Energy Technologies

Photonic surfaces designed with specific optical characteristics are becoming increasingly crucial for novel energy harvesting and storage systems. The design of these surfaces can be achieved by texturing materials using lasers. The optimal adjustment of laser fabrication parameters to achieve target surface optical properties is an open challenge. Thus, we develop a surrogate-based optimization approach. Our framework employs the Random Forest algorithm to model the forward relationship between the laser fabrication parameters and the resulting optical characteristics. During the optimization process, we use a greedy, prediction-based exploration strategy that iteratively selects batches of laser parameters to be used in experimentation by minimizing the predicted discrepancy between the surrogate model's outputs and the user-defined target optical characteristics. This strategy allows for efficient identification of optimal fabrication parameters without the need to model the error landscape directly. We demonstrate the efficiency and effectiveness of our approach on two synthetic benchmarks and two specific experimental applications of photonic surface inverse design targets. By calculating the average performance of our algorithm compared to other state of the art optimization methods, we show that our algorithm performs, on average, twice as well across all benchmarks. Additionally, a warm starting inverse design technique for changed target optical characteristics enhances the performance of the introduced approach.

Predictive synthesis of aqueous organic solutions with desired liquid-solid phase equilibria could drive progress in industrial chemistry, cryopreservation, and beyond, but is limited by the predictive power of current solution thermodynamics models. In particular, few analytical models enable accurate liquidus and eutectic prediction based only on bulk thermodynamic properties of the pure components, requiring instead either direct measurement or costly simulation of solution properties. In this work, we demonstrate that a simple modification to the canonical ideal solution theory accounting for the entropic effects of dissimilar molecule sizes can transform its predictive power. Incorporating a Flory-style entropy of mixing term that includes both the mole and volume fractions of each component, we derive size-dependent equations for the ideal chemical potential and liquidus temperature, and use them to predict the binary phase diagrams of water and 10 organic solutes of varying sizes. We show that size-dependent prediction outperforms the ideal model in all cases, reducing average error in the predicted liquidus temperature by 59% (to 5.6 K), eutectic temperature by 45% (to 9.7 K), and eutectic composition by 43% (to 4.7 mol%), as compared to experimental data. Furthermore, by retaining the ideal assumption that the enthalpy of mixing is zero, we demonstrate that, for aqueous organic solutions, much of the deviation from ideality that is typically attributed to molecular interactions may in fact be explained by simple entropic size effects. These results suggest an underappreciated dominance of mixing entropy in these solutions, and provide a simple approach to predicting their phase equilibria.

Anchoring catalysts on an engineered oxide support enables stable water electrolysis.

The absence of affordable and deployable large-scale energy storage poses a major barrier to providing zero-emission energy on demand for societal decarbonization. High temperature thermal energy storage is one promising option with low cost and high scalability, but it is hindered by the inherent complexity of simultaneously satisfying all of the material requirements. Here we design a class of ceramic-carbon composites based on co-optimizing mechanical, electrical, and thermal properties. These composites demonstrate stability in soak-and-hold tests and direct self-heating up to 1,936 °C and 750 thermal cycles from 500 to 1,630 °C without degradation. This thermal performance derives from their composition and microstructural design as verified by in situ high-temperature transmission electron microscopy and X-ray diffraction. They offer both higher energy density and lower cost than conventional storage technologies with a projected system Levelized Cost of Storage below the U.S. Department of Energy’s 2030 target 5 ¢/kWh (electric).

