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At least 397 records · Page 22

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DOE OSTI2011

Spin-Cast and Patterned Organophosphonate Self-Assembled Monolayer Dielectrics on Metal-Oxide-Activated Si

An efficient process is developed for modifying Si with self-assembled monolayers (SAMs) through in situ metal oxide surface activation and microcontact printing or spin-coating of phosphonic-acid-based molecules. The utility of this process is demonstrated by fabricating self-organized and solution-processed low-voltage organic thin-film transistors enabled by patterned and spin-cast phosphonate SAM/metal oxide hybrid dielectrics.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Process Considerations for the Production of Hydrogen via Steam Reforming of Oxygenated Gases from Biomass Pyrolysis and Other Conversion Processes

Abstract In 2021, average CO 2 emissions was 9.7 g CO 2 /g H 2 produced, primarily based on steam methane reforming (SMR) technology that currently dominates hydrogen production. The substitution of natural gas (NG) and other fossil feedstocks in SMRs with renewable gases may be considered as an option for reducing greenhouse gas emissions. This analysis explores process impacts and constraints associated with the potential introduction of biogenic gases into SMRs. The results indicate that replacing NG in the fuel train of the SMR, followed by partial replacement of NG in the feed train may be a feasible approach. For the CO‐ and CO 2 ‐rich gas compositions assumed in this analysis the results indicate that feed train may accommodate up to 25 mole % of biogenic gases using allowances in existing designs and/or with small modifications, while maintaining similar hydrogen output. NG substitution in higher proportions require more major changes because of increased flow rates and heat exchange requirements in the system. Biogenic gases with lower CO 2 and higher calorific values are advantageous for NG substitution, and dry reforming using the CO 2 present in the feed gas can reduce steam consumption and increase process efficiency within limits where coking does not become a new constraint.

08 HYDROGEN↗

DOE OSTI2026

A Step-by-Step Protocol from METASPACE to Biological Interpretation

Mass spectrometry imaging (MSI) represents an exceptional tool for exploring complex biological systems spatially at the molecular level. However, due to its multidimensional nature and large-scale data output, it presents considerable challenges when it comes to extracting meaningful biological insights. Recent advancements, such as the METASPACE platform, have enabled researchers to efficiently process, annotate, and interpret MSI datasets by leveraging machine learning and cloud-based infrastructure. In this tutorial, we present a detailed and user-friendly R-pipeline designed to help METASPACE users navigate untargeted metabolomic annotations and transform them into practical insights about their biological systems. By combining METASPACE annotations with rapid R-based screening, this workflow not only streamlined the analytical process but also enhanced the understanding of spatial molecular distribution, especially for complex systems. Here, this easy-to-follow approach has the potential for applications in diagnostics, drug discovery, environmental and ecological processes, and more. We envision this pipeline to be particularly useful for newcomers to the field of MSI and

Moreno Pedraza, Abigail↗

DOE OSTI2019

Energy flow networks: deep sets for particle jets

A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning efforts to learn directly from sets of features or “point clouds”. Adapting and specializing the “Deep Sets” framework to particle physics, we introduce Energy Flow Networks, which respect infrared and collinear safety by construction. We also develop Particle Flow Networks, which allow for general energy dependence and the inclusion of additional particle-level information such as charge and flavor. These networks feature a per-particle internal (latent) representation, and summing over all particles yields an overall event-level latent representation. We show how this latent space decomposition unifies existing event representations based on detector images and radiation moments. To demonstrate the power and simplicity of this set-based approach, we apply these networks to the collider task of discriminating quark jets from gluon jets, finding similar or improved performance compared to existing methods. We also show how the learned event representation can be directly visualized, providing insight into the inner workings of the model. These architectures lend themselves to efficiently processing and analyzing events for a wide variety of tasks at the Large Hadron Collider. Implementations and examples of our architectures are available online in our EnergyFlow package.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2011

High temperature millimeter wave radiometric and interferometric measurements of slag-refractory interaction for application to coal gasifiers

