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Final Technical Report for immediate public release of CalWave's xWave Design for PacWave for microgrids and remote communities.

Reversible epoxies using the Diels–Alder chemistry enables recycling processes through depolymerizing the polymer at higher temperature and then repolymerizing upon cooling. Compared to conventional bulk heating, photothermal heating can save time and resource and, consequently, reduce costs to reach an elevated temperature for recycling processes of the reversible epoxies. In previous studies, self‐healing of cracks and reattachments of two broken pieces have been presented using a laser; however, recycling of a sample as a whole is not feasible by using such a point light source. Herein, complete recycling processes are demonstrated utilizing an area light source, i.e., sunlight. Reversible epoxies are incorporated with carbon black and refractory plasmonic titanium nitride nanoparticles (NPs). Under concentrated (10 times) sunlight, they can generate sufficient heat (≈140 °C) to completely liquefy, reprocess, and reshape the samples multiple times. Recycling processes are validated by evaluation of mechanical properties for each cycle. Using an integrated experimental and theoretical approach, photothermal performance is investigated in terms of the dispersion and loading of photothermal NPs in the matrix, as well as the sample thickness. In this study, an insight is provided into the design of polymer/photothermal nanomaterial composites which can be sustainably recycled using abundant solar energy.

The first year of data from the Dark Energy Spectroscopic Instrument (DESI) contains the largest set of Lyman-α (Lyα) forest spectra ever observed. This data, collected in the DESI Data Release 1 (DR1) sample, has been used to measure the Baryon Acoustic Oscillation (BAO) feature at redshift z = 2.33. In this work, we use a set of 150 synthetic realizations of DESI DR1 to validate the DESI 2024 Lyα forest BAO measurement presented in [1]. The synthetic data sets are based on Gaussian random fields using the log-normal approximation. We produce realistic synthetic DESI spectra that include all major contaminants affecting the Lyα forest. The synthetic data sets span a redshift range 1.8 < z < 3.8, and are analysed using the same framework and pipeline used for the DESI 2024 Lyα forest BAO measurement. To measure BAO, we use both the Lyα auto-correlation and its cross-correlation with quasar positions. We use the mean of correlation functions from the set of DESI DR1 realizations to show that our model is able to recover unbiased measurements of the BAO position. We also fit each mock individually and study the population of BAO fits in order to validate BAO uncertainties and test our method for estimating the covariance matrix of the Lyα forest correlation functions. Finally, we discuss the implications of our results and identify the needs for the next generation of Lyα forest synthetic data sets, with the top priority being to simulate the effect of BAO broadening due to non-linear evolution.

Baryon Acoustic Oscillations can be measured with sub-percent precision above redshift two with the Lyman-α (Lyα) forest auto-correlation and its cross-correlation with quasar positions. This is one of the key goals of the Dark Energy Spectroscopic Instrument (DESI) which started its main survey in May 2021. We present in this paper a study of the contaminants to the Lyα forest which are mainly caused by correlated signals introduced by the spectroscopic data processing pipeline as well as astrophysical contaminants due to foreground absorption in the intergalactic medium. Notably, an excess signal caused by the sky background subtraction noise is present in the Lyα auto-correlation in the first line-of-sight separation bin. We use synthetic data to isolate this contribution, we also characterize the effect of spectro-photometric calibration noise, and propose a simple model to account for both effects in the analysis of the Lyα forest. We then measure the auto-correlation of the quasar flux transmission fraction of low redshift quasars, where there is no Lyα forest absorption but only its contaminants. We demonstrate that we can interpret the data with a two-component model: data processing noise and triply ionized Silicon and Carbon auto-correlations. This result can be used to improve the modeling of the Lyα auto-correlation function measured with DESI.

