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DOE OSTI2022

Engineering defect energy landscape of CoCrFeNi high-entropy alloys by the introduction of additional dopants

The concept of high-entropy alloys (HEAs) focusing on tuning the overall chemical complexity represents a novel alloy design strategy. In contrast, alloying of a metallic matrix with minor doping elements with limited and localized tunability has been a common practice to improve material performance. Combining the idea of globally engineering defect energy landscape in HEAs and the localized doping strategy in dilute alloys, in this work, we explore doping effects of minor elements in a HEA matrix to further enhance the overall and localized chemical tunability, aiming to improve its irradiation resistance. Specifically, we study the influence of minor Al, Cu, Ti, and Pd substitutional doping elements on defect energetics in a CoCrFeNi model HEA based on density-functional theory (DFT) calculations. The DFT results indicate that the formation and migration energies of vacancies can be strongly influenced when a dopant is introduced at the first nearest neighbor shells around a vacancy. On the other hand, interstitial energetics are only slightly affected. Among the four elements, Ti and Pd generally decrease vacancy formation energies and increase vacancy migration energies more significantly than Al and Cu. The doping effects become more pronounced when the concentration of the substitutional dopants increases. Based on the energy distributions obtained from DFT, we build a kinetic Monte Carlo (kMC) model to assess the impact of dopants on vacancy-mediated diffusivity in the doped HEAs. Our results suggest that Ti and Pd can lower the tracer diffusivity in the considered HEAs and act as trapping sites, whereas Cu may enhance the atomic transport. This work indicates that substitutional doping in HEAs is an effective strategy in metallurgy to further tune the defect and transport properties of complex alloys.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRSDate not supplied

Assessing Helicopter Pilots’ Detect and Avoid and Collision Avoidance Performance With ACAS Xr

The latest variant of the Federal Aviation Administration’s Airborne Collision Avoidance System (ACAS X) is being designed for both crewed and uncrewed rotorcraft. Referred to as ACAS Xr, the system joins a suite of other ACAS X variants poised to replace the Traffic Alert and Collision Avoidance System (TCAS II). ACAS Xr is tuned to support current-day helicopter platforms as well as electric Vertical Takeoff and Landing (eVTOL) vehicles that are still under development. Given this flexibility, ACAS Xr may be used by helicopter crews currently in operation or by remotely-operated eVTOL aircraft in the emerging Advanced Air Mobility (AAM) market. To cover the range of potential uses, two distinct configurations are being proposed for ACAS Xr: Collision Avoidance System (CAS) and Detect and Avoid (DAA). Under the CAS configuration, ACAS Xr provides minimal caution-level alerting but issues directive warning-level alerting and guidance. The DAA configuration, by contrast, provides caution-level alerting and guidance, in addition to the warning-level alerting and guidance. The current study was performed as part of the National Aeronautics and Space Administration’s AAM project. Six helicopter pilots were recruited to fly a variety of scripted traffic scenarios in a full-motion, crewed eVTOL simulator. Participants flew 60 encounters over two days, reacting to pre-recorded intruder aircraft that were scripted to fly into the participant’s aircraft from different approach angles, relative altitudes, and during different phases of flight. The pilots flew half of the encounters with the CAS configuration and half with the DAA configuration. Within each block of 30 encounters, pilots experienced 10 conflicts while in cruise, 10 in hover, and 10 while on approach to a heliport. Results showed that pilot response times were consistently under 5 seconds for RAs and under 10 seconds for DAA alerts, when present, during all three phases of flight. Unsurprisingly, the DAA configuration was associated with lower rates of en-route and high-severity losses of DAA well clear compared to the CAS configuration in all phases of flight except for the terminal area. Rates of losses of DAA well clear were found to be substantially higher in the Hover scenario, compared to Cruise. Pilots failed to fully comply with RAs at a rate of 0.10-0.18 in all conditions except for the DAA configuration in the Hover scenario, which was associated with a higher non-compliance rate of 0.4 due to Descend RAs issued at low altitudes. The implications of these results with regards to the ongoing development of ACAS Xr is discussed.

