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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Next Generation Fiber-Encapsulated Nanoscale Hybrid Materials for Direct Air Capture with Selective Water Rejection

Recently, NOHMs sorbents have been demonstrated to be highly stable and tunable for CO2 capture and have negligible vapor pressure, but their main drawbacks are high viscosity and high gas transfer limitation in their neat bulk phase. This project explored gas-assisted electrospinning processing coupled with promising encapsulation matrices such as Polymers with Intrinsic Microporosity (PIM-1) and polyacrylonitrile (PAN)/organopolysilizane (OPSZ) polymer/ceramic hybrid materials, which were demonstrated to be effective novel carriers for the active NOHMs sorbents. These encapsulation materials were evaluated by their thermal stability, hydrophobicity, permeability, and CO2 selectivity. The manufacturing parameters such as component composition and processing solvent were linked to bulk sorbent properties, e.g., surface area, pore volume, fiber thickness, capture capacity, capture kinetics, etc. These investigations resulted in fundamental knowledge and technical know-how that will be useful for scaling up the nanofiber mat contactor design. The encapsulation materials and design has been shown to not only increase the thermal oxidative stability of the NOHMs, but also yield significantly reduced pressure drop and increased performance of large modular air filters. 70 PAN: 30 OPSZ/NIPEI fibers exhibited the highest capture performance with 1.5 mmol CO2/g∙h kinetics the first hour under 100% CO2 condition, followed by 80 PAN: 20 OPSZ/NIPEI and 90 PAN: 10 OPSZ/NIPEI, while 90 PAN: 10 OPSZ/NIPEI fibers performed the best with 0.25 mmol CO2/g∙h of the initial kinetics under 400 ppm CO2 concentration. All the fabricated fibers demonstrated stable capture performances up to 15 cycles of adsorption and desorption. Using encapsulated NOHMs, the study found that the microwave could help regenerate the capture materials within 20 minutes without chemically degrading the materials. A high-level TEA/LCA study was performed to evaluate the feasibility of this technology at scale. These estimates show that the sorbent cost and environmental impact levels are comparable to other existing technologies. Overall, these findings increase the technological feasibility of using liquid-like, low vapor pressure NOHMs as a next generation sorbent for the direct air capture of CO2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Additive manufacturing of complexly shaped SiC with high density via extrusion-based technique – Effects of slurry thixotropic behavior and 3D printing parameters

Here, additive manufacturing of dense SiC parts was achieved via an extrusion-based process followed by electrical-field assisted pressure-less sintering. The aim of this research was to study the effect of the rheological behavior of SiC slurry on the printing process and quality, as well as the influence of 3D printing parameters on the dimensions of the extruded filament, which are directly related to the printing precision and quality. Different solid contents and dispersant- Darvan 821A concentrations were studied to optimize the viscosity, thixotropy and sedimentation rate of the slurry. The optimal slurry was composed of 77.5 wt% SiC, Y 2 O 3 and Al 2 O 3 powders, 0.25 wt% dispersant and 0.01 wt% defoamer. The printing parameters studied included extrusion pressure, nozzle size, layer height and printing speed; the one that had the most prominent effect on filament width and height was indicated as layer height. The nozzle inner diameter of 1.04 mm, speed of 350 mm/min, layer height of 0.7 mm and extrusion air pressure of 0.31 MPa were the optimal printing parameters. Furthermore, the relationship between the printing parameters and the filament dimensions was successfully predicted by using machine learning and grey system theory. Finally, the relative density of the printed SiC parts sintered at 1900 oC reached 94.7±1.5%.

36 MATERIALS SCIENCE↗
DOE OSTI2022

KilonovaNet : Surrogate models of kilonova spectra with conditional variational autoencoders

ABSTRACT Detailed radiative transfer simulations of kilonova spectra play an essential role in multimessenger astrophysics. Using the simulation results in parameter inference studies requires building a surrogate model from the simulation outputs to use in algorithms requiring sampling. In this work, we present kilonovanet, an implementation of conditional variational autoencoders (cVAEs) for the construction of surrogate models of kilonova spectra. This method can be trained on spectra directly, removing overhead time of pre-processing spectra, and greatly speeds up parameter inference time. We build surrogate models of three state-of-the-art kilonova simulation data sets and present in-depth surrogate error evaluation methods, which can in general be applied to any surrogate construction method. By creating synthetic photometric observations from the spectral surrogate, we perform parameter inference for the observed light-curve data of GW170817 and compare the results with previous analyses. Given the speed with which kilonovanet performs during parameter inference, it will serve as a useful tool in future gravitational wave observing runs to quickly analyse potential kilonova candidates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Sensitivity Analysis of Irradiated Fueled Experiments using the MOOSE Framework [Slides]

