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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Correlated 4D-STEM and EDS for the classification of fine Beta-precipitates in aluminum alloy AA 6063-T6

Tuning the properties of aluminum alloys AA 6063-T6 involves artificial aging to induce precipitate formation, particularly β’’ and β’ phases. Previous characterization challenges due to their similar appearance are addressed here by correlating 4D scanning transmission electron microscopy (4DSTEM) and energy-dispersive spectroscopy (EDS) mapping. This approach allows us to analyze the structure and composition of precipitates individually, overcoming limitations of conventional imaging and structural analysis techniques when the precipitates appear simultaneously, as is often the case. We present detailed characterizations of needle-shaped Beta precipitates, revealing distinct diffraction patterns (DPs) and compositional differences. The method's applicability extends beyond aluminum alloys, offering a promising strategy for complex composite material characterization with multimodal scanning transmission electron microscopy (STEM) techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators

Highlights: • A rapid, cost-effective, and comparative methodology was proposed to evaluate the HVs of burning MSW online. • Models ranked in descending order of performance are ANFIS, RF, SVM, MLP. • A well trained model is capable of characterizing the variation trend of HVs accurately. - Abstract: The heating values, particularly lower heating values of burning municipal solid waste are critically important parameters in operating circulating fluidized bed incineration systems. However, the heating values change widely and frequently, while there is no reliable real-time instrument to measure heating values in the process of incinerating municipal solid waste. A rapid, cost-effective, and comparative methodology was proposed to evaluate the heating values of burning MSW online based on prior knowledge, expert experience, and data-mining techniques. First, selecting the input variables of the model by analyzing the operational mechanism of circulating fluidized bed incinerators, and the corresponding heating value was classified into one of nine fuzzy expressions according to expert advice. Development of prediction models by employing four different nonlinear models was undertaken, including a multilayer perceptron neural network, a support vector machine, an adaptive neuro-fuzzy inference system, and a random forest; a series of optimization schemes were implemented simultaneously in order to improve the performance of each model. Finally, a comprehensive comparison study was carried out to evaluate the performance of the models. Results indicate that the adaptive neuro-fuzzy inference system model outperforms the other three models, with the random forest model performing second-best, and the multilayer perceptron model performing at the worst level. A model with sufficient accuracy would contribute adequately to the control of circulating fluidized bed incinerator operation and provide reliable heating value signals for an automatic combustion control system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Screening Fuels for Autoignition with Small-Volume Experiments and Gaussian Process Classification

Partially reacting candidate fuels under highly dilute conditions across a range of temperatures provides a means to classify the candidates based on traditional ignition characteristics using much lower quantities (sub-mL) than the full octane tests. Using a classifier based on a Gaussian Process model, synthetic species profiles obtained by plug flow reactor simulations at seven temperatures are used to demonstrate that the configuration can be used to classify 95% of the samples correctly for autoignition sensitivity exceeding a threshold (S = 8) and 100%of the samples correctly for research octane number exceeding a threshold (RON = 90). Molecular beam mass spectrometry (MBMS) experimental data at four temperatures is then used as the model input in a real-world test. Despite the nontrivial relationship between the MBMS measurements and speciation as well as experimental noise it is still possible to classify 95% of the samples correctly for RON and 85% of the samples correctly for S in a 'leave-one-out' cross validation exercise. The test data set consists of 45 fuels and includes a variety of primary reference fuels, ethanol blends and other oxygenates.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

MARLOWE: An Untargeted Proteomics, Statistical Approach to Taxonomic Classification for Forensics

General proteomics research for fundamental science typically addresses laboratory- or patient-derived samples of known origin and composition. However, in a few research areas, such as environmental proteomics, clinical identification of infectious organisms, archeology, art/cultural history, and forensics, attributing the origin of a protein-containing sample to the organisms that produced it is a central focus. A small number of groups have approached this problem and developed software tools for taxonomic characterization and/or identification using bottom-up proteomics. Most such tools identify peptides via database search, and many rely on organism-specific peptides as markers. Our group recently introduced MARLOWE, a software tool for taxonomic characterization of unknown samples based on de novo peptide identification and signal-erosion-resistant strong peptides, which are shared peptides distributed in a taxonomy-dependent manner. In the current work, we further characterize the utility of MARLOWE using publicly available proteomics data from forensically-relevant samples. MARLOWE characterizes samples based on their protein profile, and returns ranked organism lists of potential contributors and taxonomic scores based on shared strong peptides between organisms. Overall, the correct characterization rate ranges between 44 and 100%, depending on the sample type and data acquisition parameters (with lower numbers associated with lower-quality data sets). MARLOWE demonstrates successful characterization of true contributors and close relatives, and provides sufficient specificity to distinguish certain microbial species. MARLOWE demonstrates its ability to provide insight into potential taxonomic sources for a wide range of sample types without prior assumptions about sample contents. As a result, this approach can find utility in forensic science and also broadly in bioanalytical applications that utilize proteomics approaches for taxonomic characterization.

Bacteria↗