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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 361 records · Page 20

Deep learning of parameterized equations with applications to uncertainty quantification

We propose a learning algorithm for discovering unknown parameterized dynamical systems by using observational data of the state variables. Our method is built upon and extends the recent work of discovering unknown dynamical systems, in particular those using deep neural network (DNN). We propose a DNN structure, largely based upon the residual network (ResNet), to not only learn the unknown form of the governing equation but also take into account the random effect embedded in the system, which is generated by the random parameters. Once the DNN model is successfully constructed, it is able to produce system prediction over longer term and for arbitrary parameter values. For uncertainty quantification, it allows us to conduct uncertainty analysis by evaluating solution statistics over the parameter space.

97 MATHEMATICS AND COMPUTING↗

Understanding the dynamics of vacuole trafficking and secretion for enhanced plant storage reserves

Vacuoles are essential in plants and serve roles in both protein and organelle degradation for reutilization and storage of high energy molecules such as storage proteins which are critical for seed germination under normal and stress conditions and impact biomass production and nutrition. In addition, plants secrete many different molecules such as cell wall components, lipids, waxes and others that are important for plant protection, structure and biomass. Vacuole accumulation and secretion are closely linked and highly regulated pathways within the endomembrane trafficking network. We have discovered many drug-like synthetic small molecules that can suppress vacuole trafficking and enhance secretion; other chemicals can enhance vacuole trafficking and suppress secretion. The ability of our compounds to modulate these pathways indicates that these pathways are tightly coupled and highly regulated. Thus, to achieve the goals of enhanced energy reserves or enhanced secretion it is essential to understand how this dynamic is controlled. Bioactive chemicals have provided us unique insight into natural mechanisms controlling the dynamics of vacuole trafficking and secretion that was not possible previously. One such compound, ES2, negatively modulates secretion while enhancing vacuole trafficking. This is the first compound discovered that targets an essential secretory complex known as the exocyst and will reveal important regulatory domains controlling secretion and vacuole trafficking. During the current funding period, we determined the structure of the EXO70A1 in plants and identifying its binding site. To understand the dynamics of vacuole trafficking it is key to have compounds that inhibit vacuole trafficking as well as enhancing it (ES2). We have used sophisticated image analysis to identify another novel compound ES17 that enhances secretion while negatively modulating vacuole trafficking (opposite of ES2). We have already made rapid progress. Our preliminary data indicates that ES17 very specifically targets a late stage of vesicle fusion with the tonoplast which is highly regulated. Thus, ES17 is an ideal compound to dissect the regulation of vacuole targeting dynamics. In addition, we have now synthesized a biotinylated version of ES17 that is fully functional, permitting the use of chemical proteomics to identify the cognate target with a high probability of success. We propose to investigate the ES17 target which can negatively modulate vacuole trafficking and combine this with knowledge of the ES2 target which can promote vacuole trafficking. Dissecting the mechanisms controlling the dynamics of secretion vs vacuole accumulation and the use of synthetic modulators will permit us to understand and regulate trafficking to enhance secretion to the plasma membrane and cell wall or the accumulation of biological energy reserves for increased biomass, nutritional value, stress resistance, and the secretion of specialized plant products.

59 BASIC BIOLOGICAL SCIENCES↗

Evaluation of Hands-On Cybersecurity Skills Development

Cybersecurity is a relatively new and quickly advancing industry, though there is a deficit in industry professionals throughout the world. This deficit is a result of the inability of education to keep up with the continuously changing cyber-threat landscape. A lack of effective, accessible, hands-on training platforms is a barrier for students and professionals who seek to gain the skills and abilities necessary to enter the workforce. Additionally, the lack of adequate training makes it difficult for industry experts to upskill or stay up to date on new advancements within the field of cybersecurity. The goal of this cohort is to discover what makes an effective or ineffective training platform, what platforms are currently available, and to determine the next steps for hands-on learning. Hands-on experience is necessary to create workforce ready professionals, yet it is underutilized by students and professionals of cybersecurity due to costs, availability, and ease of use. This cohort has uncovered hands-on training solutions, identified many effective training solutions, and explored potential improvement to cybersecurity training. These findings resulted in the cohort moving beyond research to test the resources that currently exist or are in development, and to apply that knowledge to discovering and developing engaging hands-on training.