Modeling and simulation (M&S) methods are able to predict uncertainties in experimental parameters (e.g., power and fission density) during irradiation. A shortfall exists in predicting how sensitive some of the parameters will behave during the experimental process. Sensitivity and Uncertainty Quantification (SUQ) is critical in support of qualification and licensing reactor fuels. The application of a method to quantify the uncertainty in these experiments is critical to the prediction of their performance. In this work, we propose the use of a polynomial chaos expansion (PCE) method to quantify the sensitive parameters in these simulations and, in an extension, their experimental surrogates. We propose to perform M&S using PCE uncertainty quantification on a previously irradiated fueled experiment in order to provide a validation case for Griffin and expand its use as a verification and validation (V&V) tool for experiments with a neutronics component. Griffin is an advanced, deterministic neutronics analysis code built using the MOOSE (multiphysics object-oriented simulation environment) framework which can provide state-of-the-art neutronic analysis on M&S of experiments. We will use the stochastic tools module (STM) in MOOSE to provide PCE uncertainty quantification on the proposed experimental setup. Idaho National Laboratory (INL) does not yet have an in-house developed code with V&V approval for experiments performed on-site; this work would provide a necessary addition of support for experiments performed at INL. The Nuclear Regulatory Commission (NRC) has explicitly requested uncertainties in calculated values such as fuel power and burnup, and the development of this capability would benefit the relationship between INL and the NRC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Prediction of Silicon Content in a Blast Furnace via Machine Learning: A Comprehensive Processing and Modeling Pipeline

Silicon content plays an important role in determining the operational efficiency of blast furnaces (BFs) and their downstream processes in integrated steelmaking; however, existing sampling methods and first-principles models are somewhat limited in their capability and flexibility. Current data-based prediction models primarily rely on a limited set of manually selected furnace parameters. Additionally, different BFs present a diverse set of operating parameters and state variables that are known to directly influence the hot metal’s silicon content, such as fuel injection, blast temperature, and raw material charge composition, among other process variables that have their own impacts. The expansiveness of the parameter set adds complexity to parameter selection and processing. This highlights the need for a comprehensive methodology to integrate and select from all relevant parameters for accurate silicon content prediction. Providing accurate silicon content predictions would enable operators to adjust furnace conditions dynamically, improving safety and reducing economic risk. To address these issues, a two-stage approach is proposed. First, a generalized data processing scheme is proposed to accommodate diverse furnace parameters. Second, a robust modeling pipeline is used to establish a machine learning (ML) model capable of predicting hot metal silicon content with reasonable accuracy. The method employed herein predicted the average Si content of the upcoming furnace cast with an accuracy of 91% among 200 target predictions for a specific furnace provisioned by the XGBoost model. This prediction is achieved using only the past shift’s operating conditions, which should be available in real time. This performance provides a strong baseline for the modeling approach with potential for further improvement through provision of real-time features.

Chemistry↗
DOE OSTI2024

Bayesian stability and force modeling for uncertain machining processes

Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.

42 ENGINEERING↗
DOE OSTI2023

Molten salt electrochemical upcycling of CO 2 to graphite for high performance battery anodes

The efficient transformation of CO 2 into a value-added material is a potential strategy to help mitigate climate effects caused by CO 2 emissions. One potential CO 2 conversion product is graphite which is an important and versatile material extensively used in many applications including as an anode for lithium-ion batteries (LIBs). Commercial graphite, however, is traditionally synthesized via the energy intensive Acheson process (>3000 °C) and the performance of such graphite can be limited under fast charging conditions which is important for vehicle electrification. We report the electrochemical transformation of CO 2 to highly crystalline nano-graphite with a controlled microstructure in a carbonate molten salt at 780 °C. The use of a nickel foam electrode and controlled electrochemical parameters during the molten salt conversion process yielded pure graphite at a lower temperature compared to the Acheson process. Moreover, when investigated as an anode material for LIBs, the CO 2 -converted graphite exhibited high reversible capacity, long cycle life, and excellent rate capability even under fast charging conditions. This process provides a way to potentially reduce carbon emissions through the utilization of waste CO 2 by converting it into value-added graphite suitable for fast charging, high-energy-density batteries for vehicle electrification.

