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Material Measurement Laboratory

Materials Science and Engineering Division

Projects/Programs

Displaying 76 - 94 of 94

Polymers for Next-Generation Lithography

Completed
We work closely with the semiconductor industry to develop and apply measurements with high-spatial and chemically-specific resolution to elucidate the critical materials properties and process kinetics at nanometer scales that are needed to advance next-generation photolithography, including both

Protein rheology measurements

Ongoing
Intended Impact Why does the health-care industry care about rheology and scattering of protein solutions? Operations. Viscosity is a critical and fundamental constitutive input for drug delivery, device design and manufacturing. For example, high viscosity leads to poor syringeability due to

Renewable Polymers

Completed
In this project, use innovative measurements to optimize existing materials synthesis and discover new routes to polymers from renewable feedstocks by providing characterization methods that can describe the mechanisms and of enzymatic catalysis of polymers from renewable feedstocks and synthetic

Scientific Workflow

Ongoing
Digitally capturing the "scientific workflow" will be a key component to modernizing scientific data management. Defined in this context, a scientific workflow is the encapsulation of all processes and accompanying relevant data necessary to reproduce and validate an experiment. Thus, a workflow

AI self-quality assurance using learning curves in feedback loops

Ongoing
One application of artificial intelligence (AI) in materials is the acceleration of materials innovation, which is the mission of the Materials Genome Initiative. However, to decrease the cost and time-to-market, we must continuously assess the quality of models with new facts. AI quality assurance

Semiconductor Nanowire Metrology: Electronics, Photonics, and Sensors

Completed
One of the key issues hampering progress in semiconductor nanotechnology is the absence of standardized nanostructures. We address this need through the controlled fabrication and assembly of semiconductor nanowire test structures with well-defined properties. To achieve this, we have manufactured

Springback

Springback, or the elastic change in shape when a part is released from the manufacturing process, has been a problem for decades and accounts for a significant fraction of the expense that the US auto industry spends each year trying out die sets for new body designs. The inability to predict the

Stroboscopic TEM for Ultrafast Materials Science

Ongoing
Our people: June Lau co-invented the stroboscopic microscope and had been with this project since inception. She is a physicist, and the PI of this project. She received 2 non-provisional patents for the design of the microscope, and one provisional patent on a specimen holder for microwave delivery

Teaching Liquid State Theory to an Artificial Neural Network (ANN)

Ongoing
Scientific questions: Can an ANN allow us to predict the structure of fluids that are impossible to predict numerically via liquid-state theory? Can we learn something about liquid-state theory itself by the nature of the trained ANN? What features do the hidden layers capture?

Templated Assembly of Block Copolymer Films

Completed
Viable nanomanufacturing of templated block copolymers will require a capability to control orientation and line edge roughness of trillions of structures to within a single nanometer, however there are no existing platforms that meet this need. We are developing small angle x-ray and neutron

Tension-Compression Testing

Ongoing
The inability to reliably predict the mechanical behavior of new automotive alloys during forming has generated strong demand for more advanced constitutive relationships and property data necessary to calibrate them. There is a particular need for models that incorporate combined kinematic and

Thermodynamic & Kinetic Data for Sustainable Energy

Completed
Knowledge of the thermodynamic, phase equilibria and diffusion properties of potential novel materials can greatly accelerate their development. However, the data needed for new, multi-component materials are often not available. The CALPHAD (Computer Coupling of Phase Diagrams and thermochemistry)

Uniaxial Tension Beyond Necking

Ongoing
A major limitation of the uniaxial tensile test (ASTM E8) is that after a certain amount of tensile strain, the sample eventually undergoes a mechanical instability - it forms a neck. Although the sample continues to plastically deform, the stress can no longer be unambiguously determined as load

Using AI to Determine Structure-Property Relations in Materials

Ongoing
The Material Measurement Lab at NIST employs artificial intelligence for the prediction and discovery of materials characteristics. Our applications of artificial intelligence (AI) accelerate materials research as well as help the community learn about AI's capabilities and gain confidence in

WebFF: Force-field repository for organic and soft materials

Ongoing
WebFF is an open and extensible force-field (FF) repository, designed to support the Materials Genome Initiative (MGI) for organic and related soft materials. The repository is built using the NIST Materials Data Curation System (MDCS) which supports ontology based database descriptions using XML

X-ray Stress Measurement

Ongoing
X-ray diffraction is commonly used to measure a materials crystal structure, crystal lattice orientation, and the spacing of the lattice planes. Our X-ray systems are designed to measure the spacing of one or two specifically selected lattice plane reflections for a given material. The systems use

Yield Surface Measurement

Ongoing
The overarching philosophy for this project is to develop standard tests that produce clean multiaxial yield data for sheet metal that will be of use to the automotive industry in lightweighting efforts. To this end, it is required that the sheet being tested remain in the as-received condition (no