air taxis↗

NASA NTRSDate not supplied

Assessing Helicopter Pilots’ Detect and Avoid and Collision Avoidance Performance with ACAS Xr

air taxis↗

DOE OSTI2020

Tuning Catalytic Sites on Zr 6 O 8 Metal–Organic Framework Nodes via Ligand and Defect Chemistry Probed with tert -Butyl Alcohol Dehydration to Isobutylene

Metal–organic frameworks (MOFs) have drawn wide attention as candidate catalysts, but some essential questions about their nature and performance have barely been addressed. (1) How do OH groups on MOF nodes act as catalytic sites? (2) What are the relationships among these groups, node defects, and MOF stability, and how do reaction conditions influence them? (3) What are the interplays between catalytic properties and transport limitations? To address these questions, we report an experimental and theoretical investigation of the catalytic dehydration of tert -butyl alcohol (TBA) used to probe the activities of OH groups of Zr 6 O 8 nodes in the MOFs UiO-66 and MOF-808, which have different densities of vacancy sites and different pore sizes. The results show that (1) terminal node OH groups are formed as formate and/or acetate ligands present initially on the nodes react with TBA to form esters, (2) these OH groups act as catalytic sites for TBA dehydration to isobutylene, and (3) TBA also reacts to break node–linker bonds to form esters and thereby unzip the MOFs. The small pores of UiO-66 limit the access of TBA and the reaction with the formate/acetate ligands bound within the pores, whereas the larger pores of MOF-808 facilitate transport and favor reaction in the MOF interior. However, after removal of the formate and acetate ligands by reaction with methanol to form esters, interior active sites in UiO-66 become accessible for the reaction of TBA, with the activity depending on the density of defect sites with terminal OH groups. The number of vacancies on the nodes is important in determining a tradeoff between the catalytic activity of a MOF and its resistance to unzipping. Computations at the level of density functional theory show how the terminal OH groups on node vacancies act as Brønsted bases, facilitating TBA dehydration via a carbocation intermediate in an E1 mechanism; the calculations further illuminate the comparable chemistry of the unzipping.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Tuning the Porous Structure in PMMA-Templated Mesoporous MoO 2 for Pseudocapacitive Li-Ion Electrodes

MoO 2 is an exciting candidate for next-generation energy storage. It can be used for fast-charging applications in nanoscale form, but its kinetic performance is often limited by insulating MoO 3 surface oxide layers. Here, we developed methods to produce polymer-templated porous MoO 2 powders where electrical conductivity was well-maintained throughout the structure, even in the presence of some surface oxidation. Porosity, pore size, and crystallite size were controlled by varying the amount and size of the colloidal templates and through calcination temperature. The electrochemical performance was correlated with nanoscale structure: samples with high porosity, medium pore sizes, and good crystallinity display optimal rate capabilities, with over 100 mAh g −1 delivered in 3 min and 93% capacity retention after 1000 cycles. Kinetic studies were performed on samples with the largest and smallest crystallite sizes to understand the charge storage mechanism. In the sample with the smallest crystallite size, 85% of the total stored charge was capacitive, compared to 60% for the largest crystallite size. Sloping voltage profiles in materials with smaller domain sizes further suggest suppression of intercalation-induced phase transitions. This work thus provides insights into the mechanisms of charge storage in nanoscale MoO 2 and design parameters for the production of fast charging materials.

25 ENERGY STORAGE↗

DOE OSTI2022

Electrical Analysis of Pulsed Laser Annealed Poly-Si:Ga/SiOx Passivating Contacts