25 ENERGY STORAGE↗
DOE OSTI2024

Investigation of the Δ I = 1 / 2 Rule and Test of C P Symmetry through the Measurement of Decay Asymmetry Parameters in Ξ − Decays

Using ( 10 087 ± 44 ) × 10 6 J / ψ events collected with the BESIII detector, numerous Ξ − and Λ decay asymmetry parameters are simultaneously determined from the process J / ψ → Ξ − Ξ ¯ + → Λ ( p π − ) π − Λ ¯ ( n ¯ π 0 ) π + and its charge-conjugate channel. The precisions of α Λ 0 for Λ → n π 0 and α ¯ Λ 0 for Λ ¯ → n ¯ π 0 compared to world averages are improved by factors of 4 and 1.7, respectively. The ratio of decay asymmetry parameters of Λ → n π 0 to that of Λ → p π − , ⟨ α Λ 0 ⟩ / ⟨ α Λ − ⟩ , is determined to be 0.873 ± 0.01 2 − 0.010 + 0.011 , where the first and the second uncertainties are statistical and systematic, respectively. The ratio is smaller than unity more than 5 σ , which signifies the existence of the Δ I = 3 / 2 transition in Λ for the first time. Besides, we test for C P symmetry in Ξ − → Λ π − and in Λ → n π 0 with the best precision to date. Published by the American Physical Society 2024

Physics↗
DOE OSTI2021

Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets

A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it possible to synthesize solutions to a wide range of nonlinear stochastic boundary value problems described by partial differential equations (PDEs) for which a stochastic computational model (SCM) is available and which depend on a vector-valued random control parameter. The cost of a single numerical evaluation of this SCM is assumed to be such that only a limited number of points can be computed for constructing the training dataset (small data). Each point of the training dataset is made up of realizations from a vector-valued stochastic process (the stochastic solution) and the associated random control parameter on which it depends. The presented PLoM constrained by PDE allows for generating a large number of learned realizations of the stochastic process and its corresponding random control parameter. These learned realizations are generated so as to minimize the vector-valued random residual of the PDE in the mean-square sense. Appropriate novel methods are developed to solve this challenging problem. Three applications are presented. The first one is a simple uncertain nonlinear dynamical system with a nonstationary stochastic excitation. The second one concerns the 2D nonlinear unsteady Navier–Stokes equations for incompressible flows in which the Reynolds number is the random control parameter. Here, the last one deals with the nonlinear dynamics of a 3D elastic structure with uncertainties. The results obtained make it possible to validate the PLoM constrained by stochastic PDE but also provide further validation of the PLoM without constraint.

Machine learning↗
DOE OSTI2021

Thermal Processing Conditions for the Synthesis of Near Theoretical Density Li 5 La 3 Ta 2 O 12 Ceramics for Ceramic Dual-Mode Detectors

A family of Li-containing garnet compounds, extensively investigated for Li-ion solid electrolyte applications, show great potential to be developed into transparent ceramic dual-mode scintillators. In particular, Li 5 La 3 Ta 2 O 12 (LLTaO) shows promise as an efficient dual-mode scintillator due to its high Li content, high density, non-hygroscopicity, and high effective-Z. Often the synthesis of LLTaO ceramics involves solid state processing techniques starting with oxide and carbonate powders, calcining, sintering for the removal of residual porosity, and post-processing. This study focuses on two objectives: (1) establishing thermal processing boundary conditions for a flexible alkoxide-based sol-gel method for synthesizing fine-grained powders; and (2), consolidating powders via hot-pressing to form high-density ceramics. In this study, near single-phase ceramics were achieved by hot-pressing sol-gel powders with varying amounts of excess Li. Rietveld refinements were used to analyze X-ray diffraction data and provide a quantitative evaluation of the phases present in the hot pressed ceramics. Some of the diffraction data showed large low angle peak asymmetry, suggesting the presence of two cubic Li 5 La 3 Ta 2 O 12 garnet phases with different lattice parameters. Depending on the synthesis and processing conditions, the refinement revealed ~ 90 wt% concentration of a phase with a ≈ 12.8 Å and ~ 10 wt% concentration of a phase with a ≈ 12.9 Å, indicating a remaining excess of Li in the hot pressed ceramics in the large lattice parameter phase. These synthesis experiments allowed for the determination of a baseline for Li loss during hot-pressing. X-ray excited radioluminescence and photoluminescence excitation and emission spectra were obtained from the hot pressed ceramics. In conclusion, the measured emission spectra featured broad tantalate emission and line emissions likely from a rare-earth impurity.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme

