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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

State of Innovation 2025: Progress in Accelerating Next-Generation Cement and Concrete Technologies

The cement and concrete sectors are entering a decisive period as next-generation technologies advance from laboratory research to demonstration, early deployment, and first-of-a-kind commercial plants. Building on the 2024 State of Innovation report, the 2025 outlook highlights both the rapid acceleration of innovation and the urgent need for coordinated action across the value chain. Venture capital activity into the cement and concrete space stabilized following the record surge of 2022-2023, yet landmark financings, such as Sublime Systems' $200 million round and Terra CO2's $124 million Series B, signal continued investor confidence in companies approaching commercialization. Corporate procurement has become a powerful new catalyst, with Microsoft, Amazon, and CRH (Cement Roadstone Holdings) Ventures providing long-term commitments that underpin the first wave of next-generation cementitious products. The sector is shifting from early-stage experimentation toward the scaling of well-capitalized leaders capable of bridging the critical "capitalization gap." Early innovators continue to expand the toolkit through novel binders, electrochemical cements, biogenic limestone, and carbonate mineralization pathways. Going into 2026, cost competitiveness, durability validation, and scalability enabled by resilient supply chains remain the decisive factors for market adoption. At the 2025 Next Generation Cement and Concrete Critical Technologies Meeting, attendees emphasized dual-track funding strategies that integrate federal grants with private capital as key to enabling market breakthrough. State programs and corporate demand are sustaining momentum, while successful companies increasingly demonstrate both economic value and reduced dependence on imported materials. The National Concrete Pavement Technology Center and others underscored that broad integration of next-generation materials will hinge on standards compatibility, verified field performance, and workforce readiness. Colorado continues to serve as a proving ground through pilot programs that combine supplier training, phased implementation, and real-world data to de-risk innovation and provide replicable models for other regions. The 2025 Cement and Concrete Critical Technologies Workshop reinforced that scaling next-generation materials will require alignment among technology innovation, performance validation, and market demand. Stakeholders must move beyond siloed efforts toward collaborative frameworks that coordinate standards, funding, and infrastructure deployment. As a neutral convener and technical validator, the National Laboratory of the Rockies (NLR) plays a pivotal role in bridging innovation and market adoption through collaborative research, technology validation, and entrepreneurship programs. By uniting innovators, incumbents, policymakers, contractors, and investors, NLR and its partners are helping chart a credible pathway toward widespread commercialization in the decade ahead.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

InAs Terahertz Metalens Emitter for Focused Terahertz Beam Generation

Metasurfaces have opened doors to combining multiple photonic functionalities in a single compact device. In particular, the ability to generate short terahertz (THz) pulses with precise wavefront engineering in a single THz metasurface redefined the role metasurfaces can play in THz systems. Here, an InAs metalens emitter which generates and focuses a THz pulse beam is demonstrated using a 130 nm thick InAs metasurface designed as a binary‐phase Fresnel zone plate. The THz beam is focused to a spot of ≈430 μm at 1 THz with a short focal length of 5 mm and large numerical aperture of 0.5. Nanoscale InAs Mie resonators comprising the metasurface enable THz generation with an amplitude as high as 20 times compared to plasmonic THz emitters and several times compared to a 1 mm thick ZnTe crystal. This InAs metasurface emitter provides a new paradigm for designing THz imaging, spectroscopy, and communication systems, where THz beam generation and shaping are performed with a single device without compromising the generation efficiency, while eliminating losses and avoiding limitations of phase matching of conventional nonlinear optics approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

Abstract Background The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. Purpose The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain‐relevant assessments via the analysis of relevant image statistics. Methods As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis. To create the training dataset, an established 3D virtual breast phantom was adapted. The resulting dataset comprised about 108 000 images of size 512 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM‐generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. Results Fifty‐eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top‐ranked submission employed a conditional latent diffusion model, whereas the joint runners‐up employed a generative adversarial network, followed by another network for image superresolution. In general, we observed that the overall ranking of the top 9 submissions according to our evaluation method (i) did not match the FID‐based ranking, and (ii) differed with respect to individual feature families. Another important finding from our additional analyses was that different DGMs demonstrated similar kinds of artifacts. Conclusions This Grand Challenge highlighted the need for domain‐specific evaluation to further DGM design as well as deployment. It also demonstrated that the specification of a DGM may differ depending on its intended use.

