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DOE OSTI2025

Global Regulations for Sustainable Battery Recycling: Challenges and Opportunities

With the rapid expansion of transportation electrification worldwide, the demand for electric vehicles (EVs) has increased dramatically, creating new and sustainable growth opportunities for the global economy. However, as the most expensive component of EVs, lithium-ion batteries pose significant sustainability challenges due to raw material consumption and supply chain constrains, as well as the complexities of end-of-life battery disposal and recycling. To address these concerns, many countries are actively establishing regulations to promote sustainable pathways for battery reuse and recycling. Despite these efforts, existing battery recycling regulations remain often inefficient and vary significantly across different countries in legal enforcement, producer responsibility, waste classification, recycling targets, design standards, public engagement, and financial incentives, particularly given the complexities of the global supply chain and resource distribution within the battery industry. Understanding these regulatory differences and establishing a unified framework are therefore crucial to ensuring sustainable and efficient battery recycling. This review provides a comprehensive analysis of the necessity of establishing robust regulations for sustainable development of battery recycling industry. The evolution and refinement of battery recycling regulations are deeply reviewed to identifying persistent gaps and challenges in key countries. Furthermore, we discuss the challenges associated with regulatory enforcement and propose strategies for developing a more cohesive legislative framework to ensure the effective utilization of retired batteries.

25 ENERGY STORAGE↗

DOE OSTI2023

Frequency Recovery in Power Grids Using High-Performance Computing

Maintaining electric power system stability is paramount, especially in extreme contingencies involving unexpected outages of multiple generators or transmission lines that are typical during severe weather events. Such outages often lead to large supply-demand mismatches followed by subsequent system frequency deviations from their nominal value. The extent of frequency deviations is an important metric of system resilience, and its timely mitigation is a central goal of power system operation and control. This paper develops a novel nonlinear model predictive control (NMPC) method to minimize frequency deviations when the grid is affected by an unforeseen loss of multiple components. Our method is based on a novel multi-period alternating current optimal power flow (ACOPF) formulation that accurately models both nonlinear electric power flow physics and the primary and secondary frequency response of generator control mechanisms. We develop a distributed parallel Julia package for solving the large-scale nonlinear optimization problems that result from our NMPC method and thereby address realistic test instances on existing high-performance computing architectures. Our method demonstrates superior performance in terms of frequency recovery over existing industry practices, where generator levels are set based on the solution of single-period classical ACOPF models.

nonlinear model predictive control↗

DOE OSTI2025

Growth–Mortality Coordination Differs Among Xerophytic Versus Mesophytic Tree Species During Severe Drought

ABSTRACT Forest composition is changing, yet the consequences for terrestrial carbon cycling are unclear. In the eastern United States, water‐demanding “mesophytic” tree species are replacing “xerophytic” oaks (Quercusspp.) and hickories (Caryaspp.), raising concerns that forest productivity will become increasingly sensitive to more frequent and severe drought conditions predicted for the region. However, we have a limited understanding of the extent to which the mortality risk of xerophytes versus mesophytes is coordinated with their growth sensitivity during drought. Here, we evaluated growth and mortality dynamics for 20 abundant eastern United States tree species following a severe drought in the summer of 2012. We synthesized data from ~4500 forest inventory plots and used an approach that quantified relative drought responses between co‐located trees to minimize impacts from environmental heterogeneity. We found that mesophytes were just as likely to perish as co‐occurring xerophytes but were more sensitive to drought in terms of diminished growth. These findings suggest that xerophytic decline is likely to lead to reduced carbon uptake during drought and that management efforts to conserve oak‐hickory stands will be decisive to sustain the carbon mitigation potential of these forests. However, we also found that growth‐mortality relationships differed between functional groups. Among xerophytes, growth and survival during drought were decoupled. Among mesophytes, there was a high degree of coordination, where species that experienced greater mortality also experienced greater growth reductions. Therefore, mesophytes with high growth sensitivity to water deficits are likely to be the most vulnerable to drought‐driven die‐off events moving forward.