Hydrogen geo-storage is attracting substantial interdisciplinary interest as a cost-effective and sustainable option for medium- and long-term storage. Hydrogen can be stored underground in diverse formations, including aquifers, salt caverns, and depleted oil and gas reservoirs. The wetting dynamics of the hydrogen-brine-rock system are critical for assessing both structural and residual storage capacities, and ensuring containment safety. Through molecular dynamics simulations, we explore how varying concentrations of cushion gases (CO₂ or CH₄) influence the wetting properties of hydrogen-brine-clay systems under geological conditions (15 MPa and 333 K). We employed models of talc and the hydroxylated basal face of kaolinite (kaoOH) as clay substrates. Our findings reveal that the effect of cushion gases on hydrogen-brine-clay wettability is strongly dependent on the clay-brine interactions. Notably, CO₂ and CH₄ reduce the water wettability of talc in hydrogen-brine-talc systems, while exerting no influence on the wettability of hydrogen-brine-kaoOH systems. Detailed analysis of free energy of cavity formation near clay surfaces, clay-brine interfacial tensions, and the Willard-Chandler surface for gas-brine interfaces elucidate the molecular mechanisms underlying wettability changes. Our simulations identify empirical correlations between wetting properties and the average free energy required to perturb a flat interface when clay-brine interactions are less dominant. Here, our thorough thermodynamic analysis of rock-fluid and fluid-fluid interactions, aligning with key experimental observations, underscores the utility of simulated interfacial properties in refining contact angle measurements and predicting experimentally relevant properties. These insights significantly enhance the assessment of gas geo-storage potential. Prospectively, the approaches and findings obtained from this study could form a basis for more advanced multiscale simulations that consider a range of geological and operational variables, potentially guiding the development and improvement of geo-storage systems in general, with a particular focus on hydrogen storage.

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

This study investigates the theoretical design parameters and thermal performance of a kW-scale continuous oxidation reactor for high temperature (~1000 °C) thermochemical energy storage (TCES) applications. The concept comprises a counter-current particle-based system that includes a reaction zone with a heat exchanger to extract the heat produced from the oxidation reaction. Both above and below the hot reactive volume are sensible heat recuperation zones to enable the feed and removal of particles and oxidizing gas near ambient temperature during steady state operation. Two operation types for the reaction zone are studied, a fluidized bed reactor (FBR) and a moving bed reactor (MBR). The results of the parametric analysis suggest that the MBR requires a smaller volume per kW of heat produced, achieving power densities in excess of 2500 kW/m3 compared to ~ 900 kW/m³ in the FBR. Additionally, the MBR achieves between 0.71 and 0.99 oxidation conversions compared to between 0.23 and 0.38 conversions in the FBR with the same volumes and flowrates. However, the FBR has the potential to maintain a uniform reactor temperature which can produce heat transfer fluid (HTF) outlet temperatures as high as the reactor temperature, i.e., ~1000 °C, whereas the MBR produces variable reactor temperatures that can create overheating zones and low HTF outlet temperatures (< 800 °C) depending on the operating conditions selected. Future work should aim at understanding the coupled fluid dynamics, heat and mass transfer, and thermochemical reaction for any given combination of reactor volume and contacting patterns. Here, these studies should be complemented by experimental work on particle-gas TCES reactors.

High-resolution imaging using Transmission Electron Microscopy (TEM) is essential for applications such as grain boundary analysis, microchip defect characterization, and biological imaging. However, TEM images are often compromised by electron energy spread and other factors. In TEM mode, where the objective and projector lenses are positioned downstream of the sample, electron–sample interactions cause energy loss, which adversely impacts image quality and resolution. This study introduces a simulation tool to estimate the electron energy loss spectrum (EELS) as a function of sample thickness, covering electron beam energies from 300 keV to 3 MeV. Leveraging recent advances in MeV-TEM/STEM technology, which includes a state-of-the-art electron source with 2-picometer emittance, an energy spread of 3 × 10^-5, and optimized beam characteristics, we aim to minimize energy spread. By integrating EELS capabilities into the BNL Monte Carlo (MC) simulation code for thicker samples, we evaluate electron beam parameters to mitigate energy spread resulting from electron–sample interactions. Based on our simulations, we propose an experimental procedure for quantitively distinguishing between elastic and inelastic scattering. The findings will guide the selection of optimal beam settings, thereby enhancing resolution for nanoimaging of thick biological samples and microchips.

The primary purposes of the survey were to update ridership by the jurisdiction of residence for use in Metrobus’ operating subsidy allocation and to collect demographic, travel, and access data for Title VI compliance, system planning, and operation analyses. It was not a customer opinion survey; the focus was on ridership and travel characteristics.

The primary purposes of the survey were to update ridership by the jurisdiction of residence for use in Metrorail’s operating subsidy allocation and to collect demographic, travel, and access data for Title VI compliance, system planning, and operation analyses. It was not a customer opinion survey; the focus was on ridership and travel characteristics.