97 MATHEMATICS AND COMPUTING↗

Thermal Neutron Scattering Improvements and Fixes for MCNP6.3 [Slides]

Major error (10,000+ pcm) discovered when using uranium-dioxide or uranium-nitride included in ENDF80SaB2 release. Improvements are being made towards preparing the code for ENDF/B-VIII.1 (ACE) format updates. A minor error (0-10’s pcm) was discovered while working on new capability development for Lab Directed Research & Development (LDRD) project. Previously, only a single coherent or incoherent elastic channel (along with an inelastic channel) was allowed in a thermal neutron scattering evaluation and subsequent processed ACE file. Based on what is planned in ENDF/B-VIII.1 release, there will be (a) new thermal scattering evaluation(s) which uses this mixed-mode coherent and incoherent elastic scattering blocks. MCNP6.3 has been modified to be able to handle such data in the future. Some reorganization has been made in the cross section calculation (acetot, sabcol, colidn) in the presence of TSL data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

City-Wide Distributed Roof-Top Photovoltaic System Adoption Forecast, Grid Impact Simulation, & Neighborhood Microgrid Contribution Assessment

The adoption of distributed photovoltaic (PV) systems grew significantly in recent years. Market projections anticipate future growth for both residential and commercial installations. To understand grid impacts associated with distributed PV, useful hosting capacity studies require accurate representations of the spatial distribution of PV adoptions. Prediction of PV locations and numbers depends on median income data, building use zoning maps, and permit records to understand existing trends and predict future adoption rates and locations throughout an entire city. Using the PV adoption data, advanced and realistic simulations were performed to capture the distributed PV impacts on the grid. Also, using graph theory community detection hundreds of neighborhood microgrids can be discovered for the entire city by identifying densely connected loads that are sparsely connected to other communities. Then, based on the PV adoption predictions, this work identified the contribution of PV within each of the newly discovered graph theory defined microgrid communities.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Robust Event Diagnostics Platform: Integrating Tensor Analytics and Machine Learning into Real-time Grid Monitoring

The objective of this project is to develop a robust event diagnostics (RED) platform by integrating state-of-the-art tensor analytics and machine learning into real-time grid monitoring. The proposed platform can effectively analyze and discover the information hiding within the provided PMU data for effective real-time grid monitoring. The proposed RED platform provides a set of robust diagnostics tools for grid operation and management, including 1) data quality assessment, 2) data completion, 3) event detection, and 4) robust event classification. All the functionalities of the RED platform can help the operator to make informed decisions and respond in a timely manner. The developed RED platform will serve as an innovative advisory tool to reliably identify key events and discover new insights about the events and grid characteristics in the PMU data, and contribute to the efficient, safe, reliable operation and design of the nation’s electric system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Mining G.O.L.D. (Geothermal Opportunities Leveraged Through Data): Exploring Synergies Between the Geothermal and Mining Industries

This report analyzes potential collaborations between the geothermal and locatable mineral industries (focused on the portion of the Basin and Range Province within Nevada, United States of America). The objectives of this study included analyzing: 1. The type and quality of data collected by the locatable mineral industry to determine feasibility for geothermal resource exploration; 2. The regulatory pathways and potential barriers that could prevent development of geothermal resources discovered via a mining claim (and vice versa) in the United States; 3. The historical development of geothermal resources discovered via mineral exploration data in the United States and illustrations of co-located mining and geothermal power projects; 4. The value propositions for both the locatable mineral and the geothermal industries to collaborate. This article concludes that many of the data collected by the mining industry as part of locatable mineral exploration (e.g., copper, gold, lithium) would also be useful for identifying and developing previously unknown geothermal resources (and in some notable cases, already have led to geothermal resource development). In addition, for minimal costs, the mining industry could catalogue these data and potentially monetize the data itself or use the data in the future to develop a geothermal project. Leveraging these locatable mineral data to develop geothermal resources and/or co-located minerals and geothermal resources would represent significant cost savings when compared to developing geothermal resources under a business-as-usual scenario as well as compared to current generating technologies (e.g., diesel-powered generators) employed at remote mining operations. Ultimately, leveraging mining industry data, knowledge, and expertise serves to effectively expand the geothermal exploration workforce, increase the rate of geothermal resource discovery, and potentially reduce geothermal electricity's levelized cost of energy (LCOE) by 23%-29%.

