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DOE OSTI2022

Li Morphology Evolution during Initial Cycles in a Gel Composite Polymer Electrolyte

Understanding and controlling lithium morphology evolution and lithium dendrite formation and growth during cycling is one of the key challenges for high-energy lithium metal batteries. This challenge applies to liquid electrolyte batteries as well as solid-state and semi-solid-state batteries. Our current knowledge about the evolution of the Li morphology is mostly obtained from liquid electrolyte-based studies in a Li–Li symmetrical cell configuration. The knowledge obtained in such conditions may not readily transfer into solid-state or semi-solid-state batteries. In this work, Li morphology evolution during initial cycling in a full cell configuration with the LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NMC 622) cathode and a semi-solid-state gel composite electrolyte is monitored via post-mortem photographs and scanning electron microscopy at multiple length scales. The gel composite electrolyte contains a cross-linked poly(ethylene oxide)-based polymer electrolyte, ceramic fillers, and a liquid plasticizer. The results show that severe surface pitting occurs as early as the second stripping cycle. Pit formation and continuous dissolution during the stripping process are the main cause of the Li surface roughening and dendrite growth mechanism in the model gel composite electrolyte. Comparing Li dendrite growth mechanisms in liquid, polymer, and ceramic solid electrolytes, the dendrite growth mechanism observed in this model electrolyte resembles that of the liquid electrolyte the most. This study suggests that strategies to control Li morphology and prevent dendrite growth in a gel composite electrolyte should be similar to strategies applicable to liquid electrolytes.

25 ENERGY STORAGE↗

DOE OSTI2025

Cleavage of C-O and C-C Bonds in Lignin-Derived Compounds to Produce Aromatics Using Molybdenum-Containing MFI Zeolites

Lignin, the most abundant source of renewable arenes, is a viable feedstock for the production of aromatic compounds. However, the prevalence of resilient C-C bonded oligomeric fragments in lignin-derived streams can compromise monomer yields during reductive catalytic fractionation (RCF). To address this issue, we developed a bifunctional molybdenum-containing MFI (Mo/H-MFI) zeolite catalyst capable of cleaving both C-O and C-C bonds in lignin-derived molecules to produce aromatic monomers. Using propylguaiacol as a model compound, we demonstrated the importance of proximity between metallic molybdenum carbide sites and the Bronsted acid sites in the zeolite in achieving high carbon yields (~80%) of benzene, toluene, propylbenzene, and phenol while maintaining catalyst stability (>98% stable conversion for 20 h). A reaction network involving both C-O and C-C bond cleavage pathways was proposed based on kinetic studies using key intermediates as feeds. Finally, we successfully depolymerized partially deoxygenated lignin oil obtained from the RCF of poplar using a continuous, two-pass catalytic process. This work highlights the potential of the bifunctional Mo/H-MFI catalyst in upgrading complex lignin feedstocks and provides a methodological approach for converting lignin-derived compounds into platform aromatic chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Hollow-core optical fibre sensors for operando Raman spectroscopy investigation of Li-ion battery liquid electrolytes

Improved analytical tools are urgently required to identify degradation and failure mechanisms in Li-ion batteries. However, understanding and ultimately avoiding these detrimental mechanisms requires continuous tracking of complex electrochemical processes in different battery components. Here, we report an operando spectroscopy method that enables monitoring the chemistry of a carbonate-based liquid electrolyte during electrochemical cycling in Li-ion batteries with a graphite anode and a LiNi 0.8 Mn 0.1 Co 0.1 O 2 cathode. By embedding a hollow-core optical fibre probe inside a lab-scale pouch cell, we demonstrate the effective evolution of the liquid electrolyte species by background-free Raman spectroscopy. The analysis of the spectroscopy measurements reveals changes in the ratio of carbonate solvents and electrolyte additives as a function of the cell voltage and show the potential to track the lithium-ion solvation dynamics. The proposed operando methodology contributes to understanding better the current Li-ion battery limitations and paves the way for studies of the degradation mechanisms in different electrochemical energy storage systems.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

The effects of high-pressure annealing on magnetostructural transitions and magnetoresponsive properties in stoichiometric MnCoGe

In this study, phase transitions (structural and magnetic) and associated magnetocaloric properties of stoichiometric MnCoGe have been investigated as a function of annealing pressure. Metastable phases were generated by annealing at 800 °C followed by rapid cooling under pressures up to 6.0 GPa. The x-ray diffraction results reveal that the crystal cell volume of the metastable phases continuously decreases with increasing thermal processing pressure, leading to a decrease in the structural transition temperature. The magnetic and structural transitions merge and form a first-order magnetostructural transition between the ferromagnetic orthorhombic and paramagnetic hexagonal phases over a broad temperature range (>80 K) spanning room temperature, yielding considerable magnetic entropy changes. These findings demonstrate the utility of thermal processing under high pressure, i.e., high-pressure annealing, to control the magnetostructural transitions and associated magnetocaloric properties of MnCoGe without altering its chemical composition.

