Di Huang

Neonicotinoid insecticides used for agricultural pest control pose severe threats to non-target organisms and ecosystems due to their high water solubility, environmental persistence, and bioaccumulation. To address the water pollution caused by such insecticides, this work effectively removed neonicotinoid insecticides by loading the PTQ10:Y6 and PTQ10:PC71BM onto coconut shell charcoal and investigated the dominant charge dynamics differences between the non-fullerene Y6 and the fullerene derivative PC71BM system photocatalysts. Compared with the fullerene derivative PC71BM, the non-fullerene Y6 system photocatalyst exhibited lower charge recombination, higher carrier mobility, and stronger transient photocurrent response. As a result, when 100 mg of PTQ10:Y6 photocatalyst is used to treat 50 ml imidacloprid (10 mg l−1), the PTQ10:Y6 catalyst achieved a degradation efficiency of over 96% within 30 min and maintained an efficiency of 92% after 15 consecutive cycles. Moreover, two possible degradation pathways and products are inferred through the Fukui function and liquid chromatography–mass spectrometry. Toxicological analysis indicated that the toxicity of almost all the intermediate products is very low. More importantly, the Y6 photocatalyst can also widely degrade solutions such as thiacloprid and thiamethoxam with a concentration of 10 mg l−1 within 30 min. This work reveals that due to the superior charge dynamics characteristics, the Y6 heterojunction photocatalyst system is expected to be applied in the remediation of pesticide pollution.

Asymmetric structure design in non-fullerene acceptors (NFAs) has driven a significant breakthrough in the power conversion efficiency (PCE) of organic solar cells (OSCs). However, finding high-performing moieties out of the vast amount of candidates and their potential combinations is a significant challenge. In this work, we propose a machine learning (ML)-guided high-throughput screening approach to identify optimal acceptor moieties for both symmetric and asymmetric A–DA′D–A type NFAs. By standardizing and splitting the NFA structures and using one-hot encoding to construct feature vectors, a reliable XGBoost model is established to predict structure–activity relationships. Combined with interpretability analysis, core contributors to solar cell performance, such as end groups, conjugated scaffolds, and central cores, were identified. From over a million virtually generated molecules, we screened the molecules to find the best acceptor matches with a donor polymer (e.g., PM6) exhibiting predicted PCEs greater than 18%. The Sankey diagram visualizes the best combined paths for NFAs, highlighting the great potential of asymmetric design. Furthermore, quantum chemical calculations verified that the picked-out acceptor molecules (K01–K12) with asymmetric structures and predicted PCEs of over 19% are potentially promising in terms of energy levels, electrostatic potentials, and charge transfer characteristics. Moreover, assessment of synthesis accessibility indicated their good experimental feasibility, and applicability to multiple donor systems. This work not only facilitates a transition from empirical trial-and-error to a data-driven approach, but also advances molecular engineering from symmetric simplicity to asymmetric function-oriented design.

An ultra-thin PVDF interlayer controls phase distribution, suppresses low- n phases, and enhances out-of-plane orientation, crystallinity, and efficiency in 2D RP perovskite.

In perovskite solar cells, grain boundaries are considered one of the major structural defect sites, and consequently affect solar cell performance. Therefore, a precise edge detection of perovskite grains may enable to predict resulting solar cell performance. Herein, a deep learning model, Self‐UNet, is developed to extract and quantify morphological information such as grain boundary length (GBL), the number of grains (NG), and average grain surface area (AGSA) from scanning elecron microscope (SEM) images. The Self‐UNet excels conventional Canny and UNet models in edge extraction; the Dice coefficient and F1‐score exhibit as high as 91.22% and 93.58%, respectively. The high edge detection accuracy of Self‐UNet allows for not only identifying tiny grains stuck between relatively large grains, but also distinguishing actual grain boundaries from grooves on grain surface from low quality SEM images, avoiding under‐ or over‐estimation of grain information. Moreover, the gradient boosted decision tree (GBDT) regression integrated to the Self‐UNet exhibits high accuracy in predicting solar cell efficiency with relative errors of less than 10% compared to the experimentally measured efficiencies, which is corroborated by results from the literature and the experiments. Additionally, the GBL can be verified in multiple ways as a new morphological feature.

