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The role of the alkali metal cations in halide perovskite solar cells is not well understood. Using synchrotron-based nano–x-ray fluorescence and complementary measurements, we found that the halide distribution becomes homogenized upon addition of cesium iodide, either alone or with rubidium iodide, for substoichiometric, stoichiometric, and overstoichiometric preparations, where the lead halide is varied with respect to organic halide precursors. Halide homogenization coincides with long-lived charge carrier decays, spatially homogeneous carrier dynamics (as visualized by ultrafast microscopy), and improved photovoltaic device performance. We found that rubidium and potassium phase-segregate in highly concentrated clusters. Alkali metals are beneficial at low concentrations, where they homogenize the halide distribution, but at higher concentrations, they form recombination-active second-phase clusters.

Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite absorber, called the capping layer. In this study, a machine-learning framework is presented to optimize this layer. We featurize 21 organic halide salts, apply them as capping layers onto methylammonium lead iodide (MAPbI 3 ) films, age them under accelerated conditions, and determine features governing stability using supervised machine learning and Shapley values. We find that organic molecules’ low number of hydrogen-bonding donors and small topological polar surface area correlate with increased MAPbI 3 film stability. The top performing organic halide, phenyltriethylammonium iodide (PTEAI), successfully extends the MAPbI 3 stability lifetime by 4 ± 2 times over bare MAPbI 3 and 1.3 ± 0.3 times over state-of-the-art octylammonium bromide (OABr). Through characterization, we find that this capping layer stabilizes the photoactive layer by changing the surface chemistry and suppressing methylammonium loss. The stability of perovskite solar cells can be improved by using hybrid-organic perovskites capping-layers atop the active material. Here the authors use machine learning to optimize capping layers by monitoring time to degradation of differently capped lead-halide perovskite solar cells.

Stability of perovskite-based photovoltaics remains a topic requiring further attention. Cation engineering influences perovskite stability, with the present-day understanding of the impact of cations based on accelerated ageing tests at higher-than-operating temperatures (e.g. 140°C). By coupling high-throughput experimentation with machine learning, we discover a weak correlation between high/low-temperature stability with a stability-reversal behavior. At high ageing temperatures, increasing organic cation (e.g. methylammonium) or decreasing inorganic cation (e.g. cesium) in multi-cation perovskites has detrimental impact on photo/thermal-stability; but below 100°C, the impact is reversed. The underlying mechanism is revealed by calculating the kinetic activation energy in perovskite decomposition. We further identify that incorporating at least 10 mol.% MA and up to 5 mol.% Cs/Rb to maximize the device stability at device-operating temperature (<100°C). We close by demonstrating the methylammonium-containing perovskite solar cells showing negligible efficiency loss compared to its initial efficiency after 1800 hours of working under illumination at 30°C. Current view of the impact of A-site cation on the stability of perovskite materials and devices is derived from accelerated ageing tests at high temperature, which is beyond normal operation range. Here, the authors reveal the great impact of ageing condition on assessing the photothermal stability of mixed-cation perovskites using high-throughput robot system coupled with machine learning.

The development of eco-friendly adhesives is a major research direction in the wood-based material industry. Previous research has already demonstrated the mixture of sucrose and citric acid could be utilized as an adhesive for the manufacture of particleboard. Herein, based on the chemical characteristics of sucrose, a synthesized sucrose-citric acid (SC) adhesive was prepared, featuring suitable viscosity and high solid content. The investigation of synthesis conditions on the bond performance showed that the optimal mass proportion between sucrose and citric acid was 25/75, the synthesis temperature was 100 °C, and the synthesis time was 2 h. The wet shear strength of the plywood bonded with SC adhesive, which was synthesized at optimal conditions and satisfied the China National Standard GB/T 9846-2015. The synthesis mechanism was studied by both 13C NMR analysis and HPLC, and the chemical composition manifesting caramelization reaction occurred during the synthesis process. The results of ATR FT-IR indicated the formation of a furan ring, carbonyl, and ether groups in the cured insoluble matter of the SC adhesive, which indicated dehydration condensation as the reaction mechanism between sucrose and citric acid.

Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems. By defining acceleration and enhancement metrics for materials optimization objectives, we find that surrogate models such as Gaussian Process (GP) with anisotropic kernels and Random Forest (RF) have comparable performance in BO, and both outperform the commonly used GP with isotropic kernels. GP with anisotropic kernels has demonstrated the most robustness, yet RF is a close alternative and warrants more consideration because it is free from distribution assumptions, has smaller time complexity, and requires less effort in initial hyperparameter selection. We also raise awareness about the benefits of using GP with anisotropic kernels in future materials optimization campaigns.

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal-halides spanning three dimensionalities and seven space groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross-validated accuracies for dimensionality and space group classification of 93 and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16° 2θ, which enables an XRD pattern to be obtained and classified in 5.5 min or less.

Sucrose is one of the most abundantly available renewable chemicals in the world, and it is expected to be utilized as a raw material for wood-based material products. Herein, a novel adhesion system that was based on sucrose and ammonium dihydrogen phosphate (ADP) was synthesized into an adhesive with 80% solid content, and this eco-friendly was utilized on the fabrication of plywood. The effects of the synthesis conditions on the plywood bond performance and synthesis mechanism were investigated. The optimal synthesis conditions were as follows: the mass proportion between sucrose and ADP was 90/10, the synthesis temperature was 90 °C, and the synthesis time was 3 h. The bonding performance of the plywood that was bonded by optimal SADP adhesive satisfied the GB/T 9846-2015 standard. The chemical analysis was performance tested by using High-Performance Liquid Chromatography (HPLC), Attenuated Total Reflection-Fourier Transform Infrared Spectra (ATR-FTIR), and Pyrolysis Gas Chromatography and Mass Spectrometry (Py-GC/MS) to understand the chemical transformation during the synthesis process. The chemical analysis results confirmed that the hydrolysis and conversation reaction of sucrose occurred in the synthesized SADP adhesive, and ADP promoted the pyrolysis efficiency of sucrose.

The development of eco-friendly adhesives for wood composite products has been a major topic in the field of wood science and product engineering. Although the research on tannin-based and soybean protein-based adhesives has already reached, or at least nears, industrial implementation, we also face a variety of remaining challenges with regards to the push for sustainable adhesives. First, petroleum-derived substances remain a pre-requisite for utilization of said adhesive systems, and also the viscosity of these novel adhesives continues to limit its ability to serve as a drop-in substitute. Within this study, we focus upon the development of an eco-friendly plywood adhesive that does not require any addition of petroleum derived reagents, and the resultant liquid adhesive has both high solid contents as well as a manageably low viscosity at processing temperatures. Specifically, a system based on sucrose and ammonium dihydrogen phosphate (ADP) was synthesized into an adhesive with ~80% solid content and with viscosities ranging from 480–1270 mPa·s. The bonding performance of all adhesive-bound veneer specimens satisfied GB/T 9846-2015 standard at 170 °C hot pressing temperature. To better explain the system’s efficiency, in-depth chemical analysis was performed in an effort to understand the chemical makeup of the cured adhesives as well as the components over the time course of curing. Several new structures involving the fixation of nitrogen speak to a novel adhesive molecular network. This research provides a possibility of synthesizing an eco-friendly wood adhesive with a high solid content and a low viscosity by renewable materials, and this novel adhesive system has the potential to be widely utilized in the wood industry.

In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs’ potential to enhance human–machine and human–human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects. Collaborative self-diving research is crucial to research acceleration amidst ever more complex problems. Here, authors identify the key challenges to the dual cultivation of centralised self-driving user facilities and networks of self-driving labs.

While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, extracting fungible knowledge representations from experimental data remains an elusive task. In this manuscript, we use ML to infer the underlying differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). Using a sparse regression algorithm, we find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85 °C is described minimally by a second-order polynomial. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous to self-propagating reactions. We examine the robustness of our conclusions to experimental variance and Gaussian noise and describe the experimental limits within which this methodology can be applied. Our study highlights the promise and challenges associated with ML-aided scientific discovery by demonstrating its application in experimental chemical and materials systems.