Physics↗

DOE OSTI2023

Much of zero emissions commitment occurs before reaching net zero emissions

Abstract We explore the response of the Earth’s coupled climate and carbon system to an idealized sequential addition and removal of CO 2 to the atmosphere, following a symmetric and continuous emissions pathway, in contrast to the discontinuous emissions pathways that have largely informed our understanding of the climate response to net zero and net negative emissions to date. We find, using both an Earth system model and an ensemble of simple climate model realizations, that warming during the emissions reduction and negative emissions phases is defined by a combination of a proportionality of warming to cumulative emissions characterized by the transient climate response to emissions (TCRE), and a deviation from that proportionality that is governed by the zero emissions commitment (ZEC). About half of the ZEC is realized before reaching zero emissions, and the ZEC thus also controls the timing between peak cumulative CO 2 emissions and peak temperature, such that peak temperature may occur before peak cumulative emissions if ZEC is negative, underscoring the importance of ZEC in climate policies aimed to limit peak warming. Thus we argue that ZEC is better defined as the committed warming relative to the expected TCRE proportionality, rather than as the additional committed warming that will occur after reaching net zero CO 2 emissions. Once established, the combined TCRE and ZEC relationship holds almost to complete removal of prior cumulative CO 2 emissions. As cumulative CO 2 emissions approach zero through negative CO 2 emissions, CO 2 concentrations drop below preindustrial values, while residual long-term climate change continues, governed by multicentennial dynamical processes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Bennett-Brassard 1984 quantum key distribution using conjugate homodyne detection

Optical homodyne detection has been widely used in continuous-variable (CV) quantum information processing for measuring field quadrature. In this paper we explore the possibility of operating a conjugate homodyne detection system in “photon counting” mode to implement discrete-variable (DV) quantum key distribution (QKD). A conjugate homodyne detection system, which consists of a beam splitter followed by two optical homodyne detectors, can simultaneously measure a pair of conjugate quadratures X and P of the incoming quantum state. In classical electrodynamics, X 2 + P 2 is proportional to the energy (the photon number) of the input light. In quantum optics, X and P do not commute and thus the above photon-number measurement is intrinsically noisy. This implies that a blind application of standard security proofs of QKD could result in pessimistic performance. We overcome this obstacle by taking advantage of two special features of the proposed detection scheme. First, the fundamental detection noise associated with vacuum fluctuations cannot be manipulated by an external adversary. Second, the ability to reconstruct the photon number distribution at the receiver's end can place additional constraints on possible attacks from the adversary. As an example, we study the security of the BB84 QKD using conjugate homodyne detection and evaluate its performance through numerical simulations. This study may open the door to a family of QKD protocols, complementary to the well-established DV-QKD based on single-photon detection and CV-QKD based on coherent detection.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

C2QA - Bosonic Qiskit

The practical benefits of hybrid quantum information processing hardware that contains continuous-variable objects (bosonic modes such as mechanical or electromagnetic oscillators) in addition to traditional (discrete-variable) qubits have recently been demonstrated by experiments with bosonic codes that reach the break-even point for quantum error correction [1]–[5] and by efficient Gaussian boson sampling simulation of the Franck-Condon spectra of triatomic molecules [6] that is well beyond the capabilities of current qubit-only hardware. The goal of this Co-design Center for Quantum Advantage (C2QA) project is to develop an instruction set architecture (ISA) for hybrid qubit/bosonic mode systems that contains an inventory of the fundamental operations and measurements that are possible in such hardware. The corresponding abstract machine model (AMM) would also contain a description of the appropriate error models associated with the gates, measurements and time evolution of the hardware. This information has been implemented as an extension of IBM Qiskit. IBM Qiskit is an open-source software development toolkit (SDK) for simulating the quantum state of a quantum circuit and for running the same circuits on prototype hardware within the IBM Quantum Experience. We introduce the Bosonic Qiskit software to enable the simulation of hybrid qubit/bosonic systems using the existing Qiskit software development kit [7]. This implementation can be used for simulating new hybrid systems, verifying proposed physical systems, and modeling systems larger than can currently be constructed. We also cover tutorials and example use cases included within the software to study JaynesCummings models, bosonic Hubbard models, plotting Wigner functions and animations, and calculating maximum likelihood estimations using Wigner functions.

