Low-Temperature Processing for Atomically Precise Dopant Incorporation with CMOS Integration.
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Computer vision and deep learning tools that advance the ability to establish processing-structure-property-performance (PSPP) relations are presented. The Bayesian binning method for image segmentation enables quantitative analysis of microstructural features in an automated way, while the analysis of shapes and relative orientation of these features reveals local deformation maps indicative of both, material flow and residual stresses due to materials processing. The deep learning method leads to the previous knowledge agnostic mapping of empirically observed microstructural zones in friction stir welding (FSW) process and synthetic microstructure generation capability that is statistically equivalent to experimentally collected data.
Additively manufactured (AM) eutectic high-entropy alloys (EHEAs), such as nano-lamellar AlCoCrFeNi 2.1 , have excellent strength, ductility, and wear resistance even at elevated temperatures, but their corrosion behavior in aggressive acids at different length scales remain poorly understood. This work investigates the corrosion behavior of laser powder bed–fused (L-PBF) AlCoCrFeNi 2.1 as a function of annealing temperatures, probing degradation mechanisms from nanoscopic to macroscopic length scales. The alloy is dual phase consisting of a ductile FCC L1 2 phase and a high-strength BCC B2 phase. Rapid solidification during L-PBF produces a far-from-equilibrium nano-lamellar structure with nearly homogeneous elemental distribution, which tends to evolve upon annealing toward Cr/Co/Fe-enriched FCC and Al/Ni-enriched B2. Three conditions were studied: as-printed, 600 °C/5 h, and 1000 °C/1 h, over which B2 lamellae coarsen, lamellar spacing increases, and elemental segregation becomes more prominent. Microstructure and chemistry were characterized by scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS), while in-situ electrochemical atomic force microscopy (EC-AFM) was used to link early (<5 h) local dissolution to microstructure after exposure in sulfuric acid. EC-AFM highlights preferential dissolution of the BCC/B2 phase where the surrounding matrix is Cr-depleted and directly quantifies the dissolution rates within individual phases, tracks the transition from early nano-scale attack to partial repassivation, to correlate height differences with current and impedance responses. To monitor longer-term behavior (up to 96 h), ex-situ AFM, SEM, and confocal imaging were combined with conventional bulk electrochemical tests, bridging nanoscale observations to micro/meso-scale damage morphologies. At the meso-scale, the deepest dissolution channels align with the build-direction lamellae and melt-pool boundaries, indicating that printing directionality guides the propagation of these localized corrosion sites. Annealing modifies corrosion by restructuring BCC/FCC phase fractions, lamellar spacing, and Cr/Al segregation, thereby changing the cathode/anode ratio and passive film stability. The results clarify how as-printed and annealed nano-lamellar architectures differ in their susceptibility to selective dissolution; how elemental segregation competes with residual stresses along the build direction. With these insights, future work will use CALPHAD-guided alloy modification to stabilize higher Cr contents in the B2 phase while retaining a dominant FCC+B2/BCC microstructure, with the goal of designing mechanically robust, corrosion-resistant EHEAs for safety-critical applications to leverage the LLNL’s broader national and global security mission.
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Slide deck for UK/US off-gas technology exchange meeting, presenting the summary from an engineering paper-study of UNF off-gas treatment. These slides were modified from a presentation by Robert Jubin, presented in Seoul, Korea in 2017
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Wastewater is produced across nearly all human activities and requires treatment to safeguard human health and the natural environment. Treatment of wastewater often requires a large amount of thermal energy, resulting in wasted heat after the treatment process. Because hydrocarbon reforming needs both water and heat, the integration of wastewater treatment with hydrocarbon reforming, a process that produces synthesis gas rich in hydrogen, offers an excellent opportunity to utilize this waste heat and the impurities in wastewater to produce valuable hydrogen gas, to minimize waste from industrial processes, and to integrate water treatment with the hydrogen economy. Yet, no comprehensive literature review has been conducted to examine the integration of reforming and wastewater. To address this lack, we summarize the variety of catalytic reforming techniques available in the open literature and review the current literature on wastewater reforming with these techniques. Subsequently, we conduct a review of common types of wastewater contaminants and their possible effects on reforming catalyst performance and life. Lastly, three underexamined wastewater sources are identified, namely, oilfield wastewater, geothermal water, and mining and mineral processing wastewater, and their potential for future study as a reforming feedstock is examined.
The integration of additive manufacturing (AM) capabilities on Computer Numerical Control (CNC) systems allows for the expansion of additive manufacturing to a wide range of part and tool repair operations. This multi-tasking integration, termed hybrid manufacturing, has been researched by others in the past, and Autodesk has been critical in developing process planning and toolpath algorithms for hybrid systems. Objectives and Tasks: Hybrid manufacturing systems enable both additive and subtractive capabilities in a single manufacturing workcell. These systems have the potential to impact a variety of industries, including the tool and die industry due to their repair, refurbishment, and complex geometry manufacturing capabilities. While there has been significant development of toolpath planning for both subtractive and additive processes independently, there has been little, if any, development of hybrid toolpath planning to integrate both processes during the manufacturing design and toolpath generation stage of a product’s lifecycle. Furthermore, additive toolpath planning has been limited to planar manufacturing, but this limitation could be overcome as hybrid CNC machines have multi-axis control. The objectives of this research include: - Development and demonstration of independent three-, four-, and five-axis toolpath generation algorithms for both additive and subtractive processes, and - Development, demonstration, and integration of three-, four-, and five-axis hybrid process planning and toolpath generation algorithms for hybrid additive and subtractive processes. The team will leverage the widely used Autodesk Fusion 360 product design and manufacturing (CAD/CAM) platform to achieve these objectives. Autodesk will provide the expertise in CAD tools, as well as access to their new CAD/CAM manufacturing tools (3-, 4-, and 5-axis milling, additive toolpath generation). ORNL will provide expertise in additive manufacturing toolpath generation, process planning, and manufacturing validation. By the end of the program, the team will have developed and validated multi-axis milling, additive manufacturing, and hybrid manufacturing on an industrial hybrid CNC system (Mazak 500-VC).