The continuing developments in semiconductor device technologies have prompted the need for advanced nanoscale processing techniques. Laser chemical processing offers significant advantages, including spatial selectivity, high localization, minimal material damage, and fast operation. Pulsed laser-induced dissociation of gas species serves as an essential process step, contributing to doping, etching, and other chemical modifications of semiconductor materials. However, the mechanisms behind the laser-gas interactions and subsequent surface modifications remain elusive. Here, we demonstrate ultraviolet picosecond laser-induced atomic layer etching of silicon in a gaseous chlorine environment, achieving self-limited etching with a precision of 0.93 nm/cycle. Through in situ optical emission spectroscopy, we elucidate the transition energy states of laser-excited products during chlorination. Complementing our experimental findings, we perform numerical modeling that reveals the complex spatiotemporal dynamics of chlorine species, encompassing their generation, recombination, diffusion, and transient surface reaction with the silicon substrate. Our study demonstrates optical diagnostics of laser-induced chlorination in atomic layer etching, which can provide valuable insights into ultrafine chemical nanostructuring of semiconductor materials.

Abstract: In-situ two-dimensional X-ray absorption spectroscopy (XAS) imaging was employed to analyze cerium ion (Ce3+) migration in the through-plane direction in proton exchange membrane fuel cells (PEMFCs), offering fundamental insights supporting improvement of their power density and membrane durability. The transport of Ce3+ was visualized in both unreinforced thick Nafion membranes (Nafion 115, 127 µm) and reinforced thin (12 µm) perfluorosulfonic acid (PFSA) membranes under either an electrical potential gradient or a water activity gradient. The diffusion coefficients of Ce3+ were ascertained based on its behavior after removal of these gradients in both membrane types. Additionally, using a one-dimensional cation transport model, the mobility and electroosmotic drag coefficients of Ce3+ were derived from experimentally obtained data of the thick Nafion membrane. Our measurements also demonstrate that the migration of Ce3+ in the thick membranes was notably impeded by the presence of ferrous ion (Fe2+) impurities. Because Fe2+ is known to accelerate membrane degradation by promoting hydroxyl radical formation, this effect might further exacerbate membrane degradation. It therefore warrants careful consideration.

Buildings account for more than one third of global energy consumption and carbon emissions, making the optimization of their energy use crucial for sustainability. Advanced Metering Infrastructures data offers a rich source of information for understanding and improving building energy performance, yet existing frameworks for leveraging this data are limited. This paper presents a comprehensive data-mining framework for analyzing Advanced Metering Infrastructures data at multiple temporal and spatial scales, beneficial for building owners, operators, and utility companies. Utilizing hourly electricity consumption data for the east region of Portland, Oregon, the study systematically extracts key statistics such as start hour, duration, and peak hour of load periods across daily, weekly, and annual evaluation windows. The framework employs a list of techniques including load-level detection, home vacancy detection, and weather-sensitivity analysis and statistical methods to provide detailed insights into building energy dynamics. As an unsupervised study, it reveals patterns and trends without predefined labels or categories. Key findings highlight the substantial impact of the COVID-19 pandemic on residential energy use, uncover patterns like intraday load variations, weekly consumption trends, and annual weather sensitivity. The insights gained can potentially inform better energy management strategies, support grid operations and planning, guide policy-making for energy efficiency improvements, as well as improve input and assumptions in the building energy modeling. This study opens pathways for future research, including integrating more data sources and collaborating with utility companies to validate hypotheses and further explore building energy use insights.

Microadditive manufacturing has revolutionized the production of complex, nano- to microscale components across various fields. This work investigates two-photon (2P) and three-photon (3P) fluorescence imaging, as well as third-harmonic generation (THG) microscopy, to examine periodic microarchitected lattice structures fabricated using multiphoton lithography (MPL). By immersing the structures in refractive index matching fluids, we demonstrate high-fidelity 3D reconstructions of both fluorescent structures using 2P and 3P microscopy as well as low-fluorescence structures using THG microscopy. These results show that multiphoton fluorescence (MPF) imaging offers reduced signal decay with respect to depth compared to single-photon techniques in the examined structures. We further demonstrate the ability to nondestructively identify intentional internal modifications of the structure that are not immediately visible with scanning electron microscope (SEM) images and compression-induced fractures, highlighting the potential of these techniques for quality control and defect detection in microadditively manufactured components.