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Displaying 1 - 25 of 2114

Quantifying Mechanical Abrasion of AgNP Nanocomposites used in 3D Printing: Influence of AgNP content on abrasion products and rate of microplastic production

June 7, 2024
Joana Sipe, William Berger, Nathan Bossa, Melissa Chernick, Keana C. K. Scott, Alan Kennedy, Treye Thomas, Christine Ogilvie, Mark Wiesner
Nanomaterials are used in polymer composites to enhance plastic products' properties. Silver nanoparticles (AgNPs) are added to composites for their antimicrobial and fungicidal properties. Polyethylene terephthalate glycol polymer (PETG) AgNP composite

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

May 7, 2024
Kamal Choudhary, Daniel Wines, Kevin Garrity, aldo romero, Jaron Krogel, Kayahan Saritas, Panchapakesan Ganesh, Paul Kent, Pascal Friederich, Vishu Gupta, Ankit Agrawal, Pratyush Tiwary, ichiro takeuchi, Robert Wexler, Arun Kumar Mannodi-Kanakkithodi, Avanish Mishra, Kangming Li, Adam Biacchi, Francesca Tavazza, Ben Blaiszik, Jason Hattrick-Simpers, Maureen E. Williams
Reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have

Microstructural effects on the rotating bending fatigue behavior of Ti-6Al-4V produced via laser powder bed fusion with novel heat treatments

April 30, 2024
Nicholas Derimow, Jake Benzing, David Newton, Chad Beamer, Ping Lu, Frank DelRio, Newell Moser, Orion Kafka, Ryan Fishel, Lucas Koepke, Chris Hadley, Nik Hrabe
The rotating bending fatigue (RBF) behavior (fully reversed, R = -1) of additively manufactured (AM) Ti-6Al-4V alloy produced via laser powder bed fusion (PBF-L) was investigated with respect to different microstructures achieved through novel heat

Operando XPS for Plasma Process Monitoring: A Case Study on the Hydrogenation of Copper Oxide Confined under h-BN

April 29, 2024
Trey Diulus, Andrew Naclerio, J. Anibal Boscoboinik, Ashley Head, Evgheni Strelcov, Piran Kidambi, Andrei Kolmakov
We demonstrate that ambient pressure x-ray photoelectron spectroscopy (APXPS) can be used for in situ studies of dynamic changes in surface chemistry in a plasma environment. Hexagonal boron nitride (h-BN) was used in this study as a model system since it

Self-Assembly of Hierarchical High-h Fluorinated Block Copolymers with an Orthogonal Smectic-within-Lamellae 3 nm Sub-Lattice and Vertical Surface Orientation

April 16, 2024
Bijal Patel, Hongbo Feng, Whitney Loo, Chad R. Snyder, Christopher Eom, Julia Murphy, Daniel Sunday, Paul Nealey, Dean DeLongchamp
Hierarchical structure-within-structure assemblies offer a route toward increasingly complex and multifunctional materials while pushing the limits of block copolymer self-assembly. We present a detailed study of the self-assembly of a series of

Evaluating Models That Predict Epoxy Conversion Using Rheological Properties

April 15, 2024
Stian Romberg, Paul Roberts, Chad R. Snyder, Anthony Kotula
Simultaneous rheology and conversion measurements of neat and composite epoxy resins reveal that conventional models neither accurately nor fully describe the relationship between rheology and conversion. We find that models predicting thermoset conversion

Investigation of melt pool dynamics and solidification microstructures of laser melted Ti-6Al-4V powder using X-ray synchrotron imaging

April 10, 2024
Nicholas Derimow, Madelyn Madrigal-Camacho, Orion Kafka, Jake Benzing, Edward Garboczi, Samuel J. Clark, Suveen Mathaudhu, Nik Hrabe
Titanium alloy (Ti-6Al-4V) is widely used in additive manufacturing (AM) industry. However, as laser powder-bed fusion (PBF-L) additive manufacturing (AM) advances towards reliable production of titanium parts, a thorough understanding of the process

Precision Calcination Mechanism of CaCO 3 to High-Porosity Nanoscale CaO CO 2 Sorbent Revealed by Direct In Situ Observations

April 5, 2024
Jenny Martinez, Jenna Wardini, Xueli Zheng, Lauren Moghimi, Jason Rakowsky, Jonathan Means, Huiming Guo, Ivan Kuzmenko, Jan Ilavsky, Fan Zhang, Pratik Dholabhai, Leora Dresselhaus-Marais, William Bowman
Deploying energy storage and carbon capture at scale is hindered by the substantial endothermic penalty of decomposing CaCO3 to CaO and CO2, and the rapid loss of CO2 absorption capacity by CaO sorbent particles due to sintering at the high requisite

Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks

March 27, 2024
Md. Habibur Rahman, Prince Gollapalli, Panayotis Manganaris, Satyesh Kumar Yadav, Ghanshyam Pilania, Arun Kumar Mannodi-Kanakkithodi, Brian DeCost, Kamal Choudhary
First principles computations reliably predict the energetics of point defects in semiconductors, but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially

Assessing Composite Structure in Metal-Organic Framework-Polymer Mixed-Matrix Membranes

March 15, 2024
Avery Baumann, Peter Beaucage, Richard Vallery, David Gidley, Ryan Nieuwendaal, Chad R. Snyder, Jan Ilavsky, Fu Chen, Christopher Stafford, Christopher Soles
Metal-organic frameworks (MOFs) are renowned for their tunable structure, porosity, and internal chemistry with demonstrated applications in molecular separations, storage, and conversion. While they are widely usable, the powdery characteristics of MOF

Correlating Near-Infrared Spectra to Bulk Properties in Polyolefins

February 28, 2024
Bradley Sutliff, Shailja Goyal, Tyler Martin, Peter Beaucage, Debra Audus, Sara Orski
The industry standard for sorting plastic wastes is near-infrared (NIR) spectroscopy, which offers rapid and nondestructive identification of various plastics. However, NIR does not provide insights into the chain composition, conformation, and topology of

The development of an augmented machine learning approach for the additive manufacturing of thermoelectric materials

February 28, 2024
Connor V. Headley, Roberto J. Herrera del Valle, Ji Ma, Prasanna Balachandran, Vijayabarathi Ponnambalam, Saniya LeBlanc, Dylan Kirsch, Joshua B. Martin
Through the integration of machine learning (ML) techniques alongside additive manufacturing (AM) experimentation, we demonstrate an iterative process to rapidly predict laser-material interactions and melt pool geometries throughout the build parameter
Displaying 1 - 25 of 2114