Hannah-Noa Barad

All-Oxide Photovoltaics

Recently, a new field in photovoltaics (PV) has emerged, focusing on solar cells that are entirely based on metal oxide semiconductors. The all-oxide PV approach is very attractive due to the chemical stability, nontoxicity, and abundance of many metal oxides that potentially allow manufacturing under ambient conditions. Already today, metal oxides (MOs) are widely used as components in PV cells such as transparent conducting front electrodes or electron-transport layers, while only very few MOs have been used as light absorbers. In this Perspective, we review recent developments of all-oxide PV systems, which until today were mostly based on Cu2O as an absorber. Furthermore, ferroelectric BiFeO3-based PV systems are discussed, which have recently attracted considerable attention. The performance of all-oxide PV cells is discussed in terms of general PV principles, and directions for progress are proposed, pointing toward the development of novel metal oxide semiconductors using combinatorial methods.

Quantum efficiency and bandgap analysis for combinatorial photovoltaics: sorting activity of Cu-O compounds in all-oxide device libraries.

All-oxide-based photovoltaics (PVs) encompass the potential for extremely low cost solar cells, provided they can obtain an order of magnitude improvement in their power conversion efficiencies. To achieve this goal, we perform a combinatorial materials study of metal oxide based light absorbers, charge transporters, junctions between them, and PV devices. Here we report the development of a combinatorial internal quantum efficiency (IQE) method. IQE measures the efficiency associated with the charge separation and collection processes, and thus is a proxy for PV activity of materials once placed into devices, discarding optical properties that cause uncontrolled light harvesting. The IQE is supported by high-throughput techniques for bandgap fitting, composition analysis, and thickness mapping, which are also crucial parameters for the combinatorial investigation cycle of photovoltaics. As a model system we use a library of 169 solar cells with a varying thickness of sprayed titanium dioxide (TiO2) as the window layer, and covarying thickness and composition of binary compounds of copper oxides (Cu–O) as the light absorber, fabricated by Pulsed Laser Deposition (PLD). The analysis on the combinatorial devices shows the correlation between compositions and bandgap, and their effect on PV activity within several device configurations. The analysis suggests that the presence of Cu4O3 plays a significant role in the PV activity of binary Cu–O compounds.

Utilizing Pulsed Laser Deposition Lateral Inhomogeneity as a Tool in Combinatorial Material Science

Pulsed laser deposition (PLD) is widely used in combinatorial material science, as it enables rapid fabrication of different composite materials. Nevertheless, this method was usually limited to small substrates, since PLD deposition on large substrate areas results in severe lateral inhomogeneity. A few technical solutions for this problem have been suggested, including the use of different designs of masks, which were meant to prevent inhomogeneity in the thickness, density, and oxidation state of a layer, while only the composition is allowed to be changed. In this study, a possible way to take advantage of the large scale deposition inhomogeneity is demonstrated, choosing an iron oxide PLD-deposited library with continuous compositional spread (CCS) as a model system. An Fe₂O₃-Nb₂O₅ library was fabricated using PLD, without any mask between the targets and the substrate. The library was measured using high-throughput scanners for electrical, structural, and optical properties. A decrease in electrical resistivity that is several orders of magnitude lower than pure α-Fe₂O₃ was achieved at ∼20% Nb-O (measured at 47 and 267 °C) but only at points that are distanced from the center of the PLD plasma plume. Using hierarchical clustering analysis, we show that the PLD inhomogeneity can be used as an additional degree of freedom, helping, in this case, to achieve iron oxide with much lower resistivity.

Data Mining and Machine Learning Tools for Combinatorial Material Science of All-Oxide Photovoltaic Cells

Growth in energy demands, coupled with the need for clean energy, are likely to make solar cells an important part of future energy resources. In particular, cells entirely made of metal oxides (MOs) have the potential to provide clean and affordable energy if their power conversion efficiencies are improved. Such improvements require the development of new MOs which could benefit from combining combinatorial material sciences for producing solar cells libraries with data mining tools to direct synthesis efforts. In this work we developed a data mining workflow and applied it to the analysis of two recently reported solar cell libraries based on Titanium and Copper oxides. Our results demonstrate that QSAR models with good prediction statistics for multiple solar cells properties could be developed and that these models highlight important factors affecting these properties in accord with experimental findings. The resulting models are therefore suitable for designing better solar cells.

Process-Function Data Mining for the Discovery of Solid-State Iron-Oxide PV

Data mining tools have been known to be useful for analyzing large material data sets generated by high-throughput methods. Typically, the descriptors used for the analysis are structural descriptors, which can be difficult to obtain and to tune according to the results of the analysis. In this Research Article, we show the use of deposition process parameters as descriptors for analysis of a photovoltaics data set. To create a data set, solar cell libraries were fabricated using iron oxide as the absorber layer deposited using different deposition parameters, and the photovoltaic performance was measured. The data was then used to build models using genetic programing and stepwise regression. These models showed which deposition parameters should be used to get photovoltaic cells with higher performance. The iron oxide library fabricated based on the model predictions showed a higher performance than any of the previous libraries, which demonstrates that deposition process parameters can be used to model photovoltaic performance and lead to higher performing cells. This is a promising technique toward using data mining tools for discovery and fabrication of high performance photovoltaic materials.

How Transparent Oxides Gain Some Color: Discovery of a CeNiO3 Reduced Bandgap Phase As an Absorber for Photovoltaics

In this work, we describe the formation of a reduced bandgap CeNiO3 phase, which, to our knowledge, has not been previously reported, and we show how it is utilized as an absorber layer in a photovoltaic cell. The CeNiO3 phase is prepared by a combinatorial materials science approach, where a library containing a continuous compositional spread of Ce xNi1- xO y is formed by pulsed laser deposition (PLD); a method that has not been used in the past to form Ce-Ni-O materials. The library displays a reduced bandgap throughout, calculated to be 1.48-1.77 eV, compared to the starting materials, CeO2 and NiO, which each have a bandgap of ∼3.3 eV. The materials library is further analyzed by X-ray diffraction to determine a new crystalline phase. By searching and comparing to the Materials Project database, the reduced bandgap CeNiO3 phase is realized. The CeNiO3 reduced bandgap phase is implemented as the absorber layer in a solar cell and photovoltages up to 550 mV are achieved. The solar cells are also measured by surface photovoltage spectroscopy, which shows that the source of the photovoltaic activity is the reduced bandgap CeNiO3 phase, making it a viable material for solar energy.