Testing Os Staining Approach for Visualizing Soil Organic Matter Patterns in Intact Samples via X-ray Dual-Energy Tomography Scanning
Abstract not provided
Zheng, Hongbing↗
SEARCH · Engineering Papers
Results for “process modeling”
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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Regionalized Life-Cycle Water Impacts of Microalgal-Based Biofuels in the United States
While algal biofuels have the potential to reduce the national reliance on fossil fuels, high water consumption associated with algal biomass cultivation represents a major concern potentially compromising the sustainable commercialization of this technology. This study focuses on quantifying the water footprint (WF) and water scarcity footprint (WSF) of renewable diesel derived from algal biomass and provides insights into where algal cultivation is less water-intensive than traditional ethanol and biodiesel feedstocks. Results are generated with an engineering process model developed to predict the life-cycle water consumption, considering green, blue, and gray water, of algae facilities across the United States at a high spatiotemporal resolution. The total WFs for Florida and Arizona are determined to be 13.1 and 17.6 m 3 GJ –1 , respectively. The blue WF in Arizona is shown to be 8.5 times larger than in Florida, while the green WF is 4.5 times smaller, but when combined into a total WF, there is just a 26% difference between the two locations. The analysis reveals that the total life-cycle WFs of algal renewable diesel are smaller than the optimal WFs of corn ethanol and soybean biodiesel. Algal systems benefit from higher growth rates and offer the opportunity to manage wastewater streams, therefore generating smaller green and gray WFs than those of conventional biofuels. Here, the WSF analysis identifies the Gulf Coast as the most suitable region for algal cultivation, with cultivation in the western US shown to exacerbate local water stress levels.
54 ENVIRONMENTAL SCIENCES↗
Atomic Precision Processing of Two-Dimensional Materials for Next-Generation Microelectronics
The growth of the information era economy is driving the pursuit of advanced materials for microelectronics, spurred by exploration into “Beyond CMOS” and “More than Moore” paradigms. Atomically thin 2D materials, such as transition metal dichalcogenides (TMDCs), show great potential for next-generation microelectronics due to their properties and defect engineering capabilities. This perspective delves into atomic precision processing (APP) techniques like atomic layer deposition (ALD), epitaxy, atomic layer etching (ALE), and atomic precision advanced manufacturing (APAM) for the fabrication and modification of 2D materials, essential for future semiconductor devices. Additive APP methods like ALD and epitaxy provide precise control over composition, crystallinity, and thickness at the atomic scale, facilitating high-performance device integration. Subtractive APP techniques, such as ALE, focus on atomic-scale etching control for 2D material functionality and manufacturing. In APAM, modification techniques aim at atomic-scale defect control, offering tailored device functions and improved performance. Achieving optimal performance and energy efficiency in 2D material-based microelectronics requires a comprehensive approach encompassing fundamental understanding, process modeling, and high-throughput metrology. Finally, the outlook for APP in 2D materials is promising, with ongoing developments poised to impact manufacturing and fundamental materials science. Integration with advanced metrology and codesign frameworks will accelerate the realization of next-generation microelectronics enabled by 2D materials.
