Asegun Henry

terahertz (THz) illumination leads to a ten-fold decrease in the differential impedance compared to the excitation of acoustic and optical phonons. Additionally, we differentiate the ultrafast responses of LLTO due to ultrafast heating and THz-range vibrations using laser-driven spectroscopy (LUIS), finding a unique long-lived response for the THz-range excitation. These findings provide new insights into coupled ion migration mechanisms, indicating the important role of THz-range coupled phonon-ion hopping modes in enabling fast ion conduction at room temperature.

Thermal radiative energy transport is essential for high-temperature energy harvesting technologies, including thermophotovoltaics (TPVs) and grid-scale thermal energy storage. However, the inherently low emissivity of conventional high-temperature materials constrains radiative energy transfer, thereby limiting both system performance and technoeconomic viability. Here, we demonstrate ultrafast femtosecond laser-material interactions to transform diverse materials into near-blackbody surfaces with broadband spectral emissivity above 0.96. This enhancement arises from hierarchically engineered light-trapping microstructures enriched with nanoscale features, effectively decoupling surface optical properties from bulk thermomechanical properties. These laser blackened surfaces (LaBS) exhibit exceptional thermal stability, retaining high emissivity for over 100 hours at temperatures exceeding 1000{\deg}C, even in oxidizing environments. When applied as TPV thermal emitters, Ta LaBS double electrical power output from 2.19 to 4.10 W cm-2 at 2200{\deg}C while sustaining TPV conversion efficiencies above 30%. This versatile, largely material-independent technique offers a scalable and economically viable pathway to enhance emissivity for advanced thermal energy applications.

Thermophotovoltaics (TPV) are a promising new approach for converting heat to electricity. Their performance is primarily characterized by two metrics: efficiency and power density. While recent works have shown high efficiency, it is important to understand how both of these metrics impact the techno-economics of a TPV system as efforts to commercialize the technology advance. In this work, we develop the first unification of efficiency and power density into a single techno-economic metric based on the levelized cost of electricity (LCOE). We find that the LCOE can be broken into two parts: heating cost, including infrastructure and inputs for providing heat to the TPV cells, and cell cost, the capital cost of the TPV cells. We show that systems with high heating costs should prioritize TPV efficiency, while systems with high cell costs should prioritize power density. We then develop a model to identify the most impactful cell properties in improving the important performance metric and reducing system LCOE. Namely, improving spectral control with increased back-surface reflectance is the most effective to reduce LCOE in systems with high infrastructural costs, while increasing the view factor and reducing front-surface reflectance are most critical in systems with high TPV cell cost. Improving just one or two of these properties can reduce the LCOE by 25-75%, reaching competitive values ~ 8 cents/kWh-e, less than the average cost of electricity in the US. This study thus elucidates which TPV performance metric is more important for system technoeconomics and how to maximize it.

Thermophotovoltaics (TPV) are a promising new approach for converting heat to electricity. Their performance is primarily characterized by two metrics: efficiency and power density. While recent works have shown high efficiency, it is important to understand how both of these metrics impact the techno-economics of a TPV system as efforts to commercialize the technology advance. In this work, we develop the first unification of efficiency and power density into a single techno-economic metric based on the levelized cost of electricity (LCOE). We find that the LCOE can be broken into two parts: heating cost, including infrastructure and inputs for providing heat to the TPV cells, and cell cost, the capital cost of the TPV cells. We show that systems with high heating costs should prioritize TPV efficiency, while systems with high cell costs should prioritize power density. We then develop a model to identify the most impactful cell properties in improving the important performance metric and reducing system LCOE. Namely, improving spectral control with increased back-surface reflectance is the most effective to reduce LCOE in systems with high infrastructural costs, while increasing the view factor and reducing front-surface reflectance are most critical in systems with high TPV cell cost. Improving just one or two of these properties can reduce the LCOE by 25-75%, reaching competitive values ~ 8 cents/kWh-e, less than the average cost of electricity in the US. This study thus elucidates which TPV performance metric is more important for system technoeconomics and how to maximize it.

