Abstract: We describe two synthesis approaches to colloidal Cu3P nanocrystals using trioctylphosphine (TOP) as phosphorus precursor. One approach is based on the homogeneous nucleation of small Cu3P nanocrystals with hexagonal plate-like morphology and with sizes that can be tuned from 5 to 50 nm depending on the reaction time. In the other approach, metallic Cu nanocrystals are nucleated first and then they are progressively phosphorized to Cu3P. In this case, intermediate Janus-like dimeric nanoparticles can be isolated, which are made of two domains of different materials, Cu and Cu3P, sharing a flat epitaxial interface. The Janus-like nanoparticles can be transformed back to single-crystalline copper particles if they are annealed at high temperature under high vacuum conditions, which makes them an interesting source of phosphorus. The features of the Cu Cu3P Janus-like nanoparticles are compared with those of the Wiped microstructure discovered more than two decades ago in the rapidly quenched Cu Cu3P eutectic of the Cu P alloy, suggesting that other alloy/eutectic systems that display similar behavior might give origin to nanostructures with flat, epitaxial Interface between domains of two diverse materials. Finally, the electrochemical properties of the copper phosphide plates are studied, and they are found to be capable of undergoing lithiation/delithiation through a displacement reaction, while the Janus-like Cu Cu3P particles do not display an electrochemical behavior that would make them suitable for applications in batteries.

Abstract: Natural organisms contain rich elements and naturally optimized smart structures, both of which have inspired various innovative concepts and designs in human society. In particular, several natural organisms have been used as element sources to synthesize low-cost and environmentally friendly electrocatalysts for the oxygen reduction reaction (ORR) in fuel cells and metal-air batteries, which are clean energy devices. However, to date, no naturally optimized smart structures have been employed in the synthesis of ORR catalysts, including graphene-based materials. Here, we demonstrate a novel strategy to synthesize graphene-graphite films (GGFs) by heating butterfly wings coated with FeCl3 in N-2, in which the full power of natural organisms is utilized. The wings work not only as an element source for GGF generation but also as a porous supporting structure for effective nitrogen doping, two-dimensional spreading, and double-face exposure of the GGFs. These GGFs exhibit a half-wave potential of 0.942 V and a H2O2 yield of < 0.07% for ORR electrocatalysis; these values are comparable to those for the best commercial Pt/C and all previously reported ORR catalysts in alkaline media. This two-in-one strategy is also successful with cicada and dragonfly wings, indicating that it is a universal, green, and cost-effective method for developing high-performance graphene-based materials.

Abstract: Repeated intraperitoneal injections of lead acetate in rats caused a calcification of the skin of the abdomen near the site of the injections. In the lead-induced calcifications, electron dense collagen bundles could be observed. On the surface of the collagen fibrils, needle-like crystals were visible. With energy-dispersive X-ray analysis, phosphorus, calcium and lead were detected in the electron dense collagen bundles. X-ray maps of the P-Kα, Ca-Kα, and Pb-Lα plus Pb-Lβ lines showed an equivalent distribution along the collagen fibrils for phosphorus and calcium. The occurrence of the most electron dense areas in the STEM-image was comparable to the lead distribution. A good correlation existed between the structural and the elemental images of the same area. Although the medicinal use of preparations containing lead is no longer recommended, some are still prescribed. From our results we can conclude that they should not be applied to injured or inflamed skin.

Abstract: Statistical parameter estimation theory is proposed as a quantitative method to measure unknown structure parameters from electron microscopy images. Images are then purely considered as data planes from which structure parameters have to be determined as accurately and precisely as possible using a parametric statistical model of the observations. For this purpose, an efficient algorithm is proposed for the estimation of atomic column positions and intensities from high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) images. Furthermore, the so-called CramérRao lower bound (CRLB) is reviewed to determine the limits to the precision with which continuous parameters such as atomic column positions and intensities can be estimated. Since this lower bound can only be derived for continuous parameters, alternative measures using the principles of detection theory are introduced for problems concerning the estimation of discrete parameters such as atomic numbers. An experimental case study is presented to show the practical use of these measures for the optimization of the experiment design if the purpose is to decide between the presence of specific atom types using STEM images.

Abstract: The effects of the mold preheating and the silicon content of ductile iron on the percentage of carbides, graphite nodule counts and shrinkage volume were investigated. The results showed that the percentage of carbides and the shrinkage volume decreased when the mold preheating increased. The ductile iron with the carbon equivalent of 4.45% and the silicon content of 2.5% without any porosity defects was achieved when the mold preheating was 450 °C. Increasing the silicon content in the range of 2.1%3.3% led to the increase in graphite nodule count and graphite size and the decrease in percentage of carbides. It is due to the increase in induced expansion pressure during the graphite formation with the increasing of silicon content. The suitable condition for casting a sound product of ductile iron without the riser at the mold preheating temperature of 300 °C is the silicon content of 3.3% and carbon equivalent of 4.7%.

