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Schematic of the predictive wind farm flow control model
a, The ABL velocity profile is illustrated in red. Incident ABL winds u∞(z) are measured in the field experiments using a LiDAR (Light Detection And Ranging, black box and blue cone). Each turbine is equipped with cup and sonic anemometers (black circles) and generates a wake region (black shaded region). To predict the effect of a control strategy on the power of the collective wind farm, we model the power production of upwind turbines operating in freestream conditions, P̂u\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{P}}_\mathrm{u}$$\end{document}, and the waked turbines, P̂w\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\hat{P}}_\mathrm{w}$$\end{document}. b, The flow control model proposed in this study is the combination of a power–yaw model P̂(u∞(z),γ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{P}({u}_{\infty }(z),\gamma )$$\end{document}, which predicts the power production of a yawed turbine based on the incident wind u∞(z) and the yaw-misalignment γ, and a data-assisted wake model, which predicts the wake velocity deficit Δu. The power–yaw model and the wake model are described in Supplementary Methods. c, The wake velocity model parameters are calibrated using wind farm data for which the turbines are operating in baseline, yaw-aligned conditions, γ = 0. The wake model is then used to predict the farm power given a yaw control strategy, γ ≠ 0.
Collective wind farm operation experimental setup
a, Photo of the utility-scale wind farm of interest in this study, which is located in northwest India. b, Top view of the wind turbines of interest with the coordinates of the farm normalized by the wind turbine rotor diameter, D. The x and y directions correspond to easting and northing, respectively. The adjacent reference turbine is denoted as ‘Ref’. c, Measured wind rose during the experimental period as recorded by the reference wind turbine. The radial distance from the centre corresponds to the probability of the wind speed and wind direction in the wind rose, given as percentages. d, Commanded yaw-misalignment sequence, γc, for the fixed yaw-misalignment flow control model validation experiment. The commanded yaw misalignments do not depend on the incident wind conditions. During the model validation experiment, each commanded yaw-misalignment value is held fixed for 1 h.
Model predictions and field experiment results from the static yaw-misalignment model validation field experiment for three yaw-misalignment values
a–c, Streamwise velocity contours predicted by the flow control model for turbine 1 yaw misalignments of γ1 = −20° (a), γ1 = 0° (b) and γ1 = 20° (c) are shown for wind directions α from the north between 0 ± 2.5°. The wind speed is 7 ± 1.5 m s⁻¹ and the turbulence intensity is 5 ± 2.5%, as measured by the reference turbine. To account for the finite wind speed bin width, the power for each 1min\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1\,\min$$\end{document} averaged data sample for each turbine is normalized by the corresponding power of the adjacent reference turbine, and the normalized power is denoted as P. The yaw angles implemented in a and c are predicted to steer the wake region towards (a) or away (c) from the downwind turbines, depending on the yaw angle. Steering the wake away from the downwind turbines (c) is predicted to increase array power production (power prediction in f). d–f, Comparison of the wake model predictions to the measured power production for the three-turbine array for turbine 1 yaw misalignments of γ1 = −20° (d), γ1 = 0° (e) and γ1 = 20° (f). The powers are normalized by the power production of the leading turbine 1 with γ1 = 0° (P/P1γ0)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(P / P_{1}^{\gamma_0})$$\end{document} so that the results can be interpreted as a fractional gain in power compared with baseline control. The field experiment measurements are shown as circles, and the model predictions are shown as diamonds. The shaded region corresponds to 95% confidence intervals around the mean, estimated with bootstrapping. The blue and red correspond to yaw alignment and yaw misalignment, respectively. The flow control model is calibrated to the yaw-aligned data γ1 = 0°. The flow control model is then used to predict the power production for yaw-misaligned operation. For this plot, the measured angular velocity is used in the power–yaw model. The effect of the turbine 1 yaw misalignment on power production of the waked turbines (2 and 3) depends on the direction (sign) of the yaw. The field experiment measurements demonstrate a −5.1% and +28.6% change in the three-turbine array mean power production compared with γ1 = 0° for γ1 = −20° and γ1 = +20°, respectively. For γ1 = −20° (d), both the data and the predictive model result in a slight decrease in the power production of turbine 1 compared with γ1 = 0°. For γ1 = +20° (f), both the data and the predictive model result in a slight increase in the power production of turbine 1 compared with γ1 = 0°. This small gain is associated with the incident wind velocity profiles in the ABL which occurred during operation with γ1 = +20° (Supplementary Figs. 5 and 6); such a small gain has also been shown for certain flow conditions and yaw misalignments in previous studies16,31.
Source data
Results from the static yaw-misalignment field experiment for flow control model validation
The relative gain in the total power for turbines 1, 2, and 3 as a function of the yaw misalignment of turbine 1 from the static yaw-misalignment field experiment is shown, ∑iNtPi(γ1)/∑iNtPi(γ1=0)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathop{\sum }\nolimits_{i}^{{N}_\mathrm{t}}{P}_{i}({\gamma }_{1})/\mathop{\sum }\nolimits_{i}^{{N}_\mathrm{t}}{P}_{i}({\gamma }_{1}=0)$$\end{document} where Nt = 3 is the number of turbines and Pi is the power production of each turbine. The power and number of data samples are shown for different inflow wind directions, α, and yaw-misalignment angles, γ: a–l, α = −10° (a,d), α = −7.5° (b,e), α = −5° (c,f), α = −2.5° (g,j), α = 0° (h,k) and α = 2.5° (i,l). The yaw angles are shown in degrees. The origin corresponding to zero yaw misalignment is shown with dashed lines. The yaw-misalignment values tested were between −25° and 25° (yaw values beyond ∣25°∣ were not considered for loading limits). The wind turbines are approximately aligned for northwesterly inflow (α ≈ −5°). The power is normalized by the power produced with zero turbine 1 misalignment γ1 = 0° so that the results can be interpreted as a fractional gain in power compared with baseline control. The wind speed is 7 ± 1.5 m s⁻¹, and the turbulence intensity is 5 ± 2.5%, as measured by the reference turbine. To account for the finite wind speed bin width, the power for each 1min\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1\,\min$$\end{document} averaged data sample for each turbine is normalized by the corresponding power of the adjacent reference turbine, and the normalized power is denoted as P. Conditional averages with n > 25 data samples are considered. In blue, we show the field experiment mean data with 95% confidence intervals from bootstrapping. Mean flow control model predictions with 95% confidence intervals from bootstrapping are shown in red. The flow control model predictions use the predicted wind turbine angular velocities. The power-maximizing yaw-misalignment angle for turbine 1 predicted by the flow control model is given in gold. Flow control model predictions assuming that the power production of yawed turbines is P̂(γ)~cos3(γ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{P}(\gamma ) \sim {\cos }^{3}(\gamma )$$\end{document} are shown in green. The impact of the yaw misalignment of turbine 1 on the power of each of the three individual turbines of interest is shown in Supplementary Note 1 (Supplementary Figs. 3–9).
Source data
Results from utility-scale collective wind farm operation to maximize energy production
a, Energy gain from the farm energy maximization wake steering field experiment. The results for wind speeds between 6 < u < 8 m s⁻¹ and 0 < u < 20 m s⁻¹ (full wind speed range) are shown in orange and blue, respectively. The shaded region corresponds to 95% confidence intervals around the mean estimated with bootstrapping. The wind turbines are approximately aligned for northwesterly inflow (α ≈ −5°), shown with a solid black vertical line. The vertical dashed lines indicate the wind direction bounds for which collective operation is applied (γ ≠ 0°), −20° < α < 15°. b, Number of unique 1 min averaged data samples collected for each wind direction for wake steering operation. The number of data points collected for baseline yaw-aligned operation is similar. c, The yaw-misalignment set points applied to the farm, which maximize wind farm power production in the flow control model, γ*, for each turbine. The power-maximizing yaw angles, γ*, depend on the incident wind conditions of: wind speed, wind direction and turbulence intensity. The yaw angles for incident wind speeds of 5 ± 1 m s⁻¹ and turbulence intensities of 7.5 ± 1.25% are shown. The full yaw-misalignment lookup table is provided in the supporting dataset.
Source data
In wind farms, turbines are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. Here we operate a wind turbine array collectively to maximize array production through wake steering. We develop a physics-based, data-assisted flow control model to predict the power-maximizing control strategy. We first validate the model with a multi-month field experiment at a utility-scale wind farm. The model is able to predict the yaw-misalignment angles which maximize array power production within ± 5° for most wind directions (5–32% gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 3.0% ± 0.7% and 1.2% ± 0.4% for wind speeds between 6 m s−1 and 8 m s−1  and all wind speeds, respectively. The predictive model can enable a wider adoption of collective wind farm operation. Individual operation of turbines in wind farms results in energy losses from wake interactions. Here Howland et al. report on an experimentally validated model to implement collective operation of turbines, which increases the farm’s energy production.
