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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Assessing free tropospheric quasi-equilibrium for different GCM resolutions using a cloud-resolving model simulation of tropical convection

Abstract This study examines the free-tropospheric quasi-equilibrium at different global climate model (GCM) resolutions using the simulation of tropical convection by a cloud-resolving model during the Tropical Western Pacific International Cloud Experiment. The simulated dynamic and thermodynamic fields within the model domain are averaged over subdomains of different sizes equivalent to different GCM resolutions. These coarse-grained fields are then used to compute CAPE and its change with time, and their relationships with simulated convection. Results show that CAPE change with time is controlled predominantly by variations of thermodynamic properties in the planetary boundary layer for all subdomain sizes ranging from 64 to 4 km. Lag correlation analysis shows that CAPE generation by the free-tropospheric dynamical advection (dCAPE ls ) leads convective precipitation but is in phase with convective mass flux at 600 mb and 500 mb vertical velocity for all subdomain sizes. However, the correlation coefficients and regression slopes decrease as the subdomain size decreases for subdomain sizes smaller than 16 km. This is probably due to increased randomness of convection and more scale-dependence of the relationships when the subdomain size reaches the grey zone. By examining the sensitivity of the relationships of convection with dCAPE ls to temporal scales in different subdomain size, it shows that the quasi-equilibrium between dCAPE ls and convection holds well for timescales of 30 min or longer at all subdomain sizes. These results suggest that the free tropospheric quasi-equilibrium assumption may still be useable even for GCM resolutions in the grey zone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Legacies of precipitation influence primary production in Panicum virgatum

Precipitation is a key driver of primary production worldwide, but primary production does not always track year-to-year variation in precipitation linearly. Instead, plant responses to changes in precipitation may exhibit time lags, or legacies of past precipitation. Legacies can be driven by multiple mechanisms, including persistent changes in plant physiological and morphological traits and changes to the physical environment, such as plant access to soil water. Here, we used three precipitation manipulation experiments in central Texas, USA to evaluate the magnitude, duration, and potential mechanisms driving precipitation legacies on aboveground primary production of the perennial C 4 grass, Panicum virgatum. Specifically, we performed a rainout shelter study, where eight genotypes grew under different precipitation regimes; a transplant study, where plants that had previously grown in a rainout shelter under different precipitation regimes were moved to a common environment; and a mesocosm study, where the effect of swapping precipitation regime was examined with a single genotype. Across these experiments, plants previously grown under wet conditions generally performed better than expected when exposed to drought. Panicum virgatum exhibited stronger productivity legacies of past wet years on current-year responses to drought than of past dry years on current-year responses to wet conditions. Additionally, previous year tiller counts, a proxy for meristem availability, were important in determining legacy effects on aboveground production. As climate changes and precipitation extremes—both dry and wet—become more common, these results suggest that populations of P. virgatum may become less resilient.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Closure of Earth’s Global Seasonal Cycle of Energy Storage

Abstract The global seasonal cycle of energy in Earth’s climate system is quantified using observations and reanalyses. After removing long-term trends, net energy entering and exiting the climate system at the top of the atmosphere (TOA) should agree with the sum of energy entering and exiting the ocean, atmosphere, land, and ice over the course of an average year. Achieving such a balanced budget with observations has been challenging. Disagreements have been attributed previously to sparse observations in the high-latitude oceans. However, limiting the local vertical integration of new global ocean heat content estimates to the depth to which seasonal heat energy is stored, rather than integrating to 2000 m everywhere as done previously, allows closure of the global seasonal energy budget within statistical uncertainties. The seasonal cycle of energy storage is largest in the ocean, peaking in April because ocean area is largest in the Southern Hemisphere and the ocean’s thermal inertia causes a lag with respect to the austral summer solstice. Seasonal cycles in energy storage in the atmosphere and land are smaller, but peak in July and September, respectively, because there is more land in the Northern Hemisphere, and the land has more thermal inertia than the atmosphere. Global seasonal energy storage by ice is small, so the atmosphere and land partially offset ocean energy storage in the global integral, with their sum matching time-integrated net global TOA energy fluxes over the seasonal cycle within uncertainties, and both peaking in April.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Stochastic Simulation of Daily Suspended Sediment Concentration Using Multivariate Copulas

