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DOE OSTI2026

Phenomena-based graph representations and applications to chemical process simulation

Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture the physical phenomena taking place in the process (e.g., material and energy conservation, phase equilibrium, reactions). In this work, we show that graph-theoretic representations of the physical phenomena within unit operations can help navigate and decompose equations to systematically identify alternative approaches for fast and robust numerical solutions. Specifically, we present a graph-theoretic abstraction that captures the connectivity between the model variables/equations and use this abstraction to group variables/equations into fundamental phenomena. We show that phenomena-based decomposition of the underlying equations can help decouple nonlinearities and enforce material/energy conservation at the process level to accelerate convergence. The proposed decomposition approach differs from the more traditional sequential modular simulation approach, in which equations are grouped and decomposed by unit operations. We implemented the phenomena-based decomposition in BioSTEAM—an open-source process simulation platform in Python—and demonstrated that this approach can converge a variety of separation process models. Compared to sequential modular simulation, the phenomena-based approach can converge idealized systems faster, but it can be slower for (or even fail to converge) highly coupled and nonideal process systems.

Convergence↗

DOE OSTI2025

Process intensification approach to enhancing heat and mass transfer: Radio frequency (RF) assisted drying of paper and board

Conventional multi-cylinder drying of paper and board typically relies on both conductive drying from steam-heated cylinders and convective drying, where heated air flows over the paper web surface. Conduction primarily contributes to heat transfer, while convection is the main driver of mass transfer. However, conventional drying systems are heavily dependent on steam, usually powered by fossil fuels, and are often energy-inefficient with high levels of waste. Additionally, these surface-driven processes result in a lower percentage of energy absorption compared to the energy supplied, leading to significant energy losses. To improve this long-standing process, an experimental system was developed to investigate a process intensification approach involving the integration of Radio Frequency (RF) heating, a volumetric electromagnetic technology, alongside traditional conduction and convection drying methods. This study also emphasizes the use of sensors to continuously monitor key parameters such as moisture content, supply system temperatures, sample temperatures, air flows, and drying rates in real-time. The effect of RF as an auxiliary energy source in localized environments at varying moisture levels was explored to optimize industrial drying systems, quantify potential improvements, and provide insights for future studies. Experimental results from trials combining RF with convection and with the base case alternating conduction-convection drying processes are presented. It is shown that RF is a viable process intensification approach for paper drying improving the drying rate and energy intensity at higher moisture contents. These findings offer valuable insights for process intensification and contribute to the process development, modeling, and simulation of advanced paper drying techniques.

42 ENGINEERING↗

DOE OSTI2018

Toward supplying food, energy, and water demand: Integrated solar desalination process synthesis with power and hydrogen coproduction

The increase in population coupled with rising per capita income and associated change in consumption habits will put unprecedented stress on food, energy and water (FEW) resources. Sustainable and reliable fresh water supply is central for life and also for all sectors that support our existence. Uncertainty on water security prompted interest in investigation of renewable energy driven desalination processes. One particularly promising option is to produce fresh water from the two most abundant resources on earth: solar energy and seawater. In this study, using Solar Electricity, Water, Food and Chemical (SEWFAC) process synthesis concept, we explore and identify synergistic integration alternatives of multi stage flash desalination, solar thermal power, and hydrogen production processes. The promising options have been analyzed by detailed process simulation and optimization using an integrated Aspen Plus and MATLAB modeling environment. The proposed process designs can meet the water and electricity demand with rather high conversion efficiencies. Furthermore, integration of solar hydrogen production and hydrogen-fired power plant can enable continuous production of fresh water and electricity in solar-rich water-poor regions. In addition to other metrics, we have evaluated the performance of the desalination process from power point of view with a new metric, Electricity Equivalent Water (EEW) to demonstrate the marginal energy penalty of desalination. Integration of thermal desalination processes with electricity and hydrogen production is a synergistic alliance and can play a pivotal role in approaching FEW nexus.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2015

Processing and properties of a solid energy fuel from municipal solid waste (MSW) and recycled plastics