Radiology, Nuclear Medicine & Medical Imaging↗
DOE OSTI2021

Floating platform effects on power generation in spar and semisubmersible wind turbines

The design and financing of commercial-scale floating offshore wind projects require a better understanding of how power generation differs between newer floating turbines and well-established fixed-bottom turbines. In floating turbines, platform mobility causes additional rotor motion that can change the time-averaged power generation. In this work, OpenFAST simulations examine the power generated by the National Renewable Energy Laboratory's 5-MW reference turbine mounted on the OC3-UMaine spar and OC4-DeepCWind semisubmersible floating platforms, subjected to extreme irregular waves and below-rated turbulent inflow wind from large-eddy simulations of a neutral atmospheric boundary layer. For these below-rated conditions, average power generation in floating turbines is most affected by two types of turbine displacements: an average rotor pitch angle that reduces power, caused by platform pitch; and rotor motion upwind-downwind that increases power, caused by platform surge and pitch. The relative balance between these two effects determines whether a floating platform causes power gains or losses compared to a fixed-bottom turbine; for example, the spar creates modest (3.1%–4.5%) power gains, whereas the semisubmersible creates insignificant (0.1%–0.2%) power gains for the simulated conditions. Furthermore, platform surge and pitch motions must be analyzed concurrently to fully capture power generation in floating turbines, which is not yet universal practice. Finally, a simple analytical model for predicting average power in floating turbines under below-rated wind speeds is proposed, incorporating effects from both the time-averaged pitch displacement and the dynamic upwind-downwind displacements.

17 WIND ENERGY↗
DOE OSTI2024

A new Monte Carlo generator for BSM physics in B → K*ℓ+ℓ− decays with an application to lepton non-universality in angular distributions

Abstract Within the widely used EvtGen framework, we have added a new event generator model forB → K * ℓ + ℓ − with improved standard model (SM) decay amplitudes and possible BSM physics contributions, which are implemented in the operator product expansion in terms of Wilson coefficients. This event generator can then be used to estimate the statistical sensitivity of a simulated experiment to the most general BSM signal resulting from dimension-six operators. We describe the advantages and potential of the newly developed ‘Sibidanov Physics Generator’ in improving the experimental sensitivity of searches for lepton non-universal BSM physics and clarifying signatures. The new generator can properly simulate BSM scenarios, interference between SM and BSM amplitudes, and correlations between different BSM observables as well as acceptance bias. We show that exploiting such correlations substantially improves experimental sensitivity. As a demonstration of the utility of the MC generator, we examine the prospects for improved measurements of lepton non-universality in angular distributions forB→K * ℓ + ℓ − decays from the expected 50 ab −1 data set of the Belle II experiment, using a four-dimensional unbinned maximum likelihood fit. We describe promising experimental signatures and correlations between observables. The use of lepton-universality violating ∆-observables significantly reduces uncertainties in the SM expectations due to QCD and resonance effects and is ideally suited for Belle II with the large data sets expected in the next decade. Thanks to the clean experimental environment of ane + e − machine, Belle II should be able to probe BSM physics in the Wilson coefficientsC 7 and$$ {C}_7^{\prime } $$ C 7 ′ , which appear at lowq 2 in the di-electron channel.