It is challenging to build a deep learning predictive model using traditional data mining methods due to the scarcity of available data, and the model's internal decision-making process is often nonintuitive and difficult to explain. In this work, a directed message passing neural network model with transfer learning (TL) and chemprop interpreter is proposed to improve energy levels prediction and visualization for organic photovoltaic materials. The established model shows the best performance, with coefficient of determination reaching 0.787 for HOMO and 0.822 for LUMO in a small testing set after TL, compared to the other four models. Then, the chemprop interpreter analyzes local and global effects of 12 molecular structures on the energy levels for organic materials. After a comprehensive analysis of the energy level effects of nonfullerene Y-series, IT-series, and other organic materials, 12 new IT-series derivatives are designed. 1,1-dicyano-methylene-3-indanone (IC) end group halogenation can reduce HOMO and LUMO energy levels to varying degrees, while IC end group modified by electron-withdrawing aromatic groups can increase HOMO and LUMO energy levels and obtain relatively smaller electrostatic potential (ESP) to reducing intermolecular interactions. The influence of side-chain modification on energy levels is limited. It is worth mentioning that the predicted results of IT-series derivatives match density functional theory calculations. The model also shows good generalization and transferability for predicting the energy levels of other organic electronic materials. This work not only provides a cost-effective model for predicting the energy levels of organic photovoltaic materials but also explains the potential bridge between molecular structure and electronic properties.

Traditionally, squaraine dyes have been studied and employed in biomedical research due to their excellent optical properties, and the molecules are being adopted in different research fields such as organic solar cells. In this study, we investigate correlations between solar cell performance and processing parameters of all‐small‐molecule bulk heterojunction solar cells comprising squaraine (SQ) as electron donor (D) and non‐fullerene small molecules (e.g., ITIC) as electron acceptor (A) with the help of machine learning (ML) and design of experiment (DoE) methods. Among the five predictive ML models tested with the selected parameters, the eXtreme gradient boosting model shows the satisfactory results with quite high coefficient of determination of 0.999 and 0.997 in training and testing sets, respectively. By measuring the contribution of each input variable to solar cell efficiency, four process parameters, that is, the total concentration, the ratio of D/A, the rotational speed of spin coating, and the annealing temperature, are found to be the key features strongly correlated to solar cell efficiency. From contour plots in DoE, the highest solar cell efficiency of approximately 5% can be predicted under the conditions of 15 mg mL⁻¹ in solution concentration, a 1:2 mix ratio of D and A, rotational speeds ranging from 800 to 900 rpm, and annealing temperatures within 100–110°C. Using the suggested parameter conditions, we fabricated solar cells, achieving a quite high efficiency of approximately 4%. Besides the global optimization conditions, we also employ the solvent vapor annealing combination to the thermal annealing to facilitate further mobilization of molecules and more optimized microstructure of bulk heterojunction films, resulting in a further enhancement in solar cell efficiency of more than 20%.

The preparation parameters of PM6:Y6 non-fullerene organic solar cells (OSCs) have significant influence on the power conversion efficiency (PCE). Herein, machine learning (ML) models are applied for analyzing the quantitative effects of the PCE on PM6:Y6 non-fullerene OSCs from the perspective of fabrication parameters. Random Forest (RF) model has the best evaluation performance and is considered as the best model among the six different algorithms. The Pearson correlation coefficient, coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error of the test set in the RF model are 0.836, 0.668, 0.695, 0.538, and 0.035, respectively. In addition, the most important preparation parameters for PCE are the effective area of OSCs, type of solvent and solvent additives, spin-coating speed, total concentration, annealing temperature, annealing time from SHAP analysis. The optimized PCE can be obtained in PM6:Y6 non-fullerene OSCs when the effective area is between 4 and 6 mm2, the spin-coating speed is 3000 rpm, and the total concentration is 18 mg ml−1, respectively. The contour map further reflects that the optimal annealing temperature are 80 or 105 °C, and the corresponding annealing time are 5 or 9 min, respectively. Based on the above preparation conditions, we verified through the experimental design that the optimal PCE is 16.91%, and the relative error with the ML prediction is only 1.24%. Our research provides low time and cost guidance on preparation parameter matching for the development of high-performance the PM6:Y6 non-fullerene OSCs.

Exciton dissociation and charge transport are important processes in the photoelectric conversion of organic solar cells (OSCs), directly affecting the performance of OSCs. In order to facilitate exciton dissociation, phase separation size should be as small as possible. However, for the sake of continuous charge transport, a large phase separation size is also necessary. To balance these two conflicting needs, it is of utmost importance to regulate the phase separation of the active layer. This Review mainly elaborated the smart tactics commonly adopted to phase separation optimization, aiming to understand how these treatments affect both the active layer phase separation and the performance of OSCs. Furthermore, we present an outlook on the new opportunities of phase separation regulation of the active layer. Finally, this Review may provide some reference for the researchers engaged in the research of OSCs.