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This study demonstrates the feasibility of producing Pt-Re/Al2O3 catalysts via the scalable solvent deficient precipitation method achieving enhanced performance in the dehydrogenation of liquid organic hydrogen carriers. Effects similar to wet impregnated Pt-Re catalysts were observed, indicating the transferability of the Re promotion concept across catalyst synthesis approaches. In continuous gas-phase dehydrogenation of methyl cyclohexane, Re addition improved the Pt-based activity, selectivity, and stability, while reducing coking and effective activation energy. For perhydro benzyltoluene dehydrogenation, Re increased catalyst activity across various loadings with a molar ratio of Re/Pt = 0.5 yielding the highest hydrogen production rate. Higher Re loadings led to a rapid performance decline due to side reactions and catalyst deactivation, likely caused by deep dehydrogenation pathways and strong binding of the dehydrogenated product benzyltoluene to the active sites. Kinetic studies revealed that Re reduces the effective activation energy, although diffusional limitations were observed due to the small pore sizes of the produced catalyst materials. Successful 10-fold scale-up of the solvent-deficient precipitation was achieved without water, binders, or additives with performances comparable to small-scale syntheses. This highlights the potential of this simple, cost-effective, and scalable approach for the high-throughput production of bimetallic Pt-based catalysts.
To aggregate rankings into a social ranking, one can use scoring systems such as Plurality, Veto, and Borda. We distinguish three types of methods: ranking by score, ranking by repeatedly choosing a winner that we delete and rank at the top, and ranking by repeatedly choosing a loser that we delete and rank at the bottom. The latter method captures the frequently studied voting rules Single Transferable Vote (aka Instant Runoff Voting), Coombs, and Baldwin. In an experimental analysis, we show that the three types of methods produce different rankings in practice. We also provide evidence that sequentially selecting winners is most suitable to detect the "true" ranking of candidates. For different rules in our classes, we then study the (parameterized) computational complexity of deciding in which positions a given candidate can appear in the chosen ranking. As part of our analysis, we also consider the Winner Determination problem for STV, Coombs, and Baldwin and determine their complexity when there are few voters or candidates.
Multiscale problems are ubiquitous in physics. Numerical simulations of such problems by solving partial differential equations (PDEs) at high resolution are computationally too expensive for many-query scenarios, such as uncertainty quantification, remeshing applications, and topology optimization. This limitation has motivated the development of data-driven surrogate models, where microscale computations are substituted by black-box mappings between macroscale quantities. While these approaches offer significant speedups, they typically struggle to incorporate microscale physical constraints, such as the balance of linear momentum. In this contribution, we propose the Equilibrium Neural Operator (EquiNO), a physics-informed PDE surrogate in which equilibrium is hard-enforced by construction. EquiNO achieves this by projecting the solution onto a set of divergence-free basis functions obtained via proper orthogonal decomposition (POD), thereby ensuring satisfaction of equilibrium without relying on penalty terms or multi-objective loss functions. We compare EquiNO with variational physics-informed neural and operator networks that enforce physical constraints only weakly through the loss function, as well as with purely data-driven operator-learning baselines. Our framework, applicable to multiscale FE^ 2 computations, introduces a finite element-operator learning (FE-OL) approach that integrates the finite element (FE) method with operator learning (OL). We apply the proposed methodology to quasi-static problems in solid mechanics and demonstrate that FE-OL yields accurate solutions even when trained on restricted datasets. The results show that EquiNO achieves speedup factors exceeding 8000-fold compared to traditional methods and offers a robust and physically consistent alternative to existing data-driven surrogate models.
This review covers developments in and applications of XRF techniques such as EDXRF, WDXRF, TXRF, XRF microscopy using technologies such as synchrotron sources, X-ray optics, X-ray tubes and detectors in laboratory, mobile and hand-held systems.
