Study of bismuth-based perovskite-like materials for solar cell and supercapacitor applications

Low-cost, stable and solution-processable materials have attracted significant research interest for their green and economical applications in solar energy harvesting and energy storage. The rapid emergence of lead-halide perovskites solar cells along with their ever-increasing efficiency has led to a new era of solar energy research. Lead-based perovskite solar cells are central to the efficiency rise, however, the toxicity of lead and its adverse effects on the environment have raised lots of concern. At the same time, research on supercapacitors electrode materials with desirable properties are crucial, as supercapacitors possess high power density, quick charge-discharge rates and long cycle life. In the first chapter, background knowledge about solar technology will be discussed, with emphasis on perovskite solar cells within the third generation of photovoltaic technology. Moreover, studies on bismuth-halide complexes will be discussed in detail to pave the road for explanation and discussion of the main research topic. In the first two chapters of the results and discussion section, hybrid iodobismuthates with heterocyclic cations have been synthesized and studied, including [C5H6N][BiI4] ([PY][BiI4]), [C6H8N][BiI4] ([MEPY][BiI4]), C3H5N2SBiI4 ([AT][BiI4]) with 1D structure, together with 0D [C3H5NS]3Bi2I9 ([TH]3[Bi2I9]) and [C4H6N2]3Bi2I9 ([PZ]3[Bi2I9]). Their structures have been investigated by single crystal X-ray diffraction, and one-dimensional [BiI4]- anionic chains built by edge-sharing [BiI6]3- octahedra were found in all the 1D materials; alongside short I…I, I…C contacts and hydrogen bonding of [PY][BiI4] and [AT][BiI4], giving rise to three-dimensional intermolecular interactions. All the compounds are semiconductors, with band gap values around 2.0 eV. Larger band edge dispersions of electronic band structures for 1D materials were calculated compared to 0D materials, and the contributions from the organic moieties to the conduction band minimum has been derived by density functional theory. Solid-state optical and electrochemical studies performed on materials as thin films were carried out, and their stabilities under ambient environment have been demonstrated. [PY][BiI4] and [AT][BiI4] were used as the absorber layer in printable mesoscopic solar cells without hole-transport material, leading to efficiencies up to 0.9% (0.47% for [AT][BiI4]), showing a promising new approach towards the development of lead-free third-generation photovoltaic materials. Additionally, solar cells with carbon counter electrode and [AT][BiI4] as light harvester have shown interesting results as lead-free solar energy generator/storage integration devices. In chapter 5, band gap engineering of 0D Cs3[Bi2I9] and 1D [PY][BiI4] has been studied and achieved by incorporating 2- anions (including S2-, SiF62-, and TiF62-) into the crystal lattice, by the drop-casting and bismuth xanthate thermal decomposition method. In both cases, about 0.3-0.4 eV decrease in band gap values can be found. Powder XRD data collected on the dianion-treated thin films shows no structural change to the original samples, suggesting the crystal structure of [PY][BiI4] and Cs3[Bi2I9] remained unchanged after the dianionic treatment. The existence of sulfur in S-treated [PY][BiI4] as thin films has been confirmed by EDS and SEM, and HRTEM images showed good crystallinity of the doped [PY][BiI4] thin films. Raman Spectroscopy on 2- treated thin films showed the existence of Bi-S bonds in both cases, suggesting that some S atoms are partially substituting iodine atoms in the crystal lattice. In Chapter 6 of this thesis, eco-friendly, solution-processable and stable Bi-based materials are studied for energy storage applications in electric double-layer capacitors (EDLCs). Firstly, we demonstrated a novel synthetic route for films of an underexplored 3-D hexagonal bismuth chalcohalide, Bi13S18I2, and investigated its potential as the active electrode material in EDLC-type supercapacitors. The synthetic procedure has been optimised and comprises of the lowest annealing temperature (150°C) and the shortest processing time (1 h) currently reported. When integrated in a symmetrical EDLC with an aqueous NaClO4 electrolyte, the Bi13S18I2-based device achieves a remarkable areal capacitance of 210.68 mF cm-2 with 99.7% capacitance retention after 5000 cycles. Both the Bi13S18I2 powder and thin-film electrodes have been characterized through XRD, XPS, Raman spectroscopy, and SEM. Secondly, organic-inorganic [CN2SH5]3[BiI6] (TBI) was synthesized and characterized. Single crystal X-ray diffraction study reveals that TBI crystallizes in monoclinic system, with discrete [BiI6]3- octahedra as the inorganic motif. Utilizing TBI as the active supercapacitor electrode material, together with carbon cloth current collector and neutral NaClO4 water solution as the electrolyte, we have achieved an electrode areal capacitance over 3.22 F 〖cm〗^(-2) and specific capacitance over 1030 F g-1 when the device operates as an EDLC. The supercapacitor device shows superior capacitance retention after 5,000 charge-discharge cycles. The superior stability, low-cost, and facile synthesis of both Bi13S18I2 and TBI prove the promising potential of Bi-based materials for supercapacitor applications. To sum up, the initial work in this thesis expands the material types available for developing lead-free Bi-based solar absorbers and supercapacitor electrode materials. This work also sheds light on the material design and device optimization of Bi-based perovskite-like materials for solar cell and supercapacitor applications