36 MATERIALS SCIENCE↗
Techno-economic Analysis and Life-Cycle Analysis of Renewable Diesel Fuels Produced with Waste Feedstocks
Wet waste feedstocks represent an important category of resources that could be utilized to produce biofuels. Diversion of the wet waste resources from going through conventional waste management practices to utilization as feedstocks for energy production also benefits from avoided cost and pollutant emissions of waste management and disposal. This study investigates the economic and environmental implications of producing bio-blendstocks for mixing controlled compression ignition (MCCI) engines from two waste-to-fuel pathways: hydroprocessed esters and fatty acids (HEFA) from yellow grease, and swine manure hydrothermal liquefaction (HTL) followed by biocrude upgrading. Detailed process models were developed for both pathways, which informed the techno-economic analysis (TEA) and life-cycle analysis (LCA). Conventional swine manure management practice was also modeled in detail as the business-as-usual (BAU) scenario for the swine manure HTL pathway. The estimated minimum fuel selling prices (MFSP) were $4.47/gasoline gallon equivalent (GGE) and $3.57/GGE for the yellow grease to HEFA and swine manure HTL pathways, respectively. The life-cycle greenhouse gas (GHG) emissions of the two pathways were 11.2 and -33.3 g CO2e/MJ, respectively, for the yellow grease to HEFA and swine manure HTL pathways. The credits of avoided emissions from conventional swine manure management were the main reason for the negative GHG emissions of the swine manure HTL pathway. The marginal GHG emissions abatement costs were estimated to be $100 – $253/tonne CO2e and $5 – $103/tonne CO2e for the yellow grease HEFA and swine manure HTL pathways, respectively, for a diesel price ranging between $2.0/GGE and $3.5/GGE. Since the yellow grease HEFA pathway is already commercialized, it can benefit from the $200/tonne carbon credit in the California Low Carbon Fuel Standard (LCFS) market, which could help the yellow grease HEFA pathway to achieve near-zero marginal GHG emissions abatement cost.
renewable diesel, techno economic analysis, life c↗
Optimizing Desalination Operations for Energy Flexibility
Despite the value of energy optimization in desalination processes, modeling dynamic operations for monthly billing periods has remained a computational challenge. This work proposes a framework for energy flexibility optimization, which includes new modeling features for independent operation of parallel skids, start-up delays associated with chemical stabilization, the consideration of industrial energy tariff structures, and inclusion of hourly electrical carbon intensities. This is done using a modular and computationally efficient formulation that guarantees a globally optimal solution with standard optimization solvers. In this study, the approach is demonstrated in two distinct case studies: a seawater desalination plant in Santa Barbara, CA, and an indirect potable reuse facility in San Jose, CA. Trends predicted from the model are validated against operational facility measurements from a demand response shutdown event. Preliminary results show that optimizing energy flexibility can result in 18.51% monthly cost savings over energy efficiency-optimized operation. The value extracted from a facility-wide shutdown during peak electricity price hours is hampered by start-up delays in post-treatment chemical stabilization. In cases in which a facility does not have much excess capacity, using a flow equalization tank or operating over a wide recovery range may be cost-effective.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
The Interaction Between Climate Forcing and Feedbacks
A Perturbed Parameter Ensemble (PPE) with the Community Atmosphere Model version 6 (CAM6) is used to better understand the sensitivity of aerosol forcing and cloud feedbacks to changes in model processes. Aerosol forcing through aerosol-cloud interactions is mostly negative (a cooling) due to shortwave radiation, while feedbacks are positive or negative in different regions due to contrasting longwave and shortwave effects. Both forcing and feedbacks are related to the mean climate state. Higher magnitude cloud radiative effects generally mean larger magnitude net negative forcing and larger magnitude net positive feedback. Aerosol forcing is broadly related to the susceptibility of clouds to drop number. Feedbacks also related to susceptibility, but to a lesser extent and in different regions to aerosol forcing. Aerosol forcing and cloud feedbacks are anti-correlated in the CAM6 PPE such that stronger negative forcing is associated with stronger positive feedbacks. Even the processes governing forcing and feedback sensitivity in the PPE are similar. These include the warm rain formation process, ice loss processes and deep convective intensity.
54 ENVIRONMENTAL SCIENCES↗
Upcycling waste polystyrene to adipic acid through a hybrid chemical and biological process
Oxidative catalytic depolymerization of polystyrene (PS) can produce benzoic acid, but the annual consumption of benzoic acid is ~40 times lower than PS. For this catalytic oxidation method to be a viable means to manage PS waste, benzoic acid should be converted to higher-volume chemicals. We demonstrate a hybrid chemical and biological process that uses PS as feedstock for production of adipic acid, a high-volume co-monomer for nylon 6,6 via benzoic acid. Mn/Br co-catalyzed autoxidation of PS to benzoic acid proceeds with a yield of up to 94% in a solvent mixture of benzoic acid and water. The PS-derived benzoic acid undergoes bioconversion at near-quantitative yield to muconic acid, which is readily converted to adipic acid through catalytic hydrogenation. Process modeling, techno-economic analysis, and life cycle assessment estimate an adipic acid minimum selling price of $3.18/kg, with a 61% decrease in greenhouse gas emissions relative to production from fossil fuels.