15 GEOTHERMAL ENERGY↗

Synthetic Microbial Consortium for Biological Breakdown and Conversion of Lignin

The plant polymer lignin is the most abundant renewable source of aromatics on the planet and conversion of it to valuable fuels and chemicals is critical to the economic viability of a lignocellulosic biofuels industry and to meeting the DOE’s 2022 goal of $\$2.50$/gallon mean biofuel selling price. Presently, there is no efficient way of converting lignin into valuable commodities. Current biological approaches require mixtures of expensive ligninolytic enzymes and engineered microbes. This project was aimed at circumventing these problems by discovering commensal relationships among fungi and bacteria involved in biological lignin utilization and using this knowledge to engineer microbial communities capable of converting lignin into renewable fuels and chemicals. Essentially, we aimed to learn from, mimic and improve on nature. We discovered fungi that synergistically work together to degrade lignin, engineered fungal systems to increase expression of the required enzymes and engineered organisms to produce products such as biodegradable plastics precursors.

09 BIOMASS FUELS↗

Surface Science Studies of Selective Fischer-Tropsch Chemistry on Cobalt Carbide Surfaces

Advances in catalysis science are critical to the progress of the United States. Catalysis is employed, in one way or another, in the production of almost every chemical and product manufactured. The catalysis research that we undertook here, regarding the role of specific facets of cobalt carbide in the Fischer-Tropsch production of low molecular weight olefins (FTO), is motivated by the work of many previous investigators. In particular two research groups from China had discovered FTO catalysts that work very selectively for low molecular weight olefins coupled with low production of methane. These are potentially very important findings that could find significant industrial utility if improvements can be discovered. With the research undertaken here we chose to focus on one of these studies and especially the Co 2 C surfaces that they implicate as being responsible for this exciting FTO catalysis with low production of methane. In this study, Zhong and co-workers investigated cobalt-manganese oxide catalysts for the Fischer-Tropsch to Olefins process. After reaching steady-state their catalyst showed a good selectivity to light olefins and low selectivity to methane with a carbon monoxide conversion of 31.8% at one atmosphere (with a H 2 /CO ratio of two and a temperature of 250°C). Lower H 2 /CO ratios and lower pressures increased the olefin production. Their catalyst characterization studies indicated that cobalt carbide quadrangular nanoprisms with preferentially exposed (101) and (020) facets were important in favoring lower olefin production and inhibiting methane formation. The authors stated that these cobalt carbide nanoprisms are a promising new catalyst system for directly converting synthesis gas to lower olefins. Motivated by the studies of Zhong and co-workers, our goal with the research here was to contribute to a better understanding of catalysis science through the study of surface chemical reactions related to the Fischer-Tropsch to Olefins reaction on specific facets of planar model cobalt carbide catalysts in ultrahigh vacuum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Solid Phase Gradient Alloying Method via ShAPE

Alloying has been used to confer desirable properties to various metallic systems since the bronze age. For example, Ni, Mn, and Cr are alloyed into an Fe matrix to make steel, which has improved strength and corrosion resistance (as displayed in Fig. 1). In addition to the type of alloying elements, the amount of alloying elements added into a matrix is also very critical for a material’s mechanical property, physical property, machinability, and cost. Conventionally, new alloys are discovered by combining alloy precursors and elemental constituents into a mixture and melting them together. The solidified product or ingot represents a usually non-homogeneous combination of the chemistry with a microstructure dictated by the physics of solidification. It is time and energy intensive to optimize the alloying amounts via the casting process and hence, economically not conducive to rapid alloy discovery. In addition, the cast microstructure seen in the as-cast ingot is often not the ideal, nor even desired structure for optimized performance. Cast materials often need further processing such as homogenization, annealing, and deformation work put in through rolling, forging, extruding, etc. to have favorable microstructures and properties. A faster method to discover new alloy combinations and evaluate them in their worked or processed form is needed. Recently, Laser Engineered Net Shaping was applied to fabricate gradient compositional material for alloy designing. However, the mechanical properties of this gradient alloy were poor due to the existence of impurities, oxidation and cracks that are inherent in the melt-solidification process. Because of these limitations, we propose to use a solid-phase process (ShAPE) to create bulk materials (extrudates) that vary in composition from one end of the extruded solid to the other. Various manufacturing methods for alloy design including conventional casting method, laser based combinatorial method and current solid phase gradient alloying method are displayed and compared in Fig. 2. ShAPE machine and a schematic of ShAPE process are displayed in Fig. 3. Starting material is processed by a rotating and plunging die to form an extrudate. We are proposing through this methodology to invent a new, bulk-scale combinatorial technique. With this novel technique, alloying element can be dissolved into the matrix with a continued gradient without bulk melting. Formation of inter-metallics and defects originating from melt processing can be avoided. In addition, the ShAPE process creates the mixed alloy chemistry, and meanwhile subjects the material to severe plastic strain, which induces the favorable “worked” microstructure. Products from the ShAPE process can be tested in hardness directly providing a path to rapid evaluation of mechanical properties. The ability to create graded structures using friction stir processing (FSP) has been demonstrated, however using the ShAPE process we believe will lead to much faster and higher fidelity results. When combined with high throughput screening methods of physical, mechanical and microstructural property characterization, efficiency and accuracy of alloy design can be significantly improved.