quantum, boson, Qiskit↗

DOE OSTI2025

Environmental DNA as a tool for hydropower impact assessments: current status, special considerations, and future integration

Globally there is an urgent need to find sustainable solutions to balance energy production with the protection of vulnerable species and conservation of biodiversity. This is particularly critical for freshwater ecosystems, habitats, and species that may be impacted by hydropower development and operations needed to meet energy grid demands. Reliable and accurate environmental impact assessments (EIAs) that identify the biological, physical, or social impacts of hydropower are key to ensure biodiversity, ecosystem, and societal sustainability. The analysis of environmental DNA (eDNA) has the potential to transform hydropower EIAs, management and mitigation planning, and decision-making procedures. Further, the incorporation of eDNA surveys into EIAs during both hydropower planning and continued operations may streamline regulatory processes by improving our understanding of potentially impacted biota and habitats and evaluating environmental impacts mitigation. Here, we: (i) highlight current understanding and use of eDNA in freshwater environments; (ii) examine critical considerations for eDNA integration into hydropower EIAs and biological monitoring; (iii) identify knowledge gaps in eDNA analysis and applications unique to hydropower-regulated systems; and (iv) discuss future opportunities to bolster the incorporation of eDNA into hydropower research including regulatory acceptance and public engagement. While we acknowledge that there are several factors that may complicate the broad adoption of eDNA as a tool for assessing the impacts of hydropower, we anticipate that growing confidence in eDNA through hydropower-specific protocols, calibrations, and validations will overcome these inherent uncertainties.

aquatic biodiversity↗

DOE OSTI2021

Passive sensing of a microparticle modified hybrid, fiber-reinforced composite

The widespread commercial adoption of high-performance, fiber-reinforced composites has pushed research interests toward the next generation of composites. These new composites are tasked with integrating additional functionalities into the structures without causing a trade-off in mechanical performance. One such functionality that has received significant interest is sensing. This is especially important for composites using high-performance fibers (e.g., carbon fiber) because their strain-to-failure is relatively low, resulting in brittle fracture. Besides, fiber damage can be hidden within the composite, potentially leading to premature catastrophic failure if not detected. In prior research, we demonstrated continuous feed-through deposition of ceramic nanoparticles on carbon fiber’s surface that simultaneously enhanced both the piezoresistive response and interlaminar shear strength. In this work, a similar continuous feed-through deposition process was used to demonstrate passive sensing and energy harvesting by integrating ferroelectric microparticles on the surface of electrically nonconductive fibers. The sensing and energy harvesting capabilities were characterized by mechanically straining composite beams and measuring the power generated. The improvements in mechanical properties are shown through interlaminar shear strength tests. Therefore, this research aims to demonstrate a high throughput, commercially scalable approach to coat fibers with ferroelectric microparticles that enable passive sensing as well as improved mechanical performance when fabricated into a fiber-reinforced composite.

Bowland, Christopher↗

DOE OSTI2024

Improving the Cost-Effectiveness of Algal CO2 Utilization by Synergistic Integration With Power Plant and Wastewater Treatment Operations

Creating an economic demand for carbon utilization products will require lowering the overall cost of the products to compete within the current market. Photosynthetic uptake of carbon dioxide is an emerging pathway for product development in the animal feed market that globally amounts to over 400 Billion USD and is expected to continue growing. This project aims to continue the development of a process that utilizes carbon dioxide while increasing the cost competitiveness of algae as an animal feed product. The overall goal of the project is to demonstrate an engineering-scale open raceway pond algae cultivation system (180 m2) including integration of technologies that utilize coal-fired power plant CO2 and wastewater nutrient inputs. The system is designed to maximize the cost-effectiveness and environmental benefits of algal biomass production for commodity animal feed.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2021

National Cost-Effectiveness of ANSI/ASHRAE/IES Standard 90.1–2019

The purpose of this analysis is to examine the cost-effectiveness of the 2019 edition of ANSI/ASHRAE/IES Standard 90.1, which is developed by the ASHRAE Standard Standing Project Committee (SSPC) 90.1, and is the model energy standard for all commercial buildings and multifamily residential buildings over three floors. PNNL analyzed the cost-effectiveness of changes in Standard 90.1-2019, compared to the previous 90.1-2016 edition, as applied in commercial buildings across the United States. In reviewing proposed changes to Standard 90.1, the SSPC considers the cost-effectiveness of individual changes (addenda). Due to the continuous nature of the development process, however, ASHRAE does not evaluate the entire package of addenda from one edition of the standard to the next, which is of particular interest to adopting state and local governments. Providing states with an analysis of cost-effectiveness facilitates a more comprehensive understanding of the impacts associated with updated model energy codes, informs the state decision-making process and its authorities, and ultimately encourages greater adoption of updated of energy codes. This information also informs the development of future editions of Standard 90.1.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Methane Pyrolysis for CO2-free H2 and Carbon Nanomaterials (Abstract)

