The endangerment of bees and new developments in beekeeping: a social science perspective using the example of Germany

The endangerment of honeybees and pollination in agriculture is related to social causes and entails social consequences. The paper reflects the bee issue in terms of symbolism, discourses and practices, different perspectives and positions, and social dynamics. It then presents two case studies that apply qualitative methods to assess developments that are harmful or favourable to bees and new trends in beekeeping. The first case study addresses the booming development of urban beekeeping. This part of the paper also discusses the alternatives that organic beekeeping has to offer. The second case study investigates beekeeping for the purpose of pollination in orchards. It points to possibilities of cooperation between growers and beekeepers but also marks the main problems, which are related to insecticide use and the development of the pollination market.     

Statistical analysis of excitonic transitions in single,free-standing GaN nanowires: Probing impurity incorporation in the poissonian limit

Single, free-standing GaN nanowires grown by plasma-assisted molecular-beam epitaxy have been investigated with low temperature micro-photoluminescence. The quantitative analysis of the luminescence spectra of around 100 nanowires revealed that each nanowire exhibits its own individual spectrum. A significant fraction of nanowires exclusively emits at energies corresponding to either surface-donor-bound or free excitons, demonstrating that optical properties of individual nanowires are determined by a few impurity atoms alone. The number of impurities per nanowire and their location within the nanowires varies according to Poissonian statistics.     

Dopant-free, polymorphic design of TiO2 nanocrystals by flame aerosol synthesis

A dopant-free aerosol synthesis of highly crystalline TiO₂ nanoparticles (20–35 nm) with tunable polymorphic content is demonstrated by rapid flame spray pyrolysis. By controlling precisely the total ambient oxygen partial pressure of the combustion in a quartz tube enclosure, anatase content as high as 96 wt% (4 wt% rutile) was obtained at high oxic flame conditions, while rutile content as high as 94 wt% (6 wt% anatase) was obtained under anoxic flames. The polymorphic variability lies within a narrow range of combustion equivalence ratios, that is, 1.0<Φ<1.5. Unlike any other flame aerosol syntheses, the anatase and rutile crystallite sizes were similar within each sample. Under highly oxic flame conditions (Φ<1.0), twinnings between anatase {0 1 1} planes could be observed, inferring oriented attachment taking place. Such mechanism could not, however, be seen under anoxic flame (Φ>1.0) possibly due to physical hindrance by surface carbonaceous content (typically <2 wt%). The carbon content can be easily removed by short calcination without significantly affecting the surface areas and crystallite properties of the original TiO₂ nanocrystals, preserving hence its pristine state.     

Accelerated cure of phenol–formaldehyde resins: Studies with model compounds

2‐Hydroxymethylphenol (2‐HMP) and 4‐hydroxymethylphenol (4‐HMP) were used as model compounds to study the reactions that occur during cure of phenol–formaldehyde (PF) resin to which cure accelerators (ethyl formate, propylene carbonate, γ‐butyrolactone, and triacetin) have been added. The addition of cure accelerators significantly increased the rate of condensation reactions. The cure accelerators were consumed during the reaction, indicating that they do not act as true catalysts. Major dimeric and trimeric reaction products were isolated and their structures determined. The results are consistent with a mechanism in which the hydroxymethyl group of 2‐HMP (or 4‐HMP) is first transesterified by the cure accelerator. The ester group is then displaced by reaction with the negatively charged ortho or para position of a second molecule (S_N2 mechanism) or is converted to a reactive quinone methide intermediate, which subsequently reacts with the negatively charged ortho or para position of a second molecule (quinone methide mechanism). When accelerators were added to the reaction mixture, the self‐condensation of 2‐HMP was faster than that of 4‐HMP. As is well documented in the literature, the exact opposite is true without added accelerators. This result would seem to indicate that the phenolic oxygen helps activate the esterified ortho‐hydroxymethyl group. The number and nature of crosslinks in a PF resin cured with added cure accelerator might be different than those in a PF resin cured without an added cure accelerator. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 3256–3263, 2002     

Advances in simultaneous strategies for dynamic process optimization

Following on the popularity of dynamic simulation for process systems, dynamic optimization has been identified as an important task for key process applications. In this study, we present an improved algorithm for simultaneous strategies for dynamic optimization. This approach addresses two important issues for dynamic optimization. First, an improved nonlinear programming strategy is developed based on interior point methods. This approach incorporates a novel filter-based line search method as well as preconditioned conjugate gradient method for computing search directions for control variables. This leads to a significant gain in algorithmic performance. On a dynamic optimization case study, we show that nonlinear programs (NLPs) with over 800,000 variables can be solved in less than 67 CPU minutes. Second, we address the problem of moving finite elements through an extension of the interior point strategy. With this strategy we develop a reliable and efficient algorithm to adjust elements to track optimal control profile breakpoints and to ensure accurate state and control profiles. This is demonstrated on a dynamic optimization for two distillation columns. Finally, these algorithmic improvements allow us to consider a broader set of problem formulations that require dynamic optimization methods. These topics and future trends are outlined in the last section.     

Exploiting Ternary Solubility Phase Diagrams for Resolution of Enantiomers: An Instructive Example

Ternary phase diagrams are essential research tools in several scientific fields. They provide fundamental understanding and guidance in designing separation experiments. While their utility and relevance is undisputed in the chemical and chemical engineering community, yet much progress remains to be done in understanding and exploiting ternary phase diagrams for crystallization-based chiral separation purposes. A guide in the interpretation of the ternary solubility phase diagram of a chiral molecule to design the separation of its enantiomers is provided. On the basis of the discussion of fundamental relationships in the phase diagram, basic enantioseparation experiments are performed for D-/L-methionine in water exploiting the characteristic shift of the eutectic composition in the chiral system. The rational approach followed in separation process design is described together with the experimental procedures applied and the results obtained.     

Big data for microstructure‐property relationships: A case study of predicting effective conductivities

The analysis of big data is changing industries, businesses and research as large amounts of data are available nowadays. In the area of microstructures, acquisition of (3‐D tomographic image) data is difficult and time‐consuming. It is shown that large amounts of data representing the geometry of virtual, but realistic 3‐D microstructures can be generated using stochastic microstructure modeling. Combining the model output with physical simulations and data mining techniques, microstructure‐property relationships can be quantitatively characterized. Exemplarily, we aim to predict effective conductivities given the microstructure characteristics volume fraction, mean geodesic tortuosity, and constrictivity. Therefore, we analyze 8119 microstructures generated by two different stochastic 3‐D microstructure models. This is—to the best of our knowledge—by far the largest set of microstructures that has ever been analyzed. Fitting artificial neural networks, random forests and classical equations, the prediction of effective conductivities based on geometric microstructure characteristics is possible. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4224–4232, 2017