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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Scalable and accurate multi-GPU-based image reconstruction of large-scale ptychography data

Abstract While the advances in synchrotron light sources, together with the development of focusing optics and detectors, allow nanoscale ptychographic imaging of materials and biological specimens, the corresponding experiments can yield terabyte-scale volumes of data that can impose a heavy burden on the computing platform. Although graphics processing units (GPUs) provide high performance for such large-scale ptychography datasets, a single GPU is typically insufficient for analysis and reconstruction. Several works have considered leveraging multiple GPUs to accelerate the ptychographic reconstruction. However, most of these works utilize only the Message Passing Interface to handle the communications between GPUs. This approach poses inefficiency for a hardware configuration that has multiple GPUs in a single node, especially while reconstructing a single large projection, since it provides no optimizations to handle the heterogeneous GPU interconnections containing both low-speed (e.g., PCIe) and high-speed links (e.g., NVLink). In this paper, we provide an optimized intranode multi-GPU implementation that can efficiently solve large-scale ptychographic reconstruction problems. We focus on the maximum likelihood reconstruction problem using a conjugate gradient (CG) method for the solution and propose a novel hybrid parallelization model to address the performance bottlenecks in the CG solver. Accordingly, we have developed a tool, called PtyGer ( Pty chographic G PU(multipl e )-based r econstruction), implementing our hybrid parallelization model design. A comprehensive evaluation verifies that PtyGer can fully preserve the original algorithm’s accuracy while achieving outstanding intranode GPU scalability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Self-consistent integrated modeling of combined hybrid discharge-laser produced plasma devices for extreme ultraviolet metrology

Discharge- and laser-produced plasma (DLPP) devices are being used as light sources for extreme ultraviolet (EUV) generation. A key challenge for both, DPP and LPP, is achieving sufficient brightness to support the throughput requirements of nanometrology tools. To simulate the environment of a hybrid DLPP device and optimize the EUV output, we have developed an integrated HEIGHTS-DLPP computer simulation package. The package integrates simulation of two evolving plasmas (DPP and LPP) and includes modeling of a set of integrated self-consistent processes: external power source and plasma energy balance, plasma resistive magnetohydrodynamics, plasma heat conduction, detailed radiation transport (RT), and laser absorption and refraction. We simulated and optimized DLPP devices using Xe gas as a target material. We synchronized the external circuit parameters, chamber gas parameters, and laser beam temporal and spatial profiles to achieve maximum EUV output. The full 3D Monte Carlo scheme was integrated for detailed RT and EUV output calculations in Xe using more than 3600 spectral groups. The modeling results are in good agreement with Julich Forschungszentrum experimental data. Theoretical models, developed and integrated into the HEIGHTS package, showed wide capabilities and flexibility. In conclusion, the models and package can be used for optimization of the experimental parameters and settings, investigation of DLPP devices with complex design, analyzing the impact of integrated spatial effects and working timeline arrangement on the final EUV output, and EUV source size, shape, and angular distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

TEAMER: Crossflow Turbine Fairing Geometry Optimization - Report and CFD Modeling Files

The dataset includes computational fluid dynamics (CFD) models and simulation files for crossflow turbines as well as a detailed project report. The report documents the project undertaken by the Ocean Renewable Power Company (ORPC) to design and optimize a modular fairing for the Modular RivGen Marine Hydrokinetic (MHK) turbine, which enhances the efficient deployment and operation of turbine arrays. The project focused on optimizing the hydrodynamic performance of the fairing using CFD, with an emphasis on two key geometric parameters: the fairing's cross-sectional shape and the spacing between the rotor and the fairing. The analysis aimed to maximize net power output while also assessing discretized loading to evaluate ultimate and fatigue loads on the turbine components. The numerical modeling was conducted using both the commercial CFD software Star-CCM+ and the open-source code openFOAM, with the latter utilizing the actuator line library, turbinesFOAM. This dual-code approach was intended to increase confidence in the results and demonstrate the viability of using open-source tools for high-fidelity marine energy modeling. This dataset includes all necessary files for actuator line simulations in openFOAM, as well as 2D blade-resolved CFD results, along with Python and Java scripts for setting up and post-processing simulations.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Ocean & Geohazard Analysis Tool