Biodiversity & Conservation↗

DOE OSTI2022

Boron Nitride Solid-State Neutron Detectors

The goal of this project is to build upon the work performed under the Texas Tech IDEAS project (DE-AR0000964 monitored by Dr. Isik Kizilyalli) to bring the developed B-10 enriched hexagonal boron nitride (h- 10 BN or 10 BN or BN) semiconductor neutron detector technology to the next level of maturity. In comparison to He-3 ( 3 He) gas detectors, BN neutron detectors possess all the intrinsic advantages of semiconductor devices: light weight, compact size, fast response speed, high spatial resolution, high temperature and low voltage operation capability, ease of mass production via existing semiconductor infrastructures, low cost of operation and maintenance, flexible form factors, and durability. The project will develop a prototype h- 10 BN neutron detector as a potential replacement for the 3 He gas neutron detectors (LND 25185 or equivalent) used in various neutron survey meters currently produced and commercialized by Ludlum Measurements, Inc. (LMI). To increase the overall detection efficiency, it is essential to maximize the collection efficiency of charge carriers (electrons and holes) generated by the nuclear reaction, which demands a further push to achieve 10 BN epilayers with improved material quality. The material growth processes will be guided by characterization of structural, optical, and electrical properties of h-BN. Selective devices will be calibrated at LMI. During ARPA-E supporting period, the key material parameters of h-BN produced by metal organic chemical vapor deposition (MOCVD), including the layer thickness, mobility-lifetime product, surface recombination field and detector size for maintaining a high charge collection efficiency all have been improved by several orders of magnitude. These improvements have enabled the realization of high-performance h 10BN semiconductor thermal neutron detectors. Presently, h- 10 BN thermal neutron detectors developed under ARPA-E’s support hold the record high detection efficiency among all solid-state detectors at 59%. With further development in manufacturing, there is no question that h-BN detectors will replace the traditional He-3 gas detectors in certain application areas by offering obvious advantages of semiconductor technologies over gas detectors and opportunities for users to dedicate the scarce and expensive supply of He-3 gas to other application areas where substitutes of He-3 gas are not possible.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Harnessing Peptide Binding to Capture and Reclaim Phosphate

With rising consumer demands, society is tapping into wastewater as an innovative source to recycle depleting resources. Novel reclamation technologies have been recently explored for this purpose, including several that optimize natural biological processes for targeted reclamation. However, this emerging field has a noticeable dearth of synthetic material technologies that are programmed to capture, release, and recycle specified targets; and of the novel materials that do exist, synthetic platforms incorporating biologically inspired mechanisms are rare. We present here a prototype of a materials platform utilizing peptide amphiphiles that has been molecularly engineered to sequester, release, and reclaim phosphate through a stimuli-responsive pH trigger, exploiting a protein-inspired binding mechanism that is incorporated directly into the self-assembled material network. This material is able to harvest and controllably release phosphate for multiple cycles of reuse, and it is selective over nitrate and nitrite. We have determined by simulations that the binding conformation of the peptide becomes constrained in the dense micelle corona at high pH such that phosphate is expelled when it otherwise would be preferentially bound. However, at neutral pH, this dense structure conversely employs multichain binding to further stabilize phosphate when it would otherwise be unbound, opening opportunities for higher-order conformational binding design to be engineered into this controllably packed corona. With this work, we are pioneering a new platform to be readily altered to capture other valuable targets, presenting a new class of capture and release materials for recycling resources on the nanoscale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Numerical Modeling & Size Optimization of Thermal Energy Storage for Iron & Steel Production

Iron and steel production are responsible for 90 million MtCO2 per year in the United States. Hydrogen direct reduction of iron (H2DRI) is a promising pathway for a more sustainable iron production than commercially deployed technologies which rely on natural gas. The H2DRI process requires hydrogen at a temperature of up to 950 degrees C fed into a reduction furnace to produce pellets or briquettes that are used in the downstream iron and steelmaking process. In this work, we propose to use an electrical thermal energy storage (ETES) system, that can use renewable electricity to store high-temperature heat and dispatch it upon demand. Such a system can buffer the H2DRI plant from the variability of electricity prices by charging during curtailment and running the plant from storage during times of peak electricity price. We have developed heat transfer models for two different ETES systems that can be used to heat up hydrogen to the required temperatures: a particle-based ETES and a firebrick ETES. These models are used to evaluate the performance of such a system and support the sizing and preliminary cost estimation. The preliminary results using both models show that designing ETES systems for an industrial-scale H2DRI furnace is feasible. The firebrick ETES system has limited operational duration, which might limit the price buffering effect unless significantly oversized. The particle ETES system heat exchanger has industry-feasible dimensions, but its storage capacity would be decided upon the number of particle storage silos.

