Why copper catalyzes electrochemical reduction of nitrate to ammonia.

Electrochemical reduction of nitrate (NO3RR) has drawn significant attention in the scientific community as an attractive route for ammonia synthesis as well as alleviating environmental concerns for nitrate pollution. To improve the efficiency of this process, the development of catalyst materials that exhibit high activity and selectivity is of paramount importance. Copper and copper-based catalysts have been widely investigated as potential catalyst materials for this reaction both computationally and experimentally. However, less attention has been paid to understanding the reasons behind such high activity and selectivity. Herein, we use Density Functional Theory (DFT) to identify reactivity descriptors guiding the identification of active catalysts for the NO3RR, establish trends in activity, and explain why copper is the most active and selective transition metal for the NO3RR to ammonia among ten different transition metals, namely Au, Ag, Cu, Pt, Pd, Ni, Ir, Rh, Ru, and Co. Furthermore, we assess NO3RR selectivity by taking into account the competition between the NO3RR and the hydrogen evolution reaction. Finally, we propose various approaches for developing highly active catalyst materials for the NO3RR.

Exploring the Effects of Ionic Defects on the Stability of CsPbI3 with a Deep Learning Potential.

Inorganic metal halide perovskites, such as CsPbI3 , have recently drawn extensive attention due to their excellent optical properties and high photoelectric efficiencies. However, the structural instability originating from inherent ionic defects leads to a sharp drop in the photoelectric efficiency, which significantly limits their applications in solar cells. The instability induced by ionic defects remains unresolved due to its complicated reaction process. Herein, to explore the effects of ionic defects on stability, we develop a deep learning potential for a CsPbI3 ternary system based upon density functional theory (DFT) calculated data for large-scale molecular dynamics (MD) simulations. By exploring 2.4 million configurations, of which 7,730 structures are used for the training set, the deep learning potential shows an accuracy approaching DFT-level. Furthermore, MD simulations with a 5,000-atom system and a one nanosecond timeframe are performed to explore the effects of bulk and surface defects on the stability of CsPbI3 . This deep learning potential based MD simulation provides solid evidence together with the derived radial distribution functions, simulated diffraction of X-rays, instability temperature, molecular trajectory, and coordination number for revealing the instability mechanism of CsPbI3 . Among bulk defects, Cs defects have the most significant influence on the stability of CsPbI3 with a defect tolerance concentration of 0.32 %, followed by Pb and I defects. With regards to surface defects, Cs defects have the largest impact on the stability of CsPbI3 when the defect concentration is less than 15 %, whereas Pb defects act play a dominant role for defect concentrations exceeding 20 %. Most importantly, this machine-learning-based MD simulation strategy provides a new avenue to explore the ionic defect effects on the stability of perovskite-like materials, laying a theoretical foundation for the design of stable perovskite materials.

High throughput screening of promising lead-free inorganic halide double perovskites via first-principles calculations.

Perovskite solar cells (PSCs) have been intensively investigated and made great progress due to their high photoelectric conversion efficiency and low production cost. However, poor stability and the toxicity of Pb limit their commercial applications. It is particularly important to search for new non-toxic, high-stability perovskite materials. In this study, 760 Cs2B2+B'2+X6 (X = F, Cl, Br, I) inorganic halide double perovskites are screened based on high-throughput first-principles calculations to obtain an ideal perovskite material. The band gaps of this type of double perovskite are mainly determined by the elements X and B2+, decreasing monotonously with the increase in the atomic number of X (from F to I). We obtain 14 optimal and unreported materials with suitable band gaps as potential alternative materials for Pb-based photovoltaic absorbers in PSCs. This theoretical investigation can provide theoretical guidance for developing novel lead-free PSC materials.

The effect of coordination environment on the kinetic and thermodynamic stability of single-atom iron catalysts.

