• Energy Research
• 2021-2025close
• NL close
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• Eindhoven University of Technology close

Abstract Pellet edge localized mode (ELM) triggering is a well-established scheme for decreasing the time between two successive ELM crashes below its natural value. Reliable ELM pacing has been demonstrated experimentally in several devices, increasing the ELM frequency considerably. However, it was also shown that the frequency cannot be increased arbitrarily due to a so-called lag-time. During this time, after a preceding natural or triggered ELM crash, neither a natural ELM crash occurs nor is it possible to trigger an ELM crash by pellet injection. For this article, pellet ELM triggering simulations are advanced beyond previous studies in two ways. Firstly, realistic E × B and diamagnetic background flows are included. And secondly, the pellet is injected at different stages of the pedestal build-up. This allows us to recover the lag time for the first time in simulations and investigate it in detail. A series of nonlinear extended MHD simulations is performed to investigate the plasma dynamics resulting from an injection at different time points during the pedestal build-up. The experimentally observed lag-time is qualitatively reproduced. In particular, a sharp transition is observed between the regime where no ELMs can be triggered and the regime where pellet injection causes an ELM crash. Via variations of pellet parameters and injection time, the two regimes are studied and compared in detail, revealing pronounced differences in the nonlinear dynamics. The toroidal mode spectrum is significantly broader when an ELM crash is triggered, enhancing the stochasticity and therefore also the losses of thermal energy along magnetic field lines. In the heat fluxes to the divertor targets, pronounced toroidal asymmetries are observed. In the case of high injection velocities leading to deep penetration, the excitation of core modes like the 2/1 neoclassical tearing mode is also observed.

In the emerging field of perovskite solar cells, rational hole selective layer development is considered a double engine of this progress. To tap into the full potential and accelerate the commercialization path, machine learning (ML) is being tasked for perovskite screening. However, sincere efforts have not led to the design of hole selective layers based on the different organic core groups to yield efficient solar cells. Herein, it is demonstrated how ML can be applied to the advancement of hole transport materials (HTMs). The influence of HTMs with various core groups on the optoelectronic features and photovoltaic performance is evaluated and it is validated using both the random forest model and AutoML framework, General Automated Machine Learning Assistant (GAMA). To this end, the GAMA is utilized to predict the suitability of HTMs and it returns a 0.0542 ± 0.0470 RMSE score for 15 different materials on average. Correlation between experimental and predicted results is established, and GAMA is implemented for HTM suitability prediction. This paves the way for judicious and effective ways of the development of HTMs. In particular, the prediction approach from GAMA is an effective, reliable, and fast methodology and is pioneering in the field of HTM screening.

Abstract Nowadays, nearly 50% of the hydrogen produced worldwide comes from Steam Methane Reforming (SMR) at an environmental burden of 10.5 tCO2,eq/tH2, accelerating the consequences of global warming. One way to produce clean hydrogen is via methane pyrolysis using melts of metals and salts. Compared to SMR, significant less CO2 is produced due to conversion of methane into hydrogen and carbon, making this route more sustainable to generate hydrogen. Hydrogen is produced with high purity, and solid carbon is segregated and deposited on the molten bath. Carbon may be sold as valuable co-product, making industrial scale promising. In this work, methane pyrolysis was performed in a quartz bubble column using molten gallium as heat transfer agent and catalyst. A maximum conversion of 91% was achieved at 1119 °C and ambient pressure, with a residence time of the bubbles in the liquid of 0.5 s. Based on in-depth analysis of the carbon, it can be characterized as carbon black. Techno-economic and sensitivity analyses of the industrial concept were done for different scenarios. The results showed that, if co-product carbon is saleable and a CO2 tax of 50 euro per tonne is imposed to the processes, the molten metal technology can be competitive with SMR.