Promises and challenges of perovskite solar cells Correa-Baena, Juan-Pablo; Saliba, Michael; Buonassisi, Tonio ...
Science (American Association for the Advancement of Science),
11/2017, Volume:
358, Issue:
6364
Journal Article
Peer reviewed
Open access
The efficiencies of perovskite solar cells have gone from single digits to a certified 22.1% in a few years’ time. At this stage of their development, the key issues concern how to achieve further ...improvements in efficiency and long-term stability. We review recent developments in the quest to improve the current state of the art. Because photocurrents are near the theoretical maximum, our focus is on efforts to increase open-circuit voltage by means of improving charge-selective contacts and charge carrier lifetimes in perovskites via processes such as ion tailoring. The challenges associated with long-term perovskite solar cell device stability include the role of testing protocols, ionic movement affecting performance metrics over extended periods of time, and determination of the best ways to counteract degradation mechanisms.
Indoor photovoltaic cells have the potential to power the Internet of Things ecosystem, including distributed and remote sensors, actuators, and communications devices. As the power required to ...operate these devices continues to decrease, the type and number of nodes that can now be persistently powered by indoor photovoltaic cells are rapidly growing. This will drive significant growth in the demand for indoor photovoltaics, creating a large alternative market for existing and novel photovoltaic technologies. With the re-emergence of interest in indoor photovoltaic cells, we provide an overview of this burgeoning field focusing on the technical challenges that remain to create energy autonomous sensors at viable price points and to overcome the commercial challenges for individual photovoltaic technologies to accelerate their market adoption.
The Internet of Things (IoT) ecosystem promises large networks of connected devices collecting the big data upon which our medical, manufacturing, infrastructure, and energy industries will be monitored and optimized. Billions of wireless sensors are expected to be installed over the coming decade, with almost half to be located inside buildings. Currently, the use of batteries to power these devices places significant constraints on their power consumption, where the range and frequency of data transmission are curtailed to achieve sufficient battery life, and the range of applications is also limited to the ones that allow battery replacement. Additional operation and maintenance costs are also incurred by providing replacement batteries.
Indoor photovoltaics has the potential to solve these hardware issues, providing greater reliability and operational lifetimes in wireless sensor networks. Persistently powering individual nodes by harvesting ambient light using small ∼cm2 photovoltaic cells is becoming possible for more and more wireless technologies and devices. Characterizing IPV cells is a growing research field with the performance of a considerable number of different PV technologies having now been measured under ambient light sources. Given the interest in commercializing different photovoltaic cells in this growing market, we discuss here the outstanding research questions that must be answered by the indoor photovoltaic community to enable self-powered, indoor-located IoT nodes.
Indoor photovoltaic cells have the potential to power the Internet of Things ecosystem. As the power required to operate devices continues to decrease, the type and number of nodes that can now be persistently powered by indoor photovoltaic cells are rapidly growing. This will drive significant growth in the demand for indoor photovoltaics, creating a large alternative market for existing and novel photovoltaic technologies. We provide a perspective on this burgeoning field, outlining the technical and commercial challenges to be overcome.
The role of the alkali metal cations in halide perovskite solar cells is not well understood. Using synchrotron-based nano-x-ray fluorescence and complementary measurements, we found that the halide ...distribution becomes homogenized upon addition of cesium iodide, either alone or with rubidium iodide, for substoichiometric, stoichiometric, and overstoichiometric preparations, where the lead halide is varied with respect to organic halide precursors. Halide homogenization coincides with long-lived charge carrier decays, spatially homogeneous carrier dynamics (as visualized by ultrafast microscopy), and improved photovoltaic device performance. We found that rubidium and potassium phase-segregate in highly concentrated clusters. Alkali metals are beneficial at low concentrations, where they homogenize the halide distribution, but at higher concentrations, they form recombination-active second-phase clusters.
Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called ...the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI
) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules' low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI
film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI
stability lifetime by 4 ± 2 times over bare MAPbI
and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss.
Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of ...cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (<100°C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30°C.
Pathways for solar photovoltaics Jean, Joel; Brown, Patrick R; Jaffe, Robert L ...
Energy & environmental science,
01/2015, Volume:
8, Issue:
4
Journal Article
Peer reviewed
Solar energy is one of the few renewable, low-carbon resources with both the scalability and the technological maturity to meet ever-growing global demand for electricity. Among solar power ...technologies, solar photovoltaics (PV) are the most widely deployed, providing 0.87% of the world's electricity in 2013 and sustaining a compound annual growth rate in cumulative installed capacity of 43% since 2000. Given the massive scale of deployment needed, this article examines potential limits to PV deployment at the terawatt scale, emphasizing constraints on the use of commodity and PV-critical materials. We propose material complexity as a guiding framework for classifying PV technologies, and we analyze three core themes that focus future research and development: efficiency, materials use, and manufacturing complexity and cost.
Direct solar-to-fuels conversion can be achieved by coupling a photovoltaic device with water-splitting catalysts. We demonstrate that a solar-to-fuels efficiency (SFE) > 10% can be achieved with ...nonprecious, low-cost, and commercially ready materials. We present a systems design of a modular photovoltaic (PV)-electrochemical device comprising a crystalline silicon PV minimodule and lowcost hydrogen-evolution reaction and oxygen-evolution reaction catalysts, without power electronics. This approach allows for facile optimization en route to addressing lower-cost devices relying on crystalline silicon at high SFEs for direct solar-to-fuels conversion.
Crystalline silicon comprises 90% of the global photovoltaics (PV) market and has sustained a nearly 30% cumulative annual growth rate, yet comprises less than 2% of electricity capacity. To sustain ...this growth trajectory, continued cost and capital expenditure (capex) reductions are needed. Thinning the silicon wafer well below the industry-standard 160 μm, in principle reduces both manufacturing cost and capex, and accelerates economically-sustainable expansion of PV manufacturing. In this analysis piece, we explore two questions surrounding adoption of thin silicon wafers: (a) What are the market benefits of thin wafers? (b) What are the technological challenges to adopt thin wafers? In this analysis, we re-evaluate the benefits and challenges of thin Si for current and future PV modules using a comprehensive technoeconomic framework that couples device simulation, bottom-up cost modeling, and a sustainable cash-flow growth model. When adopting an advanced technology concept that features sufficiently good surface passivation, the comparable efficiencies are achievable for both 50 μm wafers and 160 μm ones. We then quantify the economic benefits for thin Si wafers in terms of poly-Si-to-module manufacturing capex, module cost, and levelized cost of electricity (LCOE) for utility PV systems. Particularly, LCOE favors thinner wafers for all investigated device architectures, and can potentially be reduced by more than 5% from the value of 160 μm wafers. With further improvements in module efficiency, an advanced device concept with 50 μm wafers could potentially reduce manufacturing capex by 48%, module cost by 28%, and LCOE by 24%. Furthermore, we apply a sustainable growth model to investigate PV deployment scenarios in 2030. It is found that the state-of-the-art industry concept could not achieve the climate targets even with very aggressive financial scenarios, therefore the capex reduction benefit of thin wafers is advantageous to facilitate faster PV adoption. Lastly, we discuss the remaining technological challenges and areas for innovation to enable high-yield manufacturing of high-efficiency PV modules with thin Si wafers.
This technoeconomic analysis revisits the concept of thin silicon wafer for its potential cost benefits and technological challenges.
Abstract
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled ...approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal-halides spanning three dimensionalities and seven space groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross-validated accuracies for dimensionality and space group classification of 93 and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16° 2
θ
, which enables an XRD pattern to be obtained and classified in 5.5 min or less.
PDF
