Synthesis and applications of zinc oxide for removal of various pollutants: A review

The versatility of zinc oxide applications in the removal of various pollutants has attracted a wide interest of researchers in the past decade. Numerous studies reported on zinc oxide synthesis pathways and resulting nanoparticle morphologies, applications, formation mechanisms and synthesis parameters. In this review the reported zinc oxide synthesis methods are classified into chemical, physical and biological routes; they are evaluated in terms of the required chemicals, synthesis conditions and the resulting morphologies and properties of zinc oxide. The chemical route of zinc oxide synthesis covers precipitation, micro-emulsion, solgel, solvothermal and hydrothermal paths. The physical route includes laser ablation and high energy ball milling, while the biological route covers plant extracts and microbe mediated synthesis. The mechanisms of zinc oxide formation of the mentioned routes are based on one or more of the following processes: particle nucleation, diffusional growth, Ostwald ripening, particle aggregation and sintering. The most influencing synthesis parameters overall are temperature, drying duration and additives’ effect. Higher temperatures (>200°C) commonly produce larger particles of zinc oxide (> 80 nm); the prolong duration (> 60 min) often results in the agglomeration and sintering of zinc oxide particles. However, additives may mitigate agglomeration extent. Overall, the chemical route is more preferable due to its flexibility that is also linked to the greater variability of zinc oxide particles. The physical method produces more consistent zinc oxide particles but requires higher energy inputs. The biological method is very promising and associated with low chemicals consumptions and good quality of zinc oxide.

Urban Land Expansion Simulation Considering the Diffusional and Aggregated Growth Simultaneously: A Case Study of Luoyang City

Restricted by urban development stages, natural conditions, urban form and structure, diffusional growth occupies a large proportion of area in many cities. Traditional cellular automata (CA) has been widely applied in urban growth studies because it can simulate complex system evolution with simple rules. However, due to the limitation of neighborhood conditions, it is insufficient for simulating urban diffusional growth process. A maximum entropy mode was used to estimate three layers of probability spaces: the probability layer of cell transformation from non-urban status to urban status (PLCT), the probability layer for aggregated growth (PLAP), and the probability layer for diffusional growth (PLOP). At the same time, a maxent category selected CA model (MaxEnt-CSCA) was designed to simulate aggregated and diffusional urban expansion processes simultaneously. Luoyang City, with a large proportion of diffusional urban expansion (65.29% in 2009–2018), was used to test the effectiveness of MaxEnt-CSCA. The results showed that: (1) MaxEnt-CSCA accurately simulated aggregated growth of 47.40% and diffusional growth of 37.13% in Luoyang from 2009 to 2018, and the overall Kappa coefficient was 0.78; (2) The prediction results for 2035 showed that future urban expansion will mainly take place in Luolong District and the counties around the main urban area, and the distribution pattern of Luolong District will change from the relative diffusion state to the aggregation stage. This paper also discusses the applicable areas of MaxEnt-CSCA and illustrates the importance of selecting an appropriate urban expansion model in a region with a large amount of diffusional growth.

MA Cation-Induced Diffusional Growth of Low-Bandgap FA-Cs Perovskites Driven by Natural Gradient Annealing

Low-bandgap formamidinium-cesium (FA-Cs) perovskites of FA1-xCsxPbI3 (x<0.1) are promising candidates for efficient and robust perovskite solar cells, but their black-phase crystallization is very sensitive to annealing temperature. Unfortunately, the low heat conductivity of the glass substrate builds up a temperature gradient within from bottom to top and makes the initial annealing temperature of the perovskite film lower than the black-phase crystallization point (~150°C). Herein, we take advantage of such temperature gradient for the diffusional growth of high-quality FA-Cs perovskites by introducing a thermally unstable MA+ cation, which would firstly form α-phase FA-MA-Cs mixed perovskites with low formation energy at the hot bottom of the perovskite films in the early annealing stage. The natural gradient annealing temperature and the thermally unstable MA+ cation then lead to the bottom-to-top diffusional growth of highly orientated α-phase FA-Cs perovskite, which exhibits 10-fold of enhanced crystallinity and reduced trap density (~3.85×1015 cm−3). Eventually, such FA-Cs perovskite films were fabricated into stable solar cell devices with champion efficiency up to 23.11%, among the highest efficiency of MA-free perovskite solar cells.

