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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Dissolved Air Flotation for Rapid Dewatering and Separation of Legacy Sludge Wastes - 19065

As a low footprint, high efficiency separation process, Dissolved Air Floatation (DAF) could effectively be retrofitted into existing waste management facilities at nuclear sites such Sellafield and Hanford to rapidly separate particulates from aqueous suspensions. The simplicity (no moving parts) and size of this technology coupled with low cost of construction and reagent purchase would also be ideal for ease of facility decommissioning with minimum impact to secondary waste generation. For this study, methyl isobutyl carbinol (MIBC) was used in this research as a frothing agent to produce a preferable stable foam. Mg(OH){sub 2} was selected as a test material as it is the result of long term corrosion of Magnox fuel in UK nuclear fuel storage ponds. Due to the cationic nature of the Mg(OH){sub 2} test material, the anionic surfactant, Sodium Dodecyl Sulphate (SDS) was used as collector to modify the surface properties to increase hydrophobicity. The performance of anionic SDS in floating 2.5%v/v Mg(OH){sub 2} was compared to the adsorption isotherm of SDS on Mg(OH){sub 2} to determine monolayer coverage surfactant dose which was investigated using Total Organic Carbon (TOC). SDS was found to increase particulate recovery to 93% with some water carry-over observed and it was found that particle bubble attachment was optimum for a select particle size distribution. This study proved that potential application of flotation as an efficient viable dewatering technique for common magnesium hydroxide based legacy wastes, using cheap, readily available collector and frother agents. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Predicting Biological Cleanliness: An Empirical Bayes Approach for Spacecraft Bioburden Accounting

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Impact of Temperature and Optical Error on the Combined Optical and Thermal Efficiency of Solar Tower Systems for Industrial Process Heat: Preprint

Concentrating solar thermal (CST) power towers can provide high flux concentrations at commercial scale. As a result, CST towers exhibit potential for high-temperature solar industrial process heat (SIPH) applications. However, at higher operating temperatures, thermal radiation losses can be significant. This study explores the trade-off between thermal and optical losses for SIPH applications using a collection of three case studies at operating temperatures that range from 900-1,550 degrees C. We assume blackbody radiation to represent the thermal losses at the receiver and we use ray tracing to estimate the optical losses. The results show the impact of process temperature on the maximum attainable system efficiency, as well as the higher flux concentration requirements as the temperature increases.

concentrating solar power↗
DOE OSTI2024

The Overlooked Potential of Sulfated Zirconia: Reexamining Solid Superacidity Toward the Controlled Depolymerization of Polyolefins

Closed-loop recycling via an efficient chemical process can help alleviate the global plastic waste crisis. However, conventional depolymerization methods for polyolefins, which compose more than 50% of plastics, demand high temperatures and pressures, employ precious noble metals, and yield complex mixtures of products limited to single-use fuels or oils. Superacidic sulfated zirconia (SZrO) with Hammet Acidity Functions (H 0 ) = -12 (i.e., stronger than 100% H 2 SO 4 ) is an industrially deployed heterogeneous catalyst capable of activating hydrocarbons under mild conditions and is shown to decompose polyolefins at ambient pressure and temperatures near 200 °C. Additionally, confinement of active sites in porous supports is known to radically increase selectivity, coking and sintering resistance, and acid site activity, presenting a possible approach to low-energy polyolefin depolymerization. However, a critical examination of the literature on SZrO led us to a surprising conclusion: despite 40 years of catalytic study, engineering, and industrial use, the surface chemistry of SZrO is poorly understood. Ostensibly spurred by SZrO's impressive catalytic activity, the application-driven study of SZrO has resulted in deleterious ambiguity in requisite synthetic conditions and insufficient characterization of acidity, porosity, and active site structure. Here, this ambiguity has produced significant knowledge gaps surrounding the synthesis, structure, and mechanisms of hydrocarbon activation for optimized SZrO, stunting the potential of this catalyst in olefin cracking and other industrially relevant reactions, such as isomerization, esterification, and alkylation. Toward mitigating these long extant issues, we herein identify and highlight these current shortcomings and knowledge gaps, propose explicit guidelines for characterization of and reporting on characterization of solid acidity, and discuss the potential of pore-confined superacids in the efficient and selective depolymerization of polyolefins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Phase II Nevada Water Resources Data, Modeling, and Visualization (DMV) Center (Final Report)

