The electrochemical hydrogen evolution reaction (HER) is growing in significance as society begins to rely more on renewable energy sources such as wind and solar power. Thus, research on designing new, inexpensive, and abundant HER catalysts is important. Here, we describe how a simple experiment combined with results from density functional theory (DFT) can be used to introduce the Sabatier principle and its importance when designing new catalysts for the HER. We also describe the difference between reactivity and catalytic activity of solid surfaces and explain how DFT is used to predict new catalysts based on this. Suited for upper-level high school and first-year university students, this exercise involves using a basic two-cell electrochemical setup to test multiple electrode materials as catalysts at one applied potential, and then constructing a volcano curve with the resulting currents. The curve visually shows students that the best HER catalysts are characterized by an optimal hydrogen binding energy (reactivity), as stated by the Sabatier principle. In addition, students may use this volcano curve to predict the activity of an untested catalyst solely from the catalyst reactivity. This exercise circumvents the complexity of traditional experiments while it still demonstrates the trends of the HER volcano known from literature.

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Objectives for state actions Typology of state demand flexibility indicators Example state actions illustrating progress to date Trends and gaps Opportunities for DOE to assist states For more information Appendix A. Grid-Interactive Efficient Buildings Roadmap recommendations Appendix B. Detailed list of state indicators for advancing demand flexibility Appendix C. Example local actions • Part II -Traditional Energy Efficiency Indicators for Electricity and Gas -Available at https://emp.lbl.gov/publications/state-indicators-advancing-demand 3 Contents Also see infographic at the link immediately above Demand flexibility (DF): Capability provided by DERs to reduce, shed, shift, modulate or generate electricity; also called energy flexibility or load flexibility. Demand response (DR): Change in the rate of electricity consumption in response to price signals or specific requests of a utility. Demand-side management (DSM): The modification of energy demand by customers through strategies, including EE, DR, distributed generation, energy storage, electric vehicles, and/or time-ofuse pricing structures.Distributed energy resource (DER): A resource sited close to customers that can provide all or some of their immediate power needs and/or can be used by the utility system to either reduce demand or provide supply to satisfy the energy, capacity, or ancillary service needs of the grid. Energy efficiency (EE): Ongoing reduction in energy use to provide the same or improved function. Grid-interactive efficient building (GEB):An energy-efficient building that uses smart technologies and on-site DERs to provide demand flexibility while cooptimizing for energy cost, grid services, and occupant needs and preferences in a continuous and integrated way. Peak demand: The maximum load during a specified period of time.

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

All time-series input data temporally and geospatially aligned 42 Answers the question: How would PVESS have performed in providing backup power during specific historical long-duration interruption events? *Vulnerable county selected using United States Federal Emergency Management Agency's social vulnerability index. **We assume that PVESS are not damaged by extreme weather during these historical events

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