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DOE OSTI2022

Solution-processable copper(I) iodide-based inorganic-organic hybrid semiconductors composed of both coordinate and ionic bonds

Three new one-dimensional (1D) All-In-One (AIO) type CuI-based hybrid semiconductors with both dative and ionic bonds between the inorganic and organic components have been successfully designed and synthesized by using the ligands containing both cationic center and coordination available sites. Structural analysis confirms that all compounds share the same formula of Cu 4 I 6 (L) 2 (L = ligand) and are composed of 1D-Cu 4 I 6 2- anionic chains that coordinate to cationic ligands. Additionally, these materials demonstrate high stability towards heat and moisture and excellent solution processability due to their unique bonding nature. They emit low energy light ranging from green to orange color (530-590 nm). The origin and mechanism of their emissions were studied by both experimental and theoretical methods. More importantly, all compounds can be easily fabricated into films by solution process, a desired but rare feature for most of CuI-based hybrids built of extended networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Experimental investigation of low velocity and high temperature solid particle impact erosion wear

Next generation Concentrating Solar Power (CSP) plants utilizing solid particles as the heat transfer medium (HTM) are expected to achieve greater operational efficiencies. However, the erosion from the solid particles can cause significant damage to component materials. Low particle speeds (1–2 m/s) are proposed as a means of preventing excessive damage to containment materials. Within the current investigation, solid particle erosion of three potential containment materials, stainless-steel grade 316L, a nickel alloy (Inconel 740H), and a refractory material (Tufcrete 60 M), are examined at a particle impact speed of 1.6 m/s. CarboBead-HSP 40/70 particles, a candidate HTM for CSP systems, impact the specimens at a relatively high impact angle of 60° based on containment design criteria. Experiments were conducted at ambient temperature and 800 °C to examine erosion of the materials at very low impact speeds and determine how temperature effects erosion. Initial results revealed surprising outcomes inconsistent with available literature: ambient temperature erosion of SS316L is an order of magnitude lower than IN740H, but erosion (erosion-corrosion) of SS316L at 800 °C surpasses IN740H by two orders of magnitude. Furthermore, results suggest removal of oxide layers (erosion-corrosion) is responsible for the significant erosion that was observed even at these low particle speeds.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Degradation of Anion Exchange Membranes by Cation Elimination: Impact on Water Uptake, Nanostructure, and Ionic Mobility

Anion exchange membranes (AEMs) are an attractive platform for fuel cell and electrolysis technologies since they enable the use of cheaper, nonplatinum group metal electrodes and components. However, the widespread adoption of AEM-based devices is limited by chemical degradation of the AEM in the highly alkaline medium of operation. Experimental studies report three major pathways of degradation, including substitution (S N 2), elimination (E2), and backbone cleavage. The decline in membrane performance is likely due to a cumulative effect of several pathways, which has proven to be difficult to disentangle through experiments. Here we use coarse-grained molecular simulations to isolate the impact of E2 degradation, where cationic sites are replaced by an alkene group, on AEM water uptake, nanoscale morphology, structure of the ion-conducting channels, water mobility, and ionic conductivity. Our studies focus on the well-studied model AEM polyphenylene oxide with tetraalkylammonium cationic sites (PPO-TMA). We find that about half of the hydrophobic groups resulting from the degradation retract into the hydrophobic domains of the membrane. The reduction in ion exchange capacity (IEC) and the hydrophobicity of the alkene groups decrease both the equilibrium water uptake (%WU) and the number of water molecules per cation (λ) of the membrane. Interestingly, the evolution of λ with the IEC seems to follow that of undegraded PPO-TMA membranes. Additionally, we ascribe this to the similarity between the structure of the degraded monomer and the neutral one in the polymer. We conclude that most of the performance losses originate in the lower hydration of the degraded membrane.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A Machine Learning Bias Correction on Large–Scale Environment of High–Impact Weather Systems in E3SM Atmosphere Model