Water electrolysis is a key technology to establish CO 2 -neutral hydrogen production. Nonetheless, the near-surface structure of electrocatalysts during the anodic oxygen evolution reaction (OER) is still largely unknown, which hampers knowledge-driven optimization. Here using operando X-ray absorption spectroscopy and density functional theory calculations, we provide quantitative near-surface structural insights into oxygen-evolving CoO x (OH) y nanoparticles by tracking their size-dependent catalytic activity down to 1 nm and their structural adaptation to OER conditions. We uncover a superior intrinsic OER activity of sub-5 nm nanoparticles and a size-dependent oxidation leading to a near-surface Co–O bond contraction during OER. We find that accumulation of oxidative charge within the surface Co ³⁺ O 6 units triggers an electron redistribution and an oxyl radical as predominant surface-terminating motif. This contrasts the long-standing view of high-valent metal ions driving the OER, and thus, our advanced operando spectroscopy study provides much needed fundamental understanding of the oxygen-evolving near-surface chemistry.
  • Lisa M. ThompsonLisa M. Thompson
Understanding what drives household-level decisions to keep cooking with polluting cookstoves and fuels must be grounded in theory for sustained change to occur. New research examines the literature through a behavioural model and finds that affordability, technical aspects, and fuel supply are the main drivers of fuel stacking.
In recent years, the investment community has become increasingly aware of the investment risks from both the physical effects of climate change and the regulatory responses to facilitate the transition to a net-zero economy. The potential impact of climate transition risks is especially large for fossil energy companies, given their central role in producing carbon emissions. Here we discuss how concerns about climate risks influence the way investors allocate their capital and exercise their oversight of firms, and how this investor response affects companies in the energy sector. We then explore how different energy firms have responded to climate-related pressures from their investors and other stakeholders. We conclude by highlighting promising areas of research for understanding how climate risks affect the interaction between financial markets and the energy sector. Managing climate risk exposures of assets and loan portfolios is increasingly important for actors within financial markets. This Review examines how such risks affect investor behaviour and hence the wider energy sector, and highlights areas for future research into the interactions between them.
Hard to decarbonize homes represent a complex problem that has historically been neglected in favour of the lower hanging fruit of easier to treat properties. To enable an equitable net zero transition, we must understand these homes in a holistic manner take into account the impacts of different routes to decarbonization on occupants.
of papers identified in the literature review
a–d, Papers are broken down by publication dates and technologies (a), regional distribution (b), location type (c) and technology only (d). P_B stands for processed biomass.
Radial graphs showing stacking drivers for each technology
a–c, The spokes on the wheel represent individual drivers, and the black bars show the number of papers featuring each stacking driver for LPG papers, N = 35 (a), ICS papers, N = 34 (b) and electric papers, N = 24 (c). There were considerably more stacking drivers per paper for LPG (n = 6.0) than for electric (n = 3.2) or ICS (n = 2.6). Note: this figure excludes technologies featured in <10 papers.
Proportion of stacking drivers by COM-B component
Pie chart showing proportion of stacking drivers by COM-B component. There were no drivers for Physical Capability, showing as 0% on the pie chart.
Breakdown of contributions by COM-B components
a,b, These graphs show the proportion of COM-B components contributing to each technology (a) and stacking category (b). P_B stands for processed biomass. AFF stands for affordability, CUL stands for cultural compatibility, END stands for end uses of traditional stoves, EQU stands for stove and equipment compatibility, FUN stands for stove functionality, HHD stands for household dynamics, KNO stands for knowledge and training, SAF stands for safety issues, SUP stands for fuel supply issues, TEC stands for technical characteristics and TIM stands for time aspects.
Globally, 2.8 billion people cook with biomass fuels, resulting in devastating health and environmental consequences. Efforts to transition households to cooking with clean fuels are hampered by ‘fuel stacking’, the reliance on multiple fuels and stoves. Consequently, there have been few interventions that have realized the full potential of clean cooking. Here we conduct a structured literature review (N = 100) to identify drivers of fuel stacking and specify them according to a psychological model of behaviour, the Capability–Opportunity–Motivation (COM-B) model. We create a taxonomy of stacking and find that the Physical Opportunity domain accounted for 82% of drivers. Our results have important implications for intervention design as they suggest improving opportunity is the most effective pathway to adoption of cleaner fuels. The findings are used to derive recommendations about how policymakers and practitioners can proactively address drivers of stacking to foster adoption of clean cooking stoves and fuels. Realizing the full potential of clean cooking transitions requires an understanding of fuel stacking in which multiple fuels and stoves are used. Towards this end, Perros et al. analyse the literature on clean cooking interventions through a behavioural model and identify underlying drivers of stacking.
Scheme of different annealing processes and corresponding film morphology
a, Scheme of normal annealing, solvent annealing and CSA processes. b–d, Top view SEM images of mixed Sn–Pb perovskite films fabricated with normal annealing (b), solvent annealing (c) and CSA (d) processes. The scale bar is 1 µm. e, Mechanisms of grain growth during the CSA process. Small grains merge into big ones during the film side down annealing process. The as-casted intermediate perovskite film has small grains after antisolvent dropping and preheating (Step I). The film is reversed back with the film side downward to the solvent permeable covers, and the residual solvents could slowly volatilize out, merging adjacent grains together and flattening the surface (Step II). The slowed solvent releasing process leads to larger grains and smoother surface of the perovskite film. The green arrows indicate the preparation flow.
Characterizations of low-Eg perovskite films and PVSCs based on normal annealing, solvent annealing and CSA processes
a,b, Steady-state PL and TRPL decays of perovskite films processed with different methods on glass substrates. TRPL results are fitted with a bi-exponential decay formula. c, Linear fit of ln(α / αg) below Eg for the CSA-, solvent annealing- (SA) and normal annealing (NA)-processed samples using results from PDS measurements. The inset shows Urbach energy extracted from PDS measurement of different perovskite films. The histograms indicate the mean values, error bars indicate the standard deviations (n = 3) and the individual data are provided in the Source Data. d, J–V curves under reverse and forward voltage scans under AM 1.5 G 100 mW cm⁻² illumination. e, EQE spectra and the integrated JSC values are 30.8 mA cm⁻², 28.4 mA cm⁻² and 29.7 mA cm⁻² for CSA-, solvent annealing- and normal annealing-processed PVSCs, respectively. f, VOC as a function of temperature; the results are fitted with a linear function.
Source data
Film quality of wide-Eg perovskites and device performance with normal annealing, solvent annealing and CSA processes
a–c, Top view SEM images of wide-Eg perovskite films fabricated with normal annealing (a), solvent annealing (b) and CSA (c) processes. d, TRPL curves of perovskite films; the results are fitted with a bi-exponential decay formula. e,f, J–V curves under reverse voltage scan (e) under AM 1.5 G 100 mW cm⁻² illumination and EQE spectra (f); the integrated JSC values are 18.3 mA cm⁻², 17.9 mA cm⁻² and 17.4 mA cm⁻² for CSA-, solvent annealing (SA)- and normal annealing (NA)-processed PVSCs, respectively.
Photovoltaic performance of 4-T and 2-T tandem solar cells
a, J–V curves of 4-T TSCs. b, EQE spectra and the integrated JSC values are 17.0 mA cm⁻² and 11.9 mA cm⁻² for top cell and filtered bottom cell, respectively. c, Power outputs of wide-Eg top cell, filtered low-Eg bottom cell and the overall output. d, Cross-sectional SEM image of an entire 2-T TSC. e, J–V curves of 2-T TSCs fabricated with normal annealing (NA) and CSA methods. f, EQE spectra of 2-T TSCs processed by normal annealing and CSA, and the integrated JSC values are 15.6 mA cm⁻² and 15.0 mA cm⁻² for top and bottom subcells of CSA-based 2-T TSC and are 14.8 mA cm⁻² and 14.5 mA cm⁻² for top and bottom subcells of normal annealing-based 2-T TSC, respectively. g, MPP tracking of the best-performing 2-T TSC under AM 1.5 G 100 mW cm⁻² illumination. h, MPP tracking of one unencapsulated 2-T all-perovskite TSC under continuous AM 1.5 G 1 sun illumination in a glovebox; the initial efficiency is 24.5%.
The broad bandgap tunability of organic–inorganic metal halide perovskites enables the fabrication of multi-junction all-perovskite tandem solar cells with ultra-high power conversion efficiencies (PCEs). Controllable crystallization plays a crucial role in the formation of high-quality perovskites. Here we report a universal close-space annealing strategy that increases grain size, enhances crystallinity and prolongs carrier lifetimes in low-bandgap (low-Eg) and wide-bandgap (wide-Eg) perovskite films. By placing the intermediate-phase perovskite films with their faces towards solvent-permeable covers during the annealing process, high-quality perovskite absorber layers are obtained with a slowed solvent releasing process, enabling fabrication of efficient single-junction perovskite solar cells (PVSCs) and all-perovskite tandem solar cells. As a result, the best PCEs of 21.51% and 18.58% for single-junction low-Eg and wide-Eg PVSCs are achieved and thus ensure the fabrication of 25.15% efficiency 4-terminal and 25.05% efficiency 2-terminal all-perovskite tandem solar cells.
