials
• Operation of SpinBot One robot for process optimization
• Data Analysis and visualization

What can the intern expect to learn?

They will learn a variety of synthetic and analytic techniques, such as:

• Basic research skills including experience defining and planning a project.
• How to plan and execute experimental research in a safe manner
• Python programming
• Scientific collaboration, communication, and presentation skills

Principal Investigator: Colin Ophus
Other mentor: Karen Bustillo

Our group focuses on computational imaging methods applied to scanning transmission electron microscopy (STEM). We develop mathematical methods and software analysis pipelines for measuring material properties from datasets consisting of thousands or even millions of diffraction patterns.

Project Description:

In this project, the intern will develop methods and python analysis code to measure radial distribution functions from diffraction data. They will start by simulating diffraction patterns from thin, known materials and implementing existing algorithms to measure 2-atom pair distribution functions. They will extend these methods to 3-atom distribution functions and thicker samples. They will study materials ranging from liquids to amorphous glasses to nanocrystalline thin films.

Main research question

Can 3-atom distribution functions be recovered from electron diffraction patterns?

Intern’s role:

The intern will learn how to build atomic models and simulate electron diffraction patterns using quantum-mechanical scattering algorithms. They will also learn how to load and analyze diffraction patterns using our group’s python code, py4DSTEM. They will learn how to write python in a collaborative software environment, and contribute to open source code development.

What can the intern expect to learn?

The intern will learn how to build atomic models and simulate electron diffraction patterns using quantum-mechanical scattering algorithms. They will also learn how to load and analyze diffraction patterns using our group’s python code, py4DSTEM. They will learn how to write python in a collaborative software environment, and contribute to open source code development.

Principal Investigator: Ricardo Ruiz
Other mentor: Beihang Yu

The Soft-Nano Research Group, led by Dr. Ricardo Ruiz in the Nanofabrication Facility, uses Soft-Matter physics to overcome specific challenges in assembling and manipulating matter at the nanometer-length scale. This covers a variety of nanofabrication techniques such as block copolymer lithography, nanoparticle and colloidal self-assembly and bio-molecular lithography for applications in nanoelectronics, memory and semiconductor synthetic biology.

Project Description:

Control over semiconductor/bio interfaces is a key enabler for biological nanofabrication pathways and new applications at the intersection of semiconductor technology and synthetic biology. Conventional surface functionalization methods such as silane chemistries and self-assembled monolayers (SAMs) may offer only a limited level of customization for such semiconductor/bio interfaces, while polymer brushes offer a wider range of chemistries and maintain compatibility with lithographic techniques. In our current project, we have developed a class of sequence-defined, biomimetic polymers (polypeptoids) that form robust monolayers through a “grafting-to” method for surface modification of silicon substrates. For this intern project, we aim to establish a workflow that involves ellipsometry, X-ray photoelectron spectroscopy, and atomic force microscopy to quantify the grafting density of the polypeptoid brushes and its relationship with annealing time and temperature in the “grafting-to” process. The polypeptoid brush monolayers will find applications in directed assembly of biomolecular building blocks such as DNA origami. Depending on the level of progress, this project may get to test patterns of polypeptoid brushes for binding simple DNA origami shapes.

Main project goal or research question to be addressed by the intern

Quantify the grafting density of biomimetic polymer brush monolayers on silicon substrates through a proposed workflow, and determine the optimal thickness obtainable through the “grafting-to” method.

Intern’s role:

• Sample preparation and characterization in a cleanroom setting including polymer thin-film and monolayer deposition, ellipsometry, X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM).
• Analyze results and develop scientific conclusions, and compare with existing literature.
• Conduct an independent research project while working closely with a postdoc and the mentor, interact with team members through group meetings and other scientists at the Foundry.
• Observe and contribute to the safety working culture of the Molecular Foundry and LBL.

What can the intern expect to learn?

• Learn about polymer brushes, biomimetic polymers, and the emergent field of semiconductor synthetic biology
• Learn about the Molecular Foundry’s nanofabrication facility and gain experience with cleanroom work. Get hands-on experience with advanced characterization techniques such as XPS and AFM.
• Learn how to work on a research project from developing an overall research plan to planning daily tasks, practice formulating hypothesis-driven research.
• Practice scientific communication and presentation skills through daily interaction with scientists, group meetings, and at the end-of-program poster session.

Principal Investigator: Colin Ophus
Other mentor: Karen Bustillo

Our group focuses on computational imaging methods applied to scanning transmission electron microscopy (STEM). We develop mathematical methods and software analysis pipelines for measuring material properties from datasets consisting of thousands or even millions of diffraction patterns.