Estimation of daily suspended sediment concentration (SSC) is required for water resources and environment management. In this paper, a copula-based stochastic method was proposed for daily SSC simulation. Here, the multivariate copula function, constructed based on a bivariate copula and two bivariate conditional probability distributions, was used to model the temporal and cross dependence structures in daily SSCs. Then, the daily SSCs were generated by sampling from the multivariate conditional distribution. As a result, synthetic long-term SSCs data beyond the limited observation period can be provided for water resources managers, which plays a critical role in accurately estimating frequency and magnitude of extreme SSCs events. The proposed method was under rigorous examination by applying to a case study at Pingshan station in the Jinsha River Basin, China. Results showed that the generated daily SSC sequences not only had a high degree of accuracy in preserving the statistical characteristics of the daily SSC observations, but also captured both the temporal correlation and the cross-correlation between the daily streamflow and daily SSC. Specifically, the average daily relative error values corresponding to mean, standard deviation, skewness, lag-1 temporal correlation, and cross correlation were 0.87%, 4.24%, 7.52%, 0.51% and 2.02%, respectively. The multivariate copula framework proposed here can accurately and efficiently generate long-term daily SSC data for water resources management such as frequency analysis and risk assessment of extreme SSC events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-performance n-Type Polymers Based on Multiple Electron-withdrawing Groups Decorated ( E )-1,2-Di(thiophen-2-yl)ethene Building Blocks

The performance of n-type conjugated polymers lags far behind that of p-type polymers, which significantly restricts the development of organic electronics. The (E)-1,2-di(thiophen-2-yl)ethene (TVT) unit, owing to its unique advantages, has been widely applied in the design of p-type polymer semiconductors. Previous studies have demonstrated that introducing electron-withdrawing groups can lower the frontier orbital energy levels of polymers and enhance electron injection/transporting capabilities. Based on this, we proposed incorporating multiple electronwithdrawing groups, such as amide groups, fluorine atoms, and cyano groups, into the polymer backbones of TVT-based polymer to facilitate the electron transport. Here, we successfully designed and synthesized the polymers TVTDA-4FTVT and TVTDA-2F2CNTVT. Both polymers exhibited low frontier orbital energy levels. Due to its significantly higher crystallization tendency and favorable intermolecular packing structure, the organic field-effect transistor (OFET) device based on TVTDA-4FTVT demonstrated an electron mobility one order of magnitude higher than that of TVTDA-2F2CNTVT. TVTDA-4FTVT showed the highest electron mobility of 0.87 cm 2 ·V −1 ·s −1 , while TVTDA-2F2CNTVT exhibited the highest electron mobility of 0.049 cm 2 ·V −1 ·s −1 . Owing to its deeper lowest unoccupied molecular orbital (LUMO) level, the OFET devices based on TVTDA-2F2CNTVT showed good air stability after being placed in a natural environment for 15 d.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Distributed strain sensing using Bi-metallic coated fiber optic sensors embedded in stainless steel

Silica fiber optic sensors are resistant to corrosive environments and high temperatures, making them attractive candidates for harsh conditions found in nuclear and aerospace industries. Moreover, fibers can be deployed remotely for continuous measuring of spatially distributed temperatures and strains. Here, this study investigated embedding a Ni/Cu bi-metallic coated fiber in a stainless-steel 316 (SS316) matrix using laser powder bed fusion towards functionalizing metal components for site-specific health monitoring. The embedded fiber was continuously interrogated during controlled heating to 1000°C. The measured fiber strains were similar to the expected differential thermal strains between the fiber and the SS316 matrix, until divergent behavior was observed at temperatures >500°C. No debonding at the matrix–coating–fiber interfaces was observed during microscopy, but significant interactions between the coatings and matrix resulted in diffusion-driven chemistry variations and Kirkendall void formation. Applying the strain-lag theory revealed plastic behavior in the Ni coating at temperatures >500°C, limiting the strain transfer to the fiber at higher temperatures. It was estimated that the elastic modulus in the Ni coating had decreased from ~200 GPa at room temperature to below 40 GPa, starting at 600°C. The low elastic modulus above 600°C is within the margin of what the tangent modulus would be in the case of bilinear isotropic hardening. Regardless of the divergent strain transfer at higher temperatures, the fiber was exposed to the equivalent of 1.9 % engineering strain at 1000°C, but measured only a 0.7 % engineering strain due to the poor strain transfer. Although compensating for the plastic behavior of Ni proved challenging, the bonding of a brittle silica fiber to a metal matrix surviving to 1000°C invites potential iterations on coating material for future application. For example, the embedded fiber is sufficient for acoustic energy transfer, realizing high temperature distributed acoustic sensing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Environment-sensitivity functions for gross primary productivity in light use efficiency models