Physics↗
DOE OSTI2022

Derivation and assessment of regional electricity generation emission factors in the USA

Electricity production is one of the largest sources of environmental emissions - especially greenhouse gases (GHGs) - in the USA. Emission factors (EFs) vary from region to region, which requires the use of spatially relevant EF data for electricity production while performing life cycle assessments (LCAs). Uncertainty information, which is sought by LCA practitioners, is rarely supplied with available life cycle inventories (LCIs). To address these challenges, we present a method for collecting data from different sources for electricity generation and environmental emissions; discuss the challenges involved in agglomerating such data; provide relevant suggestions and solutions to merge the information; and calculate EFs for electricity generation processes from various fuel sources for different spatial regions and spatial resolutions. The EFs from the US 2016 Electricity Life Cycle Inventory (eLCI) are analyzed and explored in this study. We also explore the method of uncertainty information derivation for the EFs. We explore the EFs from different technologies across Emissions & Generation Resource Integrated Database (eGRID) regions in the USA. We find that for certain eGRID regions, the same electricity production technology may have worse emissions. This may be a result of the age of the plants in the region, the quality of fuel used, or other underlying factors. Region-wise life cycle impact assessment (LCIA) ISO 14040 impacts for total generation mix activities provide an overview of the total sustainability profile of electricity production in a particular region, rather than only global warming potential (GWP). We also find that, for different LCIA impacts, several eGRID regions are consistently worse than the US average LCIA impact for every unit of electricity generated. Here this work describes the development of an electricity production LCI at different spatial resolutions by combining and harmonizing information from several databases. The inventory consists of emissions, fuel inputs, and electricity and steam outputs from different electricity production technologies located across various regions of the USA. This LCI for electricity production in the USA will prove to be an enormous resource for all LCA researchers - considering the detailed sources of the information and the breadth of emissions covered by it.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Evaluating grid stress and reliability in future electricity grids across a range of demand, generation mix, and weather trends

The reliability of power grids in the future will depend on how system planners account for the integration of new technologies, extreme weather events, and uncertainties in demand growth from increased electrification and data centers. This study introduces an open-source, multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020 and 2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a range of population growth and economic change, generation mixes, and weather conditions. Our results show future grids with high percentage of non-renewable generation and strong economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of larger reliance on dispatchable generators and lower fossil fuel extraction costs. Scenarios with high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios relying more on dispatchable generators. These events occur because higher proportion of solar and wind energy causes net demand curves to deepen during midday (duck curves get progressively severe), exacerbating the challenge of meeting demand during summer evening peaks. This study suggests that robust and co-optimized transmission and energy storage planning could help maintain low wholesale electricity prices and high reliability levels in future electricity grids across uncertainties in generation mixes.

Electric grid reliability↗
DOE OSTI2020

Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands

With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources’ power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Temporal complementarity of marine renewables with wind and solar generation: Implications for GB system benefits

Wave and tidal energy have the potential to provide benefits to power systems with high proportions of stochastic renewable generation. This is particularly applicable with wind and solar photovoltaics, as the offsetting of these renewable resources results in more reliable renewable generation. This study utilises ten metrics to quantify the temporal complementarity and supply-demand balancing requirements of the energy mix in Great Britain, to investigate the potential magnitude of these system benefits. Wave and tidal generation profiles are created using historical resource data and hydrodynamic models. The results show that the inclusion of wave and tidal generation creates a renewable energy mix which is more available under multiple conditions: throughout a year of operation; at times of peak demand; for multiple consecutive hourly time periods; and at times when wind and solar generation are not available. Three regional case studies also show that the inclusion of marine energy allows for improved regional supply-demand matching, reducing instances of energy shortage and excess and potentially relieving transmission congestion at particularly constrained locations within GB. Finally, the implications of these findings are discussed in terms of GB wholesale market operation, system balancing and system security.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

On the impact of tidal generation and energy storage integration in PV-rich electric distribution systems

