Nido-Hydroborate-Based Electrolytes for All-Solid-State Lithium Batteries

Hydroborate-based solid electrolytes have recently been successfully employed in high voltage, room temperature all-solid-state sodium batteries. The transfer to analogous lithium systems has failed up to now due to the lower conductivity of the corresponding lithium compounds and their high cost. Here LiB₁₁H₁₄ nido-hydroborate as a cost-effective building block and its high-purity synthesis is introduced. The crystal structures of anhydrous LiB₁₁H₁₄ as well as of LiB₁₁H₁₄-based mixed-anion solid electrolytes are solved and high ionic conductivities of 1.1 × 10⁻⁴ S cm⁻¹ for Li₂(B₁₁H₁₄)(CB₁₁H₁₂) and 1.1 × 10⁻³ S cm⁻¹ for Li₃(B₁₁H₁₄)(CB₉H₁₀)₂ are obtained, respectively. LiB₁₁H₁₄ exhibits an oxidative stability limit of 2.6 V versus Li⁺/Li and the proposed decomposition products are discussed based on density functional theory calculations. Strategies are discussed to improve the stability of these compounds by modifying the chemical structure of the nido-hydroborate cage. Galvanostatic cycling in symmetric cells with two lithium metal electrodes shows a small overpotential increase from 22.5 to 30 mV after 620 h (up to 0.5 mAh cm⁻²), demonstrating that the electrolyte is compatible with metallic anodes. Finally, the Li₂(B₁₁H₁₄)(CB₁₁H₁₂)  electrolyte is employed in a proof-of-concept half cell with a TiS₂ cathode with a capacity retention of 82% after 150 cycles at C/5.     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Extracellular Vesicles and Asthma—More Than Just a Co-Existence

Extracellular vesicles (EVs) are membranous structures, which are secreted by almost every cell type analyzed so far. In addition to their importance for cell-cell communication under physiological conditions, EVs are also released during pathogenesis and mechanistically contribute to this process. Here we summarize their functional relevance in asthma, one of the most common chronic non-communicable diseases. Asthma is a complex persistent inflammatory disorder of the airways characterized by reversible airflow obstruction and, from a long-term perspective, airway remodeling. Overall, mechanistic studies summarized here indicate the importance of different subtypes of EVs and their variable cargoes in the functioning of the pathways underlying asthma, and show some interesting potential for the development of future therapeutic interventions. Association studies in turn demonstrate a good diagnostic potential of EVs in asthma.     

N-Heterocyclization in Gliotoxin Biosynthesis is Catalyzed by a Distinct Cytochrome P450 Monooxygenase

Gliotoxin and related epidithiodiketopiperazines (ETP) from diverse fungi feature highly functionalized hydroindole scaffolds with an array of medicinally and ecologically relevant activities. Mutation analysis, heterologous reconstitution, and biotransformation experiments revealed that a cytochrome P450 monooxygenase (GliF) from the human-pathogenic fungus Aspergillus fumigatus plays a key role in the formation of the complex heterocycle. In vitro assays using a biosynthetic precursor from a blocked mutant showed that GliF is specific to ETPs and catalyzes an unprecedented heterocyclization reaction that cannot be emulated with current synthetic methods. In silico analyses indicate that this rare biotransformation takes place in related ETP biosynthetic pathways.     

Spurenstoffabtrennung mit keramischen Nanofiltrationsmembranen als Rotationsscheibenfilter

Reducing micropollutant pollution of water bodies is an important objective of water management and an integral part of environmental policy. Ceramic nanofiltration membranes were developed as multichannel membranes of increased membrane area and rotating disk filters. The membranes developed show retention of over 80 % for PEG 400. The membranes are currently being tested for the separation of micropollutants from wastewater contaminated with pharmaceuticals. With the help of a downstream oxidative process, the trace substances remaining in the permeate are degraded.     

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM_2.5, black carbon, CO); household air pollution (PM_2.5, CO); personal exposure (PM_2.5, CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM_2.5 (R² = 0.45) and CO (R² = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R² = 0.76) and a root mean-squared error of 85 µg/m³, and the survey-only-based regression model was able to predict PM2.5 exposures with an R² of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R² of 0.57 and RMSE of 98 µg/m³.     

Full AC analysis of value of DER to the grid and optimal DER scheduling

This paper presents part of the work ComEd and Quanta Technology have performed to quantify the locational and temporal value of DER to avoid distribution grid upgrade investments. It focuses on the formulation of a robust and efficient algorithm for DER optimal dispatch on a distribution feeder to mitigate the violation of current and voltage limits using the allocated cost of capacity and locational marginal value of real and reactive DER injection/withdrawal.     

Effect of RF magnetron sputtering parameters on the optimization of the discharge capacity of ternary lithium oxide thin films

Journal of Materials Science: Materials in Electronics - The increasing demand for lithium-ion batteries has stimulated the investigation of new compounds in order to reduce the costs and the...     

Polyetherimide-LaNi5 composite films for hydrogen storage applications

This study investigates the preparation of polyetherimide (PEI) – LaNi₅ composites films for hydrogen storage. Prior to the polymer addition, LaNi₅ was ball-milled at different conditions (250, 350, and 450 RPM) and annealed at 500 °C for 1 h under vacuum. The composites were produced with BM-LaNi₅-350 (PEI/LaNi₅-350) and annealed BM-LaNi₅-350 (PEI/LaNi₅-350-TT). Membranes were successfully produced through solvent casting assisted by an ultrasonic bath. The particles dispersion and the film morphology did not change after hydrogenation cycles. In the H₂ sorption experiments at 43 °C and 20 bar, the films stored H₂ without incubation time; both samples reached a capacity of ~0.6 wt%. The H₂ sorption kinetics of PEI/LaNi₅-350 was comparable to that of BM-LaNi₅-350, whereas PEI/LaNi₅-350-TT presented significantly slower kinetics. LaNi₅ oxidation was hindered by PEI, showing that it can be explored to improve metal hydrides air resistance. The results demonstrated that PEI films filled with LaNi₅ are promising materials for hydrogen storage.