Water is unquestionably a critical resource throughout the United States. In the semi-arid west -- an area stressed by increase in human population and sprawl of the built environment -- water is the most important limiting resource. Crucially, science must understand factors that affect availability and distribution of water. To sustain growing consumptive demand, science needs to translate understanding into reliable and robust predictions of availability under weather conditions that could be average but might be extreme. These predictions are needed to support current and long-term planning. Similar to the role of weather forecast and climate prediction, water prediction over short and long temporal scales can contribute to resource strategy, governmental policy and municipal infrastructure decisions, which are arguably tied to the natural variability and unnatural change to climate. Change in seasonal and annual temperature, precipitation, snowmelt, and runoff affect the distribution of water over large temporal and spatial scales, which impact the risk of flooding and the groundwater recharge. Anthropogenic influences and impacts increase the complexity and urgency of the challenge. The goal of this project has been to develop a decision support framework of data acquisition, digital modeling, and 3D visualization. This integrated framework consists of tools for compiling, discovering and projecting our understanding of processes that control the availability and distribution of water. The framework is intended to support the analysis of the complex interactions between processes that affect water supply, from controlled availability to either scarcity or deluge. The developed framework enables DRI to promote excellence in water resource management, particularly within the Lake Tahoe basin. In principle, this framework could be replicated for other watersheds throughout the United States. Phase II of this project builds upon the research conducted during Phase I, in which the hydrologic framework was investigated and the development initiated. Phase II concentrates on practical implementation of the earlier work but emphasizes applications to the hydrology of the Lake Tahoe basin. Phase 1 efforts have been refined and extended by creating a toolset for geographic information systems (GIS) that is usable for disparate types of geospatial and geo-referenced data. The toolset is intended to serve multiple users for a variety of applications. The web portal for internet access to hydrologic and remotely sensed product data, prototyped in Phase I, has been significantly enhanced. The portal provides high performance access to LANDSAT-derived data using techniques developed during the course of the project. The portal is interactive, and supports the geo-referenced display of hydrologic information derived from remotely sensed data, such as various vegetative indices used to calculate water consumption. The platform can serve both internal and external constituencies using inter-operating infrastructure that spans both sides of the DRI firewall. The platform is intended grow its supported data assets and to serve as a template for replication to other geographic areas. An unanticipated development during the project was the use of ArcGIS software on a new computer system, called the IBM PureSytems, and the parallel use of the systems for faster, more efficient image processing. Additional data, independent of the portal, was collected within the Sagehen basin and provides detailed information regarding the processes that control hydrologic responses within mountain watersheds. The newly collected data include elevation, evapotranspiration, energy balance and remotely sensed snow-pack data. A Lake Tahoe basin hydrologic model has been developed, in part to help predict the hydrologic impacts of climate change. The model couples both the surface and subsurface hydrology, with the two components having been independently calibrated. Results from the coupled simulations involving both surface water and groundwater processes show that it is possible to fairly accurately simulate lake effects and water budget variables over a wide range of dry and wet cycles in the historical record. The Lake Tahoe basin is representative of the hydrology, topography and climate throughout the Sierra Nevada Range, and the entire model development is prototypical of the efforts required to replicate the decision support framework to other locales. The Lake Tahoe model in particular, could allow water managers to evaluate more accurately components of the water budget (ET, runoff, groundwater, etc) and to answer important questions regarding water resources in northern Nevada. This report discusses the geographic scale and the hydrologic complexity of the calibrated model developed as part of this project, as well as simulation results for historical and future climate projects To enable human-driven data exploration and discovery, de novo software for a globalized rendering module that extends the capability of our evolving custom visualization engine from Phase I (called SMEngine) has been developed. The new rendering component, called Horizon, supports terrain rendering capable of displaying and interrogating both remotely sensed and modeled data. The development of Horizon necessitated adaptation of the visualization engine to allow extensible integration of components such as the global rendering module and support for associated features. The resulting software is general in its GIS capability, but a specific Lake Tahoe visualization application suitable for immersive decision support in the DRIVE6 virtual reality facility has been developed. During the development, various features to enhance the value of the visualization experience were explored, including the use of hyperspectral image overlays. An over-arching goal of the visualization aspect of the project has been to develop and demonstrate the CAVE (CAVE Automatic Virtual Environment) as a practical tool for hydrologic research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Evaluating the efficiency of municipalities in collecting and processing municipal solid waste: A shared input DEA-model