Deep decarbonization of power system operations requires the maximal utilization of available renewable resources. At distribution-level operations, however, grid operators can face numerous challenges in integrating renewables at scale owing to the inherent intermittence of renewable energy resources. These include phenomena such as voltage fluctuations, which are typically mitigated through control actuators such as on-load tap changers (OLTC) as well as energy storage devices, such as battery energy storage systems (BESS). On the one hand, high intermittence of the available renewable portfolio may require increasingly aggressive control of actuators, thereby accelerating the probability of equipment failure. On the other hand, integrating BESS operations and having a diverse renewable generation portfolio can typically help stagger power/energy flow to mitigate the aforementioned adverse impacts. In this paper, we employ a Bayesian framework for equipment lifetime estimation to understand the impact of including tidal energy resources and BESS in distribution system operations for feeders having substantial distribution photovoltaic generation. Our results indicate that while tidal energy alone may slightly decrease equipment reliability, the adverse impact on reliability is significantly magnified by a generation portfolio consisting of tidal generation and photovoltaic generation. Here, we also study the tidal and photovoltaic hosting capacity problem with and without energy storage systems using equipment reliability as an added constraint. We conclude that energy storage increases the reliability-constrained hosting capacity of the distribution system.

14 SOLAR ENERGY↗
DOE OSTI2022

Automatically generated model for light alkene combustion

Light alkenes are common combustion intermediates for a variety of fuels. Therefore, understanding their oxidation and pyrolysis chemistry is key to building detailed mechanisms for heavier fuels. This work was focused on the development and evaluation of a detailed kinetic mechanism suitable for the combustion of light alkenes up to C 4 without the use of tuned parameters, instead, the parameter values come from first principles or direct measurements. The generated mechanism accurately estimates the laminar burning velocity (S u ) and ignition delay time (IDT) of light alkenes available in the literature, which represent fundamental combustion properties at a wide range of conditions. Because each parameter is thought to have a physically realistic value, not tuned to these measurements, the new model could be used as a sub-mechanism in models for other applications. The reaction network was generated with the open-source Reaction Mechanism Generator (RMG) software. Sensitivity analyses were performed under wide ranges of temperatures and pressures, allowing for the identification of the most impactful species and reactions. Based on these, a comprehensive thermochemistry database, including calculations on 550 molecules performed in this work at the CBS-QB3 level of theory, and a kinetic library, including theoretically-derived reaction rates retrieved from the literature, were built and used in the mechanism generation. The developed mechanism was compared against several existing detailed kinetic mechanisms for ethene, propene, 1-butene, 2-butene, and isobutene. Here, the newly generated model is the most accurate among the ones analyzed, in terms of fractional bias and normalized mean square error. Hence, this new model was used to analyze the chemistry of alkene combustion. Key rate coefficients were compared, to identify the cause of deviations between the models and possible areas for further improvements.

09 BIOMASS FUELS↗
DOE OSTI2022

INGRID: An interactive grid generator for 2D edge plasma modeling

A fusion boundary-plasma domain is defined by axisymmetric magnetic surfaces where the geometry is often complicated by the presence of one or more X-points; and modeling boundary plasmas usually relies on computational grids that account for the magnetic field geometry. The new grid generator INGRID (Interactive Grid Generator) presented in this work is a Python-based code for calculating grids for fusion boundary plasma modeling, for a variety of configurations with one or two X-points in the domain. INGRID first performs partitioning over the domain consisting of a small number of patches conforming to the magnetic field and wall geometry; then it generates a subgrid on each of the patches and joins them into a global grid. This domain partitioning strategy makes possible a uniform treatment of various configurations with one or two X-points in the domain. This includes single-null, double-null, and other configurations with two X-points in the domain. The INGRID design allows generating grids either interactively, via a parameter-file driven GUI, or using a non-interactive script-controlled workflow. Results of testing demonstrate that INGRID is a flexible, robust, and user-friendly grid-generation tool for fusion boundary-plasma modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Decentralized Data-Driven Estimation of Generator Rotor Speed and Inertia Constant Based on Adaptive Unscented Kalman Filter

