Effect of Ion Mass on Dynamic Correlations in Ionic Liquids.

Ionic liquids (ILs) are a class of liquid salts with distinct properties such as high ionic conductivity, low volatility, and a broad electrochemical window, making them appealing for use in energy storage applications. The ion-ion correlations are some of the key factors that play a critical role in the ionic conductivity of ILs. In this work, we present the study of the impact of ion mass on ion-ion correlations in ILs, applying a combination of broadband dielectric spectroscopy measurements and molecular dynamics simulations. We examined three ILs with the same cation but different anions to consider three different cases of cation-anion masses: M+ > M-, M+ ≈ M-, and M+ < M-. We applied the momentum conservation approach to estimate the contribution of distinct ion-ion correlations from experimental data and obtained good agreement with direct calculations of distinct ion-ion correlations from molecular dynamics simulations. Our findings reveal that relative ion mass has a strong effect on the distinct ion-ion correlations, leading to swapping of the relative amplitude of distinct cation-cation and anion-anion correlations.

Search for a Grotthuss mechanism through the observation of proton transfer.

The transport of protons is critical in a variety of bio- and electro-chemical processes and technologies. The Grotthuss mechanism is considered to be the most efficient proton transport mechanism, generally implying a transfer of protons between 'chains' of host molecules via elementary reactions within the hydrogen bonds. Although Grotthuss proposed this concept more than 200 years ago, only indirect experimental evidence of the mechanism has been observed. Here we report the first experimental observation of proton transfer between the molecules in pure and 85% aqueous phosphoric acid. Employing dielectric spectroscopy, quasielastic neutron, and light scattering, and ab initio molecular dynamic simulations we determined that protons move by surprisingly short jumps of only ~0.5-0.7 Å, much smaller than the typical ion jump length in ionic liquids. Our analysis confirms the existence of correlations in these proton jumps. However, these correlations actually reduce the conductivity, in contrast to a desirable enhancement, as is usually assumed by a Grotthuss mechanism. Furthermore, our analysis suggests that the expected Grotthuss-like enhancement of conductivity cannot be realized in bulk liquids where ionic correlations always decrease conductivity.

Vegetation cover variations associated with climate change and human activities in Nanjing metropolitan area of China.

Quantify the relative effects of climate change and human activities on vegetation cover can help us understand the vegetation cover change and its drivers. Residual analysis, defined as the difference between potential normalized difference vegetation index (NDVIpot) and observed normalized difference vegetation index (NDVIact), was used to solve this problem. The estimation of NDVIpot is one of the important issues faced by ecologists. Due to the strictly protection of environment and ecosystems, the ecosystems in nature reserves are rarely disturbed by human activities. Therefore, establishing NDVIpot estimation model based on nature reserves can improve the estimation accuracy of NDVIpot. However, the estimation of NDVIpot based on nature reserves has not yet been reported. In this paper, the NDVIpot estimation model was established based on nature reserves, and residual analysis was used to quantify the impact of human activities on NDVIact. The results show that the NDVIact in Nanjing metropolitan area (NJMA) showed a significant upward trend from 2000 to 2019, and 74.20% of NJMA showed greening trends, while 24.47% showed browning trends. The temperature and precipitation were positively correlated with normalized difference vegetation index (NDVI), and the impact of the temperature on NDVI was greater than that of precipitation. The promotion of human activities on NDVI had become more and more significant. There was 51.88% of NJMA where the human activities had a positive promotion effect on NDVI, while there was 46.29% of the regional where the human activities had a negative inhibitory effect on NDVI. The population density (POP) and gross domestic product (GDP) were mainly positively correlated with NDVI, and the impact of the POP on NDVI was greater than that of GDP. The conversion of cropland to forests and the increase of the sown area of crops also caused the NDVI to increase, while the disorderly expansion of urban land caused the NDVI to decrease.

Net Primary Productivity Variations Associated with Climate Change and Human Activities in Nanjing Metropolitan Area of China.