We propose to continue to develop a new process for producing CO2-free hydrogen (H2) from inexpensive and domestically-abundant natural gas (NG), while simultaneously reducing H2’s net production cost to $1.0/kg through the sale of valuable crystalline solid carbon co-product. Producing clean hydrogen at this price is a DOE Hydrogen Energy Earthshot goal. Cost effective production of clean H2 is also of commercial relevance to project partners Southern California Gas Company (SoCalGas) and startup company C4-MCP, who aim to further develop, demonstrate at scale, and ultimately deploy the new process technology developed on this project in order to meet regulatory demands in the State of California. In the current project we have focused on i) understanding the catalyst science for thermocatalytic decomposition of methane (TCD), which resulted in the development of a patent pending bimetallic catalyst offering favorable activity, stability, and selectivity under industrially relevant process conditions, ii) developing a novel, patent pending process to enable the separation of produced carbon and catalyst, and re-synthesis of the catalyst using recycled materials, iii) performing limited characterization of the produced carbon materials, and iv) performing detailed process modeling in order to perform techno economic assessment. The additional scope proposed here will accelerate the commercial deployment of TCD for CO2-free H2 and valuable solid carbon nanotubes (CNT) co-product, by i) scaling up the production of CNT co-product using a scalable, fluidized bed reactor (25 g catalyst scale versus the 1 g catalyst scale demonstrated to-date), ii) producing at least 40 g of CNT product, produced via multiple cycles of TCD, carbon-catalyst separation, and catalyst re-synthesis, to enable the production of sufficient quantities of solid carbon so as to explore its market potential, iii) understanding the quality of the co-product CNTs, produced at larger scale, through advanced characterization, and iv) beginning to explore multiple promising high volume carbon product applications (thermoplastics, automotive composites, battery, and cement reinforcement applications).

08 HYDROGEN↗

DOE OSTI2023

From Deposition to Encapsulation: Roll-to-roll manufacturing of organic light emitting devices for lighting (Final Report)

Organic light emitting devices (OLEDs) are promising solid state light sources due to their high efficiency, high color quality and flexible form factors. The key to enable low cost OLED lighting, is to rapidly fabricate thin film organic layers on a continuous flexible roll, called R2R processing. This project aims to investigate the feasibility of mass production of OLED lighting using the R2R process, from deposition to encapsulation. A high efficiency white OLED is fabricated on 10 cm-wide substrate rolls in a pilot R2R tool comprising of two different organic deposition: vacuum thermal evaporation (VTE) and organic vapor phase deposition (OVPD). A high quality encapsulation process to package OLEDs is demonstrated using an atomic layer deposition tool integrated to the R2R system without air exposure. The method to achieve ultrahigh deposition rates required by R2R processing is demonstrated by OVPD. Uniform organic semiconductor thin films grown by OVPD at rates as high as 50 Å/s are achieved. A comprehensive numerical model that is capable of simulating complex, multilayer WOLED structures is developed to provide an alternative to experimental iterations of OLED design and tests. A cost estimate on the R2R production of WOLEDs for lighting is developed. Assuming a WOLED luminance of 10 klm/m 2 , the cost of a WOLED light engine is anticipated to be $\$ 12.5$ /klm. With incremental reduction in material and driver costs and improved luminance, the cost of WOLED lighting can be reduced to $\$ 6.3$ /klm in the near term. These findings suggest OLED lighting can be volume manufactured by R2R vapor deposition methods with much reduced costs compared to current batch processing methods, potentially positioning WOLEDs for use in numerous premium lighting applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

High Performance Computing Facility Operational Assessment 2022: Oak Ridge Leadership Computing Facility

The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2022, the OLCF supported 1,681 users and 570 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.6 out of 5 on the annual user survey, and 96% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,212 user tickets submitted in CY 2022, OLCF staff resolved 97% within 3 business days. The facility also introduced several new services for users this year, including weekly virtual office hours with subject matter experts from ORNL and vendor partners; new views in MyOLCF that allow users to analyze allocation and compute usage for a project; the ability to build and run containers on Summit; and improved data visualization support and training resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

