The Department of Energy was a department of the United Kingdom Government. The Department was established in January 1974, when the responsibility for energy production was transferred away from the Department of Trade and Industry in the wake of the 1973 oil crisis and with the importance of North Sea oil increasing.Following the privatisation of the energy industries in the United Kingdom, which had begun some ten years earlier, the Department was abolished in 1992. Many of its functions were abandoned, with the remainder being absorbed into other bodies or departments. The Office of Gas Supply (Ofgas) and the Office of Electricity Regulation (OFFER) took over market regulation, the Energy Efficiency Office was transferred to the Department of the Environment, and various media-related functions were transferred to the Department of National Heritage. The core activities relating to UK energy policy were transferred back to the Department of Trade and Industry (DTI).The Department of Energy was a significant source of funding for energy research, and for investigations into the potential for renewable energy technologies in the UK.
Machine learning can effectively accelerate materials development in real-world sorbent applications including clean-up of polluted impoundment sites. Zeolites synthesized from coal fly ash can adsorb contaminants, such as boric acid, from water. Machine learning models were trained on molecular simulation data to predict boric acid uptake based on zeolite structure, aluminum content, extra-framework cation species, and boron concentration in solution. Overall, eXtreme Gradient Boosting models yielded the highest speed and accuracy. The models were used with a genetic algorithm to enable concentration-specific zeolite optimization for coal ash impoundment sites. Results indicate small pore zeolite frameworks such as PHI, CHA, AVE, ERI with low Si/Al ratios and a mix of Na and Ca metal cations are most effective for boric acid removal. Our use of machine learning models with a genetic algorithm has broad implications for machine learning-aided materials design.
This work provides a database of the U.S. food system’s energy consumption and GHG emissions at the national and state levels by food supply chain (FSC) stage, fuel type, and food commodity. We estimate that the U.S. FSC consumed a total 4660 TBTU (4900 PJ) of site energy, 7130 TBTU (7500 PJ) of primary energy, and generated 970 MMT of GHG emissions in 2016. Among all the stages, on-farm production is the largest energy consumer (31% primary energy) and GHG emissions contributor (70%), largely due to raising animals. Optimizing distribution can reduce the stage’s energy consumption and GHG emissions and increase products’ shelf-life. Reducing food loss and waste is another good option, as it decreases the amount of food necessary to grow, thus impacting the overall FSC. The database can help stakeholders identify stage- and region-specific strategies and measures to curtail the environmental footprint of the U.S. food system.
Intercalation of metal atoms at the SiC(0001)-graphene (Gr) interface can provide confined 2D metal layers with interesting electronic properties. The intercalated Pb monolayer (ML) has shown the coexistence of the Gr(10 x 10)-moire and a stripe phase, which still lacks understanding. Using density functional theory calculation and thermal annealing with ab initio molecular dynamics as motivated by experiment, we have studied the formation energy of Gr/Pb/SiC(0001) for different Pb coverages. Near the coverage of a Pb(111)-like ML mimicking the (10 x 10)-moire, we find a slightly more stable stripe structure, where one half of the structure has compressive strain with Pb occupying the Si-top sites and the other half has tensile strain with Pb off the Si-top sites. This stripe structure along the Gr zigzag direction has a periodicity of 2.3 nm across the [1 2 10] direction agreeing with the previous observations using scanning tunneling microscopy. Analysis with electron density difference and density of states show the tensile region has a more metallic character than the compressive region, while both are dominated by charge transfer from Pb ML to SiC(0001). The small energy difference between the stripe and Pb(111)-like structures means the two phases are almost degenerate and can coexist, which explains the experimental observations.
Cement production exceeds 4.1 billion tonnes annually, emitting 2.4 billion tonnes of CO2 annually, necessitating improved process control. Traditional models, limited to steady-state conditions, lack predictive accuracy for clinker mineralogical phases. Here, using a comprehensive two-year industrial dataset, we develop machine learning models that outperform conventional Bogue equations with mean absolute percentage errors of 1.24%, 6.77%, and 2.53% for alite, belite, and ferrite prediction respectively, compared to 7.79%, 22.68%, and 24.54% for Bogue calculations. Our models remain robust under varying operations and are evaluated for uncertainty and rare-event scenarios. Through post hoc explainable algorithms, we interpret the hierarchical relationships between clinker oxides and phase formation, providing insights into the functioning of an otherwise black-box model. The framework can potentially enable real-time optimization of cement production, thereby providing a route toward reducing material waste and ensuring quality while reducing the associated emissions under real-world conditions.
The unique geometry of kagome lattices leads to topological features such as flat bands and Dirac cones. When paired with ferromagnetism and a Fermi level near Dirac points, they offer a platform for realizing topological Chern magnetotransport. This prospect recently drew interest in the ferrimagnetic kagome metal TbMn6Sn6. However, density functional theory (DFT) calculations indicate that its 2D Chern gap lies well above the Fermi energy, raising questions about its role in anomalous Hall conductivity. Here, we study YMn6Sn5.45Ga0.55, a structurally and electronically similar material, and find that its intrinsic anomalous Hall effect is three-dimensional. This demonstrates that the Hall response in such compounds does not originate from 2D Chern gaps. Additionally, we confirm that the newly proposed empirical scaling relation for extrinsic Hall conductivity is universally governed by spin fluctuations.