Millimeter wave (MMW) radiometry can be used for simultaneous measurement of emissivity and temperature of materials under extreme environments (high temperature, pressure, and corrosive environments) such as in slagging coal gasifiers, where sensors have been identified as a key enabling technology need for process optimization. We present a state-of-the-art dual-channel MMW heterodyne radiometer with active interferometric capability that allows simultaneous radiometric measurements of sample temperature, emissivity, and flow dynamics to over 1873 K. Interferometric capability is supplied via a probe signal originating from the 137 GHz radiometer local oscillator (LO). The interferometric 'video' channels allow measurement of additional parameters simultaneously, such as volume expansion, thickness change, and slag viscosity along with temperature or emissivity. This capability has been used to demonstrate measurement of temperature and simulated coal slag infiltration into a chromia refractory brick sample as well as slag flow down a vertically placed refractory brick. Observed phenomena include slag melting and slumping, slag reboil and foam with oxygen evolution, and eventual failure of the alumina crucible through corrosion by the molten slag. These results show the promise of the MMW system for extracting quantitative and qualitative process parameters from operating slagging coal gasifiers, providing valuable information for process efficiency, control, and increased productivity.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

SPLENDAQ: A Detector-Agnostic Data Acquisition System for Small-Scale Physics Experiments

Many scientific applications from rare-event searches to condensed matter system characterization to high-rate nuclear experiments require time-domain triggering on a raw stream of data, where the triggering is generally threshold-based or randomly acquired. When carrying out detector R &D, there is a need for a general data acquisition (DAQ) system to quickly and efficiently process such data. In the SPLENDOR collaboration, we are developing the Python-based SPLENDAQ package for this exact purpose—it offers two main features for offline analysis of continuous data: a threshold triggering algorithm based on the time-domain optimal filter formalism and an algorithm for randomly choosing nonoverlapping segments for noise measurements. Further, combined with the commercially available Moku platform, developed by Liquid Instruments, we have a full pipeline of event building off raw data with minimal setup. Here, we review the underlying principles of this detector-agnostic DAQ package and give concrete examples of its utility in various applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Optimization of Beidellite/Polyaniline Production Conditions by Central Composite Design for Removal of Acid Yellow 194

In the adsorption process, the adsorbent structure and its interaction with wastewater are the main determinant of the process efficiency. For this reason, the correct selection of the production conditions for the produced adsorbents is critical. In this study, the reaction time and the concentration of acid, oxidant, substrate, and monomer were selected as independent variables which effect the structure of the polyaniline based composite material. These production variables of polyaniline/beidellite (PANI/BEI) composites were designed by response surface methodology (RSM). According to RSM, 46 different PANI/BEI composites were synthesized and were used as an adsorbent in the removal of acid yellow 194. Thus, the optimum PANI/BEI production conditions were determined. After the identification of the optimum PANI/BEI production conditions, the effect of adsorption conditions such as pH, mixing time, and dye concentration were studied by batch experiments. The maximum adsorption capacity was obtained as 123 (mg g{sup −1}) at the T = 25 °C, t = 24 h, pH 3, m{sub s} = 0.15 g adsorbent, V{sub s} = 50 ml dye solution, and ω = 200 rpm. The results presented in this study revealed that the central composite design is a suitable method to optimize the production conditions of PANI/BEI and the prepared PANI/BEI composites has an important potential as an adsorbent for the removal of metal-complex dyes such as AY194. In addition, the polymerization of aniline and the removal of dye were confirmed by different analytical techniques such as BET, XRD, SEM, and EDAX.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2018

The Effects of Low Frequency on the Microstructure and Mechanical Properties of High-Strength Al-Mg Aluminum Alloys by Wire and Double-Pulsed Arc Additive Manufacturing