The Ocean & Geohazard Analysis (OGA) software tool is designed to summarize insights into key offshore hazards drawing from a diverse set of approaches, including artificial intelligence, machine learning, probabilistic and statistical, and offshore data sources. The offshore hazards that can be analyzed include submarine landslides, extreme wind/wave/current event probabilities, earthquakes, and metocean pathways (CIAM Climatological Isolation and Attraction Model–Climatological Lagrangian Coherent Structures - Submissions - EDX (doe.gov)). Currently, the tool is developed for use in the Gulf of Mexico. The data underlying the offshore hazard analyses can be found here: https://edx.netl.doe.gov/dataset/gulf-of-mexico-risk-analysis-database-gomrad This work was conducted under the Advanced Offshore Research Portfolio, FWP Number 1022409 at National Energy Technology Laboratory, U.S. Dept. of Energy. Disclaimer This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

AIML↗
DOE OSTI2020

Development of the Mobile Systems for Conditioning of Disused Sealed Radioactive Sources in Serbia - 20105

Sealed radioactive sources (SRS) are being used worldwide in the field of medicine, agriculture, industry and research. They can be found in mobile as well as stationary devices. SRS contains radioactive material that is (a) permanently sealed in a capsule or (b) closely bounded and in a solid form. The capsule or material of an SRS should be strong enough to maintain leak tightness under the conditions of use and purpose for which the source was designed, also in case of accidents. In this case only emitted radiation is utilized. Firstly, the hazard from external radiation has to be considered, but the possibility of contamination due to fracture of the capsule should not be disregarded. The radioactive sources are composed of the radiating isotope contained in the filling medium, the single or double isotope holder that partially or totally surrounds the filling medium, the outer cover that contains the parts mentioned above and the capsule closed airtightly by welding or using some other method. The capsule must be tested for leakage periodically. If the SRS is no longer needed (e.g. replaced by a different technique) or it becomes useless for the intended application (e.g. the activity becomes too weak, the equipment containing the source works poorly or becomes obsolete, the source is damaged or leaking) it is considered disused. Disused sealed radioactive sources (DSRS) are typically conditioned and disposed if a facility is available. If the disposal option is not available, conditioned DSRS should be stored under proper conditions. In some cases, the radionuclide(s) in DSRS can be recovered/recycled or the DSRS can be repurposed for other applications. Conditioning of DSRS ensures containment of the radioactive material, provides confinement for leaking sources, provides sufficient radiation shielding, reduces storage/disposal volume by allowing consolidation of multiple sources into a single storage/disposal container, facilitates transport operations and contributes to safety and security as well. Typically, conditioning technologies are deployed either as permanently installed stationary systems in centralized or mobile on-site waste processing facilities, or in a mobile configuration. Centralized stationary facilities provide a single processing location for multiple users that requires transport of the waste to the facility. On the other hand, mobile systems may be provided for the selection and application of the optimum technology for a specific waste stream (such as DSRS) by bringing the process to the point where the waste is generated. In addition, mobile systems could offer additional flexibility by sharing equipment among multiple waste generating sites for processing campaigns that vary in duration, from very short periods to several years. The term 'mobile processing system' refers to any radioactive waste processing system or component which is designed to be transportable and which is not considered permanently installed. Two mobile system for conditioning of disused sealed radioactive sources are developed in the Public Company Nuclear Facilities of Serbia. Development of these mobile systems was supported by SRB9005 national project via Technical Cooperation of the IAEA. The first mobile system, built inside the 20 feet ISO container, will be used for conditioning of DSRS category 3 to 5. The second mobile system, built inside the 7 m long vehicle (Iveco Daily Van), will be used for dismantling of ionizing smoke detectors mostly with Am-241 sources. Designs of the mobile systems were defined in cooperation with two companies from Belgium (Belgoprocess and Leniko) and a Croatian company Ekoteh as well as with the support of the IAEA experts. The generic safety assessment and operational procedures for the mobile systems are developed. Based on safety assessment the acceptance criteria and operational limits and conditions are established. Operational procedures include: (a) equipment and material requirements, (b) assembling procedure of the mobile unit, (c) procedure for acceptance of devices for dismantling and conditioning, (d) dismantling procedure for devices to recover the DSRS, (e) characterization of DSRS, (f) encapsulation procedure of DSRS, (g) disassembling procedure of the mobile unit, and (h) keeping records, identification and traceability. In addition, radiation safety, health safety, security and emergency preparedness plans are prepared. The generic safety assessment and operational procedures could be updated with site specific requirements, DSRS inventory, and different needs for future customers. Developed mobile systems could be used in all situations when it is feasible to perform conditioning of DSRS on the spot in the county and worldwide. Development of these mobile units was just the first step to create the Reference Center for Radioactive Waste Treatment and Disused Radioactive Sources Conditioning for Small Facilities which can become a regional training center in the future, and/or as a tool for comprehensive national search and secure programmes. In the next phases development of e-learning platforms and blended learning packages as well as application for the IAEA Qualified Technical Centre (QTC) for the management of DSRS is foreseen. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

