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1H and 13C nuclear magnetic resonance spectra were recorded on a Bruker Avance III 400 or Magritek Spinsolve 60 spectrometer at 293 K. Chemical shifts are reported as δ in parts per million (ppm) and referenced to the chemical shift of the residual solvent resonances (CDCl3: 1H: δ = 7.26 ppm, 13C: δ = 77.16 ppm). Polymer molecular weight and dispersity were determined using a Malvern Viscotek GPCmax size exclusion chromatograph instrument fitted with a Viscotek TDA 305 detector unit equipped with refractive index and light scattering detectors. Samples were dissolved in tetrahydrofuran at a concentration of approximately 1 mg mL-1 and eluted through a guard column and two Agilent PLGel 5 µm mixed C columns (300 x 7.5 mm) at a flow rate of 1 ml.min-1; the elution pathlength was heated to 30 °C for the duration. Molecular weights were calibrated against known poly(methyl acrylate) standards. Differential scanning calorimetry was conducted using a TA Instruments Discovery 2500. Samples were analysed in non-hermetic aluminium pans and compared against an empty reference pan of the same type. Loaded sample masses were between 3 and 10 mg. Samples were subjected to two complete heat/cool cycles from -50 °C to 150 °C (-85 °C to 150 °C for lower Tg samples) and both heating and cooling rates were set at 10 °C min-1. UV/Vis transmittance and absorption spectra were measured with a PerkinElmer Lambda 750 spectrophotometer. Transmittance spectra of films were measured using wavelength scan with a resolution of 1 nm at a scan speed of 267 nm/min and a slit width of 2 nm. Samples were directly mounted to the sample holder. Solution spectroscopy was carried out on solutions in THF in quartz SUPRASIL® cuvettes (10 mm pathlength). Absorption spectra of luminophore solutions were taken using a wavelength scan with a resolution of 0.5 nm at a scan speed of 141.20 nm/min and a slit width of 2 nm. A reference sample of THF in an identical cuvette was used to apply a 100% transmission correction. Steady-state PL spectroscopy was performed on a Fluorolog-3 spectrophotometer (Horiba Jobin Yvon). Solid-state emission spectra were recorded using the front-face configuration. Solution emission spectra were recorded using the right-angle configuration, over 10 averaged scans. The excitation and emission slits were adjusted so that the maximum PL intensity was within the range of linear response of the detector and were kept the same between samples if direct comparison between the emission intensity was required. Emission and excitation spectra were corrected for the wavelength response of the system and the intensity of the lamp profile over the excitation range, respectively, using correction factors supplied by the manufacturer. Photoluminescence quantum yields (ΦPL) were measured using a Quanta-phi integrating sphere (Horiba Jobin Yvon) mounted on the Fluorolog-3 spectrophotometer. The UC emission and phosphorescence spectra, threshold intensity (I_th), UC quantum yield (UC) and lifetime measurements were performed using an FLS1000 time-correlated single photon counting (TCSPC) spectrometer (Edinburgh Instruments Ltd.). The samples were excited with a 532 nm laser (MGL-III-532, 200mW). To determine I_th, the laser power was adjusted using a Thorlabs PM100A Power Meter Console combined with a S120VC Si photodiode power sensor (range: 200-1100 nm) before the measurement, across the 5 to 8000 mW cm-2. The ΦUC was measured with an integrating sphere (SNS125 5-inch sphere, three windows, International Light Technologies). The sample was placed at the center of the sphere using a sample holder. A baffle is placed in front of the observation window, which blocks any scattering and reflection of the laser from the sample surface. The angle of the sample holder is adjustable. The normal direction of the sample holder is 22.5˚ to the excitation beam line, which leads the reflection of the laser to the inner surface of the sphere. The laser power was measured with a photodiode before each ΦUC measurement. Both the emission of the sample (380-500 nm) and scattering of the laser beam (530-534 nm) were measured. A neutral density filter (O.D.