The stability of a single-atom catalyst is directly related to its preparation and applications, especially for high-loading single-atom catalysts. Here, the effect of a coordination environment induced by nitrogen (N) atoms coordinated with iron on the kinetic and thermodynamic stabilities of single-atom iron catalysts supported with carbon-based substrates (FeSA/CS) was investigated by density functional theory (DFT) calculations. Five FeSA/CS with different numbers of N atoms were modelled. The kinetic stability was evaluated by analyzing the migration paths of iron atoms and energy barriers. The thermodynamic stability was studied by calculating the adsorption and formation energies. Our results indicated that the coordination environment induced by N can promote the kinetic and thermodynamic stability of FeSA/CS. N atoms on the substrate promote the kinetic stability by raising the energy barrier for iron migration and not only increase the thermodynamic stability, but also contribute to catalyst synthesis. Doping N on the substrate enhances charge transfer between the iron atoms and substrates simultaneously improving the kinetic and thermodynamic stabilities. This theoretical research provides guidance for synthesizing stable and high loading single-atom catalysts by tuning the coordination environment of single-atom elements.

Non-Newtonian rheology in suspension cell cultures significantly impacts bioreactor shear stress quantification.

The fields of regenerative medicine and tissue engineering require large-scale manufacturing of stem cells for both therapy and recombinant protein production, which is often achieved by culturing cells in stirred suspension bioreactors. The rheology of cell suspensions cultured in stirred suspension bioreactors is critical to cell growth and protein production, as elevated exposure to shear stress has been linked to changes in growth kinetics and genetic expression for many common cell types. Currently, little is understood on the rheology of cell suspensions cultured in stirred suspension bioreactors. In this study, we present the impact of three common cell culture parameters, serum content, cell presence, and culture age, on the rheology of a model cell line cultured in stirred suspension bioreactors. The results reveal that cultures containing cells, serum, or combinations thereof are highly shear thinning, whereas conditioned and unconditioned culture medium without serum are both Newtonian. Non-Newtonian viscosity was modeled using a Sisko model, which provided insight on structural mechanisms driving the rheological behavior of these cell suspensions. A comparison of shear stress estimated by using Newtonian and Sisko relationships demonstrated that assuming Newtonian viscosity underpredicts both mean and maximum shear stress in stirred suspension bioreactors. Non-Newtonian viscosity models reported maximum shear stresses exceeding those required to induce changes in genetic expression in common cell types, whereas Newtonian models did not. These findings indicate that traditional shear stress quantification of cell or serum suspensions is inadequate and that shear stress quantification methods based on non-Newtonian viscosity must be developed to accurately quantify shear stress.

Predictive Modeling of Energy and Emissions from Shale Gas Development.

Contributions of individual preproduction activities to overall energy use and greenhouse gas (GHG) emissions during shale gas development are not well understood nor quantified. This paper uses predictive modeling combining the physics of reservoir development operations with depositional attributes of shale gas basins to account for energy requirements and GHG emissions during shale gas well development. We focus on shale gas development from the Montney basin in Canada and account for the energy use during drilling and fluid pumping for reservoir stimulation, in addition to preproduction emissions arising from energy use and potential gas releases during operations. Detailed modeling of activities and events that take place during each stage of development is described. Relative to the hydraulic fracturing activity, we observe significantly higher energy intensity for the well drilling and mud circulation activities. Well completion flowback gas is found to be the predominant potential source of GHG emission. When these results are expressed on an annual basis, consistent with the convention of most climate policy goals and directives, environmental impacts of our growing natural gas economy are better appreciated. Estimated likely GHG emission from new development wells in 2017 in the Montney Formation alone is 2.68 Mt CO2e. However, on a preproduction requirements basis and dependent on mean estimated ultimate recovery (EUR), energy return on invested energy for shale gas from the Montney Formation in Canada is estimated to be about 3400. The approach described here can be reliably extended to areas, globally, where natural gas development is becoming prominent.

Unconventional Heavy Oil Growth and Global Greenhouse Gas Emissions.