Understanding carrier loss mechanisms at microscopic regions is imperative for the development of high-performance polycrystalline inorganic thin-film solar cells. Despite the progress achieved for kesterite, a promising environmentally benign and earth-abundant thin-film photovoltaic material, the microscopic carrier loss mechanisms and their impact on device performance remain largely unknown. Herein, we unveil these mechanisms in state-of-the-art Cu2ZnSnSe4 (CZTSe) solar cells using a framework that integrates multiple microscopic and macroscopic characterizations with three-dimensional device simulations. The results indicate the CZTSe films have a relatively long intragrain electron lifetime of 10–30 ns and small recombination losses through bandgap and/or electrostatic potential fluctuations. We identify that the effective minority carrier lifetime of CZTSe is dominated by a large grain boundary recombination velocity (~10⁴ cm s⁻¹), which is the major limiting factor of present device performance. These findings and the framework can greatly advance the research of kesterite and other emerging photovoltaic materials.
As climate change accelerates, governments will be forced to adapt to its impacts. The public could respond by increasing mitigation behaviours and support for decarbonization, creating a virtuous cycle between adaptation and mitigation. Alternatively, adaptation could generate backlash, undermining mitigation behaviours. Here we examine the relationship between adaptation and mitigation in the power sector, using the case of California’s public safety power shut-offs in 2019. We use a geographically targeted survey to compare residents living within power outage zones to matched residents in similar neighbourhoods who retained their electricity. Outage exposure increased respondent intentions to purchase fossil fuel generators while it may have reduced intentions to purchase electric vehicles. However, exposure did not change climate policy preferences, including willingness to pay for either wildfire or climate-mitigating reforms. Respondents blamed outages on their utility, not local, state or federal governments. Our findings demonstrate that energy infrastructure disruptions, even when not understood as climate adaptations, can still be consequential for decarbonization trajectories. Climate change adaptation policies could influence public decarbonization behaviours positively or negatively, impacting further mitigation and adaptation efforts. This study examines public responses to planned power outages in California and finds that the outages shaped some energy behavioural intentions but did not alter climate or energy policy preferences.
Whether additional natural gas infrastructure is needed or would be detrimental to achieving climate protection goals is currently highly controversial. Here we combine five perspectives to argue why expansion of the natural gas infrastructure hinders a renewable energy future and is no bridge technology. We highlight that natural gas is a fossil fuel with a significantly underestimated climate impact that hinders decarbonization through carbon lock-in and stranded assets. We propose five ways to avoid common shortcomings for countries that are developing strategies for greenhouse gas reduction: manage methane emissions of the entire natural gas value chain, revise assumptions of scenario analyses with new research insights on greenhouse gas emissions related to natural gas, replace the ‘bridge’ narrative with unambiguous decarbonization criteria, avoid additional natural gas lock-ins and methane leakage, and take climate-related risks in energy infrastructure planning seriously.
Flexible all-perovskite tandem photovoltaics open up new opportunities for application compared to rigid devices, yet their performance lags behind. Now, researchers show that molecule-bridged interfaces mitigate charge recombination and crack formation, improving the efficiency and mechanical reliability of flexible devices.
In the intensive search for novel battery architectures, the spotlight is firmly on solid-state lithium batteries. Now, a strategy based on solid-state sodium–sulfur batteries emerges, making it potentially possible to eliminate scarce materials such as lithium and transition metals.
One of the key targets for further development of sodium-ion batteries is to improve their cycle life. Now, an electrolyte formulation is proposed to tackle the dissolution of both the solid-electrolyte interphases and the transition metals in cathodes, leading to enhanced cyclability.
International maritime shipping—powered by heavy fuel oil—is a major contributor to global CO2, SO2, and NOx emissions. The direct electrification of maritime vessels has been underexplored as a low-emission option despite its considerable efficiency advantage over electrofuels. Past studies on ship electrification have relied on outdated assumptions on battery cost, energy density values and available on-board space. We show that at battery prices of US$100 kWh−1 the electrification of intraregional trade routes of less than 1,500 km is economical, with minimal impact to ship carrying capacity. Including the environmental costs increases the economical range to 5,000 km. If batteries achieve a US$50 kWh−1 price point, the economical range nearly doubles. We describe a pathway for the battery electrification of containerships within this decade that electrifies over 40% of global containership traffic, reduces CO2 emissions by 14% for US-based vessels, and mitigates the health impacts of air pollution on coastal communities. The maritime shipping industry is heavily energy-consuming and highly polluting, and, as such, is urgently seeking low-emission options. Here the authors examine the feasibility of battery-electric ships and show that the battery price declines could facilitate the electrification of short to medium-range shipping.
Critics have opposed clean energy public investment by claiming that governments must not pick winners, green subsidies enable rent-seeking behaviour, and failed companies means failed policy. These arguments are problematic and should not determine the direction of energy investment policies.
Electrification models used to plan future energy systems have become increasingly sophisticated, but typically oversimplify the financial landscape. New research shows that more accurate accounting of the costs of capital significantly changes the least-cost pathway to providing electrification across sub-Saharan Africa.
The UN Sustainable Development Goal 7 (SDG 7) on energy access and Goal 5 (SDG 5) on gender equality are inextricably linked. A new study utilizing field-based data from India unpacks how levels of women’s empowerment in households influences their awareness, usage, satisfaction, and preference for energy services.
Metal halide perovskite solar cells have shown promising performance, but mainly on small-area devices and under laboratory conditions. Now, researchers have demonstrated the fabrication of large-area devices assembled and packaged into modules and reported on their operation outdoors.
Nitrogen-coordinated iron catalysts are exciting potential replacements for platinum at the cathode of proton-exchange membrane fuel cells, but still tend to have poor long-term durability. Now, a thin and porous nitrogen-doped carbon film deposited at the surface of a highly active but unstable Fe–N–C catalyst is shown to drastically improve its stability.
Nitrogen-coordinated single atom iron sites (FeN4) embedded in carbon (Fe–N–C) are the most active platinum group metal-free oxygen reduction catalysts for proton-exchange membrane fuel cells. However, current Fe–N–C catalysts lack sufficient long-term durability and are not yet viable for practical applications. Here we report a highly durable and active Fe–N–C catalyst synthesized using heat treatment with ammonia chloride followed by high-temperature deposition of a thin layer of nitrogen-doped carbon on the catalyst surface. We propose that catalyst stability is improved by converting defect-rich pyrrolic N-coordinated FeN4 sites into highly stable pyridinic N-coordinated FeN4 sites. The stability enhancement is demonstrated in membrane electrode assemblies using accelerated stress testing and a long-term steady-state test (>300 h at 0.67 V), approaching a typical Pt/C cathode (0.1 mgPt cm−2). The encouraging stability improvement represents a critical step in developing viable Fe–N–C catalysts to overcome the cost barriers of hydrogen fuel cells for numerous applications. Fe–N–C materials are promising oxygen reduction catalysts for proton-exchange membrane fuel cells but still lack sufficient long-term durability for practical applications. Here the authors fabricate an Fe–N–C material with a thin N–C layer on the surface, leading to a highly durable and active catalyst.
Design and performance of all-perovskite tandem solar cells and modules
a, Schematic and layer stack of all-perovskite tandem solar cells applied in this work. The champion tandem solar cells and modules employ sputtered indium tin oxide layers (thickness ~15 nm) and percolated Au thin films (nominal thickness ~1–2 nm) as a recombination layer, respectively. NBG and WBG are the abbreviations of the narrow and wide bandgap. b, Current density–voltage (J–V) curve and power conversion efficiency tracked at the maximum power point of the champion tandem devices for 5 min (in the inset). c, EQE of top and bottom subcell as well as the sum of both (grey symbolled line), and total absorbance calculated by 1 − reflectance (black solid line) for monolithic all-perovskite tandem solar cells. The light and dark blue regions denote the parasitic absorption and reflection losses, respectively. The corresponding losses in current density are provided. d, Schematic illustration of the two-terminal all-perovskite tandem solar module (not to scale) interconnection denoting the active area and scribing lines (for more details see Supplementary Fig. 21). The colour used for the module layers is the same as for tandem solar cells. e, J–V characteristics of individual tandem cell stripes of the module and the respective fill factors after a stepwise inclusion of cell stripes included in the measurement. f, Power, voltage and current at the maximum power point of the champion tandem solar module under continuous AM 1.5G illumination. g, Normalized power (Norm.), voltage and current at the maximum power point under temperature stress at 85 °C in nitrogen atmosphere. Panels a–c refer to tandem solar cells while panels d–g to tandem modules.
