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It is shown how appropriately engineered nanoporous carbons provide materials for reversible hydrogen storage, based on physisorption, with exceptional storage capacities (∼80 g H 2 /kg carbon, ∼50 g H 2 /liter carbon, at 50 bar and 77 K). Nanopores generate high storage capacities (a) by having high surface area to volume ratios, and (b) by hosting deep potential wells through overlapping substrate potentials from opposite pore walls, giving rise to a binding energy nearly twice the binding energy in wide pores. Experimental case studies are presented with surface areas as high as 3100 m 2 g −1 , in which 40% of all surface sites reside in pores of width ∼0.7 nm and binding energy ∼9 kJ mol −1 , and 60% of sites in pores of width >1.0 nm and binding energy ∼5 kJ mol −1 . The findings, including the prevalence of just two distinct binding energies, are in excellent agreement with results from molecular dynamics simulations. It is also shown, from statistical mechanical models, that one can experimentally distinguish between the situation in which molecules do (mobile adsorption) and do not (localized adsorption) move parallel to the surface, how such lateral dynamics affects the hydrogen storage capacity, and how the two situations are controlled by the vibrational frequencies of adsorbed hydrogen molecules parallel and perpendicular to the surface: in the samples presented, adsorption is mobile at 293 K, and localized at 77 K. These findings make a strong case for it being possible to significantly increase hydrogen storage capacities in nanoporous carbons by suitable engineering of the nanopore space.

Many materials in modern civil engineering applications, such as interlayers for laminated safety glass, are polymer-based. These materials are showing distinct viscoelastic (strain-rate) and temperature dependent behaviour. In literature, different mathematical representations of these phenomena exist. A common one is the 'Prony-series' representation, which is implemented in many state-of-the-art Finite-ElementAnalysis-Software to incorporate linear viscoelastic material behaviour. The Prony-parameters at a certain reference temperature can either be determined by relaxation or retardation experiments in the time domain or with a steady state oscillation in the frequency domain in the so called 'Dynamic Mechanical

’Big data’ and the use of ’Artificial Intelligence’ (AI) is currently advancing due to the increasing and even cheaper data collection and processing capabilities. Social and economical change is predicted by numerous company leaders, politicians and researchers. Machine and Deep Learning (ML/DL) are sub-types of AI, which are gaining high interest within the community of data scientists and engineers worldwide. Obviously, this global trend does not stop at structural glass engineering, so that, the first part of the present paper is concerned with introducing the basic theoretical frame of AI and its sub-classes of ML and DL while the specific needs and requirements for the application in a structural engineering context are highlighted. Then this paper explores potential applications of AI for different subjects within the design, verification and monitoring of façades and glass structures. Finally, the current status of research as well as successfully conducted industry projects by the authors are presented. The discussion of specific problems ranges from supervised ML in case of the material parameter identification of polymeric interlayers used in laminated glass or the prediction of cut-edge strength based on the process parameters of a glass cutting machine and prediction of fracture patterns of tempered glass to the application of computer vision DL methods to image classification of the Pummel test and the use of semantic segmentation for the detection of cracks at the cut edge of glass. In the summary and conclusion section, the main findings for the applicability and impact of AI for the presented structural glass research and industry problems are compiled. It can be seen that in many cases AI, data, software and computing resources are already available today to successfully implement AI projects in the glass industry, which is demonstrated by the many current examples mentioned. Future research directories however will need to concentrate on how to introduce further glass-specific theoretical and human expert knowledge in the AI training process on the one hand and on the other hand more pronunciation has to be laid on the thorough digitization of workflows associated with the structural glass problem at hand in order to foster the further use of AI within this domain in both research and industry.