High-efficiency single crystalline silicon (c-Si) solar cells require precise control of dopant diffusion profiles and highly active doping concentrations through thermal annealing. Conventional furnace annealing has been successfully employed for dopant diffusion in aluminum back-surface field (Al-BSF), passivated emitter and rear contact (PERC), and Topcon cells. However, furnace annealing limits some next-generation polycrystalline silicon (poly-Si) on SiOx passivating contacts. This limitation largely affects p-type passivating contacts by not being able to: 1) control the dopant diffusion accurately to prevent B segregation at the SiOx and c-Si interface; and 2) provide highly activated doping concentrations for low contact resistivity. In this contribution, we use a nanosecond excimer laser to examine the passivation quality and electrical performance of both B- and Ga-doped poly-Si/SiOx passivating contacts. The core of this method is to take advantage of the nonequilibrium nature of the anneal through rapid melting and recrystallizing the poly-Si in a short timescale and to achieve doping concentrations above the solid solubility limit. Simulations were performed on polished surfaces to visualize the doping diffusion profiles under different laser conditions, and secondary ion mass spectrometry (SIMS) was used to verify the experimental diffusion profiles post pulsed laser melting (PLM). The results show the dopant diffusion profiles can be tuned precisely through PLM. The electrical analysis using VdP-Hall measurements reveals that the active doping concentration reached 10^21 cm-3 for B and ~2×10^21 cm-3 for Ga in poly-Si, far exceeding their solid solubility limit in Si, with a nearly 100% dopant activation achieved for B, and 20% for Ga. This highly activated dopant profile results in a low contact resistivity of <15 mO·cm^2 for B-doped contacts and <40 mO·cm^2 for Ga-doped contacts. We demonstrate that the passivation quality of the laser annealed contacts shows a linear dependency with the laser energy density and the number of pulses. PLM was also performed on random pyramid textured samples, and we found that preferential melting exists for the tips of pyramids, which can be pursued further for selective pinhole opening. Additionally, we perform thermal stability tests of these hyperdoped passivating contacts and show that under certain laser conditions, the B-doped samples exhibit good thermal stability with near full activation for temperatures up to 800°C. Lastly, we transfer this technique to a larger area by overlapping laser spots and show uniform passivation quality and electrical performance. We are currently implementing this PLM annealed p-type passivating contacts into double-side back-junction poly-Si/SiOx passivating contact devices. Our results show that PLM can not only enable the next generation of high-efficiency c-Si photovoltaics technology but can also benefit the c-Si integrated circuit industry through hyperdoping other semiconductors with atoms that may otherwise have a low solubility.

doping diffusion profile↗

DOE OSTI2021

Fabrication of Silicon Sensors Based on Low-Gain Avalanche Diodes

Low-Gain Avalanche Diodes are a recently-developed class of silicon sensors. Characterized by an internal moderate gain that enhances the signal amplitude and if built on thin silicon substrates of a few tens of microns, they feature fast signals and exhibit excellent timing performance. Thanks to their fast timing they are planned to be exploited in timing detectors in High-Energy Physics experiments, for example for the upgrades of the ATLAS and CMS detectors at the High Luminosity Large Hadron Collider (HL-LHC) at CERN. However, to achieve a spatially uniform multiplication a large pixel pitch is needed, preventing a fine spatial resolution. To overcome this limitation, the AC-coupled LGAD approach was introduced. In this type of device, metal electrodes are placed over an insulator at a fine pitch, and signals are capacitively induced on these electrodes. The fabrication technology is similar for the two LGAD families, although a fine tuning of a few process parameters needs to be carefully studied. Other R&D efforts towards detectors that can simultaneously provide good time and spatial resolution, based on the LGAD concept, are under way. These efforts aim also to mitigate the loss of performance at high irradiation fluences due to the acceptor removal within the gain layer. In this paper we describe the main points in the fabrication of LGADs and AC-LGADs in a clean-room. We also discuss novel efforts carried on related topics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Simultaneously tuning dense skin and porous substrate of asymmetric hollow fiber membranes for efficient purification of aggressive natural gas

Highly permeable, selective, and stable asymmetric membranes are required to replace the traditional separation approaches for natural gas purification with higher energy efficiency and smaller footprints. In this work, we report on the design and engineering of defect-free asymmetric hollow fiber membranes with a thin dense skin and highly porous substrate to effectively deal with aggressive natural gas. A crosslinkable polymer with rigid molecular structure and high molecular weight was synthesized for developing spinning dope with desirable solution properties. Phase separation behavior of the polymer was carefully controlled by systematic formulation of the dope composition and optimizing spinning conditions, thereby realizing simultaneously tuning dense skins and porous substrates of the spun asymmetric hollow fiber membranes. The crosslinked hollow fiber membrane, with well-preserved delicate asymmetric nanostructures, exhibited unprecedentedly high and stable separation performance for long-term processing extremely aggressive CO 2 /CH 4 mixtures (with pressure up to 820 psi containing C6(+) hydrocarbons), thereby showing great potential for practical application of natural gas purification. This research provides a new platform to create hollow fiber membranes with both high permeance and plasticization resistance in natural gas service.