Abstract Liquid metals serve as efficient heat transfer fluids due to their exceptional thermal conductivity and broad temperature range, rendering them well-suited for demanding applications such as nuclear reactors and thermal energy storage. Nevertheless, designing liquid metal pumps poses significant challenges, including chemical compatibility with the containment vessel and various thermomechanical issues. This study focuses on elucidating these challenges and proposing potential solutions through the example of a centrifugal pump designed for handling liquid tin at an extreme operating temperature of 2400°C. To address chemical compatibility concerns, graphite is adopted as both the containment and pump material, presenting novel mechanical hurdles. Key obstacles include accommodating thermal expansion and navigating the intricacies of working with features approaching the grain size of graphite.

Thermal energy storage using sensible heating of a solid storage medium is a potential low-cost technology for long-duration energy storage. To effectively get heat in and out of the solid material, channels of heat transfer fluid can be embedded within the storage material. Here we present design principles to improve performance of channel-embedded thermal energy storage systems, and we apply these principles to a high-temperature system using graphite as the storage material and liquid tin as the heat transfer fluid. We first analyze the impact of geometry and material properties on the performance of the system, determining the ideal channel spacing and length to achieve high (dis)charge temperature uniformity. We then analyze how controlling the fluid flowrate, heating infrastructure, and heat engine can increase discharge power uniformity and accelerate charging. Finally, we model 100 high-temperature graphite storage blocks using a porous media approximation and implement the developed design principles to demonstrate significant improvement in performance for both discharging (constant discharge power for >90% of rated duration) and charging (>90% charged within 4 hours). Overall, the hierarchical design procedure presented here enables the design of cheap yet high-performing solid thermal energy storage systems.

Heat dissipation plays a crucial role in the performance and reliability of high-power GaN-based electronics. While AlN transition layers are commonly employed in the heteroepitaxial growth of GaN-on-SiC substrates, concerns have been raised about their impact on thermal transport across GaN/SiC interfaces. In this study, we present experimental measurements of the thermal boundary conductance (TBC) across GaN/SiC interfaces with varying thicknesses of the AlN transition layer (ranging from 0 to 73 nm) at different temperatures. Our findings reveal that the addition of an AlN transition layer leads to a notable increase in the TBC of the GaN/SiC interface, particularly at elevated temperatures. Structural characterization techniques are employed to understand the influence of the AlN transition layer on the crystalline quality of the GaN layer and its potential effects on interfacial thermal transport. To gain further insights into the trend of TBC, we conduct molecular dynamics simulations using high-fidelity deep learning-based interatomic potentials, which reproduce the experimentally observed enhancement in TBC even for atomically perfect interfaces. These results suggest that the enhanced TBC facilitated by the AlN intermediate layer could result from a combination of improved crystalline quality at the interface and the "phonon bridge" effect provided by AlN that enhances the overlap between the vibrational spectra of GaN and SiC.

Abstract Machine learning (ML) enables the development of interatomic potentials with the accuracy of first principles methods while retaining the speed and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks map descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor Li $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> Ge(PS $$_{6}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>6</mml:mn> </mml:msub> </mml:math> ) $$_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msub> </mml:math> (LGPS). We further investigate how these errors affect simulated properties and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Graphical abstract