36 MATERIALS SCIENCE↗

Accelerated Discovery of Solar Thermochemical Hydrogen Production Materials via High-Throughput Computational and Experimental Methods

In this project, combinatorial synthesis and testing methods were combined with high-throughput materials theory calculations to greatly accelerate the discovery of thermodynamically suitable candidates for green hydrogen production via a two-stage solar thermochemical water splitting (STCH) process. Over the course of the project, more than 8000 quinary and higher oxide compositions were computationally screened for STCH viability, and detailed stability calculations were performed for more than 30 of the most promising identified compositional archetypes. As a result, three new STCH capable compositional families were discovered and experimentally verified. The first, Ce x Sr 2-x MnO 4 (CSM), represents the first known Ruddlesden-Popper compound to show STCH activity, and thus demonstrates that perovskite-related structures may hold promise for this application. The second family, Sr 1-x Ce x MnO 3 (SCM), is the simple perovskite sister-analog to CSM. Sr 0.7 Ce 0.3 MnO 3 (SCM30), a member of this compositional family, was found to produce the highest hydrogen yields of any compound tested in this project, exceeding the end of project milestone target of > 150 μmol H 2 /gram oxide at a reduction temperature of 1350 °C, although only at steam-to-hydrogen ratios greater than 1000:1. Finally, we proved that a third novel Sr-and Mn-containing family, Sr 1-x Ca x Ti 1-y Mn y O 3 (SCTM), which was identified by Materials Project tools, also splits water. The behavior of the SCTM system was found to be similar to the previously discovered Sr 1-x La x Al 1-y Mn y O 3 (SLMA) family, albeit with lower H 2 yields. Across the three thrusts of the project (computational, combinatorial, and bulk testing), five journal articles were published. As part of Program End Analysis and Data Dissemination, relevant data used for the publications was uploaded to the HydroGEN Data Hub for public access, and in certain cases, results were added to public materials databases.

08 HYDROGEN↗

IMS Rapid Response 2023-2 (RR 2023-RND-2): Magnetic order in layered bismuthide RhMnBi 3 (Final Report)

Microelectronics are technologies (mostly theoretical at this point) to create electronic circuits with components of atomic length scale. One of the key materials innovations enabling this technology is exfoliatable two-dimensional materials. These materials allow thin sheets of only a few atoms thick to be easily created in the laboratory. More and more of these materials are reported every year, but few have been characterized in detail. Thus a major goal in materials research in pursuit of microelectronic devices is characterizing newly discovered materials to evaluate their properties. The goal of this project was the measure and understand the magnetic order in a newly discovered van der Waals magnet RhMnBi 3 . Bulk property experiments indicated very complicated magnetic behavior, with multiple phase transitions between 2K and 300K (room temperature). This potentially indicated highly sensitive and tunable magnetism appropriate for single-layer magnetic switches and sensors.

36 MATERIALS SCIENCE↗

Understanding the Mechanism of Light and Elevated Temperature Induced Degradation of p-type Silicon Solar Cells (Final Report)