36 MATERIALS SCIENCE↗

DOE OSTI2018

A Deep-Learning System for Fully-Automated Peripherally Inserted Central Catheter (PICC) Tip Detection

A peripherally inserted central catheter (PICC) is a thin catheter that is inserted via arm veins and threaded near the heart, providing intravenous access. The final catheter tip position is always confirmed on a chest radiograph (CXR) immediately after insertion since malpositioned PICCs can cause potentially life-threatening complications. Although radiologists interpret PICC tip location with high accuracy, delays in interpretation can be significant. In this study, we proposed a fully-automated, deep-learning system with a cascading segmentation AI system containing two fully convolutional neural networks for detecting a PICC line and its tip location. A preprocessing module performed image quality and dimension normalization, and a post-processing module found the PICC tip accurately by pruning false positives. Our best model, trained on 400 training cases and selectively tuned on 50 validation cases, obtained absolute distances from ground truth with a mean of 3.10 mm, a standard deviation of 2.03 mm, and a root mean squares error (RMSE) of 3.71 mm on 150 held-out test cases. This system could help speed confirmation of PICC position and further be generalized to include other types of vascular access and therapeutic support devices.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2022

Reinforcement learning for online adaptation of model predictive controllers: Application to a selective catalytic reduction unit

Here we present a novel application of reinforcement learning (RL) for online dynamic tuning of model predictive controllers (MPC). Applying a state-action-reward-state-action (SARSA) algorithm for temporal difference learning with a control-specific reward function improves the error tracking performance of a standard MPC formulation. The proposed RL approach is also readily adaptable to other MPCs, or entirely different control approaches. Practical details for the implementation of the RL-MPC algorithm are also presented. The proposed algorithm is applied to a case study of controlling nitrogen oxide (NO x ) emissions in an industrial selective catalytic reduction (SCR) unit, a control problem characterized by significant nonlinearity and time delay. Along with an RL-MPC formulation for NOx control, another MPC is proposed to mitigate ammonia slip and decrease ammonia consumption in the SCR. Results showing the efficacy of the RL-MPC for NO x control through learning and implementation on the nonlinear SCR dynamic model are presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Understanding Degradation Mechanisms and Improving Stability of Perovskite Photovoltaics

This review article examines the current state of understanding in how metal halide perovskite solar cells can degrade when exposed to moisture, oxygen, heat, light, mechanical stress, and reverse bias. It also highlights strategies for improving stability, such as tuning the composition of the perovskite, introducing hydrophobic coatings, replacing metal electrodes with carbon or transparent conducting oxides, and packaging. The article concludes with recommendations on how accelerated testing should be performed to rapidly develop solar cells that are both extraordinarily efficient and stable.

14 SOLAR ENERGY↗

DOE OSTI2025

Geo-Responsive Chemomechanics in Aluminum Oxyhydroxide via Alkali-Driven Dehydroxylation for Supercritical Geothermal Systems

Widespread use of enhanced geothermal systems can revolutionize global renewable electrical power access, yet its advancement is hindered by the inherent instability of Portland cement-based chemistries for geothermal well construction under high-temperature corrosive conditions. Here, in this work, we demonstrate the tunable mechanical performance of aluminum oxyhydroxides as cementitious materials through an alkali-controlled dehydroxylation reaction pathway for long-term applications under supercritical geothermal environments. Notably, the synthesized aluminum oxyhydroxides demonstrate remarkable stability, maintaining superior mechanical performance under supercritical conditions for over 30 days. Synchrotron X-ray diffraction, spectroscopy measurements and geochemical thermodynamic modeling uncover that the gibbsite dehydroxylation pathway functions as a key dial for tuning the chemomechanics, rendering the aluminum oxyhydroxide a strong cementitious material. By uncovering the mechanistic role of alkali-driven dehydroxylation, this work proposes a cementitious chemistry distinct from conventional Portland cement and geopolymer-dominated alkali-activated systems, laying the groundwork for developing next-generation cementitious materials for supercritical geothermal energy exploitation.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Comparison of temperature and doping dependence of elastoresistivity near a putative nematic quantum critical point

