Scalable and selective deuteration of (hetero)arenes

Isotope labelling, particularly deuteration, is an important tool for the development of new drugs, specifically for identification and quantification of metabolites. For this purpose, many efficient methodologies have been developed that allow for the small-scale synthesis of selectively deuterated compounds. Due to the development of deuterated compounds as active drug ingredients, there is a growing interest in scalable methods for deuteration. The development of methodologies for large-scale deuterium labelling in industrial settings requires technologies that are reliable, robust and scalable. Here we show that a nanostructured iron catalyst, prepared by combining cellulose with abundant iron salts, permits the selective deuteration of (hetero)arenes including anilines, phenols, indoles and other heterocycles, using inexpensive D2O under hydrogen pressure. This methodology represents an easily scalable deuteration (demonstrated by the synthesis of deuterium-containing products on the kilogram scale) and the air- and water-stable catalyst enables efficient labelling in a straightforward manner with high quality control.

Tunable Hydrocarbon Adsorption Based on a Zeolitic Imidazolate Framework in the Sodalite Topology

Manipulation of materials exhibiting stepped-shaped isotherms using simple and scalable methods is key to realizing their utility in advanced separations schemes. Through the judicious synthetic tuning of the zeolitic imidazolate...

Two-Step Synthesis of Bismuth-Based Hybrid Halide Perovskite Thin-Films

Lead halide perovskites have been revolutionary in the last decade in many optoelectronic sectors. Their bismuth-based counterparts have been considered a good alternative thanks to their composition of earth-abundant elements, good chemical stability, and low toxicity. Moreover, their electronic structure is in a quasi-zero-dimensional (0D) configuration, and they have recently been explored for use beyond optoelectronics. A significant limitation in applying thin-film technology is represented by the difficulty of synthesizing compact layers with easily scalable methods. Here, the engineering of a two-step synthesis in an air of methylammonium bismuth iodide compact thin films is reported. The critical steps of the process have been highlighted so that the procedure can be adapted to different substrates and application areas.

Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using “free” adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions.

A scalable coaxial bioprinting technology for mesenchymal stem cell microfiber fabrication and high extracellular vesicle yield

Mesenchymal stem cell (MSC)-derived extracellular vesicles (EVs) are promising candidates for regenerative medicine; however, the lack of scalable methods for high quantity EV production limits their application. In addition, signature EV-derived proteins shared in 3D environments and 2D surfaces, remain mostly unknown. Herein, we present a platform combining MSC microfiber culture with ultracentrifugation purification for high EV yield. Within this platform, a high quantity MSC solution (~3x10^8 total cells) is encapsulated in a meter-long hollow hydrogel-microfiber via coaxial bioprinting technology. In this 3D core-shell microfiber environment, MSCs express higher levels of stemness markers (Oct4, Nanog, Sox2) than in 2D culture, and maintain their differentiation capacity. Moreover, this platform enriches particles by ~1009-fold compared to conventional 2D culture, while preserving their pro-angiogenic properties. Liquid chromatography-mass spectrometry characterization results demonstrate that EVs derived from our platform and conventional 2D culturing have unique protein profiles with 3D-EVs having a greater variety of proteins (1023 vs 605), however, they also share certain proteins (536) and signature MSC-EV proteins (10). This platform, therefore, provides a new tool for EV production using microfibers in one culture dish, thereby reducing space, labor, time, and cost.

ELECTROLYTIC EFFECT ON GROWTH OF GRAPHENE QUANTUM DOTS VIA ELECTROCHEMICAL PROCESS

Quantum dots (QDs) are materials grown in confined dimension also known as 0D materials. QDs can be synthesized in many shapes and forms through various methods making the materials extremely versatile and can be fine-tuned for appropriate applications. Among the potentially scalable methods, Electrochemical process is considered as one of the top-down approaches with the highest potential for scalability and easy-to-process methodology while electrolyte and pH level can play various important roles on the final product. In this work, we grew and studied the effect of electrolytic solution in the growth of graphene quantum dots (GQDs) in colloidal forms using cheap graphite as precursor in KCl and NaOH as electrolytes in various concentrations. It can be inferred from our results that when KCl and NaOH were used in combination with citric acid, the optoelectrical properties and hydrodynamic properties of the resulting growth can be fine-tuned to match the required applications. [Formula: see text] electronics excitation was identified with small tunability of 487–500[Formula: see text]nm wavelength while the hydrodynamic size varied from 80–140[Formula: see text]nm with resulting pH range from 3.0–9.5 adjustable to appropriate applications, while the TEM results showed physical particle size of 1.7–3.7[Formula: see text]nm.

Cross-tissue analysis of allelic X-chromosome inactivation ratios resolves features of human development

X-chromosome inactivation (XCI) is a random, permanent, and developmentally early epigenetic event that occurs during mammalian embryogenesis. We harness these features of XCI to investigate characteristics of early lineage specification events during human development. We initially assess the consistency of X-inactivation and establish a robust set of XCI-escape genes. By analyzing variance in XCI ratios across tissues and individuals, we find that XCI is completed prior to tissue specification and at a time when 6-16 cells are fated for all tissue lineages. Additionally, we exploit tissue specific variability to characterize the number of cells present at the time of each tissue's lineage commitment, ranging from approximately 20 cells in liver and whole blood tissues to 80 cells in brain tissues. By investigating variance of XCI ratios using adult tissue, we resolve key features of human development otherwise difficult to ascertain experimentally and develop scalable methods easily applicable to future data.

Using plasmons to harness infrared solar light

Using plasmons to harness infrared solar light The PLASMIONICO project aims to advance the sustainable production of electricity by harnessing infrared (IR) solar light, which is typically wasted in conventional solar cells. The key concept is to allow IR light to be absorbed at nanostructured metal cathodes to launch surface plasmons (rapid oscillations of electronic charge analogous to sound waves in liquids) to generate a photocurrent. Our strategy uses inverted silicon pyramid arrays covered with thin gold films, manufactured employing low-cost and scalable methods. After optimising the infrared absorption performance, we are set to improve photocurrent delivery with promising results.