ac susceptibility studies of intra- and intergrain properties of high-$J_c$ Bi-2212 wires

Bi 2 Sr 2 CaCu 2 O x (Bi-2212) is the only high-$T_c$ superconductor (HTS) available as a multifilamentary round wire with multiple architectures and it is a very promising conductor for the realization of high-field applications. Despite their relatively simple wire fabrication by the powder-in-tube technique, Bi-2212 wires require a tightly controlled overpressure heat treatment (HT) with a multi-parameter time-temperature schedule to achieve high critical current density, $J_c$. Further, the variation of these HT parameters, changes in the wire design, wire diameter and powder quality can lead to variations in both the microstructure and the superconducting performance. Particularly noticeable are variations in $J_c$ performance and degree of filament bridging. In this work, we focus on the use of different magnetic characterization techniques to estimate the bridging level and assess the balance of INTER-grain and intra-grain superconducting properties including the irreversibility field ($H_{\text{irr}}$) and the pinning energy ($U_0$) in differently processed wires. Regardless of the actual bridging level, we find that the supercurrent flows at the filament bundle level, not just at the individual filament level. Moreover, using AC susceptibility we identify two distinct supercurrent contributions, one related to the intra-grain and one to the INTER-grain properties, whose irreversibility fields are different but without large sample-to-sample variation. Moreover, an additional component of intra-grain pinning mechanism becomes effective at low temperatures with positive effects also on the INTER-grain performance. The work clearly shows that detailed magnetic characterizations can become valuable tools to investigate the performance of differently processed Bi-2212 wires, correlating their microstructure and overall transport $J_c$, to obtain a deeper understanding of the causes of performance variation and paths to achieve further improvement.