Rapid economic development has changed land use and population density, which in turn affects the stability and carbon sequestration capacity of regional ecosystems. Net primary productivity (NPP) can reflect the carbon sequestration capacity of ecosystems and is affected by both climate change and human activities. Therefore, quantifying the relative contributions of climate change and human activities on NPP can help us understand the impact of climate change and human activities on the carbon sequestration capacity of ecosystems. At present, researchers have paid more attention to the impact of climate change and land use change on NPP. However, few studies have analyzed the response of the NPP to gross domestic product (GDP) and population density variations on a pixel scale. Therefore, this paper analyzes the impact of climate change and human activities to NPP on a pixel scale in the Nanjing metropolitan area. During the period 2000-2019, the annual mean NPP was 494.89 g C·m-2·year-1, and the NPP in the south of the Nanjing metropolitan area was higher than that in the north. The NPP was higher in the forest, followed by unused land, grassland, and cropland. In the past 20 years, the annual mean NPP showed a significant upward trend, with a growth rate of 3.78 g C·m-2·year-1. The increase in temperature and precipitation has led to an increasing trend of regional NPP, and the impact of precipitation on NPP was more significant than that of temperature. The transformation of land use from low-NPP type to high-NPP type also led to an increase in NPP. Land use change from high-NPP type to low-NPP type was the main cause of regional NPP decline. Residual analysis was used to analyze the impact of human activities on NPP. Over the last 20 years, the NPP affected by human activities (NPPhum) showed a high spatial pattern in the south and a low spatial pattern in the north, and the annual mean NPPhum also showed a fluctuating upward trend, with a growth rate of 2.00 g C·m-2·year-1. The NPPhum was influenced by both GDP and population density, and the impact of population density on NPP was greater than that of GDP.

Perspective: Morphology and ion transport in ion-containing polymers from multiscale modeling and simulations.

Ion-containing polymers are soft materials composed of polymeric chains and mobile ions. Over the past several decades they have been the focus of considerable research and development for their use as the electrolyte in energy conversion and storage devices. Recent and significant results obtained from multiscale simulations and modeling for proton exchange membranes (PEMs), anion exchange membranes (AEMs), and polymerized ionic liquids (polyILs) are reviewed. The interplay of morphology and ion transport is emphasized. We discuss the influences of polymer architecture, tethered ionic groups, rigidity of the backbone, solvents, and additives on both morphology and ion transport in terms of specific interactions. Novel design strategies are highlighted including precisely controlling molecular conformations to design highly ordered morphologies; tuning the solvation structure of hydronium or hydroxide ions in hydrated ion exchange membranes; turning negative ion-ion correlations to positive correlations to improve ionic conductivity in polyILs; and balancing the strength of noncovalent interactions. The design of single-ion conductors, well-defined supramolecular architectures with enhanced one-dimensional ion transport, and the understanding of the hierarchy of the specific interactions continue as challenges but promising goals for future research.

Variation in Vegetation and Its Driving Force in the Pearl River Delta Region of China.

Vegetation is an important part of a regional ecological environment and vegetation coverage can reflect the health of a regional ecological environment. Through an analysis of and research into changes in the vegetation NDVI (normalized difference vegetation index) and its driving factors in the Pearl River Delta region, the spatial−temporal pattern of vegetation changes and the driving factors can be measured. It is of significance to improve the ecological environment quality of the Pearl River Delta region and to promote the sustainable development of the regional economy. Based on SPOT/VEGETATION NDVI satellite remote sensing data, meteorological data, population density data, and gross domestic product (GDP) data during the period 2000−2019, this paper analyzed the temporal and spatial trends of the vegetation NDVI as well as the climate factors and human activities in the Pearl River Delta on a pixel scale. The correlations between the vegetation NDVI and precipitation, temperature, population density, GDP, and other factors were also estimated. The results showed that during the period 2000−2019, the annual mean NDVI significantly increased, with a growth rate of 0.0044 (R2 = 0.71, p < 0.0001). The NDVI in the center of the Pearl River Delta was lower than that in other regions. As far as the driving factors of the NDVI were concerned, among the climatic factors, the response of the NDVI to temperature was higher than that for precipitation in the Pearl River Delta. Human activities had changed from a negative hindering effect on the NDVI to a positive promoting effect. The correlation between the NDVI and the GDP was higher than that for population density. Policy factors such as the "Grain for Green Project" as well as an increase in the sown area of crops and land use changes were also important driving factors of the NDVI. It is suggested that the NDVI can be increased by the implementation of artificial afforestation policies, building a "Green City", and moderately increasing the sown area of crops.

Coarse-Grained Modeling of Ion-Containing Polymers.

Ion-containing polymers have continued to be an important research focus for several decades due to their use as an electrolyte in energy storage and conversion devices. Elucidation of connections between the mesoscopic structure and multiscale dynamics of the ions and solvent remains incompletely understood. Coarse-grained modeling provides an efficient approach for exploring the structural and dynamical properties of these soft materials. The unique physicochemical properties of such polymers are of broad interest. In this review, we summarize the current development and understanding of the structure-property relationship of ion-containing polymers and provide insights into the design of such materials determined from coarse-grained modeling and simulations accompanying significant advances in experimental strategies. We specifically concentrate on three types of ion-containing polymers: proton exchange membranes (PEMs), anion exchange membranes (AEMs), and polymerized ionic liquids (polyILs). We posit that insight into the similarities and differences in these materials will lead to guidance in the rational design of high-performance novel materials with improved properties for various power source technologies.