Enormous global reserves of unconventional heavy oil make it a significant resource for economic growth and energy security; however, its extraction faces many challenges especially on greenhouse gas (GHG) emissions, water consumption, and recently, social acceptability. Here, we question whether it makes sense to extract and use unconventional heavy oil in spite of these externalities. We place unconventional oils (oil sands and oil shale) alongside shale gas, coal, lignite, wood and conventional oil and gas, and compare their energy intensities and life cycle GHG emissions. Our results reveal that oil shale is the most energy intensive fuel among upgraded primary fossil fuel options followed by in situ-produced bitumen from oil sands. Lignite is the most GHG intensive primary fuel followed by oil shale. Based on future world energy demand projections, we estimate that if growth of unconventional heavy oil production continues unabated, the incremental GHG emissions that results from replacing conventional oil with heavy oil would amount to 4-21 Gt-CO2eq GtCO2eq over four decades (2010 by 2050). However, prevailing socio-economic, regional and global energy politics, environmental and technological challenges may limit growth of heavy oil production and thus its GHG emissions contributions to global fossil fuel emissions may be smaller.

Investigation of enhanced geothermal system in the Basal Cambrian Sandstone Unit, Alberta, Canada.

Given the climate challenge, society is seeking low greenhouse gas emission energy sources. In jurisdictions such as Alberta, Canada where power is largely generated through the combustion of natural gas, geothermal offers a compelling option but it remains unclear as to its economic and technical viability. Here, we examine the potential for an enhanced geothermal system in the Basal Cambrian Sandstone Unit in Alberta, Canada. Prior to geothermal operation, hydraulic fracturing is conducted to enhance the permeability of the thermal reservoir. This lowers the pressure drop required for circulating fluids through the system. The results show that the open-loop enhanced geothermal system realizes an energy produced to energy invested ratio from 4 to 9 depending on different operating rate. The results also suggest that applying hydraulic fracturing can accelerate energy harvesting and energy efficiency over the early stages of the process but the greater the injection rate, the smaller is this benefit of hydraulic fracturing stimulation.

Air invasion into three-dimensional foam induces viscous fingering instabilities.

We conducted an experimental investigation to examine the immiscible radial displacement flows of air invading three-dimensional foam in a Hele-Shaw cell. Our study successfully identified three distinct flow regimes. In the initial regime, characterized by relatively low fingertip velocities, the foam underwent a slow displacement through plug flow. During this process, the three-phase contact lines slipped at the cell walls. Notably, we discovered that the air injection pressure exhibited a proportional relationship with the power of the fingertip velocity. This relationship demonstrated excellent agreement with a power law, where the exponent was determined to be 2/3. Transitioning to the second regime, we observed relatively high velocities, resulting in the displacement of the foam as a plug within single layers of foam bubbles. The movement of these bubbles near the cell walls was notably slower. Similar to the first regime, the behavior in this regime also adhered to a power law. In the third regime, which manifested at higher air injection pressures, the development of air fingers occurred through narrow channels. These channels had the potential to isolate the air fingers as they underwent a process of "healing." Furthermore, our results unveiled a significant finding that the width of the air fingers exhibited a continuous scaling with the air injection pressure, irrespective of the flow regimes being observed.

Mass transfer limitations in embryoid bodies during human embryonic stem cell differentiation.

Due to their ability to differentiate into cell types from all the three germ layers and their potential unlimited capacity for expansion, embryonic stem cells have tremendous potential to treat diseases and injuries. Spontaneous differentiation of human embryonic stem cells (hESCs) is influenced by the size of the differentiating embryoid bodies (EBs). To further understand the dynamics between nutrient mass transfer, EB size, and stem cell differentiation, a transient mass diffusion model of a single hESC EB was constructed. The results revealed that the oxygen concentration at the centers of large EBs (400-µm radius) was 50% lower when compared to that in smaller EBs (200-µm radius). In addition, the concentration profile of cytokines within an EB depended strongly on their depletion rate, with higher depletion rates resulting in cytokine concentrations that varied significantly throughout the EB. A comparison of the results of our model with published experimental data reveals a close correlation between the fraction of cells that differentiate to a given lineage and the fraction of cells exposed to different oxygen or cytokine concentrations. This, along with other data from the literature, suggests that diffusive mass transfer influences the differentiation of hESCs within EBs by controlling the spatial distribution of soluble factors. This has important implications for research involving the differentiation of embryonic stem cells in EBs, as well as for bioprocess design and the development of robust differentiation protocols where mass transfer could be altered to control the cell differentiation trajectory.