Highlights: • Briquetting was used to produce solid fuels from municipal solid waste and recycled plastics. • Optimal drying, processing temperature and pressure were found to produce stable briquettes. • Addition of waste plastics yielded heating values comparable with typical coal feedstocks. • This processing method improves utilization of paper and plastic diverted from landfills. - Abstract: Diversion of waste streams such as plastics, woods, papers and other solid trash from municipal landfills and extraction of useful materials from landfills is an area of increasing interest especially in densely populated areas. One promising technology for recycling municipal solid waste (MSW) is to burn the high-energy-content components in standard coal power plant. This research aims to reform wastes into briquettes that are compatible with typical coal combustion processes. In order to comply with the standards of coal-fired power plants, the feedstock must be mechanically robust, free of hazardous contaminants, and moisture resistant, while retaining high fuel value. This study aims to investigate the effects of processing conditions and added recyclable plastics on the properties of MSW solid fuels. A well-sorted waste stream high in paper and fiber content was combined with controlled levels of recyclable plastics PE, PP, PET and PS and formed into briquettes using a compression molding technique. The effect of added plastics and moisture content on binding attraction and energy efficiency were investigated. The stability of the briquettes to moisture exposure, the fuel composition by proximate analysis, briquette mechanical strength, and burning efficiency were evaluated. It was found that high processing temperature ensures better properties of the product addition of milled mixed plastic waste leads to better encapsulation as well as to greater calorific value. Also some moisture removal (but not complete) improves the compacting process and results in higher heating value. Analysis of the post-processing water uptake and compressive strength showed a correlation between density and stability to both mechanical stress and humid environment. Proximate analysis indicated heating values comparable to coal. The results showed that mechanical and moisture uptake stability were improved when the moisture and air contents were optimized. Moreover, the briquette sample composition was similar to biomass fuels but had significant advantages due to addition of waste plastics that have high energy content compared to other waste types. Addition of PP and HDPE presented better benefits than addition of PET due to lower softening temperature and lower oxygen content. It should be noted that while harmful emissions such as dioxins, furans and mercury can result from burning plastics, WTE facilities have been able to control these emissions to meet US EPA standards. This research provides a drop-in coal replacement that reduces demand on landfill space and replaces a significant fraction of fossil-derived fuel with a renewable alternative.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2014

Realistic fission models, new beta-decay half-lives and the r-process in neutron star mergers

Almost half of heavy nuclei beyond iron are considered to be produced by rapid neutron capture process (r-process). This process occurs in the neutron-rich environment such as core-collapse supernovae or neutron star mergers, but the main production site is still unknown. In the r-process of neutron star mergers, nuclear fission reactions play an important role. Also beta-decay half-lives of magic nuclei are crucial for the r-process. We have carried out r-process nucleosynthesis calculations based upon new theoretical estimates of fission fragment distributions and new beta-decay half-lives for N=82 nuclei measured at RIBF-RIKEN. We investigate the effect of nuclear fission on abundance patterns in the matter ejected from neutron star mergers with two different fission fragment mass distributions. We also discuss how the new experimental beta-decay half-lives affect the r-process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Water Processing for Isotope Recovery Using Porous Zero Valent Iron

Reduction-oxidation cycles of metals can be harnessed to create a reusable tritiated water processing system. The concept is straightforward; a tritium-contaminated steam passes over a hot metal bed converting the metal to a metal oxide and liberating hydrogen isotopes for further processing and isotope separation. The bed is regenerated by converting the metal oxide back to a bare metal using protium gas, creating a clean water stream. Free oxygen is not produced during the cyclical process, making it safe for use in a hydrogen processing facility, and the only by-product is detritiated water. Porous zero valent iron (p-ZVI) has been identified as an ideal candidate material for this process due to its low cost, unique morphology, and favorable thermodynamics. Therefore, investigations of p-ZVI were conducted to better understand how a bed composed of such material would behave in a tritium processing facility. The thermal and physical properties were assessed, along with cycling and isotope effects. The results indicate that p-ZVI beds could serve as a low-cost, reusable system for the treatment of water in tritium processing facilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

The new model of chemical evolution of r-process elements based on the hierarchical galaxy formation. I. Ba and Eu