Large–scale dynamical and thermodynamical processes are common environmental drivers of high–impact weather systems causing extreme weather events. However, such large–scale environmental conditions often display systematic biases in climate simulations, posing challenges to evaluating high–impact weather systems and extreme weather events. In this paper, a machine learning (ML) approach was employed to bias correct the large–scale wind, temperature, and humidity simulated by the atmospheric component of the Energy Exascale Earth System Model (E3SM) at ~1° resolution. The usefulness of the ML approach for extreme weather analysis was demonstrated with a focus on three high–impact weather systems, including tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs). We show that the ML model can effectively reduce climate bias in large–scale wind, temperature, and humidity while preserving their responses to imposed climate change perturbations. The bias correction is found to directly improve water vapor transport associated with ARs, and representations of thermodynamical flows associated with ETCs. When the bias–corrected large–scale winds are used to drive a synthetic TC track forecast model over the Atlantic basin, the resulting TC track density agrees better with that of the TC track model driven by observed winds. In addition, the ML model insignificantly interferes with the mean climate change signals of large–scale storm environments as well as the occurrence and intensity of three weather systems. This study suggests that the proposed ML approach can be used to improve the downscaling of extreme weather events by providing more realistic large–scale storm environments simulated by low–resolution climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Endowing low fatigue for elastocaloric effect by refined hierarchical microcomposite in additive manufactured NiTiCuCo alloy

Abstract NiTiCu-based shape memory alloys have been considered as ideal materials for solid-state refrigeration due to their superb cycling stability for elastocaloric effect. However, the embrittlement and deterioration caused by secondary phase and coarse grains restrict their applications, and it is still challenging since the geometric components are required. Here, bulk NiTiCuCo parts with excellent forming quality were fabricated by laser powder bed fusion (LPBF) technique. The as-fabricated alloy exhibits refined three-phases hierarchical microcomposite formed based on the rapid cooling mode of LPBF, composed of intricate dendritic Ti 2 Ni–NiTi composite and nano Ti 2 Cu embedded inside the NiTi-matrix. This configuration endows far superior elastocaloric stability compared to the as-cast counterpart. The low fatigue stems from the strong elastic coupling between the interphases with reversible martensite transformation, revealed byin-situsynchrotron high-energy x-ray diffraction. The fabrication of NiTiCuCo alloy via LPBF fills the bill of complex geometric structures for elastocaloric NiTiCu alloys. The understanding of interphase micro-coupling could provide the guide for designing LPBF fabricated shape memory-based composites, enabling their applications for special demands on other functionalities.

Engineering↗

DOE OSTI2026

From photosynthetic electron flow to gene regulation: redox signal transduction in cyanobacteria

In cyanobacteria, the free-living ancestors of chloroplasts, photosynthesis simultaneously sustains growth and generates reactive oxygen species (ROS) that damage proteins, lipids, and DNA when light capture outpaces carbon fixation. Maintaining redox balance, therefore, requires cells to read photosynthetic electron flow as a signal that continuously tunes gene expression and protein activity. This review traces how these redox signals are transduced to transcription machinery through three routes: membrane-localized sensors, cytoplasmic redox sensors downstream of photosystem I, and ROS generated when electron sinks are saturated. Membrane-bound histidine kinases (two-component systems) relay the redox state of the plastoquinone pool to control photosystem remodeling, pigment biosynthesis, and circadian timing. Cytoplasmic one-component regulators, by contrast, sense redox directly through thiol-disulfide switches, glutathionylation, iron-sulfur clusters, and metal-catalyzed oxidation to control photosystem-cofactor, electron-carrier, and transition-metal homeostasis. Because many of these regulators persist in algal and plant chloroplasts, cyanobacteria illuminate principles of redox control across photosynthetic eukaryotes. Post-transcriptional and translational control further shapes redox-dependent gene expression programs through transcript stability, ribosome assembly, and translation initiation, extending redox regulation beyond transcription to every step of protein synthesis and even activity modulation. Finally, we connect redox regulation to photosynthetic physiology, stress resilience, and the rational engineering of cyanobacteria for sustainable bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Current-induced dynamics and tunable spectra of a magnetic chiral bobber