Abstract: Illicit drugs consumption and trafficking is spread worldwide and remains an increasing challenge for local authorities. Forensic drugs and their metabolites are released into wastewaters due to human excretion after illegal consumption of drugs and occasionally due to disposal of clandestine laboratory wastes into sewage systems, being recently classified as the latest group of emerging pollutants. Hence, it is essential to have efficient and accurate methods to detect these type of compounds in seized street samples, biological fluids and wastewaters in order to reduce and prevent trafficking and consumption and negative effects on aquatic systems. Electrochemical strategies offer a fast, portable, low-cost and accurate alternative to chromatographic and spectrometric methods, for the analysis of forensic drugs and metabolites in different matrices. Recent electrochemical strategies applied to the detection of illicit drugs in wastewaters, biological fluids and street samples are presented in this review, together with the impact of drug consumption on the environment.

Abstract: The installation of urban green infrastructure, particularly green walls, has proven to be an effective strategy for the mitigation of particulate matter (PM) pollution and the urban heat island effect. For the interaction between vegetation, PM and the local microclimate, wind flow is the main driving force. In order to investigate these interactions in detail, it is important to know how air flows through vegetation. This study proposes a method based on the DarcyForchheimer equation, where vegetation is considered as a porous medium and several plant species and the effects of plant morphological characteristics are examined both experimentally and using computer simulations. Results showed that the DarcyForchheimer model is a simple and robust way to describe air flow through vegetation regardless of its morphology. This research provides a new vision on studying aerodynamic properties of vegetation in relation to their morphology and provides opportunities for model the interaction between vegetation and its environment.

Abstract: Nitrification and denitrification, the key biological processes for thermophilic nitrogen removal, have separately been established in bioreactors at 50 °C. A well-characterized set of kinetic parameters is essential to integrate these processes while safeguarding the autotrophs performing nitrification. Knowledge on thermophilic nitrifying kinetics is restricted to isolated or highly enriched batch cultures, which do not represent bioreactor conditions. This study characterized the stoichiometry and kinetics of two thermophilic (50 °C) nitrifying communities. The most abundant ammonia oxidizing archaea (AOA) were related to the Nitrososphaera genus, clustering relatively far from known species Nitrososphaera gargensis (95.5% 16S rRNA gene sequence identity). The most abundant nitrite oxidizing bacteria (NOB) were related to Nitrospira calida (97% 16S rRNA gene sequence identity). The nitrification biomass yield was 0.200.24 g VSS g−1 N, resulting mainly from a high AOA yield (0.160.20 g VSS g−1 N), which was reflected in a high AOA abundance in the community (5776%) compared to NOB (511%). Batch-wise determination of decay rates (AOA: 0.230.29 d−1; NOB: 0.320.43 d−1) rendered an overestimation compared to in situ estimations of overall decay rate (0.0260.078 d−1). Possibly, the inactivation rate rather than the actual decay rate was determined in batch experiments. Maximum growth rates of AOA and NOB were 0.120.15 d−1 and 0.130.33 d−1 respectively. NOB were susceptible to nitrite, opening up opportunities for shortcut nitrogen removal. However, NOB had a similar growth rate and oxygen affinity (0.150.55 mg O2 L−1) as AOA and were resilient towards free ammonia (IC50 > 16 mg NH3-N L−1). This might complicate NOB outselection using common practices to establish shortcut nitrogen removal (SRT control; aeration control; free ammonia shocks). Overall, the obtained insights can assist in integrating thermophilic conversions and facilitate single-sludge nitrification/denitrification.

Abstract: Source size broadening is an important resolution limiting effect in modern STEM experiments. Here, we propose an alternative method to measure the source size broadening making use of a holographic biprism to create interference patterns in an empty Ronchigram. This allows us to measure the exact shape of the source size broadening with a much better sampling than previously possible. We find that the shape of the demagnified source deviates considerably from a Gaussian profile that is often assumed. We fit the profile with a linear combination of a Gaussian and a bivariate Cauchy distribution showing that even though the full width at half maximum is similar to previously reported measurements, the tails of the profile are considerable wider. This is of fundamental importance for quantitative comparison of STEM simulations with experiments as these tails make the image contrast dependent on the interatomic distance, an effect that cannot be reproduced by a single Gaussian profile of fixed width alone.

Abstract: Principal component analysis (PCA) noise filtering is a popular method to remove noise from experimental electron energy loss (EELS) spectrum images. Here, we investigate the statistical behaviour of this method by applying it on a simulated data set with realistic noise levels. This phantom data set provides access to the true values contained in the data set as well as to many different realizations of the noise. Using least squares fitting and parameter estimation theory, we demonstrate that even though the precision on the estimated parameters can be better as the CramérRao lower bound, a significant bias is introduced which can alter the conclusions drawn from experimental data sets. The origin of this bias is in the incorrect retrieval of the principal loadings for noisy data. Using an expression for the bias and precision of the singular values from literature, we present an evaluation criterion for these singular values based on the noise level and the amount of information present in the data set. This criterion can help to judge when to avoid PCA noise filtering in practical situations. Further we show that constructing elemental maps of PCA noise filtered data using the background subtraction method, does not guarantee an increase in the signal to noise ratio due to correlation of the spectral data as a result of the filtering process.