Scribing line and film quality assessment
a, Photograph of a tandem solar module with four tandem cell stripes and a total aperture area of 2.56 cm². b, SEM image of scribing lines indicating the P1–P3 interconnections. c, Combined fragments of Au⁻ (yellow), C2⁻ (red), InO⁻ (green) and SiO⁻ (dark violet, please see Supplementary Fig. 23). The combined 3D mapping of Au, perovskite, IO:H and SiOx fragments visualizes the complete tandem architecture and scribing lines. d, Schematic of our laser excitation to measure the LBIC signal of photogenerated currents in top and bottom subcells. e,f, LBIC signal of the top (e) and bottom (f) subcells in the tandem module excited with 530 and 850 nm lasers, respectively. g, Schematic of electroluminescence imaging and the applied optical shortpass (SP, at 775 nm) and longpass (LP, at 760 nm) filters. h,i, Electroluminescence imaging of the top (h) and bottom (i) subcells by use of 775 nm shortpass and 760 nm longpass filters, respectively. The black arrows indicate defects and/or inhomogeneities in the bottom and top subcells.
Upscaling the tandem modules and their characteristics
a, Schematic of the fabrication sequence for scalable processing of the tandem modules using a combination of blade-coating and vacuum-deposition techniques. b, The front side of the fabricated tandem module with seven cell strips and an aperture area of 12.25 cm². c, Current density–voltage (J–V) characteristic of stepwise accumulated tandem cell stripes of the module and the respective fill factors. d, Power at the maximum power point of the champion tandem solar mini-module with 12.25 cm² aperture area under continuous AM 1.5G illumination and nitrogen atmosphere. e, Photoluminescence of the tandem perovskite layers with excitation lasers of 513 nm. The insets are the photoluminescence imaging of the top and bottom subcells by use of 775 nm shortpass and 760 nm longpass filters, respectively. f, Photograph and corresponding J–V characteristics for the electroluminescence image. g, Electroluminescence of the tandem perovskite layers. The insets are the electroluminescence imaging of the top and bottom subcells by use of 775 nm shortpass and 760 nm longpass filters, respectively.
NBG perovskite growth control for corrosion-free deposition
a, Degradation rate over time as the ratio of red dots (that is, photoluminescence signal emitted from the degraded regions) to the image area in the photoluminescence images overlaid on the plot. Black indicates no degradation. The photoluminescence images are taken from the WBG/LiF/C60/SnOx/Au/PEDOT:PSS stack after blade coating of the DMF:DMSO mixture after a resting time of 0–20 s. The grey vertical lines indicate the time of formation of the NBG perovskite at different growth conditions of PV0-PV2. b, Schematic of the designed vacuum plus nitrogen flowing chamber for growth of the NBG perovskite. The bottom scheme shows the swapping of the evaporated DMF and DMSO from the surface of the blade-coated perovskite in combination with the simultaneous evacuation and venting of the chamber. The light blue and dark blue layers represent the interlayers and glass/IO:H/2PACz substrate, respectively. c, Pressure versus time characteristic for different growth conditions of NBG in the chamber (PV0-PV2) with defined perovskite formation time (grey vertical lines). d–f, SEM images of the NBG perovskite produced at different growth conditions of PV0 (d), PV1 (e) and PV2 (f).
Monolithic all-perovskite tandem photovoltaics promise to combine low-cost and high-efficiency solar energy harvesting with the advantages of all-thin-film technologies. To date, laboratory-scale all-perovskite tandem solar cells have only been fabricated using non-scalable fabrication techniques. In response, this work reports on laser-scribed all-perovskite tandem modules processed exclusively with scalable fabrication methods (blade coating and vacuum deposition), demonstrating power conversion efficiencies up to 19.1% (aperture area, 12.25 cm2; geometric fill factor, 94.7%) and stable power output. Compared to the performance of our spin-coated reference tandem solar cells (efficiency, 23.5%; area, 0.1 cm2), our prototypes demonstrate substantial advances in the technological readiness of all-perovskite tandem photovoltaics. By means of electroluminescence imaging and laser-beam-induced current mapping, we demonstrate the homogeneous current collection in both subcells over the entire module area, which explains low losses (<5%rel) in open-circuit voltage and fill factor for our scalable modules. All-perovskite tandem photovoltaics hold technological potential yet their upscaling is not trivial. Here Nejand et al. fabricate mini-modules using scalable methods and laser-scribed interconnections, achieving a 19.1% efficiency over an aperture area of 12.25 cm2.
Legal mandates are critical to supporting action on sustainable development goals and climate change targets. Yet, new research highlights the importance of initial endowments for energy transitions, and how they can lead to disparate outcomes across regions.
The capacity factor (cf) is a critical variable for quantifying wind turbine efficiency. Climate change-induced wind resource variations and technical wind turbine fleet development will alter future cfs. Here we define 12 techno-climatic change scenarios to assess regional and global onshore cfs in 2021–2060. Despite a decreasing global wind resource, we find an increase in future global cf caused by fleet development. The increase is significant under all evaluated techno-climatic scenarios. Under the likely emissions scenario Shared Socioeconomic Pathway 2–4.5, global cf increases from 0.251 in 2021 up to 0.310 in 2035 under ambitious fleet development. This cf enhancement is equivalent to a 361 TWh yield improvement under the globally installed capacity of 2020 (698 GW). To increase the contribution of the future wind turbine fleet to the Intergovernmental Panel on Climate Change climate protection goals, we recommend a rapid wind turbine fleet conversion.
As a vital step towards the industrialization of perovskite solar cells, outdoor field tests of large-scale perovskite modules and panels represent a mandatory step to be accomplished. Here we demonstrate the manufacturing of large-area (0.5 m²) perovskite solar panels, each containing 40 modules whose interfaces are engineered with two-dimensional materials (GRAphene-PErovskite (GRAPE) panels). We further integrate nine GRAPE panels for a total panel area of 4.5 m² in a stand-alone solar farm infrastructure with peak power exceeding 250 W, proving the scalability of this technology. We provide insights on the system operation by analysing the panel characteristics as a function of temperature and light intensity. The analysis, carried out over a months-long timescale, highlights the key role of the lamination process of the panels on the entire system degradation. A life-cycle assessment based on primary data indicates the high commercial potential of the GRAPE panel technology in terms of energy and environmental performances.
Charge carrier dynamics
a, TRPL measurement on ~800-nm-thick narrow-bandgap perovskite film prepared with the PEAI+GASCN additive under different excitation intensities (injection levels). b, TRPL measurements of perovskite films with thickness varying from 200 to 800 nm. The dashed lines in a and b are best fits as detailed in the Methods. norm., normalized. c, Thickness dependence of the TRPL lifetime with analysis to extract the bulk carrier lifetime and the surface recombination velocity. For each film thickness, the lifetime value is obtained from the best fit of the corresponding TRPL transient in b, and the error bar corresponds to the uncertainty (standard deviation) of the best fit. The data in c were analysed by best fit according to equation (1). d, Temperature dependence of recombination rate (or lifetime⁻¹) from TRPL measurement. T is the temperature, Ea is the activation energy associated with shallow traps and k is Boltzmann’s constant. The laser excitation wavelength was 640 nm.
Optoelectronic and morphological comparison
a,b, Comparison of the dark carrier density from Hall effect measurements (a) and its correlation with the TRPL lifetime (b) for perovskite thin films prepared without additive (pristine) and with additives as indicated. c, Typical J–V characteristics of PSCs prepared with and without additives as indicated. d–f, Scanning electron microscopy images of perovskite films prepared without additive (d), with GASCN (e) and with both PEAI and GASCN (f).
X-ray diffraction characterization
a, Comparison of X-ray diffraction patterns of 2D perovskite thin films with varying degrees of GA⁺/PEA⁺ mixing ratios. b, Comparison of the X-ray diffraction patterns of 2D perovskite with the GA⁺/PEA⁺ ratio of about 7:2 and (PEA)2GAPb2I7 perovskite along with that of (GA)2PbI4 and (PEA)2PbI4. c, Comparison of X-ray diffraction patterns of 3D Sn–Pb perovskite precursor with adding different amounts of PEAI and GAI additives. The vertical shadow lines are a guide to the eye. Specifically, the light purple lines correspond to (GA0.8PEA0.2)2PbI4; the light grey lines correspond to (PEA)2PbI4; the light yellow lines correspond to (GA)2PbI4; and the light orange lines correspond to PEA2GAPb2I7. X-ray source, Cu Kα radiation. 2θ is the angle between the X-ray detector and incident X-ray beam.
Single-junction Sn–Pb narrow-bandgap PSCs
a, J–V curve of the champion narrow-bandgap PSC along with the stable power output efficiency near the maximum power point (inset). b, Statistics of PV parameters of Sn–Pb narrow-bandgap PSCs (20 devices). c, Long-term stability of unencapsulated Sn–Pb narrow-bandgap PSC under continuous light illumination at about 30–35 °C in N2. The perovskite thin films in these devices were prepared by using the PEAI+GASCN additive. The initial efficiency is 20.8%, and data are normalized by the peak efficiency (21.9%).
