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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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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Effects of Nucleus Density and Dendritic Growth Influenced by Ti and Nb on Solidification Structure of Fe-18 Pct Cr Ferritic Stainless Steel

The effects of nucleus density and dendritic growth influenced by Ti and Nb on the solidification structure of Fe-18 pct Cr ferritic stainless steel (FSS) ingots were investigated. The equiaxed zone ratios (EZR) were 8.5, 28.8, 67.5, and 57.3 pct in the blank, Nb-bearing, Ti-bearing, and Ti-Nb-bearing FSS ingots, respectively. The grain size was the smallest in the Ti-Nb-bearing ingot. Silicon oxide was present in the blank sample and Nb-bearing ingot, but the complex nuclei that were composed of TiO{sub x}-Ti(C, N) and TiO{sub x}-(Ti, Nb){sub x}(C, N){sub y} existed in the Ti-bearing and Ti-Nb-bearing ingots, respectively. The bulk density of the complex nucleus decreased by 70 pct when the Ti content decreased from 0.16 to 0.14 mass pct. Thermodynamic calculations indicate that the phase transformation from Ti{sub 3}O{sub 5} to Ti{sub 2}O{sub 3} was delayed in the ingot with a lower Ti content and that the Ti{sub 2}O{sub 3} shows more potential for TiN nucleation compared with Ti{sub 3}O{sub 5} according to the disregistry theory. The increased EZR mechanism was illustrated by a cellular-automaton–finite-element method with actual distribution statistics of the complex nucleus and dendritic-growth-kinetic analysis. The heterogeneous nucleation effect of the complex nucleus by Ti addition and the variations in dendritic-growth kinetics by Ti and Nb contribute to the EZR increase of FSS.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessing pore network heterogeneity across multiple scales to inform CO2 injection models

Geologic heterogeneity is a key feature that must be considered when translations of scaled data are performed. This paper presents the assessment of geologic heterogeneity using a multiscale workflow that includes image analysis-based methods coupled with well log analysis to provide data in which fractals and machine learning methods estimate the carbon dioxide (CO 2 ) storage resource potential of a reservoir. The heterogeneity of rock properties of the complex Bell Creek reservoir in Montana, USA, was explored at the pore scale (~nm to mm), core scale (~mm to m), and well scale (~cm to m). The data used in this study included advanced image analysis of micro-CT (computed tomography) images (pore scale), thin sections (pore scale), plugs and core images (core scale) and well logs (well scale). The micro-CT images were segmented using a U-net segmentation approach into objects of pores and grains. Further, the segmented images were reconstructed into subvolumes of different sizes. Physical properties (porosity and permeability) and fractal dimensions were calculated for the various subvolumes, and Lorenz coefficient (Lc) values, a single parameter to describe the degree of heterogeneity within a pay zone section, were calculated from thin-section images and well logs. Porosity and fractal dimension values were used to estimate the 188-µm threshold of representative elementary volume (REV) in this study. Both the Lc and fractal dimension values were found to be negatively correlated. When these two parameters are combined, it is possible to discern differences in the complex porous networks of the samples analyzed in this study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Quantum chemical replica-exchange umbrella sampling molecular dynamics simulations reveal the formation mechanism of iron phthalocyanine from iron and phthalonitrile

Phthalocyanine (Pc) and its metal complexes (MPcs) have been used industrially since their discovery in the early 20th century. The phthalonitrile (PN) method is a well-known synthesis method in which Pc or MPc can be afforded by heating a mixture of PN and metal powders over 280 °C with only moderate yield. However, the formation mechanism of the phthalocyanines and the intermediate stages of this seemingly simple reaction have yet to be fully understood. To study this mechanism computationally, we carried out quantum chemical molecular dynamics (MD) simulations based on the density-functional tight-binding (DFTB) method, applying the replica-exchange umbrella sampling (REUS) method, starting from four PN molecules and one iron atom. The DFTB-REUS-MD simulations successfully yielded FePc, and a metastable structure very similar to FePc but with a reactive nitrene unit was also identified that might explain the incomplete conversion of the reactants into FePc. Furthermore, analysis of the MD trajectories reveals a three-step FePc formation mechanism for the PN method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Assessing Gas-Phase Ion Reactivity of 50 Elements with NO and the Direct Application for 239 Pu in Complex Matrices Using ICP-MS/MS