2212↗
DOE OSTI2021

Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications

After decades of developments, electron microscopy has become a powerful and irreplaceable tool in understanding the ionic, electrical, mechanical, chemical, and other functional performances of next-generation polymers and soft complexes. The recent progress in electron microscopy of nanostructured polymers and soft assemblies is important for applications in many different fields, including, but not limited to, mesoporous and nanoporous materials, absorbents, membranes, solid electrolytes, battery electrodes, ion- and electron-transporting materials, organic semiconductors, soft robotics, optoelectronic devices, biomass, soft magnetic materials, and pharmaceutical drug design. For synthetic polymers and soft complexes, there are four main characteristics that differentiate them from their inorganic or biomacromolecular counterparts in electron microscopy studies: (1) lower contrast, (2) abundance of light elements, (3) polydispersity or nanomorphological variations, and (4) large changes induced by electron beams. Since 2011, the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory has been working with numerous facility users on nanostructured polymer composites, block copolymers, polymer brushes, conjugated molecules, organic–inorganic hybrid nanomaterials, organic–inorganic interfaces, organic crystals, and other soft complexes. This review crystalizes some of the essential challenges, successes, failures, and techniques during the process in the past ten years. It also presents some outlooks and future expectations on the basis of these works at the intersection of electron microscopy, soft matter, and artificial intelligence. Machine learning is expected to automate and facilitate image processing and information extraction of polymer and soft hybrid nanostructures in aspects such as dose-controlled imaging and structure analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unraveling the Molecular Origin of Prey-Wrapping Spider Silk's Unique Mechanical Properties and Assembly Process Using NMR

Prey wrapping spider silk's unique mechanical properties are investigated confirming the silk's high degree of extensibility and superior toughness compared to other types of spider silk. For the first time, the pre-spinning dope phase is studied in isotope-enriched intact aciniform (AC) silk glands using solution NMR that reveals a combination of α-helical domains linked by disordered random coil chains consistent with previously proposed “beads-on-a-string” models. The model is further refined through the AlphaFold2 protein structure prediction tool. Finally, extensive magic angle spinning (MAS) solid-state (SS) NMR data for isotopically-enriched fibers is used to refine the structural model for AC silk from two species, A. aurantia and A. argentata. The SSNMR data shows that the AC silk fibers are highly α-helical, coiled-coil in structure but, also exhibit significant β-sheet components that can be traced back to the Gly-rich disordered linker regions in the pre-spinning dope phase that are converted to β-sheet structures during fiber formation. This combination of mechanical and structural characterization enhances the understanding of AC silk's liquid-to-solid transition and structure-mechanics relationship. In conclusion, these prey wrap silk results and models will provide the basis for the design of biomimetic materials inspired by the AC spider silk system.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Classification and computation of extreme events in turbulent combustion

In the design of practical combustion systems, ensuring safety and reliability is an important requirement. For instance, reliably avoiding lean blowout, flame flashback or inlet unstart is critical for ensuring safe operation. Currently, the science of predicting such events is based on prior experience, limited modeling or diagnostic tools and purely statistical approaches. Even though computational and experimental tools for studying combustion devices have vastly advanced in the last three decades, the analysis of such failure events has not been pursued widely. While the use of data for model development and calibration is being widely accepted, the extension to failure events introduces numerous challenges. In particular, the focus here is on so-called data-poor problems, where the cost of generating data is extremely high and is not easily amenable to existing computational and experimental approaches. Data-poor problems are particularly relevant when related to extreme events (also called anomalous events) that can lead to catastrophic failure of the system. It is argued that transient events that describe such failure can have different causal mechanisms. To develop the scientific inference process, a classification of such problems is used to determine specific modeling paths as well as computational tools needed. Research opportunities in the emerging field of extreme event prediction are highlighted in order to identify critical and immediate needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

cclib 2.0: An updated architecture for interoperable computational chemistry

Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots—the formalization of cclib’s intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

High-Precision Zinc Isotopic Measurement of Certified Reference Materials Relevant to the Environmental, Earth, Planetary and Biomedical Sciences