Populus trichocarpa is an important biofuel feedstock that has been the target of extensive research and is emerging as a model organism for plants, especially woody perennials. This research has generated several large ‘omics datasets. However, only few studies in Populus have attempted to integrate various data types. This review will summarize various ‘omics data layers, focusing on their application in Populus species. Subsequently, network and signal processing techniques for the integration and analysis of these data types will be discussed, with particular reference to examples in Populus.
The effects of radiation-induced defects and statistical variation in the dose and energy of MOSFET channel implants in a modern bulk CMOS technology are modeled using a process simulator in combination with analytical computations. The model integrates doping profiles obtained from process simulations and experimentally determined defect potentials into implicit surface potential equations. Solutions to these equations are then used to model radiation-induced edge leakage currents in 90-nm bulk CMOS n-channel MOSFETs. The results indicate that slight variations in the channel implant parameters can have a significant impact on the doping profile along the shallow trench isolation sidewall and thus the radiation-induced edge leakage currents.
Recent decades have seen tremendous increases in the quantity of empirical ecological data collected by individual investigators, as well as through research networks such as FLUXNET (Baldocchi et al., 2001). At the same time, advances in computer technology have facilitated the development and implementation of large and complex land surface and ecological process models. Separately, each of these information streams provides useful, but imperfect information about ecosystems. To develop the best scientific understanding of ecological processes, and most accurately predict how ecosystems may cope with global change, integration of empirical and modeling approaches is necessary. However, true integration - in which models inform empirical research, which in turn informs models (Fig. 1) - is not yet common in ecological research (Luo et al., 2011). The goal of this workshop, sponsored by the Department of Energy, Office of Science, Biological and Environmental Research (BER) program, was to bring together members of the empirical and modeling communities to exchange ideas and discuss scientific practices for increasing empirical - model integration, and to explore infrastructure and/or virtual network needs for institutionalizing empirical - model integration (Yiqi Luo, University of Oklahoma, Norman, OK, USA). The workshop included presentations and small group discussions that covered topics ranging from model-assisted experimental design to data driven modeling (e.g. benchmarking and data assimilation) to infrastructure needs for empirical - model integration. Ultimately, three central questions emerged. How can models be used to inform experiments and observations? How can experimental and observational results be used to inform models? What are effective strategies to promote empirical - model integration?
Electro-optic (EO) modulation is of interest to impart information onto an optical carrier. Inorganic crystals—most notably LiNbO 3 and BaTiO 3 —exhibit EO modulation and good stability, but are difficult to integrate with silicon photonic technology. Solution-processed organic EO materials are readily integrated but suffer from thermal degradation at the temperatures required in operating conditions for accelerated reliability studies. Hybrid organic–inorganic metal halide perovskites have the potential to overcome these limitations; however, these have so far relied on heavy metals such as lead and cadmium. Here, we report linear EO modulation using metal-free perovskites, which maintain the crystalline features of the inorganic EO materials and incorporate the flexible functionality of organic EO chromophores. We find that, by introducing a deficiency of cations, we reduce the symmetry in the perovskite crystal and produce thereby an increased EO response. The best-engineered perovskites reported herein showcase an EO coefficient of 14 pm V –1 at a modulation frequency of 80 kHz, an order of magnitude higher than in the nondefective materials. Furthermore, we observe split peaks in the X-ray diffraction and neutron diffraction patterns of the defective sample, indicating that the crystalline structure has been distorted and the symmetry reduced. Density functional theory (DFT) studies link this decreased symmetry to NH 4 + deficiencies. This demonstration of EO from metal-free perovskites highlights their potential in next-generation optical information transmission.
In northern climates, developing a zero-energy community is increasingly costly. Building loads are higher, thanks to the colder weather, and solar PV is both less effective, due to lower solar incidence, and misalignment with the buildings' energy needs (summer production, winter demands). We present a novel application for geothermal energy production in support of a zero-energy community, combining appropriate energy efficiency (demand design), geothermal production (supply design), and asset dispatch as an integrated technoeconomic package. This poster presents the process used to explore this system integration challenge, initial results, and discusses some of the technical opportunities for increasing the community-scale adoption of geothermal as an electric and thermal resource. Results indicate that under certain conditions, community-scale geothermal resources are quite competitive with today's grid tariffs and with PV, showing an LCOE within $0.01/kWh before consideration of important grid modernization factors such as resiliency and reliability.
In northern climates, developing a zero-energy community is increasingly costly. Building loads are higher, thanks to the colder weather, and solar PV is both less effective, due to lower solar incidence, and misalignment with the buildings' energy needs (summer production, winter demands). We present a novel application for geothermal energy production in support of a zero-energy community, combining appropriate energy efficiency (demand design), geothermal production (supply design), and asset dispatch as an integrated technoeconomic package. This poster presents the process used to explore this system integration challenge, initial results, and discusses some of the technical opportunities for increasing the community-scale adoption of geothermal as an electric and thermal resource. Results indicate that under certain conditions, community-scale geothermal resources are quite competitive with today's grid tariffs and with PV, showing an LCOE within $0.01/kWh before consideration of important grid modernization factors such as resiliency and reliability.