Understanding the reactivity of metal cations with various reaction gases in ICP-MS/MS is important to determine the best gas to use for a given analyte/interference pair. In this study, nitric oxide (NO) was investigated as the reaction gas following previous experimental designs. The reactions with 50 elements were investigated to examine periodic trends in reactivity, validate theoretical modeling of reaction enthalpies as a method to screen reactant gases, and provide a baseline for potential in-line gas separation methods. ICP-MS/MS studies involving actinides are typically limited to Th, U and Pu, with analyses of Np and Am rarely reported in the literature. To date, only two previous methods have investigated the use of NO in ICP-MS/MS analyses. To showcase the utility of NO, a method was developed to measure 239 Pu in the presence of environmental matrix constituent and other actinides, like what could be expected from post-detonation debris, with no chemical separation prior to analysis. 239 Pu + was reacted to form 239 Pu 16 O + , eliminating interferences derived from the sample matrix by measuring 239 Pu + intensity at m/z = 255 ( 239 Pu 16 O + ). To validate NO for 238 U 1 H + interference removal in environmental matrices, standard reference materials were diluted to 1 mg per gram of solution and spiked to 0.05 pg/g of 239 Pu and 1 µg/g 238 U (Pu/U = 5 x 10 -8 ). Measured 239 Pu concentrations were within 6% of the spiked value. In conclusion, these results demonstrate that reliable 239 Pu measurements can be made at levels relevant to nuclear forensics without the need for extensive chemical matrix separation prior to analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Extended Rice–Thomson analysis and atomistic simulations revealing grain boundary effects on fracture in refractory high-entropy alloys

Significance This work serves to extend the fundamental ductile vs. brittle fracture theory, specifically the Rice–Thomson criterion, by introducing a grain boundary ahead of an initiating crack which propagates at an oblique angle to impinge the boundary. Atomistic fracture simulations on two refractory complex concentrated alloys, the brittle NbMoTaW and the ductile Nb 45 Ta 25 Ti 15 Hf 15 , demonstrate qualitative correspondence with the extended Rice–Thomson criterion and experimental observations. Abstract Understanding how grain boundaries mediate fracture remains a critical challenge in designing ductile, high-performance refractory alloys. Here, we extend the Rice–Thomson criterion to account for the angle between cracks and the impinging grain boundaries (GBs), capturing the competition between intergranular fracture and dislocation-mediated plasticity. Using machine learning interatomic potentials, we performed molecular statics simulations to probe fracture mechanisms in nanocrystalline NbMoTaW and Nb 45 Ta 25 Ti 15 Hf 15 , each with two different grain sizes, revealing trends consistent with experimental observations and the extended Rice model. Comparison with averaged R-curves for bulk samples demonstrates that GBs enhance ductility in Nb 45 Ta 25 Ti 15 Hf 15 in both grain sizes investigated. In contrast, GBs only locally improve fracture resistance in NbMoTaW when cracks are temporarily pinned at GBs inclined at high angles from the crack, but generally promote brittle intergranular fracture. These contrasting behaviors are attributed to differences in GB cohesion, reflecting clear alloying trends that align with ab-initio calculations and trends observed experimentally. Our results bridge classical fracture theory, atomistic simulations, and experimental observations, providing a comprehensive understanding of the fracture mechanisms in nanocrystalline refractory complex concentrated alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Models and Algorithms for Equilibrium Analysis of Mixed-Material Nucleic Acid Systems

Dynamic programming algorithms within the NUPACK software suite enable analysis of equilibrium base-pairing properties for complex and test tube ensembles containing arbitrary numbers of interacting nucleic acid strands. Currently, calculations are limited to single-material systems that are either all-RNA or all-DNA. Here, to enable analysis of mixed-material systems that are critical for modern applications in vitro, in situ, and in vivo, we develop physical models and dynamic programming algorithms that allow the material of the system to be specified at nucleotide resolution. Free energy parameter sets are constructed for both RNA/DNA and RNA/2'OMe-RNA mixed-material systems by combining available empirical mixed-material parameters with single-material parameter sets to enable treatment of the full complex and test tube ensembles. New dynamic programming recursions account for the material of each nucleotide throughout the recursive process. For a complex with N nucleotides, the mixed-material dynamic programming algorithms maintain the O(N 3 ) time complexity of the single-material algorithms, enabling efficient calculation of diverse physical quantities over complex and test tube ensembles (e.g., complex partition function, equilibrium complex concentrations, equilibrium base-pairing probabilities, minimum free energy secondary structure(s), and Boltzmann-sampled secondary structures) at a cost increase of roughly 2.0-3.5×. The results of existing single-material algorithms are exactly reproduced when applying the new mixed-material algorithms to single-material systems. Accuracy is significantly enhanced using mixed-material models and algorithms to predict RNA/DNA and RNA/2'OMe-RNA duplex melting temperatures from the experimental literature as well as RNA/DNA melt profiles from new experiments. In conclusion, mixed-material analyses can be performed online using the NUPACK web app (www.nupack.org) or locally using the NUPACK Python module.

