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This is the data set associated with the journal article titled 'A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles'. The data files are in MATLAB file format. Figure 4 was generated using the vehicle speed profiles of an electric bus (EB) when it operated in London and of the EB's baseline simulation model, and using the state of charge (SOC) profiles of the EB when it operated in London and of the EB's baseline simulation model. Figure 5 was generated using the electricity consumption of the EB's baseline simulation model and of the EB's computationally efficient model for different drive cycles. It shows how the proposed computationally efficient model's electricity consumption compares with that of the baseline model. Figure 6 was generated using the speed profiles of a diesel heavy goods vehicle (HGV) when it operated on-road and of the HGV's baseline simulation model, and using the fuel consumption profiles of the diesel HGV when it operated on-road and of the HGV's baseline simulation model. It also contains the road elevation profile. Figure 7 was generated using the fuel consumption values of the diesel HGV's baseline simulation model and of the HGV's computationally efficient model for different drive cycles. It shows how the proposed computationally efficient model's diesel consumption compares with that of the baseline model. All the files were generated using MATLAB software package from Mathworks.

Frustration in the energy landscape has been quantified using a number of different metrics. These metrics have been applied to the databases in the subdirectories, which contain information about the energy landscape in the form of minima and transition states. The frustration metrics are coded within The PATHSAMPLE program, available for download at: http://www-wales.ch.cam.ac.uk/PATHSAMPLE/ Documentation can also be found at the same web address. The relevant keywords for the work described in this paper can be found at: http://www-wales.ch.cam.ac.uk/PATHSAMPLE.2.1.doc/node5.html Keywords CV, SHANNON and SHANNONR are described in the documentation. SHANNON and SHANNONR calculate frustration measures for the landscape as a function of temperature. The CV keyword gives a heat capacity calculation and hence allows an estimate of the melting temperature. Each Directory holds the database for the relevant system and input files for the PATHSAMPLE program. The output of the frustration calculation, for SHANNON, is given in Shannon.out. Some databases also contain output for the heat capacity calculation in CV.out. If required the OPTIM program is also available for download at: http://www-wales.ch.cam.ac.uk/OPTIM/ Documentation can also be found at the same web address. The program for drawing disconnectivity graphs, disconnectionDPS, can be found at: http://www-wales.ch.cam.ac.uk/software.html There is also input and output for the disconnectivity graph construction in each directory.

See the README file for a detailed description of the dataset. The data has been collected using a number of techniques. Figure 1 contains data gathered using photoelectron spectroscopy and have been saved in a .vms format. The data has been analysed and processed using CasaXPS and Origin respectively. Figure 2 contains data from photoluminescence and Transient absorbance spectroscopy and have been saved as .asc and .txt files. Figure 3 contains plots that have been gathered via X-ray diffraction and Time-of-flight Secondary Ion Mass spectroscopy techniques (ToF-SIMs). The ToF-SIMs data includes the response from all the species that evolves with ion bombardment but the species of interest are I2- and O2-. Figure 4 contains all the photovoltaic performance data in .txt format which can be opened and plotted using the Matlab script provided. The photovoltaic metrics, PCE, Jsc, VOC and FF have been generated with the Matlab script which can be taken forward for the box plot plotting as shown in Figure 4. Figure 5 is a summary of the PICTs data which has been derived from the raw data in Figure S11. Please see the ReadME files for more details.

This repository contains the data related to the Data in Brief article titled: Bulk and critical material demand for selected ‘Starter Kit’ energy system models. The data include the modeled mass of materials and their embodied emissions. A metadata file is also included to clarify the units, materials and scenario names.

Abstract for communication: "Bismuth-based compounds have recently gained increasing attention as potentially non-toxic and defect-tolerant solar absorbers. However, many of the new materials recently investigated show limited photovoltaic performance. Herein, we explore in detail one such compound through theory and experiment: bismuth oxyiodide (BiOI). We grow BiOI thin films by chemical vapor transport and find them to maintain the same tetragonal phase in ambient air for at least 197 days. Our computations suggest BiOI to be tolerant to antisite and vacancy defects. We demonstrate an all-inorganic solar cell (ITO|NiOx|BiOI|ZnO|Al) with negligible hysteresis and up to 80% external quantum efficiency under select monochromatic excitation. The short-circuit current densities and power conversion efficiencies under AM 1.5G illumination are nearly double those of previously-reported BiOI solar cells, as well as other bismuth halide and chalcohalide photovoltaics recently explored by many groups. Through a detailed loss analysis using optical characterization, photoemission spectroscopy and device modeling, we provide direction for future improvements in efficiency. Our work demonstrates that BiOI, previously considered to be a poor photocatalyst, is promising for photovoltaics."