An innovative and high deposition efficiency process, the double-pulsed arc additive manufacturing process, is used to produce high-strength Al-Mg wall components. This dissertation investigates the electrical parameter acquisition, bead appearance, porosity morphology, microstructure and mechanical properties of additively manufactured high-strength Al-Mg wall components and how these are affected by the low frequency of the double-pulsed arc additive manufacturing process. Then, using this process at different low frequencies of 0, 1, 3, 5, 7 and 9 Hz, multilayer deposition experiments were performed. It can be observed that the deposit porosity is obviously eliminated compared with cold metal transfer (CMT) process. The bead appearance of the deposited aluminum wall varies clearly with low frequencies, and at a low frequency of 3 Hz, its appearance is the best of all. The optical observation of microstructure proves that the average grain size varies significantly with low frequency, and at a low frequency of 1 Hz, it is the smallest. In addition, the deposited wall showed maximum mean microhardness and maximum ultimate tensile strength at a low frequency of 3 Hz. Considering the deposited porosity and mechanical priorities, double-pulsed arc additive manufacturing with a frequency of 3 Hz is more suitable for making high-strength Al-Mg wall components than at other low frequencies.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Viability of Using Glycerin as a Co-substrate in Anaerobic Digestion of Sugarcane Stillage (Vinasse): Effect of Diversified Operational Strategies

Vinasse, from sugar and ethanol production, stands out as one of the most problematic agroindustry wastes due to its high chemical oxygen demand, large production volume, and recalcitrant compounds. Therefore, the viability of using glycerin as a co-substrate in vinasse anaerobic digestion was tested, to increase process efficiency and biogas productivity. The effect of feeding strategy, influent concentration, cycle length, and temperature were assessed to optimize methane production. Glycerin (1.53% v/v) proved to be a good co-substrate since it increased the overall methane production in co-digestion assays. CH{sub 4} productivity enhanced exponentially as influent concentration increased, but when temperature was increased to 35 °C, biogas production was impaired. The highest methane productivity and yield were achieved using fed-batch mode, at 30 °C and at an organic loading rate of 10.1 kg COD m{sup −3} day{sup −1}: 139.32 mol CH{sub 4} m{sup −3} day{sup −1}, 13.86 mol CH{sub 4} kg COD{sub applied}, and 15.30 mol CH{sub 4} kg COD{sub removed}. Methane was predominantly produced through the hydrogenotrophic route. In order to treat all the vinasse produced by a mid-size sugar and ethanol plant, nine reactors with 7263.4 m{sup 3} each would be needed. The energy generated by burning the biogas in boilers would reach approximately 92,000 MW h per season and could save up to US$ 240,000.00 per month in diesel oil demand.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Gas-Phase Reactivity of Carbonate Ions with Sulfur Dioxide: an Experimental Study of Clusters Reactions

The reactivity of carbonate cluster ions with sulfur dioxide has been investigated in the gas phase by mass spectrometric techniques. SO{sub 2} promotes the displacement of carbon dioxide from carbonate clusters through a stepwise mechanism, leading to the quantitative conversion of the carbonate aggregates into the corresponding sulfite cluster ions. The kinetic study of the reactions of positive, negative, singly, and doubly charged ions reveals very fast and efficient processes for all the carbonate ions. .

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Predictive models enhance feedstock quality of corn stover via air classification

Feedstock heterogeneity is a fundamental obstacle to cost-competitive biobased products. Agricultural products like corn stover have anatomical components that vary in their chemical composition, mechanical properties, structure, and response to chemical and biological treatments. A technique that can enrich streams in select anatomical fractions would allow a tailored deconstruction approach to increase overall process efficiency. Air classification can be leveraged for such refining, however, fundamental characterization and understanding of the particle properties that underly the physics of air classification are only modestly documented. Here, we determine fundamental particle properties including mass-to-area ratio, drag coefficient, and partition velocity that describe how anatomical tissues of corn stover behave during air classification. In this work, mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm 2 for cob to 0.04 mg/mm 2 for leaf. Drag coefficients of longer, fibrous materials (i.e., rind, husk, and sheath) are shown to correlate with particle area (p-value < 0.001) whereas granular tissues (i.e., cob, pith, and leaf) correlate better with mass-to-area ratio (p-values < 0.001). When compared to experimental observations, a simulated two-stage air classification and size reduction scenario predicts the overall partitioning of anatomical tissues within 15% for pith, husk, rind, and cob tissues. The model predicts an air-classified fraction preferentially enriched in cob (purity = 20%), rind (purity = 74%), and pith (purity = 4.5%) with a mass yield of 47%. Empirical relations for these properties can be used to predict the partitioning of corn stover during air classification based on anatomical type and size.