2′OMe-RNA↗
DOE OSTI2017

Ultrafast optical snapshots of hybrid perovskites reveal the origin of multiband electronic transitions

In this paper, connecting the complex electronic excitations of hybrid perovskites to their intricate organic-inorganic lattice structure has critical implications for energy conversion and optoelectronic technologies. Here we detail the multiband, multivalley electronic structure of a halide hybrid perovskite by measuring the absorption transients of a millimeter-scale-grain thin film as it undergoes a thermally controlled reversible tetragonal-to-orthogonal phase transition. Probing nearly single grains of this hybrid perovskite, we observe an unreported energy splitting (degeneracy lifting) of the high-energy 2.6 eV band in the tetragonal phase that further splits as the rotational degrees of freedom of the disordered CH 3 NH 3 + molecules are reduced when the sample is cooled. This energy splitting drastically increases during an extended phase-transition coexistence region that persists from 160 to 120 K, becoming more pronounced in the orthorhombic phase. By tracking the temperature-dependent optical transition energies and using symmetry analysis that describes the evolution of electronic states from the tetragonal phase to the orthorhombic phase, we assign this energy splitting to the nearly degenerate transitions in the tetragonal phase from both the R - and M -point-derived states. Importantly, these assignments explain how momentum conservation effects lead to long hot-carrier lifetimes in the room-temperature tetragonal phase, with faster hot-carrier relaxation when the hybrid perovskite structurally transitions to the orthorhombic phase due to enhanced scattering at the Γ point.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A science-driven approach to optimize the design for a biological small-angle neutron scattering instrument

Biological small-angle neutron scattering (SANS) instruments facilitate critical analysis of the structure and dynamics of complex biological systems. However, with the growth of experimental demands and the advances in optical systems design, a new neutron optical concept is necessary to overcome the limitations of current instruments. This work presents an approach to include experimental objectives ( i.e. the science to be supported by a specific neutron scattering instrument) in the optimization of the neutron optical concept. The approach for a proposed SANS instrument at the Second Target Station of the Spallation Neutron Source at Oak Ridge National Laboratory, USA, is presented here. Further, the instrument is simulated with the McStas software package. The optimization process is driven by an evolutionary algorithm using McStas output data, which are processed to calculate an objective function designed to quantify the expected performance of the simulated neutron optical configuration for the intended purpose. Each McStas simulation covers the complete instrument, from source to detector, including realistic sample scattering functions. This approach effectively navigates a high-dimensional parameter space that is otherwise intractable; it allows the design of next-generation SANS instruments to address specific scientific cases and has the potential to increase instrument performance compared with traditional design approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Investigation of interfacial impurities in m-plane GaN regrown p-n junctions for high-power vertical electronic devices

GaN is an attractive material for high-power electronics due to its wide bandgap and large breakdown field. Verticalgeometry devices are of interest due to their high blocking voltage and small form factor. One challenge for realizing complex vertical devices is the regrowth of low-leakage-current p-n junctions within selectively defined regions of the wafer. Presently, regrown p-n junctions exhibit higher leakage current than continuously grown p-n junctions, possibly due to impurity incorporation at the regrowth interfaces, which consist of c-plane and non-basal planes. Here, we study the interfacial impurity incorporation induced by various growth interruptions and regrowth conditions on m-plane p-n junctions on free-standing GaN substrates. The following interruption types were investigated: (1) sample in the main MOCVD chamber for 10 min, (2) sample in the MOCVD load lock for 10 min, (3) sample outside the MOCVD for 10 min, and (4) sample outside the MOCVD for one week. Regrowth after the interruptions was performed on two different samples under n-GaN and p-GaN growth conditions, respectively. Secondary ion mass spectrometry (SIMS) analysis indicated interfacial silicon spikes with concentrations ranging from 5e16 cm -3 to 2e18 cm -3 for the n-GaN growth conditions and 2e16 cm -3 to 5e18 cm-3 for the p-GaN growth conditions. Oxygen spikes with concentrations ~1e17 cm -3 were observed at the regrowth interfaces. Carbon impurity levels did not spike at the regrowth interfaces under either set of growth conditions. We have correlated the effects of these interfacial impurities with the reverse leakage current and breakdown voltage of regrown m-plane p-n junctions.