The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO 2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full factorial light use efficiency (LUE) model structure, leading to a collection of 5600 distinct LUE models. Each model was optimized against daily GPP and evapotranspiration fluxes from 196 FLUXNET sites and ranked across sites based on a bootstrap approach. The GPP sensitivity to each environmental factor, including CO 2 fertilization, was shown to be significant, and that none of the previously published model structures performed as well as the best model selected. From daily and weekly to monthly scales, the best model's median Nash-Sutcliffe model efficiency across sites was 0.73, 0.79 and 0.82, respectively, but poorer at annual scales (0.23), emphasizing the common limitation of current models in describing the interannual variability of GPP. Although the best global model did not match the local best model at each site, the selection was robust across ecosystem types. The contribution of light saturation and cloudiness to GPP was observed across all biomes (from 23% to 43%). Temperature and W dominates GPP and LUE but responses of GPP to temperature and W are lagged in cold and arid ecosystems, respectively. The findings of this study provide a foundation towards more robust LUE-based estimates of global GPP and may provide a benchmark for other empirical GPP products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Causality guided machine learning model on wetland CH 4 emissions across global wetlands

Wetland CH 4 emissions are among the most uncertain components of the global CH 4 budget. The complex nature of wetland CH 4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH 4 emissions. In this study, we used the flux measurements of CH 4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH 4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH 4 emissions in all studied wetland types. Ecosystem respiration (CO 2 ) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH 4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH 4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH 4 emissions within earth system land models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Extreme hydroclimates amplify the biophysical effects of advanced green-up in temperate China

Vegetation phenology modulates climate by altering energy and water exchange between the land and atmosphere. However, how extreme hydroclimatic conditions modify these phenology-climate feedbacks is still poorly understood. Here, in this study, we used a land–atmosphere-coupled Weather Research and Forecasting model to explore the impacts of advanced green-up on air temperature under different hydroclimate conditions across temperate China and to Mechanistic analysis elucidate the underlying biophysical mechanisms. By imposing a 14-day earlier green-up in line with recent satellite observations, we found that under mean climate conditions, an earlier leaf-out induces immediate surface cooling of 0.14 °C during green-up and a lagging 0.02 °C warming during senescence averaged for temperate China. Extremely humid conditions amplify the cooling effects to 0.18 °C during green-up, extending this cooling into the senescence period. Conversely, under extremely arid conditions, earlier green-up cools air temperature by 0.09 °C, and amplified senescence warming to 0.16 °C. Mechanism analysis revealed that evapotranspiration-dominated non-radiative processes drive immediate cooling during green-up while radiation and circulation process dominates the delayed but opposite warming/cooling effects during senescence in extremely arid/humid hydroclimates. Given the projected continuation of warming trends and increased frequency of extreme climatic events, it is imperative to incorporate the biophysical effects of vegetation phenology into local climate adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes

Algae have mounting potential to serve as platform strains for engineering efforts, but tools for these species lag behind traditional model systems like E. coli and yeast. Metabolic models are one such tool that would enhance our ability to rationally engineer algae but current automated algorithms to build models do not perform well for algal genomes. We introduce a novel software pipeline, Rapid Annotation of Photosynthetic Systems (RAPS), which leverages manual curation efforts of published models to create high quality first draft metabolic networks for new species. We compared models produced by our pipeline to published models and found that these models are able to capture more genes than the published models and are more predictive of experimentally determined growth rate. We used RAPS to automate the creation of 8 first draft metabolic models of new species to enable the further study of metabolism in these species.

09 BIOMASS FUELS↗
DOE OSTI2025

In-situ monitoring of atomically dispersed Pt sites supported on OMS-2 during CO 2 activation

Atomically dispersed catalysts have drawn great interest lately, as they showcase a high density of active sites, selectivity, and high turnover frequencies in oxidation chemistry due to labile oxygen activation. In contrast, the applications of these catalysts have lagged in reduction reactions due to the ambiguity caused by the sintering and restructuring of active sites. To bridge this gap, the evolution of Pt 4+ isomorphically substituted into an octahedral molecular sieve structure (OMS-2) under reductive conditions was correlatively characterized using multiple in-situ analytical techniques such as ambient pressure X-ray photoelectron spectroscopy, environmental transmission electron microscopy, and solid-state nuclear magnetic resonance. The surface dynamics of the Pt single atoms were revealed during the Reverse Water Gas Shift (RWGS) reaction, where the active sites were identified as two-coordinated platinum single atoms. Under reaction, we show nonbinding atoms adjacent to the single atoms restructured the motif of the single atoms to Pt 2+ via ion mobility of potassium, increasing the activation energy by 25.6 kJ/mol. Here, this work also highlights the potential for increased stability of the single atom sites via isomorphic substitution of the metal oxide support, since the Pt-OMS-2 catalyst retained activity for about 33 h before deactivation, after which nanoparticles were observed in TEM images. This work offers a new perspective in single atom synthesis using the metal oxide as the host for the single atom site, instead of adatoms on the surface