Abstract. An earlier study evaluating dust life cycle in the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1) has revealed that the simulated global mean dust lifetime is substantially shorter when higher vertical resolution is used, primarily due to significant strengthening of dust dry removal in source regions. This paper demonstrates that the sequential splitting of aerosol emissions, dry removal, and turbulent mixing in the model's time integration loop, especially the calculation of dry removal after surface emissions and before turbulent mixing, is the primary reason for the vertical resolution sensitivity reported in that earlier study. Based on this reasoning, we propose a revised numerical process coupling scheme that requires the least amount of code changes, in which the surface emissions are applied before turbulent mixing instead of before dry removal. The revised scheme allows newly emitted particles to be transported aloft by turbulence before being removed from the atmosphere, and hence better resembles the dust life cycle in the real world. Sensitivity experiments show that the revised process coupling substantially weakens dry removal and strengthens vertical mixing in dust source regions. It also strengthens the large-scale transport from source to non-source regions, strengthens dry removal outside the source regions, and strengthens wet removal and activation globally. In transient simulations of the years 2000–2009 conducted using 1∘ horizontal grid spacing, 72 vertical layers, and unchanged tuning parameters of emission strength, the revised process coupling leads to a 40 % increase in the global total dust burden and an increase of dust lifetime from 1.8 to 2.5 d in terms of 10-year averages. Weakened dry removal and increased mixing ratios are also seen for other aerosol species that have substantial surface emissions, although the changes in mixing ratio are considerably smaller for the submicron species than for dust and sea salt. Numerical experiments confirm that the revised coupling scheme significantly reduces the strong and non-physical sensitivities of model results to vertical resolution in the original EAMv1. This provides a motivation for adopting the revised scheme in EAM as well as for further improvements on the simple revision presented in this paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Process optimization of complex geometries using feed forward control for laser powder bed fusion additive manufacturing

Additive manufacturing (AM) enables the fabrication of complex designs that are difficult to create by other means. Metal parts manufactured by laser powder bed fusion (LPBF) can incorporate intricate design features and demonstrate desirable mechanical properties. However, printing a part that is qualified for its intended application often involves reprinting and discarding many parts to eliminate defects, improve dimensional accuracy, and increase repeatability. The process of iteratively converging on the appropriate build parameters increases the time and cost of creating functional LPBF manufactured parts. This work describes a fast, scalable method for part-scale process optimization of arbitrary geometries. Additionally, the computational approach uses feature extraction to identify scan vectors in need of parameter adaptation and applies results from simulation-based feed forward control models. This method provides a framework to quickly optimize complex parts through the targeted application of models with a range of fidelity and by automating the transfer of optimization strategies to new part designs. The computational approach and algorithmic framework are described, a software package is implemented, the method is applied to parts with complex features, and parts are printed on a customized open architecture LPBF machine.

3D printing↗
DOE OSTI2020

Improving Long-term Monitoring of Contaminated Groundwater at Sites where Attenuation-based Remedies are Deployed

This study presents an effective approach to tackle the challenge of long-term monitoring of contaminated groundwater sites where remediation leaves residual contamination in the subsurface. Traditional long-term monitoring of contaminated groundwater sites focuses on measuring contaminant concentrations and is applicable to sites where contaminant mass is removed or degraded to a level below the regulatory standard. The traditional approach is less effective at sites where risk from metals or radionuclides continues to exist in the subsurface after remedial goals are achieved. We propose a long-term monitoring strategy for this type of waste site that focuses on measuring the hydrological and geochemical parameters that control attenuation or remobilization of contaminants while de-emphasizing contaminant-concentration measurements. Furthermore, we demonstrate how this approach would be more effective than traditional long-term monitoring, using a site in South Carolina, USA, where groundwater is contaminated by several radionuclides. A comprehensive enhanced attenuation remedy has been implemented at the site to minimize discharge of contamination to surface water. The immobilization of contaminants occurs in three locations by manipulation of hydrological and geochemical parameters, as well as by natural attenuation processes. Deployment of our proposed long-term monitoring strategy will combine subsurface and surface measurements using spectroscopic tools, geophysical tools, and sensors to monitor the parameters controlling contaminant attenuation. The advantage of this approach is that it will detect the potential for contaminant remobilization from engineered and natural attenuation zones, allowing potential adverse changes to be mitigated before contaminant attenuation is reversed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Land Processes Can Substantially Impact the Mean Climate State

Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model version 2 under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (σ = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagate to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The case for digital twins in metal additive manufacturing