Currently, fossil fuels make up a significant proportion of global energy demand and cause many concerns, such as increasing greenhouse gas emission. Therefore, there is a considerable need for cost-effective, facile and efficient processing of environmental-friendly energy harvesting and storage systems. Solar energy is one of the most promising energy sources that meet the energy demand. The silicon-based solar cells exhibit competitive power conversion efficiency and dominate the solar cell market in recent years. In contrast, organic solar cells (OSCs) have emerged as promising third-generation photovoltaic devices owing to their outstanding properties such as the potential of low-cost mass manufacturing, lightweight, mechanical flexibility and easy processability. Therefore, OSCs have received growing attention from the research community. For solar cell technologies, a smectic liquid crystal C8-BTBT was selected in Chapter 3 due to its unique thermal dynamic and crystal properties. A range of ternary OSCs with and without C8-BTBT loading at gradient weight fractions were thermally treated and fabricated. In addition, the assessment of fabricated OSCs on the photovoltaic characteristics reveals the evolution of various cell parameters with annealing temperature and C8-BTBT weight fractions. The cell with 5 wt% C8-BTBT loading exhibited the best performance after thermal annealing treatment at 120 oC. Furthermore, flexible hydrogel substrates were fabricated for flexible OSCs in Chapter 4. The PHEMA hydrogel films were optimised via adjusting photopolymerisation duration under UV light. Based on the fabricated PHEMA substrates, flexible OSCs were subsequently made, whose extracted device parameters showed comparable characteristics with those in Chapter 3. Moreover, PHEMA-based OSCs can be dissolved in different types of polar solvents, which is promising for realising sustainable and recyclable solar cells. For the development of energy storage devices, asymmetric carbon nanohorns were proposed as an active material to fabricate flexible solid‐state carbon wire (CW)‐based electrochemical supercapacitors (ss‐CWECs) which exhibited high power density and ultra‐low cutoff frequency. Based on microscopy and electrochemical characterisation, the fundamental reaction mechanism in polyvinyl‐based electrolyte system was elucidated in Chapter 5, as being associated with deprotonation reaction under the acid, base, and elevated temperature conditions. In Chapter 6, by using activated carbon, multi‐walled carbon nanotubes, and single‐wall carbon nanohorns as hybrid electrode materials (5:1:1), remarkable specific length capacitance of 48.76 mF cm−1 and charge-discharge stability (over 2000 times cycles) of ss‐CWECs were demonstrated, which are the highest reported to date. Furthermore, a high‐pass filter for eliminating ultra‐low electronic noise was demonstrated, enabling an optical Morse Code communication system to be operated. vThe collective works in this thesis demonstrate novel energy conversion and storage applications with the liquid crystal in OSCs and carbon nanoparticles in supercapacitors. These results provide a step forwards in the development of energy conversion and storage devices for a more efficient energy system.

Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics is based on the analysis of sequences of ground-taken sky images or satellite observations. Despite encouraging results, a recurrent limitation of existing deep learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a frequent temporal lag and limited ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatio-temporal neural network architecture that models cloud motion from sky images to not only predict future irradiance levels and associated uncertainties, but also segmented images, which provide richer information on the local irradiance map. We show that ECLIPSE anticipates critical events and reduces temporal delay while generating visually realistic futures. The model characteristics and properties are investigated with an ablation study and a comparative study on the benefits and different ways to integrate auxiliary data into the modelling. The model predictions are also interpreted through an analysis of the principal spatio-temporal components learned during network training. Manuscript accepted for publication in Applied Energy

Abstract The direct carbon fuel cells with solid oxide electrolyte (DC-SOFC) and anodes deposited by inject printing (EM/DCIJP) were studied. The cells were fed with carbon fuel obtained by the direct RF plasma splitting of methane. Since the (EM/DCIJP)M/DCIJP technology allows easy modification of electrode material, the effect of Cu addition to the Ni–YSZ anode was investigated. The significant improvement of the cell performance with the new anode was observed.

Conditional Moment Closure (CMC) is a suitable method for predicting scalars such as carbon monoxide with slow chemical time scales in turbulent combustion. Although this method has been successfully applied to non-premixed combustion, its application to lean premixed combustion is rare. In this study the CMC method is used to compute piloted lean premixed combustion in a distributed combustion regime. The conditional scalar dissipation rate of the conditioning scalar, the progress variable, is closed using an algebraic model and turbulence is modelled using the standard k–ɛ model. The conditional mean reaction rate is closed using a first order CMC closure with the GRI-3.0 chemical mechanism to represent the chemical kinetics of methane oxidation. The PDF of the progress variable is obtained using a presumed shape with the Beta function. The computed results are compared with the experimental measurements and earlier computations using the transported PDF approach. The results show reasonable agreement w...