Chiral bobber (ChB) is an exotic three-dimensional topological texture in chiral magnets. It is composed of a Bloch-point singularity and a skyrmion tube; the incommensurate characteristic length scales of these two components would thus lead to novel dynamics of a ChB. In this work, we report the current-induced motion and the accompanying Hall effect of a ChB. Surprisingly, we find that the Hall angle of a ChB can be tuned in a wide range from -90 ° to positive angles. A further study of spin-wave excitations identified high-frequency resonant modes, which are related to the velocity of ChB and lattice constants. The excited frequency can be two orders of magnitude higher than that of skyrmions. The broad ranges of Hall angles and frequency spectra could boost the development of spintronic devices using topological textures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Recursive Blind Forecasting of Photovoltaic Generation and Consumer Load for Microgrids

Existing forecasting frameworks that predict time-series photovoltaic (PV) generation and consumer load for micro-grids' operation and control assume near-continuous availability of real-time predictors from the field. The incoming data are used to periodically re-train the models and update forecast snapshots over a moving horizon window. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. This paper bridges the shortcoming by leveraging a previously proposed forecasting framework that is resilient to abrupt changes in data quality caused by communication losses. Assuming no availability of real-time field system data, which is typical in extreme weather events such as hurricanes, the framework uses lightweight recursive time-series models to independently forecast solar irradiance, ambient temperature, PV power, and consumer load for three horizon windows: 24 hours, 12 hours, and 1 hour. Four types of ensemble-based regression trees-simple gradient boosted trees (GBR), GBR with an adaptive component (A-GBR), random forests (RF), and extra trees (ExTR)-are leveraged and their performances are compared against a simple historical weekly mean. Numerical results show that A-GBR performs better on average by 32% for 24-hour horizon and 39% for 12-hour horizon, whereas ExTR outdoes the other models on average by 10% for 1-hour horizon.

Sundararajan, Aditya↗

DOE OSTI2020

Linkage mapping evidence for a syntenic QTL associated with flowering time in perennial C 4 rhizomatous grasses Miscanthus and switchgrass

Flowering in perennial species is directed via complex signalling pathways that adjust to developmental regulations and environmental cues. Synchronized flowering in certain environments is a prerequisite to commercial seed production, and so the elucidation of the genetic architecture of flowering time in Miscanthus and switchgrass could aid breeding in these underdeveloped species. In this context, we assessed a mapping population in Miscanthus and two ecologically diverse switchgrass mapping populations over 3 years from planting. Multiple flowering time quantitative trait loci (QTL) were identified in both species. Remarkably, the most significant Miscanthus and switchgrass QTL proved to be syntenic, located on linkage groups 4 and 2, with logarithm of odds scores of 17.05 and 21.8 respectively. These QTL regions contained three flowering time transcription factors: Squamosa Promoter-binding protein-Like, MADS-box SEPELLATA2 and gibberellin-responsive bHLH137. The former is emerging as a key component of the age-related flowering time pathway.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Design-Technology Co-Optimization for NVM-based Neuromorphic Processing Elements

An emerging use-case of machine learning (ML) is to train a model on a high-performance system and deploy the trained model on energy-constrained embedded systems. Neuromorphic hardware platforms, which operate on principles of the biological brain, can significantly lower the energy overhead of a machine learning inference task, making these platforms an attractive solution for embedded ML systems. In this paper, we present a design-technology tradeoff analysis to implement such inference tasks on the processing elements (PEs) of a Non-Volatile Memory (NVM)-based neuromorphic hardware. Through detailed circuit-level simulations at scaled process technology nodes, we show the negative impact of technology scaling on the information-processing latency, which impacts the quality-of-service (QoS) of an embedded ML system. At a finer granularity, the latency inside a PE depends on 1) the delay introduced by parasitic components on its current paths, and 2) the varying delay to sense different resistance states of its NVM cells. Based on these two observations, we make the following three contributions. First, on the technology front, we propose an optimization scheme where the NVM resistance state that takes the longest time to sense is set on current paths having the least delay, and vice versa, reducing the average PE latency, which improves the QoS. Second, on the architecture front, we introduce isolation transistors within each PE to partition it into regions that can be individually power-gated, reducing both latency and energy. Finally, on the system-software front, we propose a mechanism to leverage the proposed technological and architectural enhancements when implementing a machine-learning inference task on neuromorphic PEs of the hardware. Evaluations with a recent neuromorphic hardware architecture show that our proposed design-technology co-optimization approach improves both performance and energy efficiency of machine-learning inference tasks without incurring high cost-per-bit.