=3.0) was placed before the excitation beam for the scattering intensity measurements. Six data sets were collected to calculate the ΦUC of each sample: 1. sample in the path of the beam – “in fluorescence”; 2. sample in scattering; 3. sample facing away from beam – “out of fluorescence”, 4. sample out of scattering; 5. empty sphere fluorescence; 6. empty sphere scattering. Fluorescence decay measurements were performed using the multi-channel scaling (MCS) method on a the FLS1000 TCSPC spectrometer. The emission decay was recorded using a photomultiplier tube (PMT-980) equipped with TCC2 counting electronics. For the upconversion lifetime measurements, a wavelength of 440 nm was selected, and a short-pass filter (cut-off at 500 nm, Thorlabs) was placed in front of the detector. For the phosphorescence lifetimes, a wavelength of 660 nm was selected, and a long-pass filter (cut-off 550 nm, Thorlabs) was used. The instrument response function (IRF) was measured using Ludox® colloidal silica solution (a SiO2 particle suspension solution) and using a neutral density filter (O.D.=3) to attenuate the laser intensity. The pulse repetition rate was adjusted to ensure the full decay was detected within the time window. Data-fitting was carried out by tail fitting to each emission decay trace using a multiexponential decay function within the FAST software package (Edinburgh Instruments Ltd.). The goodness of fit was evaluated using the reduced chi-square statistics (χ2) and the randomness of the residuals. Please also see the readme file for more details on data collection and file organisation.

Perovskite-based material La₀.₂Sr₀.₂₅Ca₀.₄₅MₓTi₁₋ₓO₃₋δ (M=Fe, Co, Ni) was synthesised successfully using optimised Pechini method. SOFC system was fabricated using the synthesised material as the anode, YSZ as the electrolyte, and LSM as the cathode. Initial evaluations were conducted with hydrogen as the fuel. Electrochemical switching in humidified hydrogen is found to significantly improve the performance of the cells. Hydrogen SOFC with La₀.₂Sr₀.₂₅Ca₀.₄₅Co₀.₀₂₅Fe₀.₀₂₅Ti₀.₉₅O₃₋δ anode is found to show low polarisation resistance (3.23 Ω/cm²) and high maximum power density (227 mW/cm²). HDCFCs were setup with the fuel mixture containing eutectic K₂CO₃/Li₂CO₃ and activated charcoal. When purged with N₂, performance of HDCFCs was found inferior to that of hydrogen SOFCs. Better performance was observed with CO₂ as the purging gas. For example, in CO₂, HDCFC with La₀.₂Sr₀.₂₅Ca₀.₄₅Co₀.₀₂₅Fe₀.₀₂₅Ti₀.₉₅O₃₋δ anode showed a polarization resistance of 2.31 Ω/cm² and power density of 99.6 mW/cm². To explore the waste-to-energy applications of HDCFCs, medium density fibreboard was pyrolysed, forming a biochar with high oxygen content. Using this biochar as the fuel in the HDCFCs, unexpected OCV loss was observed for high operation temperatures when purged with N₂. With the limitation in testing temperature, worse performance was observed. CO₂ purging maintained OCV at higher temperatures, with the La₀.₂Sr₀.₂₅Ca₀.₄₅Co₀.₀₂₅Fe₀.₀₂₅Ti₀.₉₅O₃₋δ anode showing a polarisation resistance of 9.79 Ω/cm² and power density of 11.6 mW/cm². Abnormal shape in the I-V curve was observed for HDCFCs and some of the SOFCs tested. In attempt to understand this observation, EIS at different applied voltages were obtained, various conditions were applied for the I-V scans, carbonate concentrations in the fuel mixture were tested and different voltages were applied to the HDCFCs while the off-gases were analysed using gas chromatography. Redox behaviour of anode materials under different applied voltages and the change in CO and carbonate concentrations are suggested to be related to the abnormal curve.

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.