Highlights: Black-Right-Pointing-Pointer Complexity in local waste management calls for more in depth efficiency analysis. Black-Right-Pointing-Pointer Shared-input Data Envelopment Analysis can provide solution. Black-Right-Pointing-Pointer Considerable room for the Flemish municipalities to improve their cost efficiency. - Abstract: This paper proposed an adjusted 'shared-input' version of the popular efficiency measurement technique Data Envelopment Analysis (DEA) that enables evaluating municipality waste collection and processing performances in settings in which one input (waste costs) is shared among treatment efforts of multiple municipal solid waste fractions. The main advantage of this version of DEA is that it not only provides an estimate of the municipalities overall cost efficiency but also estimates of the municipalities' cost efficiency in the treatment of the different fractions of municipal solid waste (MSW). To illustrate the practical usefulness of the shared input DEA-model, we apply the model to data on 293 municipalities in Flanders, Belgium, for the year 2008.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Ion–ion interactions enhance aluminum solubility in alkaline suspensions of nano-gibbsite (α-Al(OH) 3 ) with sodium nitrite/nitrate

Despite widespread industrial importance, predicting metal solubilities in highly concentrated, multicomponent aqueous solutions is difficult due to poorly understood ion–ion and ion–solvent interactions. Aluminum hydroxide solid phase solubility in concentrated sodium hydroxide (NaOH) solutions is one such case, with major implications for ore refining, as well as processing of radioactive waste stored at U.S. Department of Energy legacy sites, such as the Hanford Site, Washington State. The solubility of gibbsite (α-Al(OH) 3 ) is often not well predicted because other ions affect the activity of hydroxide (OH – ) and aluminate (Al(OH) 4 – ) anions. In the study reported herein, we systematically examined the influence of key anions, nitrite (NO 2 – ) and nitrate (NO 3 – ), as sodium salts on the solubility of α-Al(OH) 3 in NaOH solutions taking care to establish equilibrium from both under- and oversaturation. Rapid equilibration was enabled by use of a highly pure and crystalline synthetic nano-gibbsite of well-defined particle size and shape. Measured dissolved aluminum concentrations were compared with those predicted by an α-Al(OH) 3 solubility model derived for simple Al(OH) 4 – /OH – systems. Specific anion effects were expressed as an enhancement factor (Al enhc ) conveying the excess of dissolved aluminum. At 45 °C, NaNO 2 and NaNO 3 -containing systems exhibited Al enhc values of 2.70 and 1.88, respectively, indicating significant enhancement. The solutions were examined by Raman and high-field 27 Al NMR spectroscopy, indicating specific interactions including Al(OH) 4 – –Na + contact ion pairing and Al(OH) 4 – –NO 2 – /NO 3 – ion–ion interactions. Dynamic evolution of the α-Al(OH) 3 particles including growth and agglomeration was observed revealing the importance of dissolution/reprecipitation in establishing equilibrium. These studies indicate that incomplete ion hydration, as a result of the low water activity in these concentrated electrolytes, results in: (i) enhanced reactivity of the hydroxide ion with respect to α-Al(OH) 3 ; (ii) increased concentrations of Al(OH) 4 – in solution; and (iii) stronger ion–ion interactions that act to stabilize the supersaturated solutions. This information on the mechanisms by which α-Al(OH) 3 becomes supersaturated is essential for more energy-efficient aluminum processing technologies, including the treatment of millions of gallons of Al(OH) 4 – -rich high-level radioactive waste.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Workshop Summary Report on Using AI Tools to Improve the Efficiency and Outcomes of the NEPA Process: AI for Permitting Workshop at the 2025 National Association of Environmental Professionals (NAEP) Annual Conference

On April 29, 2025, the U.S. Department of Energy and Pacific Northwest National Laboratory hosted a workshop at the National Association of Environmental Professionals 2025 Conference and Training Symposium in Charleston, South Carolina, titled, “Effective and Responsible Use of Customized AI Tools to Improve the Efficiency and Outcomes of the NEPA Process.” The objectives of this workshop were to make environmental practitioners aware of the potential for using artificial intelligence in the National Environmental Policy Act process, demonstrate examples of how artificial intelligence can be integrated effectively to improve efficiency and outcomes and solicit questions and feedback from practitioners. This report summarizes the key points from all talks and case studies, as well as audience questions and feedback on the presentation topics and the broader topic of "AI in permitting". The report concludes by highlighting the key barriers and opportunities for the implementation of AI in permitting, as discussed during the workshop.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Triplet Harvesting from Intramolecular Singlet Fission in Polytetracene

