Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for large-scale or online applications. We propose a neural-operator framework for surrogate modeling of power system components, using Deep Operator Networks (DeepONets) to learn mappings from system states and time-varying inputs to full trajectories without step-by-step integration. To enhance generalization and data efficiency, we introduce Physics-Informed DeepONets (PI-DeepONets), which embed the residuals of governing equations into the training loss. Our results show that DeepONets, and especially PI-DeepONets, achieve accurate predictions under diverse scenarios, providing over 30 times speedup compared to high-order ODE solvers. Benchmarking against Physics-Informed Neural Networks (PINNs) highlights superior stability and scalability. Our results demonstrate neural operators as a promising path toward real-time, physics-aware simulation of power system dynamics.
Materials discovery is the linchpin to advance sustainable energy technologies, which mitigate resource scarcity and climate change. Establishing a carbon-neutral society relies heavily on the introduction of highly active and stable electrocatalysts for sustainable reaction engineering. In the current expression of high-throughput materials science, both high-throughput synthesis (HTS) and high-throughput characterization (HTC) are required to effectively cover an ever-increasing search space. HTS relies on combinatorial design and multimodal growth to overcome the constraints of traditional electrocatalyst fabrication. Concomitantly, HTC rapidly elucidates relationships between the composition, structure, and processing of electrocatalysts and their (multi-)functional properties, such as, activity, selectivity, and stability. In this review, we discuss emerging trends in high-throughput experimentation (HTE) to discover, design, and optimize electrocatalytically active materials for sustainable energy sources. We highlight the potential of individual HTS and HTC building blocks to be integrated into a holistic HTE workflow in conjunction with AI-driven feedback loops to truly accelerate the discovery process. Finally, we discuss future challenges and opportunities to improve the rational design strategy of electrocatalysts for sustainable energy sources based on integrated HTE workflows.
While both corporate venture capital (CVC) and R&D are essential vehicles for innovation, the literature has largely overlooked whether and when they act as complements or substitutes in generating firm innovation outcomes. Drawing on a process view of absorptive capacity, this paper argues that the CVC-R&D relationship depends on how firms coordinate knowledge acquisition, assimilation, transformation, and exploitation. Analyzing a sample of US firms engaged in CVC or R&D activities from 1993 to 2016, this paper finds that CVC and R&D act as substitutes in generating innovation outcomes. Furthermore, this paper finds that the substitutive relationship weakens when firms use formalized and dedicated CVC structures, and it shifts toward complementarity as firms accumulate CVC investing experience. This paper contributes to the CVC-R&D relationship literature by highlighting the role of absorptive capacity process coordination, and to the organizational learning literature by showing how experiential and deliberate learning mechanisms moderate this relationship.
High-solids anaerobic membrane bioreactors (AnMBRs) are receiving growing interest for the treatment of urban organic feedstocks, including sewage sludge, food waste, distillery stillage and dairy residues, achieving superior COD removal, stable operation, and enhanced methane yields. Demonstrated applications, including several full-scale implementations, show high treatment capacities and robust biogas production. Performance improvements have been explored through emerging process intensification strategies, including optimized operation, energy-lean pretreatments, biochar-based media, and electrochemically assisted configurations, with reported biogas yield improvements of 10–60%, although most remain at low technology readiness levels. At elevated solids concentrations, typically with mixed liquor total solids of 25–70 g/L and fluxes mostly below 10 L/m2/h, membrane fouling in AnMBRs is predominantly governed by the formation of compressible cake/gel layers, reduced shear efficiency at high viscosity, and mineral-associated scaling, rather than simple pore blocking, underscoring the importance of appropriate module configuration, material selection, and sustainable operating conditions that balance flux, shear, and cleaning. Moreover, coupling with post-treatment technologies, such as bipolar membrane electrodialysis and hydroxyapatite-enhanced partial nitritation/anammox, can shift AnMBRs from waste treatment systems to circular biorefineries. Environmentally, AnMBRs can contribute to reduced methane emissions, nutrient recovery, and water reclamation as part of integrated anaerobic treatment systems, whereas their economic viability remains constrained by high operational costs and substantial investments in membranes and advanced automation. Future research should integrate long-term operational data on membrane performance with techno-economic analysis and life-cycle assessment to assess scale-up stability and cost–impact trade-offs.
As distributed energy resources (DERs) proliferate, future power system will need new market platforms enabling prosumers to trade various electricity and grid-support products. However, prosumers often exhibit complex, product interdependent preferences and face limited cognitive capacity, hindering participation in prevailing markets with complex structures and bid formats. We address this challenge by introducing a multi-product market that allows prosumers to express complex preferences through an intuitive format, by fusing combinatorial clock exchange and machine learning (ML) techniques. The iterative mechanism only requires prosumers to report their preferred package of products at posted prices, eliminating the need for forecasting product prices or adhering to complex bid formats, while the ML-aided price discovery speeds up convergence. The linear pricing rule further enhances transparency and interpretability. Finally, numerical simulations demonstrate convergence to clearing prices in approximately 15 clock iterations.