Ion diffusion is important in a variety of applications, yet fundamental understanding of the interaction of lattice vibrations (phonons and vibrational motifs) and the mobile species in solids is still missing. Particularly, for Li super ionic conductors (LISICONs), several studies have reported on the important role of the poly-anion octahedral rotations (rocking modes) in facilitating solid-state Li + ion migration.[1], [2] However, direct calculation of the contribution of these rocking modes to the Li + hop is missing, and the provided arguments in these reports are mostly based on establishing correlations between different properties of the rocking modes and the migration properties of the hopping Li + ion without directly quantifying the contribution of the rocking modes to the Li + hop. For instance, rocking modes have been argued to be able to ( i ) provide a softer lattice environment for Li + ion vibration,[3] ( ii ) increase the bottleneck area of diffusion (4-O square area),[2] ( iii ) induce additional force on the carrier sublattice,[1] and ( iv ) maintain a relatively constant coordination number for Li + ion during its hop[1] – all conducive to Li + hop in the lattice. Although insightful, we still lack understanding of the exact vibrational frequencies (density of states) of the rocking modes, and, more importantly, the degree of their contribution to Li + hop with respect to other phonon modes in the structure. In this work, using a combination of ab initio nudged elastic band (NEB) and lattice dynamics calculations based on the recently proposed formalism,[4] we identify the direct contributions of all phonons, including the rocking ones, to Li + hop in the perovskite solid-state Li + conductor Li 0.5 La 0.5 TiO 3 (LLTO). We set up our NEB Li + hop calculations considering ( i ) different orderings of the LLTO lattice,[5] and ( ii ) different Li + hopping mechanisms (single and concerted (cooperative)) that can occur in the LLTO lattice. 2 To sample such a high degree of complexity, we performed 22 independent NEB calculations of Li + hop in three different LLTO structures with distinct Li|La orderings and based on two different ion hop mechanisms (single and concerted). Our calculations determined that the following two groups of vibrational modes dominate the contributions to the Li + hop in the LLTO lattice: ( i ) rocking modes, and ( ii ) modes that induce large vibrational energies on the hopping Li + along its hopping direction. Specifically, our results confirmed that the top 5% (10%) contributing modes to the Li + hop in the LLTO lattice were responsible for 48% (61%) of the total contributions to the Li + hop, and the two rocking and highly energetic modes comprised &gt;95% (&gt;85%) of these top 5% (10%) contributing modes. Notable from our calculations was that the rocking modes were only present in the &lt; 5.5 THz frequency region, and in this &lt; 5.5 THz frequency region, they comprised 33% of the vibrational modes and contributed 50% to all the possible contributions to the Li + hop in this low frequency THz region. Moreover, through static modal excitement calculations, we determined that highly contributing rocking modes of vibration were important in solid-state Li + ion migration because of their ability to ( i ) increase the O-4 square bottleneck area of conduction[6] and ( ii ) amplify the force on the hopping Li + ion. In summary, our observations demonstrated the strong importance of the THz vibrational region to Li + hop in the lattice, which can be accessible for further explorations using THz spectroscopy techniques to deepen our understanding of the relation between solid-state transport of Li + and different vibrational motifs in the lattice. References [1] J. G. Smith and D. J. Siegel, ‘Low-temperature paddlewheel effect in glassy solid electrolytes’, Nat Commun , vol. 11, no. 1, pp. 1–11, 2020. [2] S. Stramare, V. Thangadurai, and W. Weppner, ‘Lithium lanthanum titanates: a review’, Chemistry of materials , vol. 15, no. 21, pp. 3974–3990, 2003. [3] X. Li and N. A. Benedek, ‘Enhancement of ionic transport in complex oxides through soft lattice modes and epitaxial strain’, Chemistry of Materials , vol. 27, no. 7, pp. 2647–2652, 2015, doi: 10.1021/acs.chemmater.5b00445. [4] K. Gordiz, S. Muy, W. G. Zeier, Y. Shao-Horn, and A. Henry, ‘Enhancement of ion diffusion by targeted phonon excitation’, Cell Rep Phys Sci , vol. 2, no. 5, p. 100431, 2021. [5] M. Catti, ‘Ion mobility pathways of the Li+ conductor Li0. 125La0. 625TiO3 by ab initio simulations’, The Journal of Physical Chemistry C , vol. 112, no. 29, pp. 11068–11074, 2008. [6] C. Chen and J. Du, ‘Lithium ion diffusion mechanism in lithium lanthanum titanate solid‐state electrolytes from atomistic simulations’, Journal of the American Ceramic Society , vol. 98, no. 2, pp. 534–542, 2015.