We investigate the chemical enrichment of r-process elements in the early evolutionary stages of the Milky Way halo within the framework of hierarchical galaxy formation using a semi-analytic merger tree. In this paper, we focus on heavy r-process elements, Ba and Eu, of extremely metal-poor (EMP) stars and give constraints on their astronomical sites. Our models take into account changes of the surface abundances of EMP stars by the accretion of interstellar medium (ISM). We also consider metal-enrichment of intergalactic medium by galactic winds and the resultant pre-enrichment of proto-galaxies. The trend and scatter of the observed r-process abundances are well reproduced by our hierarchical model with ∼10% of core-collapse supernovae in low-mass end (∼10 M {sub ☉}) as a dominant r-process source and the star formation efficiency of ∼10{sup –10} yr{sup –1}. For neutron star mergers as an r-process source, their coalescence timescale has to be ∼10{sup 7} yr, and the event rates ∼100 times larger than currently observed in the Galaxy. We find that the accretion of ISM is a dominant source of r-process elements for stars with [Ba/H] < –3.5. In this model, a majority of stars at [Fe/H] < –3 are formed without r-process elements, but their surfaces are polluted by the ISM accretion. The pre-enrichment affects ∼4% of proto-galaxies, and yet, is surpassed by the ISM accretion in the surface of EMP stars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Recursive Use of the Short-Time Fast Fourier Transform for Signature Analysis in Continuous Processes

Although a nuclear reactor is a hostile environment for sensing and electrical communications, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure (AMI) has been installed in the Advanced Test Reactor (ATR) to record acoustic signals that can capture its different operating regimes. AMI uses coolant pumps as continuous signal sources, coolant and structural components as transmission lines, and accelerometers to capture system motion. A recursive signal processing technique based on the short-time fast Fourier transform (STFFT) for continuous processes provides unique signatures for diagnostic and prognostic analyses from the system motion data. Here this article presents a recursive STFFT methodology that processes acoustic signals from continuous industrial processes. The article first discusses the initial STFFT use with simulated data to elucidate the basic principles necessary to understand and interpret the STFFT results from actual pump vibration data. Each repetitive use of the STFFT on pump vibration data using the results from the prior STFFT processing will generate additional complimentary time-frequency-based signatures. These signatures are generated by the coolant pumps operating under different process conditions. After each use of the STFFT, the resulting signatures provide exemplary examples of the diversity and intuitive nature of recursively using the STFFT. This article focuses on recursively using the STFFT to provide numerous complimentary and diverse signatures that will ultimately be inputs for machine learning algorithms that provide predictive data analytics. The intuitive nature of the information and signatures from recursive STFFT processing will also bring intuitive interpretation capabilities to machine learning and predictive data analytic techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

In-Situ Calibrated Digital Process Twin Models for Resource Efficient Manufacturing

The chief objective of manufacturing process improvement efforts is to significantly minimize process resources such as time, cost, waste, and consumed energy while improving product quality and process productivity. This paper presents a novel physics-informed optimization approach based on artificial intelligence (AI) to generate digital process twins (DPTs). The utility of the DPT approach is demonstrated in the case of finish machining of aerospace components made from gamma titanium aluminide alloy (γ-TiAl). This particular component has been plagued with persistent quality defects, including surface and sub-surface cracks, which adversely affect resource efficiency. Previous process improvement efforts have been restricted to anecdotal post-mortem investigation and empirical modeling, which fail to address the fundamental issue of how and when cracks occur during cutting. In this work, the integration of in-situ process characterization with modular physics-based models is presented, and machine learning algorithms are used to create a DPT capable of reducing environmental and energy impacts while significantly increasing yield and profitability. Based on the preliminary results presented here, we report an improvement in the overall embodied energy efficiency of over 84%, 93% in process queuing time, 2% in scrap cost, and 93% in queuing cost has been realized for γ-TiAl machining using our novel approach.