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Monolithic all-perovskite tandem solar cells
a, Schematic of the 2-T monolithic tandem device. b, Typical cross-sectional scanning electron microscopy image of a tandem device. c, J–V curve of the champion all-perovskite tandem solar cell along with the stable power output efficiency near the maximum power point (inset). d, EQE spectra of the front (wide-bandgap) and back (narrow-bandgap) subcells with the integrated Jsc values indicated. e, Long-term stability of the tandem device under continuous light illumination (~0.8 suns) at about 30–35 °C in N2. The perovskite thin films in the narrow-bandgap subcells were prepared by using the PEAI+GASCN additive. The initial efficiency is 25.3%, and data are normalized by the peak efficiency (25.3%).
All-perovskite tandem solar cells are promising for achieving photovoltaics with power conversion efficiencies above the detailed balance limit of single-junction cells, while retaining the low cost, light weight and other advantages associated with metal halide perovskite photovoltaics. However, the efficiency and stability of all-perovskite tandem cells are limited by the Sn–Pb-based narrow-bandgap perovskite cells. Here we show that the formation of quasi-two-dimensional (quasi-2D) structure (PEA)2GAPb2I7 from additives based on mixed bulky organic cations phenethylammonium (PEA+) and guanidinium (GA+) provides critical defect control to substantially improve the structural and optoelectronic properties of the narrow-bandgap (1.25 eV) Sn–Pb perovskite thin films. This 2D additive engineering results in Sn–Pb-based absorbers with low dark carrier density (~1.3 × 1014 cm−3), long bulk carrier lifetime (~9.2 μs) and low surface recombination velocity (~1.4 cm s−1), leading to 22.1%-efficient single-junction Sn–Pb perovskite cells and 25.5%-efficient all-perovskite two-terminal tandems with high photovoltage and long operational stability. Tong et al. form a 2D perovskite layer with two large organic cations to improve the structural and optoelectronic properties of Sn–Pb perovskites, and eventually the performance of single-junction and tandem solar cells.
Cost of capital estimates for the different financing scenarios and for the different electrification modes
The differences between the countries are based on the country default spreads (reflecting country-specific investment risk⁵²), while the differences between the electrification modes are based on variations in the debt share (Methods). The differences between the financing scenarios are based on different sources of capital and on different maturities of the off-grid finance sector. The dotted line reflects the cost of capital for the uniform financing scenario.
Results showing the newly connected populations and total investments between 2018 and 2030 based electrification mode for the defined electrification pathways and financing scenario
a, Total new connections per electrification mode for the extended-area grid pathway (columns) and ratio of MGs to SASs for each financing scenario (pies). b, Total new connections per electrification mode for the existing-area pathway (columns) and ratio of MGs to SASs for each financing scenario (pies). c, Total investments needed to finance the extended-area grid pathway. d, Total investments needed to finance the existing-area grid pathway. Values have been rounded to the nearest integer; the same operation is carried out for all numbers to ensure consistency. Numbers in the bars show the percentage of new connections (a,b) and the total investment (c,d) for each electrification mode.
Map of sub-Saharan Africa comparing financing scenarios for the defined electrification pathways
a, Uniform scenario extended-area grid pathway. b, Niche-financing scenario extended-area grid pathway. c, Uniform scenario existing-area pathway. d, Niche financing scenario existing-area pathway.
a–h, Proportion of new connections for clusters to be electrified in 2030 in DRC (a), Ethiopia (b), Nigeria (c) and Zimbabwe (d), and the distribution of LCOE for clusters to be electrified in 2030 in DRC (e), Ethiopia (f), Nigeria (g) and Zimbabwe (h). For this analysis, 5% of the outliers are neglected. The colours on both types of plot correspond to the electrification mode. The numbers on the stacked bar plots (a–d) represent the percentage of total new connections in 2030 by electrification mode.
Electrifying 600 million people in sub-Saharan Africa will require substantial investments. Integrated electrification models inform key policy decisions and electricity access investments in many countries. While current electrification models apply sophisticated geospatial methods, they often make simplistic assumptions about financing conditions. Here we establish cost of capital values, reflecting country and electrification mode (that is, grid extension, minigrids and stand-alone systems), and specific risks faced by investors and integrate them into an open source electrification model. We find that the cost of capital for off-grid electrification is much higher than currently assumed, up to 32.2%. Accounting for finance shifts approximately 240 million people from minigrids to stand-alone systems in our main scenario, suggesting a more cost-effective electrification mode mix than previously suggested. In turn, electrification models based on uniform cost of capital assumptions increase the per kWh cost of electricity by 20%, on average. Upscaling and mainstreaming off-grid finance can lower electrification cost substantially.
Progress towards energy transition in the APAC emerging economies during 2000–2017
Values ranging from 0 to 1 represent the progress towards the energy-transition targets, that is, the ratio of the annual value of the indicator over the target value defined in the global SDG database (low energy intensity values represent high indicator scores). The higher the value, the higher the level of progress towards the energy-transition targets. A value of 1 means the target is met. The dashed lines in the figure show the median value of the standardized progress for each target in 2017. Countries within each economic development category are ranked from lowest (top) to highest (bottom) in terms of the average GDP per capita during 2000–2017. PDR, People’s Democratic Republic; Korea Dem. Rep., Democratic People’s Republic of Korea; Micronesia Fed. Sts., Federated States of Micronesia.
Performance of APAC emerging economies’ energy transition with and without energy policy
The calculation goes from 2000 to 2017 because the basic model is lagged by one year. Observed SDG7 performance is represented by solid lines. Counterfactual performance is represented by dashed lines. a, the performance of access to electricity (indicator 7.1.1). b, The performance of access to clean cooking (indicator 7.1.2). c, Performance of energy intensity (indicator 7.3.1). d, The performance of renewable electricity capacity (indicator 7.b.1). The values in the figure indicate the percentage change in the average of indicators without energy policies to the average with energy policies, thus reflecting the effect of the energy policy (for energy intensity, energy policy lowers this indicator; but for the energy efficiency target, policy effect is a positive improvement).
Impact of energy policies on energy transition by country during 2000–2017
The coloured cells show the range of dispersion of the difference between energy transition with and without energy policies across different economies. The colours indicate the percentage of changes. For energy intensity, changes are negative as lower intensity means higher energy efficiency. PDR, People’s Democratic Republic; Korea Dem. Rep., Democratic People’s Republic of Korea; Micronesia Fed. Sts., Federated States of Micronesia.
Impact of different types of energy policy on energy transition in typical economies
a–t, Assessment of energy-transition indicators (access to electricity, access to clean cooking, energy intensity and renewable electricity capacity) with and without policy in developing country India (a–d), developing country Vietnam (e–h), developing country Fiji (i–l), LDC Myanmar (m–p) and transition country Kazakhstan (q–t), including the contributions of law, regulation, strategy and other types of policy. The numbers above the first and the last bar in each panel, ‘Without policy’ and ‘With policy’, indicate the values of the energy-transition indicators for each country (the units of the indicators are shown at the top of the figure), and the numbers above or below other bars indicate the change in the values caused by four types of policy (the units are as the same of indicators at the top of the figure).
The achievement of sustainable energy systems requires well-designed energy policies, particularly targeted strategies to plan the direction of energy development, regulations monitored and executed through credible authorities and laws enforced by the judicial system for the enhancement of actions and national targets. The Asia–Pacific region (APAC), responsible for more than half of global energy consumption, has enacted a large number of energy policies over the past two decades, but progress on the energy transition remains slow. This study focuses on the aggregate effect of energy policies on the progress towards sustainable targets in 42 emerging economies from 2000 to 2017. We find that energy policies have contributed to improving access to electricity (3.0%), access to clean cooking (3.8%), energy efficiency (1.4%) and renewable electricity capacity (6.9%), respectively. Among different types of energy policy (strategies, laws and regulations), strategies have greater impacts on advancing electrification, clean cooking and renewable electricity capacity than laws and regulations, whereas the laws are more effective for achieving energy efficiency.
Assessment framework for low energy demand scenarios
Each numbered panel represents a step in the methodology used to derive the low energy demand scenarios for the United Kingdom. These steps are elaborated in the Methods section. Credit: Centre for Research into Energy Demand Solutions.
Reduction in final energy demand associated with scenarios
a, Time series to 2050 of trends in total final energy consumption (PJ) across the UK economy under each scenario. b, Relative percentage reductions in final energy demand (from 2020) across each energy-using sector. Note that the underlying activity data in each of the sectors under the different scenarios is provided in Supplementary Note 4. Credit: Centre for Research into Energy Demand Solutions.