09 BIOMASS FUELS↗
Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels
Abstract A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation in science and engineering applications. Its success is largely attributed to the GP’s analytical tractability, robustness, and natural inclusion of uncertainty quantification. Unfortunately, the use of exact GPs is prohibitively expensive for large datasets due to their unfavorable numerical complexity of $$O(N^3)$$ O ( N 3 ) in computation and $$O(N^2)$$ O ( N 2 ) in storage. All existing methods addressing this issue utilize some form of approximation—usually considering subsets of the full dataset or finding representative pseudo-points that render the covariance matrix well-structured and sparse. These approximate methods can lead to inaccuracies in function approximations and often limit the user’s flexibility in designing expressive kernels. Instead of inducing sparsity via data-point geometry and structure, we propose to take advantage of naturally-occurring sparsity by allowing the kernel to discover—instead of induce—sparse structure. The premise of this paper is that the data sets and physical processes modeled by GPs often exhibit natural or implicit sparsities, but commonly-used kernels do not allow us to exploit such sparsity. The core concept of exact, and at the same time sparse GPs relies on kernel definitions that provide enough flexibility to learn and encode not only non-zero but also zero covariances. This principle of ultra-flexible, compactly-supported, and non-stationary kernels, combined with HPC and constrained optimization, lets us scale exact GPs well beyond 5 million data points.
97 MATHEMATICS AND COMPUTING↗
Advances in catalytic production processes of biomass-derived vinyl monomers
Plastic industries currently source the majority of monomers from crude oil substances. Although we have witnessed a significant interest of companies in the utilization of sustainable feedstock materials for bio-based compound synthesis in the past decade, the transition to photosynthetic or chemosynthetic plant-based production in a circular carbon economy is largely slow due to complex biomass processing, costs and related reaction factors. The upgrade of the separated platform chemicals with a deeper supporting understanding of processes, models and a root–cause analysis about the underlying distribution challenges of engineered transformation mechanisms, catalyzed conversion pathways and selectivity would be beneficial to advance applied scientific development, bio-refining and manufacturing output amount. This review provides a summary, assessment and perspective for three important polymer-forming vinyl molecules, i.e. acrylic acid, methacrylic acid and styrene. These provide a backbone to produce acrylates, polystyrene, resins, rubbers, protective surface coatings, adhesives, textiles and other obtained copolymers. A succinct analytical overview on the thermo-catalytic intermediate routes for property-wise drop-in alternatives is presented. Sugars, acrolein, allyl alcohol, ethylbenzene, glycerol, 3-hydroxypropionic acid, isobutene, itaconic acid and lactic acid are considered as the main starting reactants. Catalysts span mixed metal oxides, silicates, native or impregnated zeolite frameworks (HBEA, HZSM, and MFI) and hetero-poly acids as well as homogeneous base hydroxides or platinum group metals, supported on carbon, alumina and sulfates. Finally, the article concludes with a brief state sum-up of the results, topics and opportunities for systematic future research or scaling.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Conversion of plastic waste into high-value lubricants: techno-economic analysis and life cycle assessment
Given the low recycling rate of plastic waste in the United States due to low economic incentives, it is of great interest to develop a technology to upgrade plastic waste with favorable economics. Plastic upcycling to valuable chemicals could ensure a circular economy for plastics and reduce the environmental burden caused by their end use cycle and disposal. A conceptual facility to convert 250 metric ton (MT) per day of plastic waste was modeled; the main product was a high-quality liquid (HQL) with a similar performance to polyalphaolefin (PAO) lubricants. The modeled process had a lubricant yield of up to 90% based on the experimental results at the laboratory scale. Techno-economic analysis (TEA) and life cycle assessment (LCA) were also performed to evaluate the process economics and its environmental impact. By using a mixture of colored and natural high-density polyethylene (HDPE), the production cost was in the range of 0.6–1.98 dollars per kg of lubricant, depending on the operating conditions. The life cycle emissions were in the range of 0.48–1.2 kg CO 2e kg Lub -1 showing, for the best case scenario, a 52% reduction relative to the emissions for petroleum lubricants, and a 74% reduction relative to the emissions for PAO lubricants. Finally, the impacts of the lubricant yield, the catalyst amount, and reaction time were evaluated, and their effect on the final production cost was discussed.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Techno-economic analysis and life cycle assessment of mixed plastic waste gasification for production of methanol and hydrogen
This work examines the feasibility of a greenfield mixed plastics waste gasification facility with process modeling, TEA, and LCA.