US Department of Energy, Office of Science, High-Performance Computing Facility: 2023 Operational Assessment Oak Ridge Leadership Computing Facility

The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through effective allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2023, the OLCF supported 1,676 users and 598 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.52 out of 5 on the annual user survey, and 94% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,619 user tickets submitted in CY 2023, OLCF staff resolved 97% within 3 business days. The facility opened Frontier to full scientific operations this year. Two projects conducted on Frontier received the Association for Computing Machinery (ACM) Gordon Bell Prize and the Gordon Bell Special Prize for Climate Modeling, and a third earned a nomination as a Gordon Bell Prize finalist. The facility’s previous flagship machine, Summit, gained new life and was extended through 2024 in part to help provide resources to the Integrated Research Infrastructure (IRI) projects and the National Artificial Intelligence Research Resource (NAIRR) pilot program. The facility instantiated an Advanced Computing Ecosystem testbed in part to support IRI workflows. OLCF made interactivity easier and more accessible to users than ever through tools like Jupyter notebooks and workflows.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Addressing Experimental Challenges in Probing Dark Energy with Accuracy and Precision with the Rubin Observatory Legacy Survey of Space & Time (LSST)

With support from this award, the Stanford group under the leadership of PI Patricia Burchat focused on pixel-level algorithms that are key to the investigation of dark energy with the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The work was carried out through strong engagement in the DOE-supported LSST Dark Energy Science Collaboration (DESC), for which the PI served as Deputy Spokesperson (07/2019 - 07/2021) and Technical Coordinator (07/2021 - 07/2023). Members of the Burchat group are Full DESC members and are among scientists who have been accepted to join the Rubin Observatory System Integration, Test, and Commissioning (SIT-Com) effort. The specific intellectual focus of the group was the optimization of gravitational lensing as a probe of the evolution of the distribution of dark matter in space and time to learn about the nature of dark energy. The Stanford group pursued an integrated research program to understand in detail, and then remediate or calibrate, systematic effects that could limit the sensitivity of the LSST to cosmic shear as a dark energy probe -- particularly those related to our understanding of the atmospheric point spread function (PSF), challenges associated with blended galaxy images, and impacts of residual camera artifacts. Key to this work was developing, enhancing, and validating the fidelity of simulations that play a key role in the development of our understanding of subtle systematic biases and then using these simulations to develop techniques to characterize and mitigate biases, and associated algorithms for implementation in the LSST or DESC science pipelines. The group’s work included contributions to the open-source GalSim simulation package, which is widely used in the community; simulations and studies of spatial correlations of atmospheric PSFs; studies of temporal correlations of the atmospheric PSF; impacts of chromatic PSFs on cosmic shear calibration; continued studies of anisotropic Gaussian process interpolation of PSF models across the focal plane; and measuring and mitigating impacts of residual camera artifacts on cosmic shear. A primary impact of this work is that it increases the scientific discovery potential of the DOE and NSF investments in the Rubin Observatory Legacy Survey of Space \& Time, by addressing challenges in interpreting astronomical images through more accurate descriptions of the blurring of images due to the atmosphere. Specifically, this work impacts our ability to understand the fundamental nature of dark energy.

Burchat, Patricia Rose↗

DOE OSTI2024

Development of a machine learning model for polyethylene pyrolysis using a detailed reaction mechanism

Waste plastics have recently received significant attention as the issue of waste generation continues to increase. Thermal conversion processes, such as pyrolysis and gasification, are attractive potential technologies for utilizing waste plastics and reducing overall waste generation. Efficient utilization of plastics requires a detailed understanding of the conversion process such as pyrolysis and gasification. However, a mechanistic understanding of these processes lead to large and complex kinetic schemes that are not suited for large-scale and long-time simulation methods. Currently, most modeling approaches for pyrolysis and gasification rely on globally lumped, simplified kinetic schemes that provide results that are classified by their product type and not individual species, which limit the level of fidelity achieved via modeling. A machine learning (ML) model has been developed for the primary reactions of high-density polyethylene (HDPE) in an attempt to increase computational efficiency while still maintaining a high level of detail and accuracy. The ML model is trained on a detailed reaction mechanism containing 42 total species and 737 chemical reactions. A DeepONet branch and trunk architecture was adopted to train the model using time-steps relevant to computational fluid dynamics simulations. The ML used physics-informed loss functions to ensure mass conservation. The surrogate model has been deployed in simple MFiX CFD simulations, single particle and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗

DOE OSTI2025

US Department of Energy, Office of Science, High-Performance Computing Facility 2024 Operational Assessment Oak Ridge Leadership Computing Facility

97 MATHEMATICS AND COMPUTING↗