Project Description:

In this project, the intern will develop methods and python analysis code to extract orientation and structural information from weakly-scattering materials, such as polymers, from electron diffraction patterns. These diffraction patterns contain very broad features owing to the high disorder of the material, and therefore have intensity distributions very different from conventional diffraction patterns. The intern will need to test and optimize the best mathematical fitting functions, and then implement it efficiently in python. The intern will test their methods on various experimental datasets.

Main research question 

What is the most efficient method to measure orientation and structure in weakly-scattering materials?

Intern’s role:

The intern will learn how to analyze complex electron diffraction patterns which contain a variety of noisy signals. They will  learn how to load and analyze experimental diffraction patterns using our group’s python code, py4DSTEM. They will learn how to write python in a collaborative software environment, and contribute to open source code development.

What can the intern expect to learn?

The intern will learn how to analyze complex electron diffraction patterns which contain a variety of noisy signals. They will  learn how to load and analyze experimental diffraction patterns using our group’s python code, py4DSTEM. They will learn how to write python in a collaborative software environment, and contribute to open source code development.

Principal Investigator: Bruce Cohen

Optical microscopy is the primary means of studying complex living systems, enabling real-time analysis of individual cellular components at high spatial and temporal resolution.  The Cohen lab develops novel optical probes as biosensors, improving bioconjugation and targeting chemistries, and imaging live systems with these reagents.  We aim to integrate the development of novel luminescent nanomaterials into multidisciplinary efforts to address significant biological questions of cell function.

Project Description:

The assembly of beta amyloid peptides (A-beta) into aggregates is a hallmark of Alzheimer’s disease, but major questions remain about how this happens and which aggregates, if any, are responsible for disease progression.  Micron-scale A-beta plaques are readily apparent in pathology of Alzheimer’s brain, but multiple lines of evidence suggest that earlier, nano-scale, aggregates are the critical neurotoxic agents.  Despite the apparent importance of A-beta oligomers, there is little experimental information on the structures or assembly process of these aggregates.  To address this, we have recently developed organic fluorescent probes able to bind and report on A-beta aggregation, including at the earliest timepoints. 

Main project goal or research question to be address by the intern:

This project then entails optical characterization of labeled A-beta oligomers by fluorescence and optical microscopy, including kinetic studies, tests of varying A-beta sequences and aggregation conditions, and detailed optical characterization of fluorescent oligomers.

Intern’s role:

The intern will learn and perform confocal and TIRF microscopy, fluorescence studies, and possibly some fluorophore synthesis, depending on interests.  In addition to these techniques, the intern will learn about protein/peptide biochemistry and the science underlying amyloid diseases.

What can the intern expect to learn?

The intern will learn and perform confocal and TIRF microscopy, fluorescence studies, and possibly some fluorophore synthesis, depending on interests.  In addition to these techniques, the intern will learn about protein/peptide biochemistry and the science underlying amyloid diseases.

Principal Investigator: Jian Zhang

The Zhang group at the Foundry investigates the synthesis and emerging properties of self-assembly of molecular building blocks. The resulting close packed molecular crystals or porous framework materials exhibit tunable chemical, physical, optical, or mechanical properties for potential energy related applications, which requires a deep understanding the structure-property relationship across the spatial and temporal scale.

Project Description:

Most organic molecular crystals are brittle and fragile and exhibit poor mechanical performance, which limits certain practical applications involving electron transport, rapid mechanical actuation, and so forth. Flexible organic molecular crystals against mechanical stress are newly emerged materials with potential applications in actuators, artificial muscles, smart materials, and sensors. Reversible and irreversible deformation occur in elastic and plastic crystals, respectively. As for elastic deformation, it is suggested that the presence of weak interactions in crystals is the buffer to accommodate the external stress, which allows for stretch and shrink on the outer and inner arcs of crystals, respectively. When the stress is removed, the molecules return to a thermodynamically stable position.

Since applications of large, high-quality single crystals to achieve solution-processible micropatterns on flexible substrates pose challenges because of disintegration or development of severe defects under mechanical impact, using flexible organic molecular crystals can be an effective alternative. Thus, there is an urgent need to systematically study their macroscopic mechanical deformability and to emulate design methodologies by understanding their structure−property relationships

The main goal of the project is to determine the strength of intermolecular interactions and the geometrical arrangement in the 3D packing of flexible organic crystals based on azaquinodimethanes (AQM) molecules. Ultimately, it aims to precisely quantify and predict the macroscopic mechanical responses of AQM-based molecular crystals for applications in flexible organic electronics.

Intern’s role:

• Optimize recrystallization conditions for growing large single crystals for mechanical tests
• Determine crystal structure using single crystal XRD using in house X-ray diffractometer and ALS beamline.
• Test qualitative and quantitative mechanical properties of flexible molecular crystals
• Quantify intermolecular interaction energy and rationalize mechanical behavior

What can the intern expect to learn?