42 ENGINEERING↗

DOE OSTI2023

Modeling Tool Development and Validation for Solar Industry Process Heat Using Particle Thermal Energy Storage

U.S. industry sectors used 26.2 quadrillion Btu and accounted for 33% of total energy consumption in 2021 according to the Energy Information Agency. Industrial process heat accounts for 70% of industrial energy use with application temperatures ranging from 60 degrees -1100 degrees C. Industry processes, heavily relying on fossil fuels of cheap coal or natural gas, differ widely in operating conditions and load requirements which makes them difficult to standardize and imposes great challenges in decarbonization. Industry processes require reliable energy supply and vary widely in temperature ranges. Storing energy from renewable sources is necessary to improve reliability and to mitigate renewable intermittency when replacing carbon fuel-based heat supplies to achieve energy savings and reduce emissions. To this end, we have developed a particle-based thermal energy storage (TES) technology using low-cost and highly stable silica sand as a storage medium. The economic and performance-based analysis is key for renewable energy sources to reliably supply industry process heat and ultimately displace fossil fuels for decarbonization. The diversified industrial processes need case-by-case analysis and design. Therefore, an adaptive modeling tool is key for renewable power with energy storage to meet industry demands. Thus, a modeling tool to simulate a solar industry process heat system using the particle TES has been developed using the object-oriented equation-based language Modelica and the commercial platform of Modelon Impact. The Modelica-based software tool provides a general simulation environment for the design of reliable solar energy sources integrated with TES for various industrial process applications at different temperatures for economic competence with fossil fuels such as coal and natural gases. It uses both customized and standard component modeling modules in Modelon libraries for the flexibility to be adapted to a specific energy demand application. The particle TES system establishes a uniform energy supply platform with an efficient heat exchanger and particle thermal energy reservoir integrated with renewable powers. The particle TES system can provide a wide temperature range and can have a large storage temperature difference that increases storage energy density; therefore, it can be an adaptable energy storage system integrated with renewable power to supply 24/7 heat for industry decarbonization.

concentrated solar thermal↗

DOE OSTI2011

Status of the Development of In-Tank/At-Tank Separations Technologies for High-Level Waste Processing for the U.S. Department of Energy

Within the U.S. Department of Energy’s (DOE) Office of Technology Innovation and Development, the Office of Waste Processing manages a research and development program related to the treatment and disposition of radioactive waste. At the Savannah River (South Carolina) and Hanford (Washington) Sites, approximately 90 million gallons of waste are distributed among 226 storage tanks (grouped or collocated in “tank farms”). This waste may be considered to contain mixed and stratified high activity and low activity constituent waste liquids, salts and sludges that are collectively managed as high level waste (HLW). A large majority of these wastes and associated facilities are unique to the DOE, meaning many of the programs to treat these materials are “first-of-a-kind” and unprecedented in scope and complexity. As a result, the technologies required to disposition these wastes must be developed from basic principles, or require significant re-engineering to adapt to DOE’s specific applications. Of particular interest recently, the development of In-tank or At-Tank separation processes have the potential to treat waste with high returns on financial investment. The primary objective associated with In-Tank or At-Tank separation processes is to accelerate waste processing. Insertion of the technologies will (1) maximize available tank space to efficiently support permanent waste disposition including vitrification; (2) treat problematic waste prior to transfer to the primary processing facilities at either site (i.e., Hanford’s Waste Treatment and Immobilization Plant (WTP) or Savannah River’s Salt Waste Processing Facility (SWPF)); and (3) create a parallel treatment process to shorten the overall treatment duration.

High level waste↗

DOE OSTI2012

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., 'FOR PROCESSING') and vendor postprocessed (i.e., 'FOR PRESENTATION'), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Results: Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r= 0.82, p < 0.001) and processed (r= 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r= 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's {kappa}{>=} 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). Conclusions: The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-D-BRD-02: A Web-Based Image Processing and Plan Evaluation Platform (WIPPEP) for Future Cloud-Based Radiotherapy