42 ENGINEERING↗

DOE OSTI2021

Multiscale Machine-Learned Modeling Infrastructure Core

MuMMI Core is the central component of the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback across three scales of resolution.

Bhatia, Harsh↗

DOE OSTI2020

Increasing the Lifetime of Epoxy Components with Antioxidant Stabilizers

Epoxy thermoset resins are ubiquitous materials with extensive applications where they are used as encapsulants, composites, and adhesives/staking compounds used to secure sensitive components. Epoxy resins are inherently sensitive to thermo-oxidative aging, especially at elevated temperatures, which changes the bulk properties of the material and can lead to component failure for example by cracking due to embrittlement or by adhesion failure between the epoxy and filler material in a composite. This project investigated the effects of three commercial antioxidants (Irganox ® 1010 (I-102), butylated hydroxytoluene (BHT), or Chisorb ® 770 (HALS)) at two different loadings (2.5 and 5 wt%) on the mechanical and chemical aging of a model epoxy system (EPON TM 828 / Jeffamine ® T-403) under ambient conditions, 65, 95, and 110 °C. Additionally, synthetic routes towards an antioxidant capable of being covalently bound to the resin so as to prevent leaching were explored with one such molecule being successfully synthesized and purified. One commercial antioxidant (Irganox ® 1010) was found to reduce the degree of thermo-oxidatively induced damage in the system.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Improved Time-Stepping Methods in Global to Regional Ocean Modeling (Annual Status Report 2020)

Time stepping algorithms are an important part of ocean models, and strongly influence both the accuracy of solution and performance. There have been a number of projects investigating various improvements for ocean time-stepping schemes in the Model for Prediction Across Scales-Ocean (MPAS-Ocean), a component of the DOE Energy Exascale Earth System Model. Ocean dynamics include fast surface gravity waves, which are two-dimensional, and slower internal waves, which are three-dimensional, so ocean models use a split time-stepping scheme that separates these barotropic and baroclinic modes for efficiency. MPAS-Ocean runs on variable-resolution horizontal meshes, and must scale to tens of thousands of cores and millions of horizontal gridcells. Ocean models require time stepping algorithms that are customized to these needs, and which are tuned for performance on various resolutions and architectures.

58 GEOSCIENCES↗

DOE OSTI2021

Generic Pressurized Water Reactor Model for SAPHIRE

Generic Pressurized Water Reactor Model for SAPHIRE This report contains a data dump from a SAPHIRE Generic Pressurized Water Reactor (PWR) model. The intent is to have this information reviewed for export control and classification so that the generic model can be used external to INL. The facility model is not applicable to any existing nuclear reactors. The Generic PWR model does not represent any existing power plant and was created to support research and training purposes. Three distinct types of data are used to create a SAPHIRE risk model: - Event trees representing typical PWR sequences - Fault trees representing generic system performance - Basic events (e.g., component failure information) Where possible, we have taken information from publicly available documents and referenced that information tagged to a specific item.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Improved Time-Stepping Methods in Global to Regional Ocean Modeling (Annual Status Report)

58 GEOSCIENCES↗

DOE OSTI2022

Effect of thermal aging on microstructure and crack growth response of Alloy 152 weld