• Hands-on experience on synthesis and characterization of flexible organic crystals based on azaquinodimethanes (AQM) molecules
• Basic principles of chemical crystallography and hands-on experience on single crystal X-ray diffraction technique for structure solution and refinement
• Methods to execute a research project through daily task planning and outlining scientific questions
• Skills for scientific communication and presentation via interactions with PI and coworkers, group meetings, and poster session

Principal Investigator: Tevye Kuykendall
Other Mentor Name: Aidar Kemelbay

In the Inorganic Facility of the Molecular Foundry we develop methods for gas-phase synthesis of compound semiconductor nanostructures such as nanowires, nanotubes, and 2D films. Recently, we have focused on 2D transition metal dichalcogenides (TMDs), inspired by the discovery of emergent properties when reduced from bulk crystals to 2D layers. Transition to direct band gap, emerging charge density waves, high mobility, and valley polarization are some of the many exciting properties that have been reported in the TMD literature recently.

Project Description:

Transition metal dichalcogenides (TMDs) are an interesting class of semiconductor materials due to their emergent properties when reduced to thin two-dimensional (2D) layers. While exfoliation and vapor phase growth produce extremely high-quality 2D materials, direct fabrication at wafer scale remains a significant challenge. In previously published results, we demonstrated a method that we call “lateral conversion,” which employs chemical conversion of a metal-oxide film to TMD layers by diffusion of precursor propagating laterally between lithographically defined silica layers, resulting in patterned TMD structures with control over the thickness down to a few layers. The intern will work on further development of this synthetic method. The synthesis has two distinct components: 1) Micro lithography and substrate preparation, and 2) sample annealing and conversion to the resulting TMD. The intern will focus on processing lithographically patterned substrates using chemical vapor deposition (CVD) under a variety of conditions to optimize the growth strategy and control their morphology and crystalline quality. The main goal of the internship is to explore and optimize different synthetic conditions for growing 2D TMD semiconductor films. They will study the effect of precursor conditioning, pressure, temperature, and reactive gasses on the TMD growth. Using a variety of characterization techniques, they will narrow down the process, through successive experiments and characterization, to control size, thickness, and size distribution, producing high-quality TMD materials.

Main project goal or research question to be address by the intern:

The main goal is to explore and optimize different synthetic conditions for growing 2D TMD semiconductor films. The synthesis portion of the project will study the affect of precursor conditioning, pressure, temperature, and reactive gasses on the TMD growth.

Intern’s role (i.e., what kinds of things will they be doing):

• The intern will learn how to conduct independent research on solid state materials synthesis.
• They will be responsible for synthesizing 2D TMD films using a two-step “lateral conversion” synthesis method.
• They will learn how to characterize their samples using a variety of synthetic and analytic techniques.
• They will learn how to interpret results, and make improvements to the synthetic process using feedback for successive experiments.

They will receive careful oversight and training during the first month, until they are qualified to work independently. Additional training will be given as needed. Regular discussions will be had to interpret results and gauge progress.

What can the intern expect to learn?:

• The intern will learn a variety of synthetic and analytic techniques, such as:
• Chemical vapor deposition (CVD) synthesis
• Raman spectroscopy
• Optical microscopy
• They will learn about the lithographic process and microfabrication techniques
• They will be mentored in the creation of a final poster project and will learn how to present their data using written text, plots, photographic images, and illustrations.

Principal Investigator: Brett Helms
Other Mentor: Jeremy Demarteau

We design and develop new materials to solve problems in energy and sustainability. We harness synthetic chemistry, computational insights, X-ray characterization, and engineering to forge a molecular-level understanding of materials to realize performance advantages with them in batteries, adaptive and reconfigurable energy materials, and chemically recyclable polymers for the circular economy.

Project Description:

Plastics were never designed for recycling and it remains a significant challenge to design next generation of plastics that are both recyclable and provide performance advantages. This project will look into the design, discovery, and development of circular plastics based on the chemistry of polydiketoenamines (PDK). PDKs can be formulated as thermoplastics, elastomers, and thermosets from simple triketone and amine monomers, many of which are bio-based. We will gain insights into the underlying molecular mechanisms critical to circularity in a new plastics economy, while firmly establishing a role for creativity in polymer chemistry to provide innovative solutions.

Main project goal or research question to be address by the intern:

The intern will explore how PDK microstructure, with respect to flexible and rigid segments, can be tailored to enhance the performance of PDK plastics and assess the downstream impacts on monomer recovery after depolymerization at end of life.

Intern’s role (i.e., what kinds of things will they be doing):

The intern will carry out monomer and polymer synthesis and characterization as well as chemical recycling.

What can the intern expect to learn?

The intern will learn contemporary organic and polymer synthesis, how to assess the properties of plastics by thermomechanical characterization, and how to recycle plastics using low temperature catalytic processes.