As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand. In this study, we modeled an existing thermal energy storage unit with estimated capital costs that are sufficiently low to enable large-scale deployment in the electric power system. Our analysis emphasizes the value of using such units to cost-effectively improve renewable energy dispatchability. This study modeled an existing real-world grid rather than simulating hypothetical future electric power systems. The storage unit coupled with a photovoltaic (PV) system was modeled with different storage capacities, whereas each storage unit size had various discharge capacities. The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO$_2$ emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO$_2$ reduction scenarios, as the storage unit is utilized differently. When CO$_2$ emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit. The proposed approach can provide increased knowledge to power system planners regarding how adding PV + storage systems to existing grids can contribute to the efficient stepwise decarbonization of electric power systems.

Machine learning (ML) enables the development of interatomic potentials that promise the accuracy of first principles methods while retaining the low cost and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks can map these descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of choice of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor LGPS. We further investigate how these errors affect simulated properties with these models and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. Since linear and nonlinear regression models have different advantages and disadvantages, the results presented herein help researchers choose models for their particular application. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Such observations can be repeated by other researchers to determine the level of accuracy, and hence model complexity, needed for their particular systems of interest.

Ionic conduction in solids that exceeds 1 mS/cm is predicted to involve coupled phonon-ion interactions in the crystal lattice. Here, we use theory and experiment to measure the possible contribution of coupled phonon-ion hopping modes which enhance Li+ migration in Li0.5La0.5TiO3 (LLTO). The ab initio calculations predict that the targeted excitation of individual TiO6 rocking modes greatly increases the Li+ jump rate as compared to the excitation of vibrational modes associated with heating. Experimentally, coherently driving TiO6 rocking modes via terahertz (THz) illumination leads to a ten-fold decrease in the differential impedance compared to the excitation of acoustic and optical phonons. Additionally, we differentiate the ultrafast responses of LLTO due to ultrafast heating and THz-range vibrations using laser-driven spectroscopy (LUIS), finding a unique long-lived response for the THz-range excitation. These findings provide new insights into coupled ion migration mechanisms, indicating the important role of THz-range coupled phonon-ion hopping modes in enabling fast ion conduction at room temperature.

An exemplary embodiment of the present invention provides a reactor system comprising: a reaction vessel comprising a reactant, a heat transfer fluid and a first reaction product, wherein the heat transfer fluid has a greater density than the first reaction product such that at least a portion of the first reaction product floats on a surface of the heat transfer fluid; a first outlet positioned at a surface level of the first reaction product, the first outlet configured to output a first outlet flow comprising at least a portion of the first reaction product and at least a portion of the heat transfer fluid; wherein the heat transfer fluid is configured to provide thermal energy to the reactant in the reaction vessel to form the first reaction product.

Thermophotovoltaics (TPV) are a technology that can turn light into electricity, just like solar panels. However, instead of taking their energy from the sun, TPVs make use of the light emitted by a hot object in close proximity. We demonstrated a new record-breaking efficiency of more than 40% for TPVs, enabling the viability of this technology for ‘thermal battery’ energy storage.

As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand. In this study, we modeled an existing thermal energy storage unit with estimated capital costs that are sufficiently low to enable large-scale deployment in the electric power system. Our analysis emphasizes the value of using such units to cost-effectively improve renewable energy dispatchability. This study modeled an existing real-world grid rather than simulating hypothetical future electric power systems. The storage unit coupled with a photovoltaic (PV) system was modeled with different storage capacities, whereas each storage unit size had various discharge capacities. The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO$_2$ emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO$_2$ reduction scenarios, as the storage unit is utilized differently. When CO$_2$ emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit. The proposed approach can provide increased knowledge to power system planners regarding how adding PV + storage systems to existing grids can contribute to the efficient stepwise decarbonization of electric power systems.