Abstract Strong electronic nematic fluctuations have been discovered near optimal doping for several families of Fe-based superconductors, motivating the search for a possible link between these fluctuations, nematic quantum criticality, and high temperature superconductivity. Here we probe a key prediction of quantum criticality, namely power-law dependence of the associated nematic susceptibility as a function of composition and temperature approaching the compositionally tuned putative quantum critical point. To probe the ‘bare’ quantum critical point requires suppression of the superconducting state, which we achieve by using large magnetic fields, up to 45 T, while performing elastoresistivity measurements to follow the nematic susceptibility. We performed these measurements for the prototypical electron-doped pnictide, Ba(Fe 1− x Co x ) 2 As 2 , over a dense comb of dopings. We find that close to the putative quantum critical point, the elastoresistivity appears to obey power-law behavior as a function of composition over almost a decade of variation in composition. Paradoxically, however, we also find that the temperature dependence for compositions close to the critical value cannot be described by a single power law.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Insights into Li/Ni ordering and surface reconstruction during synthesis of Ni-rich layered oxides

Nickel-rich layered transition metal oxides (NMCs) have been intensively studied as promising cathode candidates for next-generation Li-ion batteries, known for low cost and high theoretical capacity. However, the practical capacity of NMCs is largely determined by cationic ordering and has yet to be well controlled during synthesis, largely due to the complexity and non-equilibrium nature of the reactions occurring in the sintering process. In this work, high-energy synchrotron X-ray diffraction is employed to investigate the kinetic and thermodynamic aspects of cationic ordering during synthesis of LiNi 0.7 Mn 0.15 Co 0.15 O 2 (NMC71515). It is found that cationic ordering in the bulk is coupled to surface reconstruction during synthesis, occurring concomitantly and both being greatly affected by Li 2 CO 3 decomposition and Li loss at the particle surface. Through tuning the sintering temperature and time, highly ordered NMC71515 with high capacity and excellent rate capability is synthesized. The developed approach may be applied broadly to the synthesis of high-performance Ni-rich NMC and other cathode materials.

25 ENERGY STORAGE↗

DOE OSTI2019

Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer

The rapid growth and wide applicability of Deep Learning (DL) frameworks poses challenges to computing centers which need to deploy and support the software, and also to domain scientists who have to keep up with the system environment and scale up scientific exploration through DL. We offer recommendations for deploying and scaling DL frameworks on the Summit supercomputer, currently atop the Top500 list, at the Oak Ridge National Laboratory Leadership Computing Facility (OLCF). We discuss DL software deployment in the form of containers, and compare performance of native-built frameworks and containerized deployment. Software containers show no noticeable negative performance impact and exhibit faster Python loading times and promise easier maintenance. To explore strategies for scaling up DL model training campaigns, we assess DL compute kernel performance, discuss and recommend I/O data formats and staging, and identify communication needs for scalable message exchange for DL runs at scale. We recommend that users take a step-wise tuning approach beginning with algorithmic kernel choice, node I/O configuration, and communications tuning as best-practice. We present baseline examples of scaling efficiency 87% for a DL run of ResNet50 running on 1024 nodes (6144 V100 GPUs).