The zinc (Zn) stable isotope system has emerged as a powerful tool for investigating natural and anthropogenic processes of interest to disciplines extending from the Earth and planetary sciences to biomedical research. In nature, Zn isotopes typically fractionate ( 66 Zn/ 64 Zn) within the range of –1 and +1 permil (‰), and however, analytical limitations restrict the application of the Zn isotope system. Specifically, there is a lack of well-characterised matrix-matched reference materials covering most Zn isotope applications that are essential for validating laboratory performance, particularly given the contamination prone nature of Zn. Furthermore, the increasing demand for analysis of size-limited materials and sample types displaying small, sub-permil level Zn isotope variations requires improvements in measurement procedures. To address these limitations, the δ 66 Zn values of two pure Zn solutions and eighteen reference materials including the previously uncharacterised RGM-2, BCR-279, DOLT-4, DOLT-5, NASS-7, SBC-1, SGR-1b, MESS-3 and HISS-1, and the understudied QLO-1, SDC-1, BCR-414, NASS-6 and COQ-1 materials are reported. Furthermore, a 70 Zn- 67 Zn double-spike design has been implemented that routinely achieves intermediate measurement precision better than ± 0.02‰. This represents at least a two-fold improvement when compared with other double-spike designs, providing a robust platform of δ 66 Zn values required for quality control procedures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Elevating SolTrace's Capabilities for the Next Generation of Concentrating Solar Analysis

SolTrace is an open-source Monte Carlo ray tracing software developed at NREL. SolTrace can characterize concentrating solar thermal (CST) collector optical performance and is CST technology agnostic. Shown in Fig. 1, SolTrace is a foundational tool in NREL's CST system and component modeling suite. SolTrace's generic surface elements can flexibly model novel collector and receiver designs to predict spatial and temporal flux distributions - critical to understand for CST component design, performance prediction, and system integration. Since its initial development, SolTrace has over 1,650 references on Google Scholar, over 9,800 downloads since 2017, and has served the CST research and development community as a benchmark of 3rd party verification. SolTrace provides users with many options for defining surface shape and boundaries. However, SolTrace provides limited documentation which can result in a steep learning curve for new users. Additionally, SolTrace lacks the computational performance required to evaluate optical performance of a CST system over the course of a year and/or iteratively over design parameters in a timely manner. To address this, we are working towards a new release of SolTrace that enables increased computational throughput by implementing ray tracing acceleration structures and enabling GPU parallelization. Additionally, we are working to improve SolTrace's usability, accessibility, and maintainability by (1) automating solar position time-dependent simulation processes, (2) creating general CST collector templates of grouped elements, (3) updating the user interface to better visualize model inputs and outputs, and (4) creating a user support network through forums, "how to" videos, and documentation.

14 SOLAR ENERGY↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2020

Low Temperature Activation of Methane on Metal-Oxides and Complex Interfaces: Insights from Surface Science

The abundance of inexpensive, natural gas has transformed the energy landscape, whereby revealing new possibilities for sustainable chemical technologies or impacting those that have relied on traditional fossil fuels. The primary component, methane, is underutilized and wastefully exhausted, leading to anthropogenic global warming. Historically, the manipulation of methane remained “ clavis aurea ,” an insurmountable yet rewarding challenge and thus the focus of intense research. This is primarily due to an inability to dissociate C–H bonds in methane selectively, which requires a high energy penalty and is an essential prerequisite for the direct conversion of methane into a large set of value-added products. The discovery of such processes would promise an energy gainful use of natural gas benefiting several essential chemical processes associated with C1 chemistry. This first C–H bond dissociation step of the methane molecule appears in numerous catalytic mechanisms as the rate-determining step or most essential barrier sequence for all subsequent steps that follow in the production of C–C, C–O, or C x –H y –O z bonds found in value added products. A main goal is to catalytically reduce the energy barrier for the first C–H bond dissociation to be able to achieve the activation of methane at low or moderate temperatures. As such there is great value in understanding the fundamental nature of the active sites responsible for bond breaking or formation and thus be able to facilitate better control of this chemistry, leading to the development of new technologies for fuel production and chemical conversion. Surface science studies offer enhanced perspectives for a careful manipulation of bonds over the last layer atoms of catalyst surfaces, an essential factor for the design of atomically precise catalysts and unravelling of the reaction mechanism. With the advent of new surface imaging, spectroscopy, and in situ tools, it has been possible to decipher the surface chemistry of complex materials systems and further our understanding of atomic active sites on the surfaces of metals, oxides, and carbides or metal–oxide and metal–carbide interfaces. The once considered near impossible step of C–H bond activation is now observed at low temperatures with high propensity over a collection of oxide, metal–oxide, and metal–carbide systems in a conventional or inverse configuration (oxide or carbide on metal). The enabling of C–H activation at low temperature has opened interesting possibilities for the specific production of chemicals such as methanol directly from methane, a step toward facile synthesis of liquid fuels. We highlight the most recent of these results and present the key aspects of active site configurations engineered from surface science studies which enable such a simple reactive event through careful manipulation of the last surface layer of atoms found in the catalyst structure. New concepts which help in the activation and conversion of methane are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring the effects of molecular beam epitaxy growth characteristics on the temperature performance of state-of-the-art terahertz quantum cascade lasers