The digital twin (DT) is a relatively new concept that is finding increased acceptance in industry. A DT is generally considered as comprising a physical entity, its virtual replica, and two-way digital data communications in-between. Its primary purpose is to leverage the process intelligence captured within digital models—or usually their faster-solving surrogates—towards generating increased value from the physical entities. The surrogate models are created using machine learning based on data obtained from the field, experiments and digital models, which may be physics-based or statistics-based. Anomaly detection and correction, and diagnostic closed-loop process control are examples of how a process DT can be deployed. In the manufacturing industry, its use can achieve improvements in product quality and process productivity. Metal additive manufacturing (AM) stands to gain tremendously from the use of DTs. This is because the AM process is inherently chaotic, resulting in poor repeatability. However, a DT acting in a supervisory role can inject certainty into the process by actively keeping it within bounds through real-time control commands. Closed-loop feedforward control is achieved by observing the process through sensors that monitor critical parameters and, if there are any deviations from their respective optimal ranges, suitable corrective actions are triggered. The type of corrective action (e.g. a change in laser power or a modification to the scanning speed) and its magnitude are determined by interrogating the surrogate models. Because of their artificial intelligence (AI)-endowed predictive capabilities, which allow them to foresee a future state of the physical twin (e.g. the AM process), DTs proactively take context-sensitive preventative steps, whereas traditional closed-loop feedback control is usually reactive. Apart from assisting a build process in real-time, a DT can help with planning the build of a part by pinpointing the optimum processing window relevant to the desired outcome. Again, the surrogate models are consulted to obtain the required information. In this article, we explain how the application of DTs to the metal AM process can significantly widen its application space by making the process more repeatable (through quality assurance) and cheaper (by getting builds right the first time).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Perspectives on the relationship between materials chemistry and roll-to-roll electrode manufacturing for high-energy lithium-ion batteries

As lithium-ion battery (LIB) active material and cell manufacturing costs continue to drop with wider adoption of electric vehicles, electrode and cell processing costs remain too high in terms of reaching the ultimate U.S. Department of Energy (DOE) cell cost target of $80/kWh. This paper primarily covers major materials chemistry advancements made over the last 10 years at Oak Ridge National Laboratory (ORNL) in the space of advanced manufacturing science for LIBs with the aim of simultaneously meeting the ultimate cost target, 500 Wh/kg gravimetric energy density, 10-15-min fast charge times, and 1000 deep discharge cycles. Aqeous electrode processing with a variety of active anode and cathode materials is now a standard procedure at the DOE Battery Manufacturing R&D Facility at ORNL (BMF), including the latest processes developed for Ni-rich cathodes. New results on cobalt-free LiNi 0.8 Fe 0.1 Al 0.1 O 2 (NFA 811) are also included and discussed in an electrode processing advantage context. In addition, colloidal processing advancements have been made for Si/C composite anodes for achieving >600 mA h/g capacities. Optimization of electrode coating parameters and drying protocols have been completed, which has elucidated how key processing variables need to be changed when parameters such as slurry solids loading, solvent type, and wet electrode thickness are changed. ORNL has also increased the line speeds at which thick cathodes can be processed using ultra-fast electron beam (EB) curing and substantially decreased formation cycling times to <1 day. Finally, key details of these advancements are discussed in the context of materials chemistry and process-property-performance relationships.

25 ENERGY STORAGE↗
DOE OSTI2025

Automated tabletop exfoliation and identification of monolayer graphene flakes

Over the past two decades, graphene has been intensively studied because of its remarkable mechanical, optical, and electronic properties. Initial studies were enabled by manual “Scotch Tape” exfoliation; nearly two decades later, this method is still widely used to obtain chemically pristine flakes of graphene and other 2D van der Waals materials. Unfortunately, the yield of large, pristine flakes with uniform thickness is inconsistent. Thus, significant time and effort are required to exfoliate and locate flakes suitable for fabricating multilayer van der Waals heterostructures. Here, we describe a relatively affordable tabletop device (the “eXfoliator”) that can reproducibly control key parameters and largely automate the exfoliation process. In a typical exfoliation run, the eXfoliator produces 3 or more large (≥ 400 μm 2 ) high-quality graphene monolayer flakes, allowing new users to produce such flakes at a rate comparable to manual exfoliation by an experienced user. Furthermore, we use an automated mapping system and a computer vision algorithm to locate candidate flakes. Our results provide a starting point for future research efforts to identify more precisely which parameters matter for the success of exfoliation and to optimize them.

47 OTHER INSTRUMENTATION↗