09 BIOMASS FUELS↗

DOE OSTI2012

Current Research on Thermochemical Conversion of Biomass at the National Renewable Energy Laboratory

The thermochemical research platform at the National Bioenergy Center, National Renewable Energy Laboratory (NREL) is primarily focused on conversion of biomass to transportation fuels using non-biological techniques. Research is conducted in three general areas relating to fuels synthesis via thermochemical conversion by gasification: (1) Biomass gasification fundamentals, chemistry and mechanisms of tar formation; (2) Catalytic tar reforming and syngas cleaning; and (3) Syngas conversion to mixed alcohols. In addition, the platform supports activities in both technoeconomic analysis (TEA) and life cycle assessment (LCA) of thermochemical conversion processes. Results from the TEA and LCA are used to inform and guide laboratory research for alternative biomass-to-fuels strategies. Detailed process models are developed using the best available material and energy balance information and unit operations models created at NREL and elsewhere. These models are used to identify cost drivers which then form the basis for research programs aimed at reducing costs and improving process efficiency while maintaining sustainability and an overall net reduction in greenhouse gases.

09 BIOMASS FUELS↗

DOE OSTI2020

High-yield growth kinetics and spatial mapping of single-walled carbon nanotube forests at wafer scale

Emerging commercial applications of vertically aligned, single-walled carbon nanotube (SWCNT) “forests” require synthesis that minimizes nanotube diameter while maximizing number density across substrate areas exceeding centimeter scale. To address this need, we synthesized SWCNT forests on full silicon wafers with notable reproducibility and uniformity, and co-optimized growth for small diameters and high densities across large areas to access new territory in this 3D parameter space. We mapped the spatial uniformity of key structural features using Raman microscopy, synchrotron X-ray scattering, and Rutherford backscattering spectrometry. Low C 2 H 2 flux over sub-nm Fe/Mo catalysts produced small-diameter SWCNTs (2.1 nm) at high number densities (2.26 × 10 12 cm -2 ) on wafers up to 6 in. Although removing Mo resulted in larger SWCNT diameters and lower densities (<0.7 × 10 12 cm -2 ), mass conversion rates from C 2 H 2 to SWCNT product were high and remarkably invariant for catalyst compositions and densities (i.e., 47.7% or 1.30 × 10 6 % g-catalyst -1 on 4-in. wafers). These carbon conversion efficiencies far exceed typical benchtop reactors and are on par with the best reported literature values. Our detailed elucidation of correlations among structural characteristics within this resource-efficient process is expected to guide future scale-up efforts of SWCNT forest growth beyond wafer scale.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Microwave-assisted depolymerization of PET over heterogeneous catalysts

The accumulation of plastic waste in the environment is rising and poses a significant environmental and health threat. Polyethylene terephthalate (PET), used mainly in single-use plastic bottles, accounts for a substantial fraction of this waste. The currently employed recycling strategies are inadequate to deal with the global plastic waste problem. PET glycolysis is a promising depolymerization method but has mainly been conducted using homogeneous catalysts; developing active and sustainable catalysts, and more energy-efficient processes remain challenges. Here, in this work, we implement microwave heating in PET glycolysis and identify ZnO as an excellent heterogeneous catalyst. We demonstrate the influence of ZnO's particle size and facet and hydrogen bonding on its activity. Finally, we demonstrate excellent performance for real-world post-consumer PET waste deconstruction with rapid depolymerization into bis(2-hydroxyethyl) terephthalate (BHET), achieving > 95% yields in less than 10 min. Cradle-to-gate life cycle assessment also indicates that BHET produced by ZnO-catalyzed glycolysis has lower global warming potential than the petrochemical-based production and homogeneously-catalyzed glycolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A Performance analysis of the Claridge-Culp-Liu dehumidification process: A novel approach for drying moist air based on membrane separation, vacuum compression and sub-atmospheric condensation

