Zinc loading in urea-formaldehyde nanocomposites increases nitrogen and zinc micronutrient fertilization efficiencies in poor sand substrate

Agricultural output needs significant increases to feed the growing population. Fertilizers are essential for plant production systems, with nitrogen (N) being the most limiting nutrient for plant growth. It is commonly supplied to crops as urea. Still, due to volatilization, up to 50 % of the total N application is lost. Slow or controlled release fertilizers are being developed to reduce these losses. The co-application of zinc (Zn) as a micronutrient can increase N absorption. Thus, we hypothesize that the controlled delivery of both nutrients (N and Zn) in an integrated system can improve uptake efficiency. Here we demonstrate an optimized fertilizer nanocomposite based on urea:urea-formaldehyde matrix loaded with ZnSO4 or ZnO. This nanocomposite effectively stimulates maize development, with consequent adequate N uptake, in an extreme condition – a very nutrient-poor sand substrate. Our results indicate that the Zn co-application is beneficial for plant development. However, there were advantages for ZnO due to its high Zn content. We discuss that the dispersion favors the Zn delivery as the nanoparticulated oxide in the matrix. Concerning maize development, we found that root morphology is altered in the presence of the fertilizer nanocomposite. Increased root length and surface area may improve soil nutrient uptake, potentially accompanied by increased root exudation of essential compounds for N release from the composite structure

Dissemination of PV-Battery systems in the German residential sector up to 2050: Technological diffusion from multidisciplinary perspectives

A decarbonization of the European energy system implies great changes in the residential sector. Recently, the sector does not show the necessary dynamics. Apparently decarbonizing the sector requires a new momentum. Using PV-Battery systems as key technology for the residential sector for becoming more environmentally sustainable as an example, we take a closer look at the complexity of technology diffusions in the residential sector. By employing a socio-techno-economic (SoTeEc) framework approach we consider that diffusion processes are impacted by a broad range of measurable and non-measurable factors. Our framework combines a techno-economic with a socio-economic analysis. With the techno-economic analysis we assess the drivers of technology diffusion of PV-Battery systems in private households whereas the socio-economic analysis focuses on the role of a) the household sector in the overall social-economic system and b) actor specific factors like attitudes. The link of the two approaches enables us to identify which techno-economic scenarios are feasible from socio-economic point of view and vice versa. Hence, we can identify scenarios which fulfill simultaneously requirements from techno-economic and socio-economic point of views. Such scenarios can serve as a starting point for policy recommendations

Direct investigation of the interparticle-based state-of-charge distribution of polycrystalline NMC532 in lithium ion batteries by classification-single-particle-ICP-OES

The presented case study provides mesoscopic insights into the state-of-charge (SOC) distribution of battery electrodes containing layered transition metal oxides with Li(Ni0.5Mn0.3Co0.2)O2 (NMC532). The application of classification-single-particle inductively coupled plasma optical emission spectroscopy (CL-SP-ICP-OES) enables the rapid screening of the lithium content of individual cathode active material (CAM) particles achieving a statistically viable elucidation of the mesoscale SOC distribution between different particles of the electrode. The results reveal the evolution of a persistent mesoscale SOC heterogeneity of the electrode upon delithiation at slow rates and extensive relaxation times as confirmed by time-of-flight secondary ion mass spectrometry (ToF-SIMS). The implications of local chemical and structural ramifications of the investigated NMC532 for heterogeneous active material utilization are thoroughly discussed. Furthermore, it is found that the evolved SOC heterogeneity of the electrode is strongly dependent on the current density. The correlation to the decreased capacity utilization is further investigated with a straightforward quantification approach revealing a considerable contribution to capacity fading by persistently inactive lithium in the CAM. The results highlight the importance of the analysis of persistent mesoscale SOC heterogeneity as a potential capacity fade mechanism in layered lithium transition metal oxide-based battery electrodes

The in situ generated emerging phase inside dual phase oxygen transport membranes

The in situ generated emerging phase inside the dual-phase oxygen transport membranes (DP-OTMs) plays a crucial role in boosting the overall performance of DP-OTMs. However, its detailed structure and properties are still not fully understood. Utilizing advanced transmission electron microscopy (TEM) techniques, the emerging phase GdxCe1-xFeyCo1-yO3-δ (GCFCO) inside the CexGd1-xO2-δ-FeCo2O4 (CGO-FC2O) OTMs was successfully characterized at the atomic scale. The newly formed GCFCO is primarily surrounded by the CGO, and contributes to a significant reduction of non-solute segregation at the CGO grain boundaries. Electronic characteristics of the GCFCO shows a sensitive dependence on its chemical composition, including the valence state of Ce and Fe as well as the oxygen vacancies. Additional CGO-GCFCO interfaces were introduced, where almost intact crystal structures were observed with slight Gd and Co segregation ∼1 nm at the edges. Approaching the interface, on the CGO side, only a minimum drop of the Ce valence was determined. On the GCFCO side, mixed Ce3+ and Ce4+ are partially occupying the Gd sites, while Fe and Co valence stay constant until the edge. Our study provides novel insight into the phase information within CGO-FC2O composites, which paves the path towards superior performance of various DP-OTM

Resolving ambiguities in core size determination of magnetic nanoparticles from magnetic frequency mixing data

Frequency mixing magnetic detection (FMMD) has been widely utilized as a measurement technique in magnetic immunoassays. It can also be used for the characterization and distinction (also known as “colourization”) of different types of magnetic nanoparticles (MNPs) based on their core sizes. In a previous work, it was shown that the large particles contribute most of the FMMD signal. This leads to ambiguities in core size determination from fitting since the contribution of the small-sized particles is almost undetectable among the strong responses from the large ones. In this work, we report on how this ambiguity can be overcome by modelling the signal intensity using the Langevin model in thermodynamic equilibrium including a lognormal core size distribution fL(dc,d0,σ) fitted to experimentally measured FMMD data of immobilized MNPs. For each given median diameter d0, an ambiguous amount of best-fitting pairs of parameters distribution width σ and number of particles Np with R² > 0.99 are extracted. By determining the samples’ total iron mass, mFe, with inductively coupled plasma optical emission spectrometry (ICP-OES), we are then able to identify the one specific best-fitting pair (σ, Np) one uniquely. With this additional externally measured parameter, we resolved the ambiguity in core size distribution and determined the parameters (d0, σ, Np) directly from FMMD measurements, allowing precise MNPs sample characterization

Linking brain structure and genetic risk in large-scale data: A comparison of shared versus predominantly disorder-specific genetic risk for neuropsychiatric disorders

Shared and predominantly disorder-specific common genetic risk variants for neuropsychiatric disorders have been identified in previous studies. In particular, a large-scale genome-wide association study meta-analysis across eight neuropsychiatric disorders reported 23 highly pleiotropic single-nucleotide polymorphisms (SNPs) that are associated with at least four disorders as well as 22 SNPs that were predominantly associated with schizophrenia (SCZ) risk. Yet, their influences on brain structure which might be relevant for the increased vulnerability to multiple or specific neuropsychiatric disorders is not fully understood. In this study, we investigated and compared the cumulative influences of highly pleiotropic and predominantly SCZ-specific SNPs at the level of brain structure in the general population. Comparing their brain structural associations might point to brain regions of high transdiagnostic value as well as brain regions relevant to SCZ-specific risk pathways