Energy Technologies

Light-driven reduction of CO2 into chemicals using a photoelectrochemical (PEC) approach is considered as a promising way to meet the carbon neutral target. The very top surface of the photoelectrode and semiconductor/electrolyte interface plays a pivotal role in defining the performance for PEC CO2 reduction. However, such impact remains poorly understood. Here, we report an electrodeposition-annealing route for tailoring surface composition of ZnTe photocathodes. Our work demonstrates that a Zn-rich surface on the ZnTe photocathode is essential to impact the CO2 reduction activity and selectivity. In particular, the Zn-rich surface not only facilitated the interfacial charge carrier transfer, but also acted as electrocatalyst for boosting carbon product selectivity and suppressing the hydrogen evolution reaction. This work provides a new avenue to optimize the photocathode, as well as improvement of the CO2RR performance.

Further improvements to lithium-ion and emerging battery technologies can be enabled by an improved understanding of the chemistry and working mechanisms of interphases that form at electrochemically active battery interfaces. However, it is difficult to collect and interpret spectra of interphases for several reasons, including the presence of a variety of compounds. To address this challenge, we herein present a vibrational spectroscopy and X-ray diffraction data library of ten compounds that have been identified as interphase constituents in lithium-ion or emerging battery chemistries. The data library includes attenuated total reflectance Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray diffraction data, collected in inert atmospheres provided by custom sample chambers. The data library presented in this work (and online repository) simplifies access to reference data that is otherwise either diffusely spread throughout the literature or non-existent, and provides energy storage researchers streamlined access to vital interphase-relevant data that can accelerate battery research efforts.

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model's decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

Thermally responsive ionic liquids (ILs) exhibit liquid-liquid phase separation when mixed with water and heated above a lower critical solution temperature (LCST), resulting in a water-rich (WR) and an IL-rich (ILR) phase. These binary IL-water mixtures can be employed in a variety of thermodynamic processes such as forward osmosis (FO) desalination, for which two solution properties are desirable: low phase separation temperature and high osmotic strength (osmolality). However, these two properties are interlinked, with ILs that exhibit higher osmotic strengths typically requiring higher phase separation temperatures. This behavior tends to arise from the hydrophilicity of the IL cations, which enhances osmotic strength while also elevating the phase separation temperature. In this work, we highlight a pathway to overcome this tradeoff by developing ternary IL mixtures (two ILs with varying cation hydrophilicity mixed with water), which lowers the phase separation temperature while maintaining and even enhancing the osmotic strength of the solution. We characterize the mixing behavior (osmolality, phase separation temperature, WR phase purity, and WR to ILR phase mass ratio) of four ILs as a function of their concentration in solution. We find that an enhancement of up to 81.6% in the osmolality with a concomitant reduction of up to 15.4% in the phase separation temperature can be achieved using this approach. The ternary mixture is also shown to improve the phase separation kinetics by nearly 95% compared to the binary mixture. Overall, this work highlights a new pathway to improve the performance of LCST ILs for water and energy applications.

Nafion, a perfluorosulfonic acid ionomer, has been well-studied for decades due to its key role as an ion-conductive membrane in electrochemical energy conversion and storage applications. When hydrated, this membrane phase separates into a complex hierarchical nanostructure with hydrophilic domains that facilitate ion transport. Hard X-ray scattering has been a powerful technique in understanding Nafion due to its capabilities in capturing the ionomer’s nanophase separated structure, which gives rise to contrast between polymer and water domains. More recently, resonant X-ray scattering, which tunes to elemental absorption edges to provide specificity on constituent elements, has been explored to highlight key interactions related to the sulfonic acid groups within its structure. Here, we study the Nafion nanostructure by combining hard X-ray scattering and tender resonant X-ray scattering (TReXS) at the sulfur K-edge to reveal a mesoscopic feature corresponding to a correlation length of approximately 40 nm that has been challenging to resolve with hard X-ray studies. Additionally, we study the effect of the dispersion solvent composition that plays a key role in the formation of this mesoscale feature. Notably, TReXS can attain high contrast to decipher this mesoscale morphology even for dry polymer membranes under a vacuum, which typically have reduced contrast for hard X-rays. We find that the correlation length of this mesoscale feature decreases with increasing water fraction in the dispersion, which is the opposite trend exhibited by the smaller intercrystalline feature in the same membranes. This study showcases the utility of TReXS to uncover multiscale morphological details in functional polymers that are not always revealed by other methods like hard X-ray scattering. We illustrate this with Nafion, which is a relevant ion-conducting polymer for electrochemical technologies. (Figure presented.)

Coral reefs suffer from climate change, including long-term ocean acidification (OA) and warming and short-term bleaching, tropical storms, and pollution events, all of which are increasing in frequency and severity. It is urgent yet unclear how to intervene to save coral reefs. Reversal of the ocean pH to preindustrial levels could restore coral reefs to their preindustrial growth rates; however, strategies to reverse OA on environmentally relevant scales have not been established. Anecdotally, electrolysis seems to help coral reefs recover from acidification and short-term events, but few uncontrolled studies support such claims. Here, using two independent continuum simulation approaches (COMSOL and CrunchFlow), we show the effect of electrolysis on seawater chemistry relevant to coral reef survival and growth. We conclude that near the negative electrodes, the cathodes, seawater pH, supersaturation, and carbonate concentration all increase significantly. Electrolysis of seawater, therefore, can be used to restore preindustrial ocean conditions locally to save coral reefs, an approach termed eCoral here. We anticipate these simulation results to be the starting point for controlled experiments to test whether seawater electrolysis promotes coral reef growth and restoration, as these simulations predict.

Lithium bis(fluorosulfonyl)imide-based liquid electrolytes are promising for realizing high coulombic efficiency and long cycle life in next-generation Li-metal batteries. However, the role of anions in the formation of the solid-electrolyte interphase remains unclear. Here we combine electrochemical analyses and X-ray photoelectron spectroscopy measurements, both with and without sample washing, together with computational simulations, to propose the reaction pathways of electrolyte decomposition and correlate the interphase component solubility with the efficacy of passivation. We discover that not all the products derived from interphase-forming reactions are incorporated into the resulting passivation layer, with a notable portion present in the liquid electrolyte. We also find that the high-performance electrolytes can afford a sufficiently passivating interphase with minimized electrolyte decomposition, by incorporating more anion-decomposition products. Overall, this work presents a systematic approach of coupling electrochemical and surface analyses to paint a comprehensive picture of solid-electrolyte interphase formation, while identifying the key attributes of high-performance electrolytes to guide future designs.