This article covers a basic model for analyzing the performance of the Claridge-Culp-Liu dehumidification process. The fundamental process efficiency limit for dehumidification is close to COP Carnot , but for the eight dehumidification cases examined, the limiting or ideal energy use required is 26% to 56% that of a Carnot condensing system as shown in an earlier paper. The model presented in this paper is used to show the membrane system performance reduction caused by finite membrane area, finite water vapor permeance, non-zero air permeance, non-zero system air pressure drop, non-ideal compressors, vacuum pumps, and condensers. The performance of a “conservative” membrane system based on the use of existing components is computed for eight specific conditions along with that of a “target” system that assumes expected component performance after additional future component development. The “conservative” membrane system would use 36% to 66% as much energy as a system with a COP=7 chiller to produce the same dehumidification for the eight cases examined while the “target” system would use 15% to 40% the energy of a system with a COP=7 chiller. In addition to the significant energy reduction over conventional technology, the membrane system offers the advantages of: 1) no HFC refrigerant use; 2) direct isothermal control over humidity ratio setpoint; 3) maximum capacity occurs at design conditions; and 4) system generates pure water extracted from air as a by-product.

42 ENGINEERING↗

DOE OSTI2020

A new approach of reduction of carbon dioxide emission and optimal use of carbon and hydrogen content for the desired syngas production from coal

We report this research is designed to make progress in overcoming the challenges through the development of a two-stage coal processing. Specifically, a two-stage process was used to maximize the use of the carbon in coal or increase the carbon monoxide yield or lower carbon dioxide and methane yields. Carbon dioxide-char gasification in the absence of water can generate carbon monoxide with near-zero methane, which is desired to produce high-carbon and low-hydrogen chemicals such as oxalic acid through catalytic carbon monoxide coupling and hydrolysis. The technology is applicable to any coal, although Power River Basin (PRB) coal is used as an example feedstock in this research Also, the sodium-iron catalyst can accelerate not only the reaction kinetics in both stages but also increase the hydrogen/carbon monoxide ratio in the syngas produced in the second stage. In other words, the catalyst is a multifunctional agent, which can not only intensify the overall coal process efficiency but also improve the qualities of the desired syngas products and reduce carbon dioxide emission. Thus, In the 1st stage, the catalysts can significantly reduce the CO 2 -char gasification by as high as 75.00%. In the 2nd stage, the H 2 /CO ratio of ideal syngas is ~2:1 with near-0 CH 4 generation and the CH4 production can be reduced as high as 61.29% for the CO 2 –H 2 O-Char coal gasification. The activation energy for the 2nd stage is reduced by as high as 35.43% than that of raw coal without use Na–Fe catalyst. The overall carbon footprint reduction for the study is reduced by 87.33% in the 1st stage and 96.77% compared to the direct coal combustion.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

A “Sweet” Biorefinery: Sugar-derived ionic liquids for the pretreatment of lignocellulosic biomass

Sugar-based ionic liquids (ILs) derived from biomass-based cations (choline, betaine) and sugar-derived anions were developed as sustainable pretreatment solvents to advance the concept of self-reliant biorefinery. Cholinium gluconate ([Ch][GlcA]) enabled streamlined one-pot sorghum pretreatment without the need for pH adjustment or washing, while betainium gluconate ([Bet][GlcA]) achieved higher sugar yields, improving overall process efficiency and economics. Both ILs produced hydrolysates fully compatible with microbial fermentation, demonstrating their potential for efficient, simplified biomass conversion. This work positions sugar-based ILs as powerful platforms that unite sustainable chemistry with integrated bioprocessing, marking a pivotal step toward realizing the self-reliant biorefinery.

Biomass-based ions↗