This study conducts a comparative analysis, using non-equilibrium Green’s functions (NEGF), of two state-of-the-art two-well (TW) Terahertz Quantum Cascade Lasers (THz QCLs) supporting clean 3-level systems. The devices have nearly identical parameters and the NEGF calculations with an abrupt-interface roughness height of 0.12 nm predict a maximum operating temperature (T max ) of ~ 250 K for both devices. However, experimentally, one device reaches a T max of ~ 250 K and the other a T max of only ~ 134 K. Both devices were fabricated and measured under identical conditions in the same laboratory, with high quality processes as verified by reference devices. The main difference between the two devices is that they were grown in different MBE reactors. Our NEGF-based analysis considered all parameters related to MBE growth, including the maximum estimated variation in aluminum content, growth rate, doping density, background doping, and abrupt-interface roughness height. From our NEGF calculations it is evident that the sole parameter to which a drastic drop in T max could be attributed is the abrupt-interface roughness height. We can also learn from the simulations that both devices exhibit high-quality interfaces, with one having an abrupt-interface roughness height of approximately an atomic layer and the other approximately a monolayer. However, these small differences in interface sharpness are the cause of the large performance discrepancy. This underscores the sensitivity of device performance to interface roughness and emphasizes its strategic role in achieving higher operating temperatures for THz QCLs. We suggest Atom Probe Tomography (APT) as a path to analyze and measure the (graded)-interfaces roughness (IFR) parameters for THz QCLs, and subsequently as a design tool for higher performance THz QCLs, as was done for mid-IR QCLs. Our study not only addresses challenges faced by other groups in reproducing the record T max of ~ 250 K and ~ 261 K but also proposes a systematic pathway for further improving the temperature performance of THz QCLs beyond the state-of-the-art.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Harnessing ionic complexity: A modeling approach for hierarchical ionic circuit design

Since the 1950s, soft ionic devices have evolved from individual components to an expanding library of sensors, actuators, signal transmitters, and processors. However, integrating these components into complex, multifunctional systems remains challenging due to the nonintuitive and nonlinear interactions between ionic elements. In this work, we address these fundamental challenges by developing a lumped element model that enables interrogation of the physics that governs ionic circuits, as well as rapid design and optimization. Our model captures features specific to ionic charge carriers, while preserving the hierarchical design flexibility and computational efficiency of traditional circuit modeling. We demonstrate that our model can not only fit individual device behavior but also accurately predict the behavior of larger circuits formed by combining those devices. Additionally, we show how our tool utilizes the intrinsic nonlinearities of ionic systems to enable extended functionality, revealing how factors such as ion enrichment, ion leakage, and polymer charge density influence performance. Lastly, we present a fully ionic power supply, sensor, control system, and actuator for a soft robot that adapts its motion in response to environmental salt, illustrating the tool’s potential to accelerate advancements in chemical sensing, biointerfacing, biomimetic systems, and adaptive materials.

42 ENGINEERING↗
DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