25 ENERGY STORAGE↗

DOE OSTI2025

Mesoporous Thin Film Architectures: Addressing Material Demands through Molecular Self-Assembly

Mesoporous thin films spark interest across a wide range of disciplines due to their tunable nanostructures, large internal surface areas, and strong compatibility with planar optical, electronic, and microfluidic devices. While attention in the porous materials community has shifted toward macroporous or disordered nanoporous systems, a resurgence in mesoporous thin film research is underway, driven by new molecular self-assembly methods, advanced materials chemistry, and improved characterization techniques. The integration of high-χN block copolymer design, kinetically persistent micelle templating, and postdeposition processing protocols now allows control over structural parameters such as pore size, wall thickness, porosity, and connectivity. These advances have overcome many of the thermodynamic and processing constraints that previously limited widespread adoption. Rather than serving only as high-surface-area supports, mesoporous thin films are engineered as active interfaces where responsive chemistries and nanoscale confinement act in tandem. Embedding switchable ligands, thermoresponsive polymers, redox mediators, or ion-selective groups directly within the pore walls enables real-time control over transport, optical, and electrochemical properties. These capabilities open up new directions in adaptive coatings, gated membranes, and fast-response biosensors. To further expand their functional scope, mesoporous films are integrated into hierarchical and multicomponent architectures. Techniques such as triblock terpolymer templating, crack-directed assembly, and nanoimprint lithography allow for control over spatial organization on the micron and submicron scale and pore system orientation. This enables programmable anisotropy, enhanced molecular diffusion, and wavelength-selective photonic behavior, essential for next-generation sensing, catalysis, and energy applications. Such structural and functional complexity requires equally sophisticated characterization. Multimodal and in situ techniques can track material dynamics under operational conditions. Recent progress includes extended-range ellipsometric porosimetry (EP) for hierarchical architectures, vacuum EP for interface energetics, time-resolved EP for diffusion kinetics, and correlative AFM-SAXS mapping. The introduction of advanced neutron-based spectroscopies, particularly quasielastic neutron scattering (QENS), promises to provide real-time access to ion transport dynamics and segmental motion under nanoscale confinement, offering a path toward deeper mechanistic understanding of structure-performance correlations in mesoporous systems. This Account reflects the technical advances made and the interdisciplinary collaborations that have shaped our collective vision. The particular dimensions of mesopores enable us to subtly tune interactions at the molecular, interfacial, and mesoscopic levels that permit us to harness nanoconfinement. What emerges is a versatile, modular platform capable of chemical gating, energy transduction, and sensing with a level of tunability unmatched by other porous materials. We highlight critical challenges including the need for more robust large-area processing, a deeper understanding of dynamic behavior under cycling, and better integration with device-level architectures. Our strategies support the transition of mesoporous thin films into active high-performance components in next-generation energy, environmental, and biomedical systems.

oxides↗

DOE OSTI2020

Emerging Technologies for Improved Plug Load Management Systems: Learning Behavior Algorithms and Automatic and Dynamic Load Detection

Plug loads are responsible for a significant portion of the energy consumed in commercial buildings, yet their distributed and ever-changing nature makes them one of the most challenging building end uses to manage. Plug load management systems exist today that utilize smart plugs to meter and control devices at the outlet level, however, their uptake has been relatively slow in part due to the significant labor required for installation and maintenance. Learning behavior algorithms and automatic and dynamic load detection have been identified as two technology areas that could accelerate the adoption of plug load management systems by reducing these labor demands and providing additional energy efficiency and non-energy benefits. Learning behavior algorithms learn occupant behavior and adjust plug load management systems accordingly, allowing for the automatic creation of optimized control schedules. Automatic and dynamic load detection allows a plug load management system to identify devices as they are plugged in to a building and keeps the system up to date as devices are moved throughout a building. In this paper, we present our findings with respect to the current state of these two technologies based on a review of existing research and patents, as well as a series of interviews with companies working in the plug load space. We have found that, as of now, no commercialized solutions exist for these plug load technologies and that more work is needed to bring them to market. In addition, we summarize our findings related to the technology challenges, market barriers, drivers, and opportunities for these technologies moving forward.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

