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Climate change and the need to reduce greenhouse gas emissions pose tremendous challenges to policymakers, the economy, and society. In this context, the development of clean, low-emission technologies plays a crucial role in mitigating the negative impact of fossil fuels on the climate. Hydrogen is a promising energy carrier and fuel that, thanks to its versatility, can be used in many applications. However, the adoption of hydrogen technology requires sufficient trust in its safety. To proxy the development of hydrogen safety innovations, we provide an analysis along the three knowledge and technology transfer channels of publications, patents, and standards. Our results show that research on hydrogen safety has increased significantly in the last decades, with hydrogen safety patents experiencing a general upward trend between 1980 and 2020, just recently decreasing. However, an analysis of almost 100 international hydrogen and fuel cell standards shows only a small number of references to scientific publications. This apparently limited transfer of knowledge from publications points to the need to optimize the coordination of the three knowledge and technology transfer channels for the future development of hydrogen technology. Based on the exploration of this gap, we recommend that research on the three channels for hydrogen be intensified and that the impact of hydrogen safety technology research and development on their diffusion be investigated.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.