583 research outputs found

    An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

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    Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences

    An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

    Get PDF
    Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences

    An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

    Get PDF
    Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42, 400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences. © 2021, The Author(s)

    An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

    Get PDF
    AbstractLarge datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences.</jats:p

    The challenge of studying perovskite solar cells’ stability with machine learning

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    Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technology–which has resulted in the accumulation of a significant amount of data. The best example is the “Perovskite Database Project,” which also includes stability-related metrics. From this database, we use data on 1,800 perovskite solar cells where device stability is reported and use Random Forest to identify and study the most important factors for cell stability. By applying the concept of learning curves, we find that the potential for improving the models’ performance by adding more data of the same quality is limited. However, a significant improvement can be made by increasing data quality by reporting more complete information on the performed experiments. Furthermore, we study an in-house database with data on more than 1,000 solar cells, where the entire aging curve for each cell is available as opposed to stability metrics based on a single number. We show that the interpretation of aging experiments can strongly depend on the chosen stability metric, unnaturally favoring some cells over others. Therefore, choosing universal stability metrics is a critical question for future databases targeting this promising technology

    From design to device: challenges and opportunities in computational discovery of p-type transparent conductors

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    A high-performance p-type transparent conductor (TC) does not yet exist, but could lead to advances in a wide range of optoelectronic applications and enable new architectures for, e.g., next-generation photovoltaic (PV) devices. High-throughput computational material screenings have been a promising approach to filter databases and identify new p-type TC candidates, and some of these predictions have been experimentally validated. However, most of these predicted candidates do not have experimentally-achieved properties on par with n-type TCs used in solar cells, and therefore have not yet been used in commercial devices. Thus, there is still a significant divide between transforming predictions into results that are actually achievable in the lab, and an even greater lag in scaling predicted materials into functional devices. In this perspective, we outline some of the major disconnects in this materials discovery process -- from scaling computational predictions into synthesizable crystals and thin films in the laboratory, to scaling lab-grown films into real-world solar devices -- and share insights to inform future strategies for TC discovery and design

    Breaking 1.7V open circuit voltage in large area transparent perovskite solar cells using bulk and interfaces passivation

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    Efficient semi-transparent solar cells can trigger the adoption of building integrated photovoltaics. Halide perovskites are particularly suitable in this respect owing to their tunable bandgap. Main drawbacks in the development of transparent perovskite solar cells are the high Voc deficit and the difficulties in depositing thin films over large area substrates, given the low solubility of bromide and chloride precursors. In this work, we develop a 2D and passivation strategies for the high band-gap Br perovskite able to reduce charge recombination and consequently improving the open-circuit voltage. We demonstrate 1cm 2 perovskite solar cells with Voc up to 1.73 V (1.83 eV QFLS) and a PCE of 8.2%. The AVT exceeds 70% by means of a bifacial light management and a record light utilization efficiency of 5.72 is achieved, setting a new standard for transparent photovoltaics. Moreover, we show the high ceiling of our technology towards IoT application due to a bifaciality factor of 87% along with 17% PCE under indoor lighting. Finally, the up-scaling has been demonstrated fabricating 20cm 2 -active area modules with PCE of 7.3% and Voc per cell up to 1.65V

    Bandgap-universal passivation enables stable perovskite solar cells with low photovoltage loss

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    The efficiency and longevity of metal-halide perovskite solar cells are typically dictated by nonradiative defect-mediated charge recombination. In this work, we demonstrate a vapor-based amino-silane passivation that reduces photovoltage deficits to around 100 millivolts (>90% of the thermodynamic limit) in perovskite solar cells of bandgaps between 1.6 and 1.8 electron volts, which is crucial for tandem applications. A primary-, secondary-, or tertiary-amino–silane alone negatively or barely affected perovskite crystallinity and charge transport, but amino-silanes that incorporate primary and secondary amines yield up to a 60-fold increase in photoluminescence quantum yield and preserve long-range conduction. Amino-silane–treated devices retained 95% power conversion efficiency for more than 1500 hours under full-spectrum sunlight at 85°C and open-circuit conditions in ambient air with a relative humidity of 50 to 60%

    Photovoltaic Performance of FAPbI3 Perovskite Is Hampered by Intrinsic Quantum Confinement

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    Formamidinium lead trioiodide (FAPbI3) is a promising perovskite for single-junction solar cells. However, FAPbI3 is metastable at room temperature and can cause intrinsic quantum confinement effects apparent through a series of above-bandgap absorption peaks. Here, we explore three common solution-based film-fabrication methods, neat N,N-dimethylformamide (DMF)–dimethyl sulfoxide (DMSO) solvent, DMF-DMSO with methylammonium chloride, and a sequential deposition approach. The latter two offer enhanced nucleation and crystallization control and suppress such quantum confinement effects. We show that elimination of these absorption features yields increased power conversion efficiencies (PCEs) and short-circuit currents, suggesting that quantum confinement hinders charge extraction. A meta-analysis of literature reports, covering 244 articles and 825 photovoltaic devices incorporating FAPbI3 films corroborates our findings, indicating that PCEs rarely exceed a 20% threshold when such absorption features are present. Accordingly, ensuring the absence of these absorption features should be the first assessment when designing fabrication approaches for high-efficiency FAPbI3 solar cells
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