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Convolutional neural networks for intra-hour solar forecasting based on sky image sequences

Accurate and timely solar forecasts play an increasingly critical role in power systems. Compared to longer forecasting timescales, very short-term solar forecasting has lagged behind in both research and practice. In this paper, we propose deep convolutional neural networks (CNNs) to provide operational intra-hour (10-minute-ahead to 60-minute-ahead) solar forecasts. We develop two CNN structures inspired by a widely-used CNN architecture. The CNNs are tailored to our solar forecasting regression tasks and rely solely on sky image sequences. Case studies based on six years of data (over 150,000 data points) demonstrate that the best CNN model has forecast skill scores of 20%-39% over the naive persistence of cloudiness benchmark, even at these very short timescales. The CNNs also have consistently superior performance when compared to shallow machine learning models with meteorological predictors, where the improvement averages around 7%. The sensitivity analyses show that the sky image length, resolution, and weather conditions have impacts on the deep learning model accuracy. In our intra-hour problem with specific setups, two sky images with a 10-minute 128 x 128 resolution yield the most accurate forecasts. Current limitations, future work, and deployment challenges and solutions are also discussed.

14 SOLAR ENERGY↗
DOE OSTI2022

Vehicle-to-manufacturing (V2M) system: A novel approach to improve energy demand flexibility for demand response towards sustainable manufacturing

The U.S. manufacturing sector accounts for 77% of the industrial energy consumption, but its participation in demand response (DR) programs is largely lagged behind. The limited flexibility in production scheduling under high capacity utilization and the lack of DR schemes that incorporate energy demand flexibility measures are deemed as major barriers. In this study, a framework of the interactive vehicles-to-manufacturing (V2M) energy sharing system is proposed to improve the energy demand flexibility of the aggregated system and enhance the DR effectiveness for manufacturers. The V2M-based DR scheme aims to reduce the energy cost by load shifting through joint production and energy sharing control, which can eventually promote manufacturing DR implementation even under high capacity utilization requirements and dynamic real-time electricity prices. The V2M system is modeled based on a discrete-Markov chain considering the complex interconnections among various manufacturing resources and multi-directional energy flows among manufacturing facilities, electric vehicles, and the power grid. Based on the system model, a mixed-integer nonlinear programming (MINLP) problem is formulated to identify the optimal DR scheme. The effectiveness of the proposed approach is validated through comparisons with traditional manufacturing DR schemes. Finally, the results show that a 2.1 to 6.5 times energy demand flexibility improvement and an additional 4.7% to 6.9% energy cost reduction can be achieved by the proposed approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Longitudinal Multi-omics Reveal Phase-Dependent Viral Adaptive Strategies and Functional Potential During Formation of Algal-bacterial Granular Sludge

Virus-host interactions within microbial aggregates critically influence microbiome function and stability, yet how physicochemical stresses shape the interactive dynamics remains largely unexplored. Here, we investigated virus–host dynamics during the transition of algal-bacterial granular sludge (ABGS) from activated sludge under continuous hydraulic shear using integrated metagenomics and metatranscriptomics. Hydraulic stress initially reduced host a-diversity, which coincided with a marked increase in viral lysogenicity. During this host diversity bottleneck, viral microdiversity increased, and genes related to virion structure and DNA packaging were under positive selection (pN/pS >1). As host diversity recovered, viral microdiversity declined, while viral anti-defense systems (ADS) significantly increased in abundance. Lagged correlation analysis revealed a significant positive correlation between viral ADS and host defense systems (DS), suggesting an evolutionary arms race. Furthermore, active lysogenic infections were accompanied by enrichment of DS and auxiliary viral genes (AVGs) involved in genetic information processing and amino acid metabolism, potentially enhancing host fitness. Overall, our study unveils a phase-dependent co-evolutionary interplay between viruses and hosts during ABGS formation, providing insights into the development and maintenance of microbial structural and functional resilience in engineered ecosystems.