09 BIOMASS FUELS↗
Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes
Leveraging natural language processing models including transformers, we curate four distinct datasets: tmCAT for catalysis, tmPHOTO for photophysical activity, tmBIO for biological relevance, and tmSCO for magnetism.
Kevlishvili, Ilia [Department of Chemical Engineer↗
The impact of isospin dependence of pairing on fission barriers in the fission cycling regions
A systematic analysis of the ground state and fission properties of actinides and superheavy nuclei important for the r process modeling has been performed within the framework of covariant density functional theory for the first time in Ref. A brief review of the results related to the heights of primary fission barriers and systematic uncertainties in their prediction is presented. In addition, new results on the potential impact of the isospin dependence of pairing on fission barriers in fission cycling regions is provided for the first time.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Analysis of Automated Transit Network Systems with Battery-Electric Vehicles in Automated Mobility Districts: Preprint
The paper provides an overview of the insights and findings of the National Renewable Energy Laboratory's (NREL) ongoing research on the implementation of automated mobility districts (AMDs). AMD is a term coined by NREL to describe a geographically defined district or major activity center located in a dense urban setting with mobility applications provided by automated/autonomous vehicle (AV) systems spanning internal circulation and first-mile/last-mile connections to regional transportation hubs. Research over the past five years has focused on understanding the evolution of AMDs, beginning with demonstrations of automated shuttles prior to the pandemic, to more integrated on-demand mobility systems currently in initial stages of deployment. Initial insights and findings from earlier studies include the need for designation of a "jurisdiction having authority," a clear vision of a complete system to provide end-to-end mobility services, and the requisite intelligent infrastructure to complement AV technology. NREL's most recent Phase III research investigates station boarding/alighting (curb) issues, the full electrification of fleets, and the need for a systems engineering methodology (SEM) to properly analyze the complexities resulting from the convergence of automation, on-demand mobility, and electrification of the transit systems within the AMD. The paper reviews the findings of AMD research conducted during Phase I, II, and III, with special emphasis on Phase III results with respect to a descriptive example of the proposed SEM when a "digital twin" analytical model is used to simulate the transport fleet's battery-electric vehicle miles of travel and associated duty cycles through a rigorous analytical assessment with a comprehensive modeling process.