This paper proposes an online decentralized and data-driven method for synchronous generator rotor speed and inertia estimation. First, an analytical relationship between the generator terminal voltage/current phasors and its internal rotor speed is derived using the Thevenin equivalent. The parameters of the latter are obtained via a recursive estimator, which allows us to estimate the generator's internal rotor speed from voltage/current phasors without the need for any generator information. Second, we reformulate the equivalent swing equation into the Kalman filter form, which enables us to use a priori-estimated internal rotor speed along with measured real and reactive power injections to obtain an estimate of the inertia for each online synchronous generator. This is achieved through an adaptive unscented Kalman filter. We demonstrate that the use of terminal bus frequency to approximate the rotor speed for inertia estimation by existing methods leads to large estimation biases/errors. Numerical simulations carried out on the IEEE 39-bus and Texas 2000-bus systems reveal that our proposed method is consistently more accurate than existing methods. Moreover, our proposed method is insensitive to both the type and location of system disturbances, and it is robust to different levels of measurement noise.

bus frequency↗
DOE OSTI2023

Evaluation of the economic implications of varied pressure drawdown strategies generated using a real-time, rapid predictive, multi-fidelity model for unconventional oil and gas wells

Experience has suggested that pressure maintenance in hydraulically fractured reservoirs via lower, more sustained production drawdowns may offer improved cumulative recovery and overall resource extraction efficiency compared to more rapid drawdown approaches aimed at generating high initial production. However, given the inherent variability of oil and natural gas markets, operators pursue production strategies that maximize profitability over resource extraction efficiency. This study focuses on evaluating the implications of contrasting pressure drawdown strategies on the long-term production and resulting economics for a real, producing unconventional gas well in the Marcellus Shale of the Appalachian Basin using a techno-economic analysis approach. Our research combines elements of well-specific horizontal well design, production forecasting, equipment sizing and capital cost estimation, operating cost estimation, and revenue and tax calculations. Gas production forecast outlook scenarios were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning workflow and 2) traditional reservoir simulation. A discounted cash flow model was used to evaluate the resulting economic implications for each drawdown scenario—generating output for exploring the coupled effect of factors like the timing and volume of gas production, prevailing economic and market conditions for natural gas, and overall estimated ultimate recovery on profitability metrics such as internal rate of return and net present value. Results show that there is potential to maximize the cumulative gas produced in the specific case study well by employing a lower pressure drawdown. Conversely, the greatest profitability is achieved using rapid drawdown as signified by a small, specific subset of our outlook scenarios. On an averaging basis, we find that the combinations of highest cumulative producing and most profitable scenarios occur under lower drawdowns with long (>40 years) producing timeframes, but require higher relative gas price and lower discounting considerations. Further, the machine learning predictive outlooking capability proved effective for enabling rapid generation of a multitude of scenario forecasts. As a result, a variety of prominent example cases could be generated to strike the balance of greater productivity and economic return given their associated producing features and economic conditions when compared to similar producing scenarios—critical insight that offers improved decision support for unconventional oil and gas operations.

42 ENGINEERING↗
DOE OSTI2023

Dislocation generation in diamond under extreme loading

Diamond is, by virtue of its high bonding forces and Peierls-Nabarro barrier stresses, among the hardest materials on earth due to the difficulty of generating and moving dislocations. We demonstrate, using molecular dynamics and analytical calculations, that the generation of defects is dependent on loading orientation. Shock-loading single-crystal diamond along [001] and [011] to a stress of 137 GPa did not reveal dislocations, whereas loading along [111] generated profuse dislocations. The introduction of a void at the latter orientation generated dislocations at 72.6 GPa. Two slip systems were identified: <011>{100} and <112>{111}. The threshold for plastic deformation in diamond is orientation dependent and significantly lower than reported in previous studies: 0.14 G (shear modulus). The void collapse generated localized amorphization for [001]. In conclusion, these results on the anisotropy of plasticity and void effects have relevance for the improvement of symmetry in the collapse of diamond capsules in inertial fusion experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A deep learning-based battery sizing optimization tool for hybridizing generation plants

