Kommunale Wirtschaftsförderung und Nachhaltigkeit

Nachhaltigkeit ist als Thema in der kommunalen Wirtschaftsförderung durchaus von Interesse. Allerdings zeigen die Ergebnisse einer deutschlandweiten Befragung, dass bestehende Gestaltungsspielräume noch nicht erkannt und noch nicht ausreichend genutzt werden

Structural Characterisation of Hierarchically Porous Silica: Monolith by NMR Cryo-porometry and -diffusometry

A systematic NMR cryo-porometry and -diffusometry study using nitrobenzene as a probe liquid is carried out in order to characterise pore structures of hierarchically-organised porous silica monolith possessing mesopores along with a 3D bicontinuous macropore network. The result obtained from NMR cryoporometry shows the presence of a relatively wide mesopore size distribution of 10-35 nm. Furthermore, NMR cryodiffusometry indicates that whilst the mesopores are highly tortuous (Tmeso ≈6), they have little influence on the overall tortuosity of the material (Tmacro ≈1.5), which is largely dominated by the macropores (Toverall ≈1.7)

Structural Characterisation of Hierarchically Porous Silica: Monolith by NMR Cryo-porometry and -diffusometry

A systematic NMR cryo-porometry and -diffusometry study using nitrobenzene as a probe liquid is carried out in order to characterise pore structures of hierarchically-organised porous silica monolith possessing mesopores along with a 3D bicontinuous macropore network. The result obtained from NMR cryoporometry shows the presence of a relatively wide mesopore size distribution of 10-35 nm. Furthermore, NMR cryodiffusometry indicates that whilst the mesopores are highly tortuous (Tmeso ≈6), they have little influence on the overall tortuosity of the material (Tmacro ≈1.5), which is largely dominated by the macropores (Toverall ≈1.7)

Network-based forecasting of climate phenomena

Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of high-impact phenomena: 1) El Niño events, 2) droughts in the central Amazon, 3) extreme rainfall in the eastern Central Andes, 4) the Indian summer monsoon, and 5) extreme stratospheric polar vortex states that influence the occurrence of wintertime cold spells in northern Eurasia. In this perspective, we argue that network-based approaches can gainfully complement numerical modeling