AI Benchmark Democratization and Carpentry

Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring dynamic, AI-focused workflows. Rapid evolution in model architectures, scale, datasets, and deployment contexts makes evaluation a moving target. Large language models often memorize static benchmarks, causing a gap between benchmark results and real-world performance. Beyond traditional static benchmarks, continuous adaptive benchmarking frameworks are needed to align scientific assessment with deployment risks. This calls for skills and education in AI Benchmark Carpentry. From our experience with MLCommons, educational initiatives, and programs like the DOE's Trillion Parameter Consortium, key barriers include high resource demands, limited access to specialized hardware, lack of benchmark design expertise, and uncertainty in relating results to application domains. Current benchmarks often emphasize peak performance on top-tier hardware, offering limited guidance for diverse, real-world scenarios. Benchmarking must become dynamic, incorporating evolving models, updated data, and heterogeneous platforms while maintaining transparency, reproducibility, and interpretability. Democratization requires both technical innovation and systematic education across levels, building sustained expertise in benchmark design and use. Benchmarks should support application-relevant comparisons, enabling informed, context-sensitive decisions. Dynamic, inclusive benchmarking will ensure evaluation keeps pace with AI evolution and supports responsible, reproducible, and accessible AI deployment. Community efforts can provide a foundation for AI Benchmark Carpentry.

von Laszewski, Gregor [Virginia U.]↗

DOE OSTI2020

Technology Transfer Excellence Award NTXBio

While a scientist at Los Alamos National Laboratory, Dr. Alex Koglin developed two compounds to create a better vaccine for Tuberculosis (TB). In 2015, Dr. Koglin took an entrepreneurial leave of absence from the Laboratory to start NTXBio, LLC. His objective was to develop and commercialize a TB vaccine based on two compounds he created while a scientist at the Los Alamos. In 2018, a total of 1.5 million people died from TB. According to the World Health Organization TB is the leading infectious disease resulting in death worldwide. Approximately one-quarter of the world’s population is infected with mycobacterium tuberculosis, the bacteria that causes TB. Dr. Koglin non-exclusively licensed the two compounds from Los Alamos and began further development of the technology in collaboration with the Government of South Africa and universities to address the need for a vaccine to prevent TB. In their research and development of the compounds there was no evidence of cross resistance between other bacteria and the bacteria that causes TB. The compounds were tested in mouse models and demonstrated they can treat TB. Also, the tests have not shown any of the massive side effects that the current treatments for TB are showing. The company started production of the compounds using current manufacturing processes. Using these processes NTXBio could not produce the compounds at a reasonable price to be available for patients in third world countries. The company shifted to developing a new methodology “invitro synthetic biology” that can increase the production of these compounds and other vaccine molecules in a more affordable way. Dr. Koglin’s startup NTXBio is working to positively influence and secure the world vaccine supply by developing rapid on-demand production of the full spectrum of protein vaccines. Starting with the TB vaccine and advancing to common childhood vaccines, emergency and experimental vaccines such as those needed for COVID-19. NTXBio is capable of accelerated prototyping and manufacturing with increased purity and greater stability while reducing both cost and production time. The newly developed production methodology can be applied to not only the TB compounds licensed from Los Alamos, but also to their compounds that advance vaccine production for other diseases. Their technology produces vaccines fast enough to be fully deployable in areas without the need of specialized storage conditions and response times.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Responsible Artificial Intelligence for Insider Threat Mitigation

This report examines the application of artificial intelligence (AI) technologies for insider threat mitigation (ITM) programs in nuclear security facilities. Insider threat detection presents unique challenges due to the subtle and adaptive nature of these threats, the complex signatures involved, and the scarcity of available data for analysis. Traditional human-centered approaches, while essential, face limitations in processing large amounts of data continuously and detecting subtle patterns across multiple systems. AI technologies can potentially address these limitations by providing 24/7 monitoring capabilities, identifying complex patterns that might escape human observation, and offering consistent application of security criteria. However, the deployment of AI in nuclear security contexts introduces significant new risks, including workflow disruption, expanded attack surfaces, potential for misuse, and ethical concerns regarding privacy, fairness, transparency, safety, and security. The high-consequence nature of nuclear security decisions demands careful consideration of these risks and systematic approaches to their mitigation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2024

Assessment of Electric Grid Transmission System Simulator for Human Factors Research

Over the past decades, various technologies have been developed for electric grid operations to support clean energy, meet rising electricity demands, and address infrastructure concerns. However, the human factors aspect is often overlooked during rapid integration. Questions persist about how these technologies impact human performance. Simulators play a critical role in supporting investigation of human factors design concepts and conducting comprehensive usability testing to evaluate human performance and assess human reliability. This paper aims to address human factors research simulator requirements and conduct a comparative study of six different simulators. A detailed evaluation reveals that the evaluated simulators lack the ability to customize user interfaces. Additionally, their user interface designs do not fulfill the basic human factors design principles, potentially leading to increased response variability and reduced statistical power when conducting controlled experimental research. In the future, it is essential to develop scripting tools to integrate customizable user interfaces and simulation models, ensuring meeting research requirements.