Purpose: Visualization and processing of medical images and radiation treatment plan evaluation have traditionally been constrained to local workstations with limited computation power and ability of data sharing and software update. We present a web-based image processing and planning evaluation platform (WIPPEP) for radiotherapy applications with high efficiency, ubiquitous web access, and real-time data sharing. Methods: This software platform consists of three parts: web server, image server and computation server. Each independent server communicates with each other through HTTP requests. The web server is the key component that provides visualizations and user interface through front-end web browsers and relay information to the backend to process user requests. The image server serves as a PACS system. The computation server performs the actual image processing and dose calculation. The web server backend is developed using Java Servlets and the frontend is developed using HTML5, Javascript, and jQuery. The image server is based on open source DCME4CHEE PACS system. The computation server can be written in any programming language as long as it can send/receive HTTP requests. Our computation server was implemented in Delphi, Python and PHP, which can process data directly or via a C++ program DLL. Results: This software platform is running on a 32-core CPU server virtually hosting the web server, image server, and computation servers separately. Users can visit our internal website with Chrome browser, select a specific patient, visualize image and RT structures belonging to this patient and perform image segmentation running Delphi computation server and Monte Carlo dose calculation on Python or PHP computation server. Conclusion: We have developed a webbased image processing and plan evaluation platform prototype for radiotherapy. This system has clearly demonstrated the feasibility of performing image processing and plan evaluation platform through a web browser and exhibited potential for future cloud based radiotherapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Feasibility study of using statistical process control to customized quality assurance in proton therapy

Purpose: To evaluate and improve the reliability of proton quality assurance (QA) processes and, to provide an optimal customized tolerance level using the statistical process control (SPC) methodology. Methods: The authors investigated the consistency check of dose per monitor unit (D/MU) and range in proton beams to see whether it was within the tolerance level of the daily QA process. This study analyzed the difference between the measured and calculated ranges along the central axis to improve the patient-specific QA process in proton beams by using process capability indices. Results: The authors established a customized tolerance level of ±2% for D/MU and ±0.5 mm for beam range in the daily proton QA process. In the authors’ analysis of the process capability indices, the patient-specific range measurements were capable of a specification limit of ±2% in clinical plans. Conclusions: SPC methodology is a useful tool for customizing the optimal QA tolerance levels and improving the quality of proton machine maintenance, treatment delivery, and ultimately patient safety.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-P-48: The Quantitative Evaluation and Comparison of Image Distortion and Loss of X-Ray Images Between Anti-Scattered Grid and Moire Compensation Processing in Digital Radiography

Purpose: To quantitatively analyze the influence image processing for Moire elimination has in digital radiography by comparing the image acquired from optimized anti-scattered grid only and the image acquired from software processing paired with misaligned low-frequency grid. Methods: Special phantom, which does not create scattered radiation, was used to acquire non-grid reference images and they were acquired without any grids. A set of images was acquired with optimized grid, aligned to pixel of a detector and other set of images was acquired with misaligned low-frequency grid paired with Moire elimination processing algorithm. X-ray technique used was based on consideration to Bucky factor derived from non-grid reference images. For evaluation, we analyze by comparing pixel intensity of acquired images with grids to that of reference images. Results: When compared to image acquired with optimized grid, images acquired with Moire elimination processing algorithm showed 10 to 50% lower mean contrast value of ROI. Severe distortion of images was found with when the object’s thickness was measured at 7 or less pixels. In this case, contrast value measured from images acquired with Moire elimination processing algorithm was under 30% of that taken from reference image. Conclusion: This study shows the potential risk of Moire compensation images in diagnosis. Images acquired with misaligned low-frequency grid results in Moire noise and Moire compensation processing algorithm used to remove this Moire noise actually caused an image distortion. As a result, fractures and/or calcifications which are presented in few pixels only may not be diagnosed properly. In future work, we plan to evaluate the images acquired without grid but based on 100% image processing and the potential risks it possesses.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

The Effect of Flocculent, Dispersants, and Binder on Wet-laid Process for Recycled Glass Fiber/PA6 Composite