Relative contribution of efficiency measures versus those that avoid or shift energy service demand
For each of the energy service sectors, this figure highlights the contribution of categories of interventions to reductions in energy demand in 2050, compared with the Ignore scenario. a,b, The categorization includes interventions that avoid or shift energy service demands compared with efficiency measures under Shift (a) and Transform (b), compared with Ignore. Credit: Centre for Research into Energy Demand Solutions.
Supply-side technological roll-out under net-zero scenarios
a,b, CO2 sequestration under net-zero scenarios, including carbon capture and storage (CCS) and CDR (a) and nature-based solutions (b) from 2020–2050. c, Electricity generation (TWh) by type under net-zero scenarios 2010–2050. CHP, combined heat and power; PV, photovoltaics; Retr., retrofit; Man., manufactured. Credit: Centre for Research into Energy Demand Solutions.
In recent years, global studies have attempted to understand the contribution that energy demand reduction could make to climate mitigation efforts. Here we develop a bottom-up, whole-system framework that comprehensively estimates the potential for energy demand reduction at a country level. Replicable for other countries, our framework is applied to the case of the United Kingdom where we find that reductions in energy demand of 52% by 2050 compared with 2020 levels are possible without compromising on citizens’ quality of life. This translates to annual energy demands of 40 GJ per person, compared with the current Organisation for Economic Co-operation and Development average of 116 GJ and the global average of 55 GJ. Our findings show that energy demand reduction can reduce reliance on high-risk carbon dioxide removal technologies, has moderate investment requirements and allows space for ratcheting up climate ambition. We conclude that national climate policy should increasingly develop and integrate energy demand reduction measures.
Electrolyte design principles to suppress the SEI dissolution for highly stable high-voltage sodium-ion batteries
a,b, In the conventional electrolyte (a), SEI dissolution leads to continuous side reactions of SEI formation and electrolyte decomposition, low CEs and irreversible capacity loss. In the low-solvation electrolyte (b), SEI dissolution is suppressed for stable sodium salt anion (FSI⁻)-derived SEI to stabilize cell long cycling performance. c–e, Three main electrolyte design principles to suppress the SEI dissolution: solvent selection by choosing a low dielectric constant (ε) solvent (c), reduced amount of free solvent by manipulating solvation structures (d) and salt-derived SEI with insoluble components (e). f–i, Ab initio molecular dynamics (AIMD) simulation of the NaFSI/DMC:TFP electrolyte: a snapshot of the electrolyte molecular system from the AIMD simulation (f) and a representative Na⁺ solvation structure extracted from the AIMD simulations (g), coordination number (h) and projected density of states (PDOS) (with an arbitrary unit) (i) of the NaFSI/DMC:TFP electrolyte. The Fermi level is set to 0 eV in the PDOS analysis.
Electrochemical performance of HC||NaNMC full cells
a, Electrochemical stability window. b, Cycling performance of HC||NaNMC full cells using different electrolytes cycled at 0.2 C after three formation cycles at 0.1 C. The cathode loading is 1.5 mAh cm⁻². c–e, Voltage curves as a function of cycle number of HC||NaNMC full cells using NaPF6/EC:EMC (c), NaFSI/TFP (d) and NaFSI/DMC:TFP (e) electrolytes. f–g, Rate performance of HC||NaNMC full cells at different charge C rates with a constant discharge rate at 0.33 C (f) and different discharge C rates with a constant charge rate of 0.33 C (g) after three formation cycles at 0.1 C.
XPS characterization to identity SEI dissolution
a,d, The absolute value of difference of quantified SEI atomic composition ratios between the cycled and cycled–soaked HC anodes as a function of the sputtering thickness. b,c,e,f, XPS spectra of C 1s (b,e), P 2p (c) and N 1s (f) of the HC anodes (signal depth = 0 nm). The cycled HC anodes were cycled in NaPF6/EC:EMC (a, upper of b and c), NaFSI/TFP (d, upper of e and f) electrolytes for 100 cycles. The cycled–soaked HC anodes were soaking cycled HC electrodes with EC:EMC (a, bottom of b and c) and TFP (d, bottom of e and f) solvents for 50 h.
SEI dissolution analysis by capacity loss of Cu||Na cells in three electrolytes
a, The capacity from the reduction reaction of electrolytes before and after each pause time as a function of cycle numbers. The grey area with arbitrary width means cell pause processes. b, The capacity loss for different pause times in NaPF6/EC:EMC, NaFSI/TFP and NaFSI/DMC:TFP electrolytes. Each shade of colour represents a cell, and the averages of three parallel cells are tracked with the solid lines (a).
CEI components and structures of the cycled NaNMC cathodes
a–c, P 2p (a) and S 2p (b,c) XPS spectra of the cycled NaNMC cathodes after 100 cycles in NaPF6/EC:EMC (a), NaFSI/TFP (b) and NaFSI/DMC:TFP (c) electrolytes (signal depth = 0 nm). All of the XPS results were fitted with CasaXPS software. The binding energy was calibrated with C 1s at 284.8 eV. Shirley BG type was used for background subtraction and GL(30) line shape was used for peak fitting. d–f, Cryo-TEM images of NaNMC cathodes after 100 cycles in NaPF6/EC:EMC (d), NaFSI/TFP (e) and NaFSI/DMC:TFP (f) electrolytes. Yellow dashed lines indicate the interfaces of CEI and surface reconstruction region of NaNMC. g–h, Ni 3p XPS spectra of the NaNMC cathodes (g) and HC anodes (h) after 100 cycles in HC||NaNMC cells (signal depth = 0 nm).
Sodium-ion batteries (NIBs) have attracted worldwide attention for next-generation energy storage systems. However, the severe instability of the solid–electrolyte interphase (SEI) formed during repeated cycling hinders the development of NIBs. In particular, the SEI dissolution in NIBs with a high-voltage cathode is more severe than in the case of Li-ion batteries (LIBs) and leads to continuous side reactions, electrolyte depletion and irreversible capacity loss, making NIBs less stable than LIBs. Here we report a rational electrolyte design to suppress the SEI dissolution and enhance NIB performance. Our electrolyte lowers the solvation ability for SEI components and facilitates the formation of insoluble SEI components, which minimizes the SEI dissolution. In addition to the stable SEI on a hard carbon (HC) anode, we also show a stable interphase formation on a NaNi0.68Mn0.22Co0.1O2 (NaNMC) cathode. Our HC||NaNMC full cell with this electrolyte demonstrates >90% capacity retention after 300 cycles when charged to 4.2 V. This study enables high-voltage NIBs with long cycling performance and provides a guiding principle in electrolyte design for sodium-ion batteries.
Geographic coverage and key statistics of the Gender Perception Survey for Energy Access and Use
We conducted a large-scale 45-minute survey using a stratified multistage probability sampling design. The survey encompasses 2,312 rural and urban slum households (4,624 individuals) across six Indian states, 58 districts, 506 rural villages and 79 urban slums. The states—Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Rajasthan and Uttar Pradesh—are among those with the lowest socio-economic conditions and highest health burdens from household air pollution in India. In each household, we surveyed the primary cook and their spouse to analyse within-household gender differences in the awareness, satisfaction, usage and opinion of energy services. Data for administrative boundaries adapted by Anuj Tiwari and publicly available on GitHub.
Women’s Empowerment Index (WEI)
a, Indicators used to create the WEI are grouped under six dimensions on knowledge, work, time, money, power and social norm. For each indicator, households are ranked as low (1), medium (2) or high (3) empowerment based on their survey responses. We then aggregate across all six indicators to create a composite index that corresponds to low, medium and high women’s empowerment. b, Spider graph showing the empowerment score for households with low, medium and high WEI against the six dimensions. c, Distribution of the raw empowerment scores that range from 7 to 18. Bar height represents the number of observations with the corresponding raw empowerment score, i.e. 1,254 respondents have empowerment scores of 11. There are 592 households (n = 1,184) or 26% of respondents in the low-empowerment category (raw score ≤ 10), 1,180 households (n = 2,360) or 51% of respondents in the medium-empowerment category (11 ≤ raw score ≤ 12) and 540 households (n = 1,080) or 23% of respondents in the high-empowerment category (13 ≤ raw score ≤ 18). Supplementary Table 8 shows detailed breakdowns of the key indicators and their assigned levels.
Across-household gender inequality in energy services
a,b,c, This figure shows descriptive evidence of inter-household gender differences. The number on each bar shows the proportion or percentage of females and males who report each outcome. For instance, 86 percent of male respondents are aware of PMUY, in contrast to 76 percent of female respondents. Female respondents tend to be less aware of energy services across the board (a). Women in our sample tend to use electricity at different times of the day and for different appliances than men (b). In particular, we identify TVs, fans and chargers as female-intensive appliances. Women tend to be more satisfied with community lighting and electricity and less so with LPG and primary fuel availability (c). They have a stronger preference for government subsidies on grid electricity at home.