Cloud droplet diffusional growth in homogeneous isotropic turbulence: bin microphysics versus Lagrangian super-droplet simulations

The increase in the spectral width of an initially monodisperse population of cloud droplets in homogeneous isotropic turbulence is investigated by applying a finite-difference fluid flow model combined with either Eulerian bin microphysics or a Lagrangian particle-based scheme. The turbulence is forced applying a variant of the so-called linear forcing method that maintains the mean turbulent kinetic energy (TKE) and the TKE partitioning between velocity components. The latter is important for maintaining the quasi-steady forcing of the supersaturation fluctuations that drive the increase in the spectral width. We apply a large computational domain (643 m3), one of the domains considered in Thomas et al. (2020). The simulations apply 1 m grid length and are in the spirit of the implicit large eddy simulation (ILES), that is, with small-scale dissipation provided by the model numerics. This is in contrast to the scaled-up direct numerical simulation (DNS) applied in Thomas et al. (2020). Two TKE intensities and three different droplet concentrations are considered. Analytic solutions derived in Sardina et al. (2015), valid for the case when the turbulence integral timescale is much larger than the droplet phase relaxation timescale, are used to guide the comparison between the two microphysics simulation techniques. The Lagrangian approach reproduces the scalings relatively well. Representing the spectral width increase in time is more challenging for the bin microphysics because appropriately high resolution in the bin space is needed. The bin width of 0.5 µm is only sufficient for the lowest droplet concentration (26 cm−3). For the highest droplet concentration (650 cm−3), an order of magnitude smaller bin size is barely sufficient. The scalings are not expected to be valid for the lowest droplet concentration and the high-TKE case, and the two microphysics schemes represent similar departures. Finally, because the fluid flow is the same for all simulations featuring either low or high TKE, one can compare point-by-point simulation results. Such a comparison shows very close temperature and water vapor point-by-point values across the computational domain and larger differences between simulated mean droplet radii and spectral width. The latter are explained by fundamental differences in the two simulation methodologies, numerical diffusion in the Eulerian bin approach and a relatively small number of Lagrangian particles that are used in the particle-based microphysics.

On numerical broadening of particle size spectra: a condensational growth study using PyMPDATA 1.0

The work discusses the diffusional growth in particulate systems such as atmospheric clouds. It focuses on the Eulerian modeling approach in which the evolution of the probability density function describing the particle size spectrum is carried out using a fixed-bin discretization. The numerical diffusion problem inherent to the employment of the fixed-bin discretization is scrutinized. The work focuses on the applications of MPDATA family of numerical schemes. Several MPDATA variants are explored including: infinite-gauge, non-oscillatory, third-order-terms and recursive antidiffusive correction (double pass donor cell, DPDC) options. Methodology for handling coordinate transformations associated with both particle size distribution variable choice and numerical grid layout are expounded. The study uses PyMPDATA – a new open-source Python implementation of MPDATA. Analysis of the performance of the scheme for different discretization parameters and different settings of the algorithm is performed using an analytically solvable test case pertinent to condensational growth of cloud droplets. The analysis covers spatial and temporal convergence, computational cost, conservativeness and quantification of the numerical broadening of the particle size spectrum. Presented results demonstrate that, for the problem considered, even a tenfold decrease of the spurious numerical spectral broadening can be obtained by a proper choice of the MPDATA variant (maintaining the same spatial and temporal resolution).

Comments on “Do Ultrafine Cloud Condensation Nuclei Invigorate Deep Convection?”

Here we elaborate on the deficiencies associated with the theoretical arguments and model simulations in a paper by Grabowski and Morrison (2020, hereafter GM20) that argued convective invigoration by aerosols does not exist. We show that the invigoration can be supported by both accurate theoretical analysis and explicit physics modeling with prognostic supersaturation and aerosols. Negligible invigoration by aerosols via drop freezing in GM20 was explained by a complete compensation between the heating effect from the freezing of extra liquid water and the extra loading effect during droplet ascending. But the reality is that droplet ascending then freezing occur at different locations and time scales, producing complex nonlinear responses that depend on the duration and location of the forcing. Also, this argument neglects the effect of off-loading of precipitating ice particles, increases in condensation during ascending, and riming and deposition accompanying droplet freezing. Regarding the warm-phase invigoration, the quasi-steady assumption for supersaturation as adopted in GM20 makes condensation independent of droplet number and size, therefore an incorrect interpretation of warm-phase invigoration. We illustrate that the quasi-steady assumption is invalid for updrafts of deep convective clouds in clean conditions because of the high acceleration of vertical velocity and the fast depletion of droplets by raindrop formation and accretion. Any assumption imposed on supersaturation, such as quasi-steady approximation and saturation adjustment, leads to errors in the evaluation of aerosol effects on diffusional growth and related buoyancy. Furthermore, we demonstrate that the piggybacking approach they used cannot prove or disprove the convective invigoration.