Light- and elevated-temperature-induced degradation (LeTID) was first discovered in multicrystalline Si (mc-Si) solar cells and was initially attributed to metal impurities. Later, LeTID was reported in Czochralski (Cz) and float-zone (FZ) Si, and is considered as an important efficiency loss mechanism in p-type passivated emitter rear contact (p-PERC) Cz Si solar cells. LeTID causes ~10% relative and permeant efficiency losses in these cells in warmer climate regions where the module temperature is > 50 °C. Unlike light-induced degradation (LID), which is also observed in p-PERC cells, LeTID is slower and takes weeks to months in the field to saturate. Another difference compared to LID is that regeneration in LeTID proceeds very slowly, and field regeneration could take > 25 years — essentially the life of the module. Unlike B-O defects that are responsible for LID, neither B nor O impurities are directly involved in LeTID. LeTID appears to be unique to p-type Si, and is also observed in Ga-doped Si. Currently, most experimental evidence relates LeTID to the injection of hydrogen present in the dielectric surface passivation layers, such as SiN x and Al 2 O 3 , into the monocrystalline Si (c-Si) bulk during the fast-firing step. The involvement of hydrogen is further strengthened by controlled studies that show that increasing the amount of hydrogen in the dielectric during fast-firing increases the degree of LeTID. Similar to LID, a regeneration process has been discovered for LeTID. Regeneration of LeTID defects occurs when samples are exposed to 2–4 Suns illumination at elevated temperatures of 140–220 °C for 2–15 hr. Given the slower kinetics of LeTID and sample regeneration compared to LID, this poses a challenge for the manufacturing and field reliability of p-PERC cells, which will be the leading photovoltaic technologies over the next decade. Therefore, there is a need to understand LeTID and develop strategies to mitigate this effect. The defect responsible for LeTID has been extensively studied with over 100 publications, but direct spectroscopic evidence of this defect’s structure is lacking. Without an atomistic understanding of the LeTID defect, it is difficult to assess the long-term efficacy of the current industrial mitigation strategies. This, in turn, has implications on energy production for tens of gigawatts of these cells that will be deployed yearly worldwide. Using electron paramagnetic resonance, we identified a defect associated with LeTID with a g-value of 2.006, which we attribute to an Si dangling bond in an extended defect such as a vacancy agglomerate with H possibly within or in close vicinity. These vacancy agglomerates are likely created during the firing process, during which time H atoms are also injected into the bulk from the hydrogenated SiN x dielectric layer. Our atomistic-level insight shows that the LeTID defect can be mitigated by targeted intrinsic defect engineering of the c-Si material through a slower pull rate of the Cz ingot or 1000 °C oxygen ambient processing of the Si wafer to reduce the vacancy concentration. This project was a collaborative effort between the Colorado School of Mines and the National Renewable Energy Laboratory.

14 SOLAR ENERGY↗

Comparing Automated Posterior Estimation Techniques for Modeling Strong Lenses In Ground-based Survey Data

Current and future ground-based cosmological surveys, such as the Dark Energy Survey (DES), and the Vera Rubin Observatory Legacy Survey of Space and Time (LSST), are predicted to discover thousands to tens of thousands of strong gravitational lenses. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. However, conventional lens modeling techniques are unable to scale up to the sheer number of lenses that will be discovered through upcoming surveys. Therefore, the use of automated lens analysis techniques is necessary. We demonstrate that machine learning methods can be used to automate the inference of informative model posteriors of strong lensing systems in ground-based surveys with credible uncertainty estimation. We present two Simulation-Based Inference (SBI) approaches for lens parameter estimation of galaxy-galaxy lenses. We demonstrate applications of Neural Posteriors Estima tors (NPEs) and Bayesian Neural Network (BNNs) to automate the inference of a 12-parameter lensing system for DES-like ground-based imaging data. We apply a suite of diagnostics (e.g., posterior coverage and SBC) to validate the performance of our methods. We find that NPEs outperform the BNN, producing posterior distributions that are for the most part both more accurate and more precise; in particular, several source-light model parameters are systematically biased in the BNN implementation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Discovery of False Data Injection Attacks on Power Grid Frequency Controllers with Reinforcement Learning [Poster]

While inverter-based DER (distributed energy resources) are instrumental to integrating renewable energy into the power grid, they reduce the grid's mechanical inertia, thereby increasing the risk of frequency instabilities. To compensate for frequency instability risks, the grid must also undergo a transformation to include digital technologies that allow for two-way communication between the utility and customers. The current and future state of the power grid allows for building a cleaner energy landscape. However, the grid may also become vulnerable to novel cyber threats. To preemptively protect the power grid against elaborate cyber-attacks, we propose to discover potential threats via reinforcement learning. In this work, the focus is on studying false data injection attacks that target the control logic of frequency controllers. We show that a reinforcement learning agent can successfully discover how to best inject false data into linear droop controllers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Live-Cell Quantum Dot-Based Tracking of Plant & Microbial Extracellular Vesicles