Within-household gender inequality in energy services
This figure shows the marginal effects of gender by the household-level WEI. In other words, it shows the difference in outcomes between females and males (treated as the baseline) in low-, medium- and high-WEI households after removing household-level confounders through household fixed effects. For a given panel, each colour represents a different dependent variable shown in the legend. Solid squares represent the coefficient estimates, and vertical lines are the 95% confidence intervals. Full model specifications are available in the Methods section. Standard errors are robust to heteroskedasticity and are clustered at the household level. a–f, We find significant gender disparities within the household across four outcome categories: awareness (a), satisfaction (b), time and appliance use (c,d) and preference for government subsidy and opinion on the harmful effects of smoke (e,f).
Energy access delivers broad socio-economic benefits, but few studies have examined how benefits are allocated within the household. Here we conduct a large-scale survey with 4,624 respondents across six Indian states to provide results on intra-household differences across multiple outcome dimensions of energy service, including knowledge, satisfaction, utilization and opinion. Using a Women’s Empowerment Index (WEI) to measure household-level gender equality, we find that women in low-WEI households are less aware of energy services and use less electricity than their spouses. This awareness gap manifests in differences in satisfaction, as women in higher-WEI households show more concern with energy services and fuel sources. Overall, these results signify that the ‘one-size-fits-all’ approach of providing energy access may not effectively meet the goal of sustainable energy for all. Bridging the gender gap through targeted information and learning campaigns that empower and educate women could unlock additional support for sustainable energy policies. Improved energy access can bring socio-economic benefits, yet these may not be evenly distributed within the household. Zhang et al. conduct a large-scale survey in India and find gender-based disparities in energy services within households.
Photovoltaic performance of flexible WBG PSCs with molecule-bridged hole-selective contact
a, Device structure. b, Molecular structures of bridging molecules. c, Performance comparison between flexible WBG PSCs with an aperture area of 0.049 cm² using bare NiO and MB-NiO with various MeO-2PACz molar ratios (15 devices for each type). The centre line represents the median value, the bounds of box indicate upper and lower quartiles and the whiskers represent the minimum and maximum values. d,e, J–V (d) and EQE (e) curves of the champion devices with NiO and MB-NiO.
Source data
Characterization of WBG perovskite films
a, Energy-level diagram of the WBG PSCs with NiO and MB-NiO. The energy levels of NiO, MB-NiO and perovskite (PVK) were determined from ultraviolet photoelectron spectroscopy (UPS) and ultraviolet-visible (UV-Vis) absorption measurements. The energy levels of C60, ITO and Cu are taken from previous reports45,46. b–d, SEM images (b), X-ray diffraction (c) and 2D GIWAXS patterns (d) of perovskite films deposited on NiO and MB-NiO. The qz and qr in (d) are the out-of-plane and in-plane scattering vector components, respectively. e, The (100) plane orientation distribution in perovskite films as integrated from d.
Charge carrier dynamics at hole-selective contacts
a,b, Steady-state PL spectra (a) and TRPL spectra (b) of perovskite films deposited on PET, PET/ITO/NiO and PET/ITO/MB-NiO substrates. The excitation fluence of TRPL spectra is 0.6 nJ cm–2. The PL decay curves were fitted with biexponential function. The PL spectra were taken from the PET side. c, The calculated QFLS of the perovskite film, HTL/perovskite and HTL/perovskite/electron transport layer junctions. PVK:WBG perovskite. d,e, First-principles simulation of passivation effect of the phosphoryl group in the bridging molecule (that is, 2PACz) on typical defects on perovskite (I substituted by Pb: PbI (d)) and NiO (O vacancy: VO (e)) surfaces. For clarity only, corner-sharing octahedral framework is shown. Optimized structure of the passivated surface and electronic band structures (projected onto individual orbitals) of defective and passivated surfaces are given. The valence band maximum of band structure is set to zero. For the band structure plots, four high symmetry points (that is, Γ, X, S and Y) in Brillouin zone of the orthorhombic surface defective structures are chosen for the momentum k-path. The O’ represents the O from the bridging molecule. The chemical structures in the dotted boxes at the top are the zoom-in of the perovskite (d) and NiO (e) surface defective structures.
Photovoltaic performance of flexible all-perovskite tandem solar cells with MB-NiO
a,b, Device structure (a) and cross-sectional SEM image (b) of flexible all-perovskite tandem solar cells. c,d, J–V (c) and EQE (d) curves of the champion flexible tandem cell with an aperture area of 0.049 cm². e, Stabilized power output of the champion flexible tandem cell (0.049 cm²). The inset shows the PCE distribution of 50 tandem devices, showing an average PCE of 23.8 ± 0.5%. f, PCEs of various types of flexible solar cell. Solid symbols are certified values, and open symbols are in-lab measurements. The highest reported PCE of each type of flexible solar cell is included, and a plot of PCE progress of single-junction flexible PSCs is given. g, Bending tests of flexible tandem cells based on NiO and MB-NiO with a bending radius of 15 mm. The initial PCEs of flexible tandem cells based on NiO and MB-NiO are 22.0% and 24.6%. The inset shows the digital image of a flexible tandem cell under bending. h, J–V curves of the champion flexible tandem cell with MB-NiO with an aperture area of 1.05 cm². i, Stabilized power output of the large-area flexible tandem cell. The inset shows a digital image of the large-area flexible tandem cell.
Source data
Lightweight flexible perovskite solar cells are promising for building integrated photovoltaics, wearable electronics, portable energy systems and aerospace applications. However, their highest certified efficiency of 19.9% lags behind their rigid counterparts (highest 25.7%), mainly due to defective interfaces at charge-selective contacts with perovskites on top. Here we use a mixture of two hole-selective molecules based on carbazole cores and phosphonic acid anchoring groups to form a self-assembled monolayer and bridge perovskite with a low temperature-processed NiO nanocrystal film. The hole-selective contact mitigates interfacial recombination and facilitates hole extraction. We show flexible all-perovskite tandem solar cells with an efficiency of 24.7% (certified 24.4%), outperforming all types of flexible thin-film solar cell. We also report 23.5% efficiency for larger device areas of 1.05 cm². The molecule-bridged interfaces enable significant bending durability of flexible all-perovskite tandem solar cells that retain their initial performance after 10,000 cycles of bending at a radius of 15 mm.
Structural characterization of pristine cathodes
a,b, HAADF-STEM image (a) and corresponding elemental maps (b) of BF-NCM particle. c, High-resolution HAADF-STEM image of BF-NCM particle. d, 4D-STEM nanobeam diffraction pattern of a. e–h, Crystal orientation map of BF-NCM (e,f), C-NCM (g) and N-NCM (h) measured by synchrotron Laue diffraction microscopy. The left and right panels in Fig. 1e–h represent the crystal orientation map and pole figures, respectively. The colour of each pixel in the orientation maps is determined by superimposing the zoomed pole figures onto the colour legend shown inset in e.
Electrochemical performance of the investigated cathodes
a,b, Cycling performance (a) and discharge mean voltage (b) of NCM cathodes with different DBs at C/3 with a charge cut-off voltage of 4.7 V. The capacity retention in percentage in a is defined as the ratio of the 100th to the 1st discharge capacity. c–f, Corresponding voltage profiles of BF-NCM (c), N-NCM (d), C-NCM (e) and BR-NCM (f). RT, room temperature.
O K-edge mRIXS characterization during cycling
a–c, The O K-edge mRIXS map of the BF-NCM cathode at pristine (a), first 4.8 V-charged state (b) and different charge/discharge states (c). d, The O K-edge mRIXS map of the C-NCM cathode at different charge/discharge states. e,f, Integrated RIXS intensity in the characteristic energy range, from 530.6 to 531.8 eV, of OR for BF-NCM (e) and C-NCM cathodes (f). Insets in e and f show a comparison of the charge state for different cycles. The colour in a–d represents the intensity, with white and blue for high and low, respectively.
In situ synchrotron X-ray characterizations of cathodes
a,b, In situ HEXRD of BF-NCM (a) and BR-NCM cathodes (b) during charge/discharge within 3.0–4.7 V at C/10 (λ = 0.1173 Å). c,d, Zoom-in HEXRD patterns of BF-NCM (c) and BR-NCM cathodes (d) in the voltage region of 4.3-4.7 V. The box between c and d indicates the XRD peak shift for the 4.7 V-charged state of BF-NCM and BR-NCM. e–g, Contour images of in situ HEXRD and oxygen evolution for charged BR-NCM (e), BF-NCM (f) and C-NCM cathodes (g) during heating (λ = 0.24125 Å. S, RS and L represent spinel, rock-salt and layered structure, respectively.). h, In situ HEXRD of charged BF-NCM and BR-NCM cathodes during heating from 40 °C to 210 °C and then cooling back to 40 °C (λ = 0.1173 Å). The colour in the contour plots of a, b, and e–h represents the intensity, with red and blue for high and low, respectively. The dashed horizontal lines in Fig. 4e–g indicate the critical temperature of phase transition and oxygen release. The dashed horizontal line in Fig. 4h indicates the XRD result at 210 °C. The dashed vertical line in Fig. 4h indicates the peak shift throughout the whole heating/cooling process.
