Akash Dasgupta

Abstract:

This contains all data used in the paper: ACS Energy Lett. 2022, 7, 7, 2311–2322, DOI: https://doi.org/10.1021/acsenergylett.2c01094. Data has been sorted into raw and processed, and organised by which figure they appear in. Arrays require Python and the numpy package to load (np.load('filename.npy')). All other data is in text format of some form, easily openable. Some plots require Origin labs to open, but no data in these files are inaccessible from the txt files/ csvs etc.

Abstract:

High-efficiency solar cell architectures, including silicon heterojunction (SHJ) and perovskite/silicon tandems, rely heavily on the unique properties of transparent conducting oxides (TCOs). The push towards terawatt-scale PV manufacturing means it is increasingly desirable to develop indium-free TCOs to facilitate the upscaled manufacturing of high-efficiency cell designs. Aluminium-doped ZnO (AZO) deposited by atomic layer deposition (ALD) has emerged as a promising candidate due to its combination of optical transparency and electrical conductivity. In addition, AZO has also been shown to passivate the c-Si surface. The ability for one material to provide all three properties without requiring any indium is advantageous in single junction and tandem solar devices. Herein, we demonstrate exceptional silicon surface passivation using AZO/AlOx stacks deposited with ALD, with a J0 < 1 fA cm−2 and corresponding implied open circuit voltage (iVOC) of 740 mV. We provide a comprehensive analysis of the role of ALD precursor dosing to achieve optimised performance. A broad range of characterisation approaches were used to probe the structural, compositional, and chemical properties of AZO films. These indicated that the passivation properties are governed by a delicate interplay between the Zn and Al concentrations in the film, highlighting the importance of precise process control. Optical modelling in a single junction SHJ architecture indicates these AZO films are close in performance to high-mobility indium-containing TCOs. The insights provided by this work may help to further the case of indium-free TCOs, which is critical for upscaled production of high-efficiency solar cells.

Abstract:

It is widely accepted that mobile ions are responsible for the slow electronic responses observed in metal halide perovskite-based optoelectronic devices, and strongly influence long-term operational stability. Electrical characterisation methods mostly observe complex indirect effects of ions on bulk/interface recombination, struggle to quantify the ion density and mobility, and are typically not able to fully quantify the influence of the ions upon the bulk and interfacial electric fields. We analyse the bias-assisted charge extraction (BACE) method for the case of a screened bulk electric field, and introduce a new characterisation method based on BACE, termed ion drift BACE. We reveal that the initial current density and current decay dynamics depend on the ion conductivity, which is the product of ion density and mobility. This means that for an unknown high ion density, typical in perovskite solar absorber layers, the mobility cannot be directly obtained from BACE measurements. We derive an analytical model to illustrate the relation between current density, conductivity and bulk field screening, supported by drift-diffusion simulations. By measuring the ion density independently with impedance spectroscopy, we show how the ion mobility can be derived from the BACE ion conductivity. We highlight important differences between the low- and high-ion density cases, which reveal whether the bulk electric field is fully screened or not. Our work clarifies the complex ion-related processes occurring within perovskite solar cells and gives new insight into the operational principles of halide perovskite devices as mixed ionic-electronic conductors.

Abstract:

<jats:p>In this work, we demonstrate that time-resolved photoluminescence data of metal halide perovskites can be effectively evaluated by combining Bayesian inference with a Markov-chain Monte-Carlo algorithm and a physical model. This approach enables us to infer a high number of parameters that govern the performance of metal halide perovskite-based devices, alongside the probability distributions of those parameters, as well as correlations among all parameters. Via studying a set of halfstacks, comprising electron- and hole-transport materials contacting perovskite thin films, we determine surface recombination velocities at these interfaces with high precision. From the probability distributions of all inferred parameters, we can simulate intensity-dependent photoluminescence quantum efficiency and compare it to experimental data. Finally, we estimate mobility values for vertical charge-carrier transport, which is perpendicular to the plane of the substrate, for all samples using our approach. Since this mobility estimation is derived from charge-carrier diffusion over the length scale of the film thickness and in the vertical direction, it is highly relevant for transport in photovoltaic and light-emitting devices. Our approach of coupling spectroscopic measurements with advanced computational analysis will help speed up scientific research in the field of optoelectronic materials and devices and exemplifies how carefully constructed computational algorithms can derive valuable plurality of information from simple datasets. We expect that our approach can be expanded to a variety of other analysis techniques and that our method will be applicable to other semiconductors.</jats:p> <jats:sec> <jats:title/> <jats:supplementary-material> <jats:permissions> <jats:copyright-statement>Published by the American Physical Society</jats:copyright-statement> <jats:copyright-year>2025</jats:copyright-year> </jats:permissions> </jats:supplementary-material> </jats:sec>

Abstract:

In this work we demonstrate that time-resolved photoluminescence data of metal halide perovskites can be effectively evaluated by combining Bayesian inference with a Markov-Chain Monte-Carlo algorithm and a physical model. This approach enables us to infer a high number of parameters which govern the performance of metal halide perovskite-based devices, alongside the probability distributions of those parameters, as well as correlations among all parameters. Via studying a set of "half-stacks’‘, comprising electron and hole transport materials contacting perovskite thin-films, we determine surface recombination velocities at these interfaces with high precision. From the probability distributions of all inferred parameters, we can simulate intensity-dependent photoluminescence quantum efficiency and compare it to the experimental data. Finally, we estimate mobility values for the "vertical’’ charge carrier transport, that perpendicular to the plane of the substrate, for all samples using our approach. Since this mobility estimation is derived from charge carrier diffusion over the length-scale of the film thickness and in the vertical direction, it is highly relevant to transport in photovoltaic and light emitting devices. Our approach of coupling spectroscopic measurements with advanced, computational analysis will help speed up scientific research in the field of optoelectronic materials and devices and exemplifies how carefully constructed computational algorithms can derive valuable plurality of information from simple datasets. We expect that our approach will be expandable to a variety of other analysis techniques and that our method will be applicable to other semiconductors.