Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Nanomaterial”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Current Status on the Manufacturing of Nanomaterials for Proton Exchange Membrane Energy Systems by Vapor-Based Processes

Development of novel technologies for catalyst synthesis and membrane electrode assembly (MEA) fabrication is of primary importance for further improvement of the performance and economics of proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane water electrolyzers (PEMWEs). While the traditional manufacturing methods are time-consuming, energy intensive, and require many processing steps, the new vapor-based methods provide many benefits including the development of improved catalysts and catalyst supports, deposition of uniform thin films, reduction of catalyst loading, and minimizing the number of manufacturing steps. Recent publications in the field identified spray pyrolysis, reactive spray deposition technology, chemical vapor deposition, and atomic layer deposition as advanced vapor-based catalyst synthesis and deposition methods used for fabrication of MEAs for PEMFCs and PEMWEs. Here, the MEAs fabricated via vapor-based processes have shown significant performance improvements in comparison to the state-of-the-art MEAs, which are attributed to better catalyst distribution, improved catalyst supports, and controlled, uniform catalyst layer microstructures. This review provides an overview of the vapor-based synthesis and deposition methods currently being used for the development of PEM-based devices. The advantages and disadvantages of these methods are critically compared and discussed while the outlook for future development is provided.

25 ENERGY STORAGE↗
DOE OSTI2024

Structural, Electrochemical, and (De)lithiation Mechanism Investigation of Cation-Disordered Rocksalt and Spinel Hybrid Nanomaterials in Lithium-Ion Batteries

Significant demand for lithium-ion batteries necessitates alternatives to Co- and Ni-based cathode materials. Cation-disordered materials using earth-abundant elements are being explored as promising candidates. Here, in this paper, we demonstrate a coprecipitation synthetic approach that allows direct preparation of disordered rocksalt Li 2.4 Fe 1.0 Ti 1.0 O 4.7 (r-LFTO·C) and spinel structured hybrid Li 0.5 Fe 1.0 Ti 0.9 O 3.2 ·C (s-LFTO·C) nanoparticles with a conformal conductive carbon coating. High-angle annular dark-field imaging coupled with electron energy loss spectroscopy mapping shows uniform Fe/Ti distribution with minor compositional variation among particles. Cation disorder was confirmed for both of the materials at an atomic level, with a short-range order more pronounced in r-LFTO·C. Operando X-ray absorption spectroscopy, ex situ hard X-ray photoelectron spectroscopy, ex situ soft X-ray absorption spectroscopy, and ex situ synchrotron X-ray diffraction were used to investigate (de)lithiation in the bulk and at the surface. Structurally, the r-LFTO·C demonstrated reversible partial Fe center migration between octahedral and tetrahedral sites during (de)lithiation. The r-LFTO·C evidenced that the redox of O was coincident with iron redox during initial electrochemical cycling, while iron redox dominated later cycling. In contrast, s-LFTO·C electrochemistry involved iron redox throughout the cycling process. The findings rationalize the differences in the electrochemistry where r-LFTO·C shows higher initial capacity yet poorer capacity retention over a voltage window where O redox can be accessed, while the s-LFTO·C shows lower initial capacity yet improved capacity retention.

25 ENERGY STORAGE↗
DOE OSTI2026

An Activity-Based Sensing Approach to Monitor Nanomaterial-Promoted Changes in Labile Metal Pools in Living Systems

Metal-based nanoparticles are a promising class of materials for diagnosis and treatment of cancer and other diseases. However, mechanisms of action of these nanomedicines remain insufficiently understood due in large part to our limited understanding of the dynamic equilibria between solid metal nanoparticles and labile metal ions generated from these nanoparticles within complex biological milieus. Here, we apply activitybased sensing to directly identify and investigate the fate of labile copper pools with metal and oxidation state-specificity generated by anticancer copper nanomedicines. We found that treatment of cells with copper-releasing nanoparticles alter labile Cu(I)/Cu(II) ratios through an increase in labile Cu(II), while overall labile copper levels decrease. Labile copper release triggers compensatory responses in two major antioxidant pathways, glutathione (GSH) and nuclear factor erythroid 2-related factor 2 (NRF2), as well as in metal homeostasis to limit copper availability via regulation of copper export (ATP7B) and copper import (CTR1) proteins. These findings establish the value of activity-based sensing as a generalizable approach for labile metal imaging to help decipher molecular mechanisms of bioactive metal nanoparticles and guide the development of more effective nanomedicine diagnostics and therapies to target metal-dependent disease vulnerabilities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Shape-Shifting Peptide Nanomaterials: Surface Asymmetry Enables pH-Dependent Formation and Interconversion of Collagen Tubes and Sheets

The fabrication of dynamic, transformable biomaterials that respond to environmental cues represents a significant step forward in the development of synthetic materials that rival their highly functional, natural counterparts. In this work, we describe the design and synthesis of crystalline supramolecular architectures from charge-complementary heteromeric pairs of collagen-mimetic peptides (CMPs). Under appropriate conditions, CMP pairs spontaneously assemble into either 1D ultraporous (pore diameter >100 nm) tubes or 2D bilayer nanosheets due to the structural asymmetry that arises from heteromeric self-association. Crystalline collagen tubes represent a heretofore unobserved morphology of this common biomaterial. In-depth structural characterization from a suite of biophysical methods, including TEM, AFM, high-resolution cryo-EM, and SAXS/WAXS measurements, reveals that the sheet and tube assemblies possess a similar underlying lattice structure. The experimental evidence suggests that the tubular structures are a consequence of the self-scrolling of incipient 2D layers of collagen triple helices and that the scrolling direction determines the formation of two distinct structural isoforms. Furthermore, we show that nanosheets and tubes can spontaneously interconvert through manipulation of the assembly pH and systematic adjustment of the CMP sequence. Altogether, we establish initial guidelines for the construction of dynamically responsive 1D and 2D assemblies that undergo a structurally programmed morphological transition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Classifying handedness in chiral nanomaterials using label error robust deep learning

Abstract High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

36 MATERIALS SCIENCE↗