42 ENGINEERING↗
DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Characterization of Si (sub X)Ge (sub 1-x)/Si Heterostructures for Device Applications Using Spectroscopic Ellipsometry

Spectroscopic ellipsometry (SE) characterization of several complex Si (sub X)Ge (sub 1-x)/Si heterostructures prepared for device fabrication, including structures for heterojunction bipolar transistors (HBT), p-type and n-type heterostructure modulation doped field effect transistors, has been performed. We have shown that SE can simultaneously determine all active layer thicknesses, Si (sub X)Ge (sub 1-x) compositions, and the oxide overlayer thickness, with only a general knowledge of the structure topology needed a priori. The characterization of HBT material included the SE analysis of a Si (sub X)Ge (sub 1-x) layer deeply buried (600 nanometers) under the silicon emitter and cap layers. In the SE analysis of n-type heterostructures, we examined for the first time a silicon layer under tensile strain. We found that an excellent fit can be obtained using optical constants of unstrained silicon to represent the strained silicon conduction layer. We also used SE to measure lateral sample homogeneity, providing quantitative identification of the inhomogeneous layer. Surface overlayers resulting from prior sample processing were also detected and measured quantitatively. These results should allow SE to be used extensively as a non-destructive means of characterizing Si (sub X)Ge (sub 1-x)/Si heterostructures prior to device fabrication and testing.

Sieg, R. M.↗
NASA NTRS2019

Analyses of Aliphatic Aldehydes and Ketones in Carbonaceous Chondrites

Aliphatic aldehydes and ketones are essential building blocks for the synthesis of more complex organic compounds. Despite their potentially key role as precursors of astrobiologically important molecules, such as amino acids and carboxylic acids, this family of compounds has scarcely been evaluated in carbonaceous chondrites. The paucity of such analyses likely derives from the low concentration of aldehydes and ketones in the meteorites and from the currently used chromatographic methodologies that have not been optimized for meteorite analysis. In this work, we report the development of a novel analytical method to quantify the molecular distribution and compound-specific isotopic analysis of 29 aliphatic aldehydes and ketones. Using this method, we have investigated the molecular distribution and C-13-isotopic composition of aldehydes and ketones in 10 carbonaceous chondrites from the CI, CM, CR, and CV groups. The total concentration of carbonyl compounds ranged from 130 to 1000 nmol g(exp -1) of meteorite with formaldehyde, acetaldehyde, and acetone being the most abundant species in all investigated samples. The C-13-isotopic values ranged from -67 to +64‰ and we did not observe clear relationships between C-13-content and molecular weight. Accurately measuring the relative abundances, determining the molecular distribution and isotopic composition of chondritic organic compounds is central in assessing both their formation chemistry and synthetic relationships.

Organic carbon↗
DOE OSTI2018

Structural, magnetic, dielectric and magneto-dielectric properties of (BaTiO3)0.70(Li0.3Zn0.4Fe2.3O4)0.30

Highlights: Microcrystals of BaTiO{sub 3} is incorporated in Li{sub 0.3}Zn{sub 0.4}Fe{sub 2.3}O{sub 4} using sol-gel method. Crystallographic phase and morphology of the composite is investigated. Magnetic ordering originates from the ferrimagnetic matrix of the composite. The dielectric behaviour is originated from the BaTiO{sub 3} particles in the composite. The composite shows strong magneto-electric property, useful for application. - Abstract: Structural, magnetic, dielectric and magneto-dielectric properties of (BaTiO{sub 3}){sub 0.70}(Li{sub 0.3}Zn{sub 0.4}Fe{sub 2.3}O{sub 4}){sub 0.30} [BTO-LZFO] composite are studied. To prepare the composite, microcrystals of BaTiO{sub 3} [BTO] prepared in solid-state reaction are taken along with the precursors of Li{sub 0.3}Zn{sub 0.4}Fe{sub 2.3}O{sub 4} [LZFO], prepared in sol-gel route. X-ray diffraction (XRD) study confirms the phase purity of the composite sample. High-resolution transmission electron microscopy also confirms the formation of the composite state. Analysis of magnetic data confirms the ferrimagnetic nature of the sample along with a fraction of superparamagnetic particles. The ferrimagnetism is explained by Néels’ two-sublattice model. The dielectric spectrum of the sample follows the Cole-Cole relaxation. Interestingly, the ferroelectric and dielectric behaviours of BTO are retained in the composite state of BTO-LZFO, though the magnetization is enhanced drastically. This enhanced magnetic property along with the ferroelectricity and strong magneto-dielectric coupling in the composite state confirms that BTO-LZFO can be used as a multiferroic material in different electronic devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

X-ray photoelectron spectroscopy and rotating disk electrode measurements of smooth sputtered Fe-N-C films