The papermaking industry has been using the wet-laid process to suspend paper pulp-derived fibers in water and drain the solution through a forming mesh. This process has recently been adopted to produce non-woven, wet-laid fiber-reinforced polymer matrix composite mats. The mats can be postmolded into different complex shapes using compression molding or related processes. The objective of this study was to produce composite panels from wet-laid mats and observe the effect of chemicals used during the process on the mechanical and thermal characteristics of the resulting composite. Two sets of mats were processed using recycled glass fiber with Polyamide 6 (PA6). Flocculent, dispersant and binder (poly(vinyl alcohol) (PVOH)) were added to one of the mats, and the second mat was processed without these chemicals. The addition of these chemicals enhanced the fiber distribution and reduced processing defects in the mats. This was reflected in the mechanical properties of the final product. It was noticed that the flocculent, dispersant and binder volatilized during the compression molding step. Furthermore, the additives were found not to affect the thermal properties of the consolidated part.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Narrow-Band Least-Squares Infrasound Array Processing

Infrasound data from arrays can be used to detect, locate, and quantify a variety of natural and anthropogenic sources from local to remote distances. However, many array processing methods use a single broad frequency range to process the data, which can lead to signals of interest being missed due to the choice of frequency limits or simultaneous clutter sources. In this work, we introduce a new open-source Python code that processes infrasound array data in multiple sequential narrow frequency bands using the least-squares approach. We test our algorithm on a few examples of natural sources (volcanic eruptions, mass movements, and bolides) for a variety of array configurations. Our method reduces the need to choose frequency limits for processing, which may result in missed signals, and it is parallelized to decrease the computational burden. Improvements of our narrow-band least-squares algorithm over broad-band least-squares processing include the ability to distinguish between multiple simultaneous sources if distinct in their frequency content (e.g., microbarom or surf vs. volcanic eruption), the ability to track changes in frequency content of a signal through time, and a decreased need to fine-tune frequency limits for processing. We incorporate a measure of planarity of the wavefield across the array (sigma tau, στ) as well as the ability to utilize the robust least trimmed squares algorithm to improve signal processing and insight into array performance. Our implementation allows for more detailed characterization of infrasound signals recorded at arrays that can improve monitoring and enhance research capabilities.

58 GEOSCIENCES↗

DOE OSTI2011

Wide-Area Thermal Processing of Light-Emitting Materials

Silicon carbide based materials and devices have been successfully exploited for diverse electronic applications. However, they have not achieved the same success as Si technologies due to higher material cost and higher processing temperatures required for device development. Traditionally, SiC is not considered for optoelectronic applications because it has an indirect bandgap. However, AppliCote Associates, LLC has developed a laser-based doping process which enables light emission in SiC through the creation of embedded p-n junctions. AppliCote laser irradiation of silicon carbide allows two different interaction mechanisms: (1) Laser conversion or induced phase transformation which creates carbon rich regions that have conductive properties. These conductive regions are required for interconnection to the light emitting semiconducting region. (2) Laser doping which injects external dopant atoms into the substrate that introduces deep level transition states that emit light when electrically excited. The current collaboration with AppliCote has focused on the evaluation of ORNL's unique Pulse Thermal Processing (PTP) technique as a replacement for laser processing. Compared to laser processing, Pulse Thermal Processing can deliver similar energy intensities (20-50 kW/cm2) over a much larger area (up to 1,000 cm2) at a lower cost and much higher throughput. The main findings of our investigation; which are significant for the realization of SiC based optoelectronic devices, are as follows: (1) The PTP technique is effective in low thermal budget activation of dopants in SiC similar to the laser technique. The surface electrical conductivity of the SiC samples improved by about three orders of magnitude as a result of PTP processing which is significant for charge injection in the devices; (2) The surface composition of the SiC film can be modified by the PTP technique to create a carbon-rich surface (increased local C:Si ratio from 1:1 to 2.9:1). This is significant as higher thermal and electrical conductivities of the surface layer are critical for a successful development of integrated optoelectronic devices; and (3) PTP provides low thermal budget dopant activation with a controlled depth profile, which can be exploited for high performance device development with selective patterning of the substrate. This project has successfully demonstrated that a low thermal budget annealing technique, such as PTP, is critical to defining the path for low cost electronic devices integrated on glass or polymeric substrates. This project is complimentary to the goals of the Solid State Lighting Program within DOE. It involves new manufacturing techniques for light emitting materials that are potentially much lower cost and energy efficient than existing products. Significant opportunity exists for further exploration of AppliCote's material and device technology in combination with ORNL's PTP technique, modeling, and characterization capabilities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