All cellular organisms examined to date release various types of small vesicles called extracellular vesicles (EVs). They were originally thought to be unimportant, but research into EVs as carriers of key messages for communication within large, multicellular organisms or between interacting organisms has gained interest in many different scientific areas of study. Recently, it was discovered that both plants and their pathogenic fungi generate EVs and they are important vehicles for sending plant signals to thwart invasion by fungal pathogens, and in response, fungi use EVs to help evade plant defenses. This project aimed to develop new ways to visualize EVs and their potential nucleic acid cargo isolated from the crop plant, sorghum. We adapted a click chemistry-based method for making quantum dot-based RNA sensors, which will be a useful tool for others studying small RNAs. These sensors were not used detect RNA in sorghum EVs because unlike EVs from Arabidopsis, sorghum EVs were not enriched for specific small RNAs. This interesting difference shifted the project in an exciting direction, which focused on comparing EV proteomes. Our study discovered that greater than 60% of Arabidopsis EV proteins were also found in the sorghum EV proteome, revealing a partial conservation between a eudicot and monocot plant. These studies have raised new, fundamental questions on the conserved function of EVs in plants.

59 BASIC BIOLOGICAL SCIENCES↗

Plutonium-238 Loadout Calorimeter Return To Service

The calorimeter in the transfer area in Building 7920 at the US Department of Energy’s Oak Ridge National Laboratory is used to measure the amount of 238 Pu in the Plutonium Heat Source Oxide. The calorimetry measurement is taken after the oxide is packaged in an ORISC (Oak Ridge Inner Shipping Capsule)/FSO (Fuel Storage Outer) assembly before loading into the Containment Vessel and shipping container. The equipment is used in three specific configurations: zero measurement (nothing in the sample cup), FSO measurement (actual measurement of 238 Pu), and reference calibration measurement (using the reference FSO as a known heat source). System checks were performed in July 2023 in preparation for scheduled material loadout. During these checks, personnel discovered that the reference FSO had experienced an electrical short in the heater cable and was no longer functional. The calorimeter was in the reference calibration measurement configuration at this time. Facility management was notified when the damage was discovered. The data from the Yokogawa recorder were pulled and analyzed for a span of time between September 2022 and July 2023. An unusual spike was found in the sample cup temperature data that started on October 19, 2022, and ended on October 20, 2022. The sample cup reached a temperature of 191.1°C. The maximum allowable temperature for the calorimeter thermopile to reach is 220°C based on the manufacturer statement provided in Appendix B of this report [1]. The calorimeter thermopile and the sample cup thermocouple are physically close to each other; therefore, the data suggest that the calorimeter itself never saw a temperature in excess of 191.1°C. Physical damage present on the cable, tape, and insulation suggests that the temperature reached a higher value in some locations within the sample cup than the data shows. Because of this discrepancy, testing of the calorimeter was required to confirm that the calorimeter is functioning the same as it was before the electrical short. The intent of this report is to assess the functionality of the calorimeter and determine if it is acceptable to use for 238 Pu loadout material measurement. Testing showed that the calorimeter is functionally equivalent to the initial calibration that was performed in the summer of 2021, and thus, the calorimeter was cleared to be used for its intended purposes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Enhancing photonic systems using topology and non-Hermiticity

The broad goal of this project was to develop new analytical and numerical insights for how important photonic processes can be improved using recently discovered principles in topological and non-Hermitian physics. In particular, there are two recent discoveries that we aimed to harness to achieve this goal. First, it was discovered in condensed matter physics that crystalline symmetries can protect low-dimensional topologically protected states in lattices without the need for breaking time-reversal symmetry. These so-called ‘higher-order’ topological systems represent an important development for photonic systems, where it is very difficult to break time-reversal symmetry, and which had been previously thought necessary to realize topological phenomena. Second, the last decade has seen a significant amount of interest in phenomena which are unique to non-Hermitian systems, i.e., systems which do not conserve energy. For example, spatially patterned gain and loss can be used to realize exceptional points, which are degeneracies in a system’s spectrum where the system becomes defective, while the existence of radiative losses also enables a new route to confinement through bound states in the continuum. For such non-Hermitian phenomena, photonics again represents a critical platform, as photonic systems naturally lose light to their radiative environments, making them generally non-Hermitian, and it is also possible to incorporate additional gain or loss. Based on these broad principles, we pursued a range of projects to harness bound states in the continuum in a variety of different systems and architectures, develop real-space methods for classifying topological systems to yield better photonic design principles, and a novel Brillouin-based fiber laser for sensing strain.

77 NANOSCIENCE AND NANOTECHNOLOGY↗