The disclosed technology includes pumps, pipes, valves, seals, and systems for pumping and controlling high temperature fluids, such as liquid tin, at temperatures of between 1000-3000° C. The systems and device may be partially or entirely constructed using brittle materials, such as ceramics, that are capable of withstanding extreme heat without significantly degrading, and may be secured using components made of refractory metals, such as tungsten. The systems and devices may utilize static and dynamic seals made from brittle materials, such as graphite, to enable the high temperature operation of such pumps, pipes, valves, and systems without leakage.

Energy storage systems are disclosed. The systems may store energy as heat in a high temperature liquid, and the heat may be converted to electricity by absorbing radiation emitted from the high temperature liquid via one or more photovoltaic devices when the high temperature liquid is transported through an array of conduits. Some aspects described herein relate to reducing deposition of sublimated material from the conduits onto the photovoltaic devices.

Graphite is a unique material for high temperature applications and will likely become increasingly important as we attempt to electrify industrial applications. However, high-quality graphite can be expensive, limiting the cost-competitiveness of high-quality graphite technologies. Here, we investigate the thermal properties of low-cost, low-quality, macro-porous graphite to determine the tradeoff between cost and thermal performance. We use laser flash analysis (LFA) to measure the thermal diffusivity of graphite at high temperatures. However, due to the large pores in the graphite samples preventing uniform laser flash heating, we must apply a thick coating to achieve the required flat, parallel surfaces for LFA measurements. The presence of the coating directly impacts the measured diffusivity, not only because of the added thickness but also because of the sample/coating interface profile generated. We therefore develop a methodology based on finite element modeling of a variety of sample/coating interface profiles to extract properties of the sample. Validating the methodology against a reference sample demonstrates a mean absolute percentage error of 8.5%, with potential improvement with better sample characterization. We show low-cost, low-quality graphite has a thermal conductivity of ~10 W/m/K up to 1000$^{\circ}$C, which is an order of magnitude lower than high-quality graphite, but contributions from photon conductivity may result in higher conductivities at higher temperatures. Overall, we demonstrate an approach for measuring thermal properties of macro-porous materials at high temperatures, and apply the approach to measuring thermal conductivity of porous graphite, which will aid in the design of high-temperature systems for cost-competitive decarbonization.

Graphite is a unique material for high temperature applications and will likely become increasingly important as we attempt to electrify industrial applications. However, high-quality graphite can be expensive, limiting the cost-competitiveness of high-quality graphite technologies. Here, we investigate the thermal properties of low-cost, low-quality, macro-porous graphite to determine the tradeoff between cost and thermal performance. We use laser flash analysis (LFA) to measure the thermal diffusivity of graphite at high temperatures. However, due to the large pores in the graphite samples preventing uniform laser flash heating, we must apply a thick coating to achieve the required flat, parallel surfaces for LFA measurements. The presence of the coating directly impacts the measured diffusivity, not only because of the added thickness but also because of the sample/coating interface profile generated. We therefore develop a methodology based on finite element modeling of a variety of sample/coating interface profiles to extract properties of the sample. Validating the methodology against a reference sample demonstrates a mean absolute percentage error of 8.5%, with potential improvement with better sample characterization. We show low-cost, low-quality graphite has a thermal conductivity of ~10 W/m/K up to 1000$^{\circ}$C, which is an order of magnitude lower than high-quality graphite, but contributions from photon conductivity may result in higher conductivities at higher temperatures. Overall, we demonstrate an approach for measuring thermal properties of macro-porous materials at high temperatures, and apply the approach to measuring thermal conductivity of porous graphite, which will aid in the design of high-temperature systems for cost-competitive decarbonization.