Electrocatalysts for the oxygen reduction reaction (ORR) based on complexes of iron and nitrogen in a carbon matrix (Fe-N-C) are a promising alternative to platinum group metal (PGM) based catalysts in polymer electrolyte membrane (PEM) fuel cells. Further improvements of Fe-N-C catalysts would benefit from model thin film studies of activity and stability of catalytic sites, but synthesis of Fe-N-C model thin films is challenging. Here we report on synthesis and characterization of Fe-N-C thin films produced by co-sputtering iron and carbon in a reactive nitrogen atmosphere onto removable glassy carbon rotating disk electrode (RDE) tips. Scanning electron microscopy (SEM) measurements indicate that the Fe-N-C films deposited at high temperature are smoother than the films annealed at high temperature. Electrocatalytic activity measured on the thin Fe-N-C films is greater for both high-temperature samples than for the room-temperature sample. From the analysis of X-ray photoelectron spectroscopy (XPS) data, exposure of the films to high temperatures results in increased graphitization of the carbon with the Fe-N-C films, and increased relative amount of graphitic and hydrogenated nitrogen species. Overall the results of this study demonstrate the feasibility of a thin film model system approach for studying active sites in PGM-free catalysts.

14 SOLAR ENERGY↗
DOE OSTI2021

In-situ 4D visualization and analysis of temperature-driven creep in an oil shale propped fracture

Here, the proppant embedment due to creep in shales is a known issue affecting the useable lifetime of wells in unconventional oil and gas recovery. One of the factors influencing creep is the presence of organics, whose properties can be very sensitive to temperature. In this work we investigated for the first time the role of temperature-induced creep increase in proppant embedment in an organics-rich Green River oil shale sample via in-situ synchrotron X-ray micro-computed tomography. We observed that temperatures as low as 75 °C already induce fast creep, with a fracture aperture closing rate of 13 μm/h and a loss of fracture conductivity rate of 8.7%/h, due only to proppant embedment, in the measured interval at the first heating stage. Local displacement data analysis provided evidence for markedly plastic deformation around the proppant-shale contacts, in contrast with the brittle proppant embedment observed on more cemented and less organics-rich shales at room temperature. The results highlight how the problem of temperature-dependent mechanical behavior might be more important than previously thought, in shales with a high content in organics, and that in-situ micro-imaging techniques can play a key role in understanding the underlying mechanisms, contributing to solve creep-related problems associated with hydraulic fracturing in complex scenarios.

02 PETROLEUM↗
DOE OSTI2018

Comprehensive High Throughput One‐Pot Analysis of Glycoproteins through Tool for Rapid Analysis of Glycopeptide by Permethylation (TRAP) Method.

Glycoproteins plays key roles in various cellular functions such as protein folding, protein targeting, receptor function, cell–cell recognition, protein stability, signaling, and other cellular functions. Currently a comprehensive and universal tool for the automated analysis of glycopeptides through their tandem mass spectrometry and data processing is a tedious process. We recently developed a method, Tool for Rapid Analysis of glycopeptide by Permethylation (TRAP), that simplifies glycoprotein analysis by integrating glycan sequencing and glycopeptide analysis in a single experiment. We employed this approach for the detailed analysis of glycosidic linkages, composition, glycan sequencing, glycosylation site mapping and sequencing of peptide moiety of glycopeptides of glycoproteins from both prokaryotes and eukaryotes. Here we are presenting an easy to use, robust, suitable for large as well as small sample sets and also cost‐effective glycoprotein analysis tool through TRAP approach. A permethylation protocol which can be automated and suitable for reliable permethylation of glycopeptides from the protease digest of glycoproteins was developed. Initial studies on glycopeptides from a model glycoprotein bovine RNAse B indicates that improved sensitivity and throughput of permethylation reactions can be achieved in conducting the reaction in microscale. This approach would enable convenient, rapid, and reliable high‐throughput screening of protein glycosylation and can be employed as a cost‐effective kit for glycosylated biomarker discoveries. In order to improvise the comprehensive glycosylation analysis and throughput, we have also developed an LC‐MS/MS platform for the analysis of glycopeptides processed through TRAP. We have conducted the analysis of standard glycoproteins such as bovine RNase B, human transferrin, and bovine fetuin and our preliminary results indicate that TRAP hyphened with LC‐MS/MS enables characterization of isomers of each glycoform at each glycosylation sites of complex glycoprotein mixtures. Support or Funding Information This research was supported by the National Institutes of Health (NIH) funded research, Grant R21GM122633; resource for biomedical glycomics, Grant P41GM103490; instrument grant, Grant S10OD018530; and U.S. Department of Energy grant, Grant DE‐SC0015662, to the Complex Carbohydrate Research Center. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Shajahan, Asif↗
DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