ADVANCED PROPULSION SYSTEMS↗
Adaptive four-level modeling of laser cooling of solids
Laser cooling of rare-earth doped solids has been demonstrated across a wide range of material platforms, inspiring the development of simple phenomenological models such as the four-level model to elucidate the universal properties of laser cooling under various operating conditions. However, these models usually require the input of full absorption spectra that must be provided experimentally or by additional complicated atomic modeling. In this Letter, we propose that a four-level model, when extended to admit effective energy levels adaptive to the pumping photon energy, can accurately predict the cooling efficiency as a function of temperature and pumping frequency using only few inputs such as the absorption coefficient measured at a single frequency and temperature. Our model exploits the quasi-equilibrium properties of the excitation of rare-earth ions for the determination of the effective four energy levels. The model is validated against published experimental results for a span of materials including ytterbium/thulium-doped glass and crystals. With the verified model, we derive explicit expressions for the optimal frequency and the operating bandwidth of pumping lasers. Our model significantly simplifies the modeling process of laser cooling and is expected to stimulate further development of optical refrigeration.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Design methodologies and engineering applications for ecosystem biomimicry: an interdisciplinary review spanning cyber, physical, and cyber-physical systems
Abstract Ecosystem biomimicry is a promising pathway for sustainable development. However, while typical form- and process-level biomimicry is prevalent, system-level ecosystem biomimicry remains a nascent practice in numerous engineering fields. This critical review takes an interdisciplinary approach to synthesize trends across case studies, evaluate design methodologies, and identify future opportunities when applying ecosystem biomimicry to engineering practices, including cyber systems (CS), physical systems (PS), and cyber-physical systems (CPS). After systematically sourcing publications from major databases, the papers were first analyzed at a meta level for their bibliographic context and for statistical correlations among categorical variables. Then, we investigated deeper into the engineering applications and design methodologies. Results indicate that CPS most frequently mimic organisms and ecosystems, while CS and PS frequently mimic populations-communities and molecules-tissues-organ systems, respectively (statistically highly significant). An indirect approach is most often used for mimicry at organizational levels from populations to ecosystems, while a direct approach frequently suits levels from molecules to organisms (highly significant). Dominant themes across engineering applications include symbiotic organism search algorithms for CS and ecological network analysis for CPS, while PS are highly diverse. For design methodologies, this work summarizes and details ten well-documented biomimetic process models among literature, which addresses an outdated concern for a lack of systematic methods for ecosystem biomimicry. In addition to the Biomimetics Standard ISO 18458, these methods include the Natural Step and Techno-Ecological Synergy framework, among others. Further, the analyses revealed future opportunities from less utilized design methods (e.g. interdisciplinary teams tackling indirect, ecosystem-level projects) to well-established engineering concepts ready for technological advancement (e.g. implementing membrane computing for physical applications). For future studies, this review provides a comprehensive reference for ecosystem biomimetic design practices and application opportunities across multiple engineering domains.
42 ENGINEERING↗
Urbanization drives convergence in soil profile texture and carbon content
Urban development has driven extensive modification of the global landscape. This shift in land use and land cover alters ecological functioning, and thereby affects sustainable management agendas. Urbanization fundamentally reshapes the soils that underlay landscapes, and throughout the soil profile, extends impacts of urbanization far below the landscape surface. The impacts of urbanization on deeper soils that are beyond the reach of regular land management are largely unknown, and validation of general theories of convergent ecosystem properties are thwarted by a dearth of both level of measurement effort and the substantial heterogeneity in soils and urban landscapes. Here, we examined two soil properties with strong links to ecological functioning—carbon and mineral-fraction particle size—measured in urban soils, and compared them to their pre-urbanization conditions across a continental gradient encompassing global soil diversity. We hypothesized that urbanization drove convergence of soils properties from heterogeneous pre-urban conditions towards homogeneous urban conditions. Based on our observations, we confirm the hypothesis. Both soil carbon and particle size converged toward an intermediate value in the full data distribution, from pre-urban to urban conditions. These outcomes in urban soils were observed to uniformly be fine textured soils with overall lower carbon content. Although these properties are desirable for supporting urban infrastructure (e.g. buildings, pipes), they constrain the potential to render ecosystem services. Since soil profile texture and carbon content were convergent and observed across 11 cities, we suggest that these property profiles can be used as a universal urban soil profile to: 1) provide a clear prediction for how urbanization will shift soil properties from pre-urban conditions, 2) facilitate the adoption of commonly-accepted soil profiles for process models, and 3) offer a reference point to test against urban management strategies and how they impact soil resources.
54 ENVIRONMENTAL SCIENCES↗
A deep neural network to search for new long-lived particles decaying to jets
A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length $\mathrm{c}\tau_0$ of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The potential performance of the tagger is demonstrated with a search for long-lived gluinos, a manifestation of split supersymmetric models. The tagger provides a rejection factor of 10 000 for jets from standard model processes, while maintaining an LLP jet tagging efficiency of 30-80% for gluinos with 1 mm $\leq$ $c\tau_0$ $\leq$ 10 m. The expected coverage of the parameter space for split supersymmetry is presented.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