The integration of polymer chains with organolead halide perovskite (MAPbI3) films, leading to enhanced stability and electro-optical performance, is critically affected by the molecular weight of chains. The molecular weight determines the mobility and volume of the chains, which affects the crystallization kinetics and, hence, perovskite grain size. The insulating nature of the chains is another critical factor that affects both ion migration and conduction of electronic charge. The combined effect of these factors leads to optimal performance with the use of medium-length chains. A simple model integrating the two effects accurately fits the response of the polymer-perovskite composite. Further characterization results show that the polymer-perovskite films have a three-layer architecture consisting of nanoscale polymer-rich top and bottom layers. These combined results show that the optimization of performance in polymer-perovskite devices depends critically on the size of the chains due to their multiple effects on the perovskite matrix.The first Ni(OTf)2-catalyzed hydroamination of ynamides 2 was developed by reacting with secondary amines (1 and 4). This protocol features excellent regioselectivity, a broad substrate scope of secondary aryl amines, and good functional group tolerance for ynamides. Using this method, a variety of substituted ethene-1,1-diamine compounds were prepared in moderate to excellent yields with high regioselectivities.Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https//github.com/ncats/ld50-multitask, https//predictor.ncats.io/, and https//cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.The microencapsulation technique has been proven as a powerful and flexible tool to design and develop a multifunctional additive for various applications. The significant characteristics of this technique center around the ability to control the release of the core active ingredients by tuning the porosity and the permeability of the shell. However, this original concept has faced a major roadblock in lubricant research since it causes a major breakage of the microcapsules (∼70%) under severe stressed-shearing conditions. The shell fragments generated from such unwanted events significantly influence the friction and wear performances of the counterpart, thus limiting the ongoing research of the microencapsulation technique in tribology. To solve such technical bottlenecks, we develop a new strategy of utilizing the microencapsulation technique which focuses on the smart responsiveness of the shell with the base lubricant and the synergy between the incorporated materials. In this study, the smart-responsive colloidal capsule has been developed based on our proposed concept that demonstrates outstanding performances in improving the lubricity of the conventional melt lubricant (by ∼70%) under hot metal working conditions. An unprecedented oxidation-reduction (by ∼93%) and the first instance of ultralow friction (0.07) at elevated temperatures (880 °C) have been initially achieved. This work opens a new avenue of customizing a multifunctional additive package by utilizing the smart colloidal capsules in lubrication science.The integration of metal-organic frameworks (MOF) into organic polymers represents a direct and effective strategy for developing innovative composite materials that combine the exceptional properties of MOFs with the robustness of organic polymers. However, the preparation of MOF@polymer hybrid composites requires an efficient dispersion and interaction of MOF particles with polymer matrices, which remains a significant challenge. In this work, a new simple and direct approach was applied for the development of Ln-MOF@polymer materials. A series of Ln-MOF@TGIC composites Ln-MOF = [Ln(μ3-BTC)(H2O)6] n (Ln-BTC), where Ln = Eu, Tb, Eu0.05Tb0.95; H3BTC = 1,3,5-benzenetricarboxylic acid; TGIC = triglycidyl isocyanurate were successfully obtained by applying a grinding method via the chemical bonding between uncoordinated carboxylate groups in Ln-BTC and epoxy groups in TGIC. The Ln-BTC@TGIC materials possess significant fluorescence characteristics with superior emission lifetimes and quantum yields if compared to parent Ln-MOFs. Interestingly, under the UV irradiation, a considerable color change from yellow in Eu0.05Tb0.95-BTC to red in Eu0.05Tb0.95-BTC@TGIC was observed. The energy-transfer mechanism was also rationalized by the density functional theory (DFT) calculations. The developed Ln-BTC@TGIC composites were further applied as functional fluorescent coatings for the fabrication, via a simple spraying method, of the flexible polyimide (PI) films, Ln-BTC@TGIC@PI.  https://www.selleckchem.com/products/SB-202190.html  Thus, the present work unveils a new methodology and expands its applicability for the design and assembly of stable, multicomponent, and soft polymer materials with remarkable fluorescence properties.