Nickel-based Alloy 690 and the associated weld Alloys 52 and 152 are typically used for nozzle penetrations in replacement heads for pressurized water reactor (PWR) vessels, because of their excellent overall resistance to general corrosion and environmental degradation, primarily stress corrosion cracking (SCC). However, many of the existing PWRs are expected to operate for 40-80 years. Likewise, advanced water-cooled small modular reactors (SMRs) will use Ni-Cr alloys and are expected to receive initial operating licenses for 60 years. Hence, the thermal stability of Ni-Cr alloys is a potential concern for the long-term performance of both existing and advanced nuclear power plants, and possibly spent fuel storage containers. The objective of this research is to understand the microstructural changes occurring in high-Cr, Ni-based Alloy 152 weldments during long time exposure to the reactor operating temperatures, and the effect of these changes on the service performance. One area of particular concern is the potential for long range ordering (LRO), i.e. formation of the intermetallic Ni2Cr phase under prolonged exposure to reactor temperatures and/or irradiation, which can increase strength, decrease ductility, and cause dimensional changes or lead to in-service embrittlement of components made with these alloys. Hence, this research focused on the microstructural evolution and the SCC response of Alloy 152 under accelerated thermal aging. The materials studied involved three heats of Alloy 152 used to produce a dissimilar metal weld (DMW) joining an Alloy 690 plate to an Alloy 533 low alloy steel (LAS) plate, thermally aged at three different temperatures (370°C, 400°C and 450°C) for up to 75,000h (equivalent to 60 years of service). The microstructural characterization by means of synchrotron X-ray did not find evidence of LRO in any of the three heats aged to an equivalent of 60 years of service. Testing in a primary water environment of a heat of Alloy 152 aged at 370°C to a 60-year service equivalent revealed a fatigue and corrosion fatigue crack growth responses similar to those measured on the un-aged alloy. However, the SCC CGR response of the aged sample appears to show a deterioration in performance.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Effect of thermal aging on microstructure and stress corrosion cracking behavior of Alloy 152 weldment

Nickel-based Alloy 690 and the associated weld Alloys 52 and 152 are typically used for nozzle penetrations in replacement heads for pressurized water reactor (PWR) vessels, because of their excellent overall resistance to general corrosion and environmental degradation, primarily stress corrosion cracking (SCC). However, many of the existing PWRs are expected to operate for 40-80 years. Likewise, water-cooled small modular reactors (SMRs) will use Ni-Cr alloys and are expected to receive initial operating licenses for 60 years. Hence, the thermal stability of Ni-Cr alloys is a potential concern for the long-term performance of both existing and advanced nuclear power plants, and possibly spent fuel storage containers. The objective of this research is to understand the microstructural changes occurring in high-Cr, Ni-based Alloy 152 weldments during long time exposure to the reactor operating temperatures, and the effect of these changes on the service performance. One area of particular concern is the potential for long range ordering (LRO), i.e. formation of the intermetallic Ni2Cr phase under prolonged exposure to reactor temperatures and/or irradiation, which can increase strength, decrease ductility, and cause dimensional changes or lead to in-service embrittlement of components made with these alloys. Hence, this research focused on the microstructural evolution and the SCC response of Alloy 152 under accelerated thermal aging. The materials studied involved three heats of Alloy 152 used to produce a dissimilar metal weld (DMW) joining an Alloy 690 plate to an Alloy 533 low alloy steel (LAS) plate, thermally aged at three different temperatures (370°C, 400°C and 450°C) for up to 75,000h (equivalent to 60 years of service). The microstructural characterization by means of synchrotron X-ray conducted in small, 0.2 mm - step line scans in the high-deformation regions of the weld root – covering areas spanning from the weld heat affected zone (HAZ) in Alloy 690 to the weld and weld butter on LAS - did not show evidence of LRO in any of the three Alloy 152 heats aged to an equivalent of 60 years of service. Testing in a primary water environment of two heats of Alloy 152 aged at 370°C to a 60-year service equivalent revealed a fatigue and corrosion fatigue crack growth responses similar to those measured on the un-aged alloys. However, the SCC CGR response of the aged samples appears to show a deterioration in performance, confirming our previous observation.

36 MATERIALS SCIENCE↗