Rapid land cover classification using a 36-year time series of multi-source remote sensing data

Long-time series land cover classification information is the basis for scientific research on urban sprawl, vegetation change, and the carbon cycle. The rapid development of cloud computing platforms such as the Google Earth Engine (GEE) and access to multi-source satellite imagery from Landsat and Sentinel-2 enables the application of machine learning algorithms for image classification. Here, we used the Random Forest algorithm to quickly achieve a time series land cover classification at different scales based on the fixed land classification sample points selected from images acquired in 2022, and the year-by-year spectral differences of sample points. The classification accuracy was enhanced by using multi-source remote sensing data, such as synthetic aperture radar (SAR) and digital elevation model (DEM) data. The results showed that: (i) the maximum difference (threshold) of sample points without land class change determined by counting the sample points of each band of landsat time series from 1986 to 2022 was 0.25; (ii) the kappa coefficient and observed accuracy of the same sensor from Landsat 8 are higher than the results of TM and ETM+ sensor data from 2013 to 2022; (iii) the addition of a mining land cover type increase the kappa coefficient and overall accuracy mean values of the Sentinel 2 image classification for a complex mining and -forest area. Among the land classifications by multi-source remote sensing, the combined variables spectral band + index + topography + SAR result in the highest accuracy, but the overall improvement is limited. The method proposed is applicable to remotely sensed images at different scales and using sensors under complex terrain conditions. The use of GEE cloud computing platform enabled rapid analysis of remotely sensed data to produce land cover maps with high-accuracy and a long time series

Lead-free Pseudo-three-dimensional Organic-inorganic Iodobismuthates for Photovoltaic Applications

X-ray diffraction, electronic characterisation and mesoscopic solar cell evaluation were performed for two novel iodobismuthates, C5H6NBiI4 ([py][BiI4]) and C6H8NBiI4 ([mepy][BiI4]).</p

Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks

Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader domains and tasks, there is a need to establish richer and more diverse benchmarks to better reflect the reality of the application scenarios. Graph learning is an emerging field of machine learning that urgently needs more and better benchmarks. To accommodate the need, we introduce Graph Learning Indexer (GLI), a benchmark curation platform for graph learning. In comparison to existing graph learning benchmark libraries, GLI highlights two novel design objectives. First, GLI is designed to incentivize \emph{dataset contributors}. In particular, we incorporate various measures to minimize the effort of contributing and maintaining a dataset, increase the usability of the contributed dataset, as well as encourage attributions to different contributors of the dataset. Second, GLI is designed to curate a knowledge base, instead of a plain collection, of benchmark datasets. We use multiple sources of meta information to augment the benchmark datasets with \emph{rich characteristics}, so that they can be easily selected and used in downstream research or development. The source code of GLI is available at \url{https://github.com/Graph-Learning-Benchmarks/gli}.Comment: Oral Presentation at LOG 202