Qi, Huiyuan↗
DOE OSTI2026

Xylose metabolic engineering of Issatchenkia orientalis for 3-hydroxypropionic acid production from cellulosic hydrolysate without nutrient supplementation

Bioconversion of lignocellulosic biomass offers a promising alternative to petroleum-based chemical production. However, inefficient xylose utilization and toxic compounds in cellulosic hydrolysate limit microbial fermentation, as the hydrolysate contains substantial amounts of xylose in addition to glucose. To address these challenges, we engineered Issatchenkia orientalis to produce 3-hydroxypropionic acid (3-HP) directly from sorghum hydrolysate under low-pH conditions. A heterologous xylose utilization pathway consisting of XYL1, XYL2, and XYL3 from Scheffersomyces stipitis was introduced into an engineered 3-HP producing strain, enabling efficient conversion of xylose to 3-HP. The engineered strain produced 46.8 g/L 3-HP from sorghum hydrolysate without nutrient supplementation. To eliminate the lag phase under low-pH conditions, fermentation was conducted at pH 6.0 for the first three days, after which pH control was discontinued and in situ 3-HP accumulation buffered the culture. This partial pH control strategy increased 3-HP productivity by 55% from 0.20 to 0.31 g/L∙h, while maintaining low-pH conditions. Introducing an additional copy of XYL2 further increased 3-HP titer to 53.5 g/L and the yield by 33%, from 0.30 to 0.40 g/g sugars, with pH reaching 4.5 at the end of fermentation. This represents one of the highest reported 3-HP titers and yields from cellulosic hydrolysate without additional nutrient supplementation. This work demonstrates a nutrient-independent and low-pH bioprocess for upgrading lignocellulosic hydrolysate into 3-HP, highlighting the industrial potential of engineered xylose-utilizing I. orientalis for sustainable production of platform chemicals from renewable feedstocks.

3-Hydroxypropionic acid↗
DOE OSTI2021

The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community

Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. Finally, the needed transformation of the LTG data culture requires emphasis in student education on science and management of data.

58 GEOSCIENCES↗
DOE OSTI2025

Entropy-based feature selection for capturing impacts in Earth system models with abrupt forcing

This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and without an external forcing in an Earth system model. Temporal feature selection is performed by first computing the cross-fuzzy entropy to quantify similarity of patterns between two datasets and then applying changepoint detection to identify regions of statistically constant entropy. The method is used to capture temperate north surface cooling from a 9-member simulation ensemble of the Mt. Pinatubo volcanic eruption, which injected 10 Tg of SO 2 into the stratosphere. The results estimate a mean difference decrease in near surface air temperature of -0.560 K with a 99% confidence interval between -0.864 K and -0.257 K between April and November of 1992, one year following the eruption. A sensitivity analysis with decreasing SO 2 injection revealed that the impact is statistically significant at 5 Tg but not at 3 Tg. Using identified features, a dependency graph model based on a 9-day lag had significantly fewer nodes than a graph based on monthly means. Furthermore, this demonstrates our method’s ability to perform dimension reduction while still uncovering source-to-impact pathways.

Changepoint detection↗
DOE OSTI2022

Room temperature slot-die coated perovskite layer modified with sulfonyl-γ-AApeptide for high performance perovskite solar devices

Perovskite solar cells (PSCs) exhibited remarkable progress for small aperture area cells, however, the performance of its counterpart, large aperture area cells, lags due to non-uniform and defective perovskite layers. Here, we fabricate reproducible large area homogeneous perovskite films at room temperature and without controlling humidity (up to 40 % RH) with a slot die coater on a c-TiO 2 layer deposited using a large area chemical bath. A new artificial peptide – sulfonyl-γ-AApeptide (F-GLU-S) was employed to modify the slot-die coated perovskite surface, grain boundaries and electronic defects. The multi-functional F-GLU-S with carbonyl, carboxyl, sulfonyl, benzene, and chloro groups was capable of strongly interacting with the perovskite layer and repairing the uncoordinated Pb 2+ ions and halide vacancies. As a result, both the electron and hole densities of defects were significantly suppressed; consequently, the non-radiative recombination was effectively suppressed for the modified device which can be explicitly seen in the device performance where both V oc and FF of the modified device improved considerably. Therefore, F-GLU-S modified slot-die coated MAPbI 3 – based devices demonstrated outstanding performance of 21.44 % PCE with a V oc of 1.13 V, J sc of 24.64 cm –2 , and FF 76.99 %. Furthermore, F-GLU-S passivation impeded the infiltration of moisture and oxygen due to its hydrophobic nature and defect repair potential. As a result, the modified device retained above 92 % of its original PCE after 720 h in air (room temperature and 40–60 % RH).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