Hybrid generation and energy storage systems offer the ability to increase flexibility of the combined asset. This flexibility can be used to increase provision of services already provided by the generation asset, such as timing sale of electricity to the energy market during high price periods, and also enable provision of additional services, such as ancillary services or contribute to resource adequacy. From a generation asset owner perspective, the decision to hybridize includes selecting an energy storage system that, among other factors, maximizes financial performance of the energy storage investment. Yet, existing tools to optimize energy storage sizing are either too rudimentary (i.e., based on “rules of thumb”) or too complex to implement (i.e., require specialized engineering and software knowledge and a high-performance computer to run). This work presents a novel deep learning-based battery sizing optimization tool that is designed to help generation asset owners easily assess preliminary sizing considerations for potential battery investments to hybridize their generation facility. The tool uses deep learning to predict revenue over a broad search space of potential battery sizes, estimates capital and operating costs (including accounting for battery degradation), and computes financial performance of each potential battery system investment, recommending a system with maximum financial performance. The tool is tested and validated for hydropower assets. Finally, this tool will help a greater cross-section of industry consider investments in battery systems, increasing their revenue and helping them compete in rapidly evolving electrify markets.

13 HYDRO ENERGY↗
DOE OSTI2025

Generation of radioisotopes for medical applications using high-repetition, high-intensity lasers

We used the PW high-repetition laser facility VEGA-3 at Centro de Láseres Pulsados in Salamanca, with the goal of studying the generation of radioisotopes using laser-driven proton beams. Various types of targets have been irradiated, including in particular several targets containing boron to generate α-particles through the hydrogen–boron fusion reaction. We have successfully identified γ-ray lines from several radioisotopes created by irradiation using laser-generated α-particles or protons including 43 Sc, 44 Sc, 48 Sc, 7 Be, 11 C and 18 F. We show that radioisotope generation can be used as a diagnostic tool to evaluate α-particle generation in laser-driven proton–boron fusion experiments. We also show the production of 11 C radioisotopes, ≈ 6 × 10 6 , and of 44 Sc radioisotopes, ≈ 5 × 10 4 per laser shot. This result can open the way to develop laser-driven radiation sources of radioisotopes for medical applications.

gamma ray spectroscopy↗
DOE OSTI2024

Strong Surface-Enhanced Coherent Phonon Generation in van der Waals Materials

Terahertz (THz) coherent phonons have emerged as promising candidates for the next generation of high-speed, low-energy information carriers in atomically thin phononic or phonon-integrated on-chip devices. However, effectively manipulating THz coherent phonons remains a significant challenge. Here, in this study, we investigated THz coherent phonon generation in exfoliated van der Waals (vdW) flakes of Fe 3 GeTe 2 , Fe 5 GeTe 2 , and FePS 3 . We successfully generated the THz A 1g coherent phonon mode in these vdW flakes. An innovative approach involved partially exfoliating vdW flakes on a gold substrate and partially on a silicon (Si) substrate to compare the THz coherent phonon generation between both sides. Interestingly, we observed a significantly enhanced THz coherent phonon in the vdW/gold area compared with that in the vdW/Si area. Frequency-domain Raman mapping across the vdW flakes corroborated these findings. Numerical simulations further indicated a stronger enhanced surface field in vdW/gold structures than in vdW/Si structures. Consequently, we attribute the observed enhancement in THz coherent phonon generation to the increased surface field on the gold substrate. This enhancement was consistent across the three different vdW materials studied, suggesting the universality of this strategy. Our results hold promise for advancing the design of THz phononic and phonon-integrated devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