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The heat effect of nonthermal plasma significantly enhanced the synergy between the plasma and the catalytically active sites. Consequently, nearly 100% NH₃decomposition was achieved over the low-loading Ru/Al₂O₃catalyst under adiabatic conditions. 

Supramolecular polymer blends (SPBs) represent a versatile class of polymers whose morphology directly determines their macroscopic properties. However, rational design of SPBs remains hindered by the lack of predictive models describing how molecular features and intermolecular interactions determine morphology. Here, we report a data-driven high-throughput workflow integrating modular synthesis, robotic sample formulation and processing, automated morphology characterization, and machine learning (ML) for SPBs discovery. Using a plug-and-play modular synthetic strategy, 33 hydrogen-bonding end-functional homopolymer precursors were prepared and orthogonally paired to fabricate 260 SPBs within one day. A custom automated atomic force microscopy (AFM) protocol enabled systematic morphological characterization, producing 2340 images with little human intervention. Average phase separation sizes (e.g. domain spacings) was extracted from processed AFM data using multiple complementary approaches and applied to ML model training. Leveraging the high-throughput sample formation and characterization, a high-quality database was curated for SPBs, allowing training of ML models. Guided by support vector regression (SVR) model, target morphologies of 50, 100, and 150 nm were successfully predicted and experimentally validated. This work demonstrates the potential of coupling high-throughput experimentation with ML to accelerate polymer blends phase discovery, providing one of the first large-scale, experimentally derived datasets specifically designed for supramolecular polymer research. 

This work demonstrates a series of functionalization methods to enhance the utility of thermoplastic-elastomer derived ordered mesoporous carbons, including chemical activation, heteroatom doping, and the introduction of nanoparticles. 

Degradable epoxy-amine thermosets derived from cyclic-ketal monomers offer robust performance and facile end-of-use processing, enabling recovery of diketone building blocks and pristine carbon fiber from fiber reinforced polymer composites. 

Abstract The optimal selection of alkyl chains and halogen ions in ammonium salts for addressing specific defect types in perovskite films remains unclear, although ammonium salts emerged as a promising strategy to enhance the performance of perovskite solar cells (PSCs). Herein, four ammonium salts are introduced with different alkyl chain types and halogen ions to passivate perovskite films. Branched‐alkyl chain ammonium salts exhibited superior passivation effects compared to linear‐alkyl chain salts, with the alkyl chain structure having a more significant impact on device performance than the halogen ion component. In addition, DFT calculations are performed to investigate which defect types in perovskite films are most effectively passivated by different