Li, Ruixuan↗

DOE OSTI2025

The Mystery of Baselevel Controls in the Incision History of the Central Colorado Plateau

Erosion can remain active and changing in landscapes long after tectonic drivers have ceased, potentially due to local-geologic controls, climate changes, or geodynamics. We present new fluvial incision-rate histories and terrain analyses of the Colorado River system through the central Colorado Plateau to understand what has caused the variable erosion across this post-orogenic landscape. Results from new cosmogenic and luminescence dating of fluvial terrace and upland gravel deposits in Glen and Meander Canyons establish incision-rate histories that are marked by an Early-Middle Pleistocene erosion hiatus, followed by ~200 m of rapid incision over the last ~350 kyr. Projection of fluvial topography from above knickzones of the Colorado River drainage system roughly agree with the observed magnitude of recent incision and reflect a common baselevel fall from Pliocene river integration through Grand Canyon, which is still propagating through the drainage. A response-time model indicates that baselevel fall from integration likely took 2–4 Myr to reach the central Colorado Plateau and 100s kyr to travel across the study area, potentially accounting for incision rate changes in the fluvial terrace records of Meander and Glen Canyons. The upstream-migrating incision has likely been partitioned into multiple waves across the landscape due to the local geologic controls of lava damming, salt tectonics, and heterogenous bedrock. As baselevel fall from Pliocene Colorado River integration diffuses upstream, it can only account for perhaps a quarter of the total ~2 km of exhumation in the central Colorado Plateau, demanding an unknown driver for significant erosion in the Pliocene.

58 GEOSCIENCES↗

DOE OSTI2024

Enhancing Site Screening for Underground Hydrogen Storage: Qualitative Site Quality Assessment - SHASTA: Subsurface Hydrogen Assessment, Storage, and Technology Acceleration Project

The global shift towards renewable energy sources to mitigate fossil fuel dependency and meet carbon emission targets by 2050 has underscored the importance of innovative solutions to address energy supply-demand imbalances. Underground Hydrogen Storage (UHS) has emerged as a promising strategy to store excess renewable energy in subsurface formations for future retrieval and utilization. This report focuses on enhancing the site screening process for UHS facilities. By drawing on insights from prior research to identify key criteria influencing site suitability, our objective is to present a comprehensive set of sixteen specific criteria essential for refining the selection of UHS sites. These criteria cover various aspects such as reservoir performance, legal access, regulatory compliance, economic viability, public acceptance, and safety and security considerations. Our proposed methodology allows users to evaluate potential sites using binary responses, transitioning from isolated factors to broader considerations that facilitate a qualitative assessment of site suitability. This method enables comparative analysis and informed decision-making, supporting stakeholders in the site selection process. Our approach is designed as a guiding framework rather than a rigid template for site screening, emphasizing the importance of customizing approaches to individual circumstances. As UHS site development project evolves, adopting a comprehensive selection methodology that integrates holistic evaluations will be essential for ensuring efficient and effective site screening processes tailored to specific project needs.

08 HYDROGEN↗

DOE OSTI2024

Enhancing Site Screening for Underground Hydrogen Storage: Qualitative Site Quality Assessment - SHASTA: Subsurface Hydrogen Assessment, Storage, and Technology Acceleration Project