Predicting miRNA-Disease Associations via Combining Probability Matrix Feature Decomposition With Neighbor Learning.

Predicting the associations of miRNAs and diseases may uncover the causation of various diseases. Many methods are emerging to tackle the sparse and unbalanced disease related miRNA prediction. Here, we propose a Probabilistic matrix decomposition combined with neighbor learning to identify MiRNA-Disease Associations utilizing heterogeneous data(PMDA). First, we build similarity networks for diseases and miRNAs, respectively, by integrating semantic information and functional interactions. Second, we construct a neighbor learning model in which the neighbor information of individual miRNA or disease is utilized to enhance the association relationship to tackle the spare problem. Third, we predict the potential association between miRNAs and diseases via probability matrix decomposition. The experimental results show that PMDA is superior to other five methods in sparse and unbalanced data. The case study shows that the new miRNA-disease interactions predicted by the PMDA are effective and the performance of the PMDA is superior to other methods.

Proton Transfer in Phosphoric Acid-Based Protic Ionic Liquids: Effects of the Base.

Electronic structure calculations were performed to understand highly decoupled conductivities recently reported in protic ionic liquids (PILs). To develop a molecular-level understanding of the mechanisms of proton conductivity in PILs, minimum-energy structures of trimethylamine, imidazole, lidocaine, and creatinine (CRT) with the addition of one to three phosphoric acid (PA) molecules were determined at the B3LYP/6-311G** level of theory with the inclusion of an implicit solvation model (SMD with ε = 61). The proton affinity of the bases and zero-point energy corrected binding energies were computed at a similar level of theory. Proton dissociation from PA occurs in all systems, resulting in the formation of ion pairs due to the relatively strong basicity of the bases (proton acceptors) and the effect of the high dielectric constant solvent in stabilizing the charge separation. The second and third PA molecules preferentially form "ring-like" hydrogen bonds with one another instead of forming hydrogen bonds at the donor and acceptor sites of the bases. Potential energy scans reveal that the bases with stronger proton affinity exert greater influence on the energetics of proton transfer between the individual PA molecules. However, the effects are minimal when shifted into a single-well from a double-well potential. Barrierless proton transfer was observed to occur in the CRT system with several PA molecules present, implying that the CRT may be a promising PA-based PIL.

Construction of 1,3-Oxazolidines through a Three-Component [3+2] Cycloaddition of Tetrahydroisoquinolines, Aldehydes, and Ethyl Ketomalonate.

A chemoselective and diastereoselective synthesis of fused oxazolidines was achieved by a three-component cascade reaction of tetrahydroisoquinolines (THIQs), α,ß-unsaturated aldehydes, and diethyl 2-oxomalonate. Probably due to the reactivity difference between the aldehyde and the ketone, the reaction proceeded through the condensation of THIQs with α,ß-unsaturated aldehydes and 1,3-dipolar cycloaddition of the generated azomethine ylide intermediate with 2-oxomalonate. The key features are easily available starting materials, mild reaction conditions, broad substrate scope, and high chemo- and diastereoselectivity.

Poly[[[µ(2)-1,1'-(butane-1,4-di-yl)bis-(1H-imidazole)-κN:N](µ(2)-2,6-di-methyl-pyridine-3,5-dicarboxyl-ato-κO:O)zinc] dihydrate].

In the title coordination polymer, {[Zn(C(9)H(7)NO(4))(C(10)H(14)N(4))]·2H(2)O}(n), the Zn(II) ion displays a distorted tetra-hedral geometry with two imidazole N atoms from two 1,1'-(butane-1,4-di-yl)bis-(imidazole) (bbi) ligands and two carboxyl-ate O atoms from two 2,6-dimethyl-pyridine-3,5-dicarboxyl-ate (dpdc) ligands. The bbi and dpdc ligands bridge the Zn(II) ions, forming layers parallel to (011). O-H⋯O and O-H⋯N hydrogen bonds and π-π inter-actions between the imidazole rings [centroid-centroid distance = 3.807 (5) Å] connect the layers. Two of the three uncoordinated water mol-ecules are disordered, each over two 0.25-occupancy positions.