Yin, Junqi↗

DOE OSTI2025

Experimental Analysis of Advanced Turbine for Supercritical CO2 Power Cycle

An advanced first stage high pressure turbine blade for a supercritical CO2 power cycle is tested in the Big Rig for Aerothermal Stationary Turbine Analysis (BRASTA) annular cascade at the Purdue Experimental Turbine Aerothermal Laboratory (PETAL) high pressure blow down facility alongside a baseline blade for comparison of aerodynamic performance. Both geometries are tested simultaneously using a novel off-axis rig design to allow for the blade geometries to be scaled up to achieve higher Reynolds numbers. The off-axis design necessitates the use of discrete sectors of airfoils, which are designed and additively manufactured in house using an mSLA printer. Printing the blades allows for unique routing of passage for blade surface static pressure taps that contour to the blade shape instead of requiring a straight line view from surface tap to egress, allowing all instrumentation for 15%, 50%, and 85% span to exist in the same passage. A flow conditioning gauze [1] is placed upstream of the blade passages to impart pressure, Mach, and swirl profiles to mimic the rotor relative frame inlet conditions to the blade. Downstream, a sonic valve is used to alter the backpressure to achieve different pressure ratios for different blowdown setpoints. The design of the rig, sonic wheel, and instrumentation are discussed along with the manufacturing and GD&T of the blades with respect to the build-up of this novel experimental apparatus. Initial commissioning data is obtained from inlet total pressure and temperature rakes, blade static pressure taps, and exit total pressure rakes. Oil visualization is performed using a silicone oil mixture containing titanium dioxide and pigmented for contrast of the blade suction side surface with the hub and shroud endwalls. The viscosity of the oil is tuned so that the oil does not fully thin and blow away during the time of the test, and endoscopic cameras are placed in the rig for live monitoring and recording. The preliminary oil visualization and rake profiles are presented, showcasing the operability of this additively manufactured off-axis flow path design.

Tuite, Logan [Purdue University]↗

DOE OSTI2026

Alkylidene functionalization produces highly recyclable and scalable polyhydroxyalkanoates

Recyclable polymers that can be produced at scale and readily tuned within the same polymer framework for specific properties are important to achieving a circular materials economy. To this end, synthetic poly(3-hydroxyalkanoate)s (PHAs) have emerged as high-performance, chemically recyclable variants of biological PHAs, but their difficult monomer syntheses and suboptimal recycling efficiencies pose challenges for large-scale deployment. In this study, we investigated a β-isopropylidene PHA, i-PHA, for which the lactone monomer can be synthesized by existing industrial methods from biomass-derived isobutyric acid. The alkylidene substituent prevents decarboxylative degradation typically observed during PHA depolymerization, enabling near-quantitative chemical recycling to monomer. Controlled hydrogenation of the β-isopropylidene side group produces PHAs with diverse performance metrics that are competitive with a range of commodity polymers, spanning strong fibers to ductile thermoplastics to superglue epoxy resins.

36 MATERIALS SCIENCE↗

DOE OSTI2026

PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space

Background Protein language models (PLMs) have revolutionized protein fitness prediction, yet their application to rapidly evolving viral pathogens is often confounded by extreme sequence homology. This homology leads to “data leakage” in standard random validation splits, yielding inflated performance metrics that fail to translate into real-world biosurveillance utility. Results We present Protein Representation Inference for Mutation Evaluation (PRIME), a framework that integrates domain-specific fine-tuning with a rigorous position-stratified validation protocol to evaluate viral threats. Using a dataset of 347,432 SARS-CoV-2 receptor binding domain (RBD) sequences, we demonstrate that while random training data split yields deceptive R 2 values (> 0.90), they fail to generalize to novel mutational sites. By benchmarking models up to 650 M parameters, we show that domain-specific fine-tuning of the ESM-C 600 M model with correctly stratified data provides an initial demonstration of predictive signal for binding affinity and expression at unseen mutational sites of binding affinity and expression on unseen sites (R 2 ~0.23), a significant advancement over base foundation models which exhibit no predictive power (R 2 <0). PRIME’s embedding-based clustering identified 3.03% of bat coronavirus sequences as candidates for further experimental prioritization based on their functional similarity to human-infective strains in embedding space, offering a perspective complementary to traditional phylogenetic methods. Conclusion PRIME establishes a new benchmark for the application of PLMs in pathogen surveillance. Our findings demonstrate that state-of-the-art models and fine-tuning, when paired with stratified validation, provide biologically meaningful insights into pathogen evolution and zoonotic risk.

59 BASIC BIOLOGICAL SCIENCES↗