08 HYDROGEN↗

DOE OSTI2022

Design Requirements and Software Specification for the Autonomous Energy Management Software System for Small Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems. Many of these buildings are small (<50,000 square feet), underserved, and use rooftop units for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the rooftop units heating and cooling set points, schedules, setbacks, and optimal start can result in 20 to 25 percent reduction in electricity consumption in small commercial buildings. In addition, improving demand flexibility of these buildings will result additional cost savings for the building owner. Therefore, the Department of Energy’s Building Technologies Office approved a project to address the needs for small commercial buildings. The project is led by Pacific Northwest National Laboratory (PNNL) with Intellimation LLC as the cooperative research and development agreement partner. The primary goal of the project is to develop and validate an autonomous energy management software (AEMS) system that will continuously optimize small commercial building operations by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. The work will leverage the vast experience of PNNL research and development staff who have over two decades of experience in developing and successfully transferring software technologies to the private sector. This solution will be jointly developed with Intellimation, a company that plans to use it to scale their building energy efficiency (EE) and grid services offering. Widespread deployment of the AEMS system will improve the EE and demand flexibility of the building commercial building stock. It should also support cities and states in meeting their climate change mitigation goals. This document describes the various EE and grid service features of the AEMS system, infrastructure and data required to implement those features, and how the features should be automated. It also details how the various features will be tested and validated, including field validation. The document also details what flexibility the users have and how they will be able to leverage those capabilities exercise those. The intent is to create an AEMS system that would support scalable deployment, requires minimal configuration, and is easy to maintain over its expected lifespan. The initial alpha release of AEMS system is planned for March 2023, and the beta release is planned for the summer of 2023. The final release is planned for March 2024. Section 2 of the report documents the relevant building types that AEMS is suitable for. Section 3 documents EE features that will be supported. It will also include the data requirements, hardware requirements, implementation details, and how EE features will be tested and validated. Grid service features will be documented in section 4, including data requirements, hardware requirements, implementation details, and how the services will be tested and validated. Planned next steps are described in section 5.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Optimizing the location and configuration of disaster resilience hubs under transportation and electric power network failures

Natural disasters often result in failures of transportation network components and blackouts that imperil the wellbeing of vulnerable populations. In response to these events, resilience hubs have been proposed as a pre-disaster planning strategy to improve access to critical services. This paper introduces an optimization-based approach to locate and configure electric power-generating resilience hubs considering the possibility of failures in transportation and electric power systems. The model's objective is to identify hub locations and configurations that maximize transportation accessibility to the hubs and maximize the satisfaction of basic energy needs through hub-generated electric power. Besides a budget constraint, the model accounts for limits on the levels of hub energy generation vis-à-vis community energy demands, and on the transportation network distance of communities to hubs. Three heuristics are presented for the proposed planning problem. The first heuristic is a genetic algorithm (GA) with problem-specific solution generation procedures. The other two heuristics implement greedy search techniques. Numerical experiments were conducted, using data from rural Puerto Rico, to illustrate the application of the proposed model and heuristics, and examine their performance. In the numerical experiments, the GA heuristic found better solutions than the greedy heuristics. Additionally, design solutions consisting of spatially dispersed hubs with low energy generation capacity were better than solutions with spatially concentrated high-capacity hubs. Lastly, across a wide range of hub demand scenarios, only a small number of candidate hub locations consistently ranked among the best locations for establishing a hub.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

MgSiP 2 : An Infrared Nonlinear Optical Crystal with a Large Non‐Resonant Phase‐Matchable Second Harmonic Coefficient and High Laser Damage Threshold

Superior infrared nonlinear optical (NLO) crystals are in urgent demand in the development of lasers and optical technologies for communications and computing. The critical challenge is to find a crystal with large non‐resonant phase‐matchable NLO coefficients and high laser damage threshold (LDTs) simultaneously, which however scale inversely. This work reports such a material, MgSiP 2, that exhibits a large second harmonic generation (SHG) coefficient of d 14 ≈ d 36 = 89 ± 5 pm V −1 at 1550 nm fundamental wavelength, surpassing the commercial NLO crystals AgGaS 2 , AgGaSe 2 , and ZnGeP 2 . First principles theory reveals the polarizability and geometric arrangement of the [SiP 4 ] tetrahedral units as the origin of this large nonlinear response. Remarkably, it also exhibits a high LDT value of 684 GW cm −2 , which is six times larger than ZnGeP 2 and three times larger than CdSiP 2 . It has a wide transparency window of 0.53–10.35 µm, allowing broadband tunability. Further, it is Type I and Type II phase‐matchable with large effective SHG coefficients of d eff,I ≈80.2 pm V −1 and d eff,II ≈73.4 pm V −1 . The outstanding properties of MgSiP 2 make it a highly attractive candidate for optical frequency conversion in the infrared.

36 MATERIALS SCIENCE↗