Correlation between boundary structures and OR
a,b, Schematic of OR activity of layered cathodes with DB-rich (a) and DB-free (b) structure. c–e, Simulated atomic structures of fully lithiated Li3MnCoNiO6 (c) and partially lithiated Li0.375NiCoMnO6 (d) with tilt boundary structure and the corresponding oxygen vacancy formation energy and net charge (e) of oxygen sites along the b lattice vector. f–h, Simulated atomic structures of fully lithiated Li3MnCoNiO6 (f) and partially lithiated Li0.357NiCoMnO6 (g) with twin boundary structure and the corresponding oxygen vacancy formation energy and net charge (h) of oxygen sites along the b lattice vector. Larger spheres with orange, navy, purple and grey colour represent Li, Co, Mn and Ni atoms, respectively, while oxygen atoms at DBs are labelled by small yellow, red, purple, cyan, brown and magenta spheres. Oxygen atoms in the bulk are marked by white spheres. The colours of oxygen atoms at different sites in c and f are consistent with those in d and g, respectively. Horizontal red and blue dotted lines in e and h represent the oxygen vacation formation free energy in the bulk fully and partially lithiated structures, respectively. The colours in e and h for all the symbols are consistent with the oxygen atoms colours in c, d, f and g.
Oxygen redox at high voltage has emerged as a transformative paradigm for high-energy battery cathodes such as layered transition-metal oxides by offering extra capacity beyond conventional transition-metal redox. However, these cathodes suffer from voltage hysteresis, voltage fade and capacity drop upon cycling. Single-crystalline cathodes have recently shown some improvements, but these challenges remain. Here we reveal the fundamental origin of oxygen redox instability to be from the domain boundaries that are present in single-crystalline cathode particles. By investigating single-crystalline cathodes with different domain boundaries structures, we show that the elimination of domain boundaries enhances the reversible lattice oxygen redox while inhibiting the irreversible oxygen release. This leads to significantly suppressed structural degradation and improved mechanical integrity during battery cycling and abuse heating. The robust oxygen redox enabled through domain boundary control provides practical opportunities towards high-energy, long-cycling, safe batteries. Oxygen redox instability at high voltages hinders the application of high-energy battery cathodes. Here the authors report that elimination of domain boundaries in single-crystal cathodes improves the redox stability and consequently the electrochemical performance in extended high-voltage cycling.
All-perovskite tandem devices are promising due to their high efficiency and low cost but their development is hindered by narrow-bandgap absorbers. Now, researchers combine two large organic cations to improve the optoelectronic quality of narrow-bandgap tin–lead perovskites, enabling single-junction and tandem cells with enhanced efficiency and stability.
High-performance electrolytes are urgently required in the development of reversible lithium-metal batteries that offer high energy densities. Now, a versatile liquefied gaseous electrolyte is demonstrated with inherent safety, temperature resilience, high recyclability, and promising electrochemical properties.
Controlling the crystallization of perovskite films over large areas is key to the manufacturing of solar cells, but is difficult with existing fabrication methods. Now, researchers tailor the composition of the precursor ink to fabricate uniform and phase-pure perovskite layers, enabling a 15.3%-efficient photovoltaic module with an area of 205 cm2.
High-energy density, improved safety, temperature resilience and sustainability are desirable properties for lithium-battery electrolytes, yet these metrics are rarely achieved simultaneously. Inspired by the compositions of clean fire-extinguishing agents, we demonstrate inherently safe liquefied gas electrolytes based on 1,1,1,2-tetrafluoroethane and pentafluoroethane that maintain >3 mS cm⁻¹ ionic conductivity from −78 to +80 °C. As a result of beneficial solvation chemistry and a fluorine-rich environment, lithium cycling at >99% Coulombic efficiency for over 200 cycles at 3 mA cm⁻² and 3 mAh cm⁻² was demonstrated in addition to stable cycling of Li/NMC622 full batteries from −60 to +55 °C. In addition, we demonstrate a one-step solvent-recycling process based on the vapour pressure difference at different temperatures of the liquefied gas electrolytes, which promises sustainable operation at scale. This work provides a route to sustainable, temperature-resilient lithium-metal batteries with fire-extinguishing properties that maintain state-of-the-art electrochemical performance.
Photoelectrochemical water splitting is an attractive solar-to-hydrogen pathway. However, the lifetime of photoelectrochemical devices is hampered by severe photocorrosion of semiconductors and instability of co-catalysts. Here we report a strategy for stabilizing photoelectrochemical devices that use a polyacrylamide hydrogel as a highly permeable and transparent device-on-top protector. A hydrogel-protected Sb2Se3 photocathode exhibits stability over 100 h, maintaining ~70% of the initial photocurrent, and the degradation rate gradually decreases to the saturation level. The structural stability of a Pt/TiO2/Sb2Se3 photocathode remains unchanged beyond this duration, and effective bubble escape is ensured through the micro gas tunnel formed in the hydrogel to achieve a mechanically stable protector. We demonstrate the versatility of the device-on-top hydrogel protector under a wide electrolyte pH range and by using a SnS photocathode and a BiVO4 photoanode with ~500 h of lifetime.
Flow chart of PoSo
Under the PoSo scheme, a delegation comprising participants interested in ruling the collaboration network is selected by and out of all participants. A temporary leader, selected from and by the delegates, is responsible for yielding an optimal solution. The rest of the delegates are followers, who verify and exchange the received solutions. If the delegates agree on the solution, they will broadcast the solution to every participant. A participant trusts a received solution if the solution is endorsed by the majority of the delegates. Any leader identified as dishonest or incompetent is replaced by one of the rest of the delegates and removed from the delegation. d1, d2, ..., dk represent the delegates.
Schematics of different optimization structures
a, Centralized. b, Hierarchical. c, Fully distributed. d, Blockchain as coordinator. e, PoSo. In a or e, the central operator (a) or delegation (e) determines an optimal solution for each participant with no iterations. In b or d, the coordinator (b) or delegation (d) exchanges information with participants iteratively to find an optimal solution. In c, participants exchange information with neighbours iteratively to find an optimal solution.
Electricity outputs under the optimal and non-optimal dispatch patterns
The electricity production pattern covers 24 hours. For each hour, there are two bars representing two dispatch patterns. The left bar represents the dispatch pattern before manipulation (optimal dispatch pattern). The right bar represents the dispatch pattern after manipulation (non-optimal dispatch pattern).
Traditional centralized optimization and management schemes may be incompatible with a changing energy system whose structure is becoming increasingly distributed. This challenge can hopefully be addressed by blockchain. However, existing blockchains have not been well prepared to integrate mathematical optimization, which plays a key role in many energy system applications. Here we propose a blockchain consensus mechanism tailored to support mathematical optimization problems, called Proof of Solution (PoSo). PoSo mimics Proof of Work (PoW) by replacing the meaningless mathematical puzzle in PoW with a meaningful optimization problem. This is inspired by the fact that both the solutions to the puzzle and to an optimization problem are hard to find but easy to verify. We show the security and necessity of PoSo by using PoSo to enable energy dispatch and trading for two integrated energy systems. The results show that compared with existing optimization schemes, PoSo ensures that only the optimal solution is accepted and executed by participants. Further, compared with existing blockchains, PoSo can seamlessly incorporate mathematical optimization and minimize the workload associated with searching and verifying the optimum.
Sodium-based batteries have attracted wide interests in the academic and industrial fields. However, their energy density is still lower than that of Li-based batteries. Here we report an initial anode-free Na battery with an energy density of over 200 Wh kg−1, which is even higher than that of the commercial LiFePO4||graphite battery. Through introducing graphitic carbon coating on the Al current collector and boron-containing electrolytes in the battery, we show that uniform nucleation and robust interphases enable reversible and crack-free Na deposition. Benefitting from the synergetic effects derived from the built cooperative interfaces, the cycling lifetime of the Na battery without applying additional pressure reaches 260 cycles, which is the longest life for large-size cells with zero excess Na. The insights gained from the Na plating/stripping behaviour and interfacial chemistry in this work pave the way for further development of Na batteries with even higher performance. Sodium-ion batteries have long been tipped as a promising post-Li-ion storage technology but their performance is still inferior to Li-ion batteries. Here the authors design an ampere-hour-scale battery with an initial Na-free anode configuration to achieve an energy density that rivals Li-ion batteries.
Blockchains offer a lot of opportunities for efficiency and decentralized management in energy systems. Researchers now show the electricity dispatch is a useful problem uniquely suited to serve as proof of work in a new consensus mechanism for decentralized grid management.
Top-cited authors
Yi Cui
  • Stanford University
Linda F. Nazar
  • University of Waterloo
Ji-Guang Zhang
  • Pacific Northwest National Laboratory
Jun Liu
  • Netdragon Websoft
He Yan
  • Iwate University