Abstract Singlet fission (SF), a promising mechanism of multiple exciton generation, has only recently been engineered as a fast, efficient, intramolecular process (iSF). The challenge now lies in designing and optimizing iSF materials that can be practically applied in high‐performance optoelectronic devices. However, most of the reported iSF systems, such as those based on donor–acceptor polymers or pentacene, have low triplet energies, which limits their applications. Tetracene‐based materials can overcome significant challenges, as the tetracene triplet state is practically useful, ≈1.2 eV. Here, the synthesis and excited state dynamics of a conjugated tetracene homopolymer are studied. This polymer undergoes ultrafast iSF in solution, generating high‐energy triplets on a sub‐picosecond time scale. Magnetic‐field‐dependent photocurrent measurements of polytetracene‐based devices demonstrate the first example of iSF‐generated triplet extraction in devices, exhibiting the potential of iSF materials for use in next‐generation devices.

Pun, Andrew B.↗
DOE OSTI2021

Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems

Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile memory (NVM) based on resistive switches, phase-change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi-terminal device concepts are required for more sophisticated bio-realistic functions. Of particular interest are memtransistors based on low-dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi-terminal NVM devices including dual-gated floating-gate memories, dual-gated ferroelectric transistors, and dual-gated van der Waals heterojunctions. Further, the different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase-changes properties of nanomaterials. Finally, strategies for achieving wafer-scale integration of memtransistors and multi-terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2025

Janus Superiority of Membranes in Chemical Engineering and Beyond

Janus configurations, characterized by their inherent asymmetry, enable directional mass transfer in membrane materials that drive novel and energy-efficient chemical processes. This Janus superiority spans applications from nanoscale molecular and ionic transport to macro-scale separation systems with asymmetric spatial architectures. This review provides an analysis of the material foundations including design principles, structure regulation, and scalability challenges underlying Janus membranes. Here, we explore the physics that governs their unique behavior and examine their diverse applications across chemical engineering, including phase transfer, and molecular or ionic transport. Through a multiscale perspective, we provide a comprehensive understanding of the impact of Janus superiority in advancing chemical engineering technologies. Finally, we discuss the hurdles in translating theoretical advances into practical applications and propose promising avenues for future research to harness the full potential of Janus membranes and systems in addressing global challenges related to energy, sustainability, and beyond.

Yang, Hao‐Cheng [Zhejiang University, Hangzhou (Ch↗
DOE OSTI2022

Solvent‐free enzymatic esterification of free fatty acids with glycerol for biodiesel application: Optimized using the Taguchi experimental method

Abstract Presence of free fatty acids along with glycerides poses a technical difficulty for biodiesel production. This work used a Taguchi L9 design to optimize the solvent‐free enzymatic process to result in the esterification of oleic acid with glycerol. Under optimal conditions the esterification reaction temperature of 60°C, enzyme dose of 5 wt%, glycerol: oleic acid molar ratio of 5:1, and reaction time of 3 h, a 75.235 ± 2.19% conversion of oleic acid to esters was achieved. With the addition of molecular sieves, the conversion increased to 86.73% ± 1.09%. However, using the parameters predicted by Taguchi design (60°C, 5 wt%, 5:1, and 4.5 h), 88.5% ± 1.11% of oleic acid could be converted to esters derivative. Diglycerides were the major product, and the reaction equilibrium was attained after 4 h. The immobilized enzyme could be used up to seven times with only a 10% reduction in the conversion. Thus, the process can efficiently reduce the free fatty acid content of oil to make it suitable for biodiesel production.

09 BIOMASS FUELS↗
DOE OSTI2021

3D printing of lignin: Challenges, opportunities and roads onward

Abstract As the second most abundant biopolymer on earth, and as a resource recently becoming more available in separated and purified form on an industrial scale due to the development of new isolation technologies, lignin has a key role to play in transitioning our material industry towards sustainability. Additive manufacturing (AM), the most efficient‐material processing technology to date, has likewise made great strides to promote sustainable industrial solutions to our needs in engineered products. Bringing lignin research to AM has prompted the emergence of the nascent “lignin 3D printing” field. This review presents the recent state of art of this promising field and highlights its challenges and opportunities. Following a review of the industrial availability, molecular attributes, and associated properties of technical lignins, we review R&D efforts at implementing lignin systems in extrusion‐based and stereolithography (SLA) printing technologies. Doing so underlines the adage of lignin research that “all lignins are not created equal,” and stresses the opportunity nested in this chemical diversity created mostly by differences in isolation conditions to molecularly select and tune the attributes of technical lignin systems towards desirable properties, be it by modification or polymer blending. Considering the AM design process in its entirety, we finally propose onward routes to bring the full potential to this emerging field. We hope that this review can help promote the unique value and overdue industrial role of lignin in sustainable engineered materials and products.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Hydrogenolysis of Poly(Ethylene–co–Vinyl Alcohol) and Related Polymer Blends over Ruthenium Heterogeneous Catalysts

The hydrogenolysis of polymers is emerging as a promising approach to deconstruct plastic waste into valuable chemicals. Yet, the complexity of plastic waste, including multilayer packaging, is a significant barrier to handling realistic waste streams. Herein, we reveal fundamental insights into a new chemical route for transforming a previously unaddressed fraction of plastic waste – poly(ethylene–co­­–vinyl alcohol) (EVOH) and related polymer blends – into alkane products. Additionally, we report that Ru/ZrO2 is active for the concurrent hydrogenolysis, hydrogenation, and hydrodeoxygenation of EVOH and its thermal degradation products into alkanes (C1–C35) and water. Detailed reaction data, product analysis, and catalyst characterization reveal that the in–situ thermal degradation of EVOH forms aromatic intermediates that are detrimental to catalytic activity. Increased hydrogen pressure promotes hydrogenation of these aromatics, preventing catalyst deactivation and improving alkane product yields. Calculated apparent rates of C–C scission reveal that the hydrogenolysis of EVOH is slower than low–density polyethylene. We apply these findings to achieve hydrogenolysis of EVOH/polyethylene blends and elucidate the sensitivity of hydrogenolysis catalysts to such blends. Overall, we demonstrate progress towards efficient catalytic processes for the hydroconversion of waste multilayer film plastic packaging into valuable products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Reciprocal Ternary Molten Salts Enable the Direct Upcycling of Spent Lithium‐Nickel‐Manganese‐Cobalt Oxide (NMC) Mixtures to Make NMC 622

Cathode active material is the most valuable component of spent lithium‐ion batteries, accounting for ≈30% of their overall value. Direct recycling of cathode materials involves recovering, regenerating, and reusing them without breaking down their chemical structure. This approach maximizes the added value of the cathode compound and reduces manufacturing costs by avoiding the need for virgin material production. However, one key challenge in scaling direct recycling from lab to industry is the requirement for highly purified cathode materials, contrasting with the low purity of black mass generated from battery shredding. No efficient separation process currently exists to isolate different lithium‐nickel‐manganese‐cobalt oxides (NMCs) from each other. Thus, direct recycling technologies that can operate with mixtures of multiple NMC stoichiometries will be best‐suited for industrial adoption. This study explores the direct recycling of NMC mixtures into NMC 622 using a “reciprocal ternary molten salts (RTMS)” system. Ionothermal relithiation and upcycling within the RTMS system successfully restore the layered structure, lithium content, and electrochemical performance of degraded NMCs, yielding results comparable to pristine NMC 622 (P‐NMC 622).

25 ENERGY STORAGE↗
DOE OSTI2023

Particle Size Optimization of Thermochemical Salt Hydrates for High Energy Density Thermal Storage

Thermal energy storage (TES) solutions offer opportunities to reduce energy consumption, greenhouse gas emissions, and cost. Specifically, they can help reduce the peak load and address the intermittency of renewable energy sources by time shifting the load, which are critical toward zero energy buildings. Thermochemical materials (TCMs) as a class of TES undergo a solid–gas reversible chemical reaction with water vapor to store and release energy with high storage capacities (600 kWh m −3 ) and negligible self‐discharge that makes them uniquely suited as compact, stand‐alone units for daily or seasonal storage. However, TCMs suffer from instabilities at the material (salt particles) and reactor level (packed beds of salt), resulting in poor multi‐cycle efficiency and high‐levelized cost of storage. In this study, a model is developed to predict the pulverization limit or R crit of various salt hydrates during thermal cycling. This is critical as it provides design rules to make mechanically stable TCM composites as well as enables the use of more energy‐efficient manufacturing process (solid‐state mixing) to make the composites. The model is experimentally validated on multiple TCM salt hydrates with different water content, and effect of R crit on hydration and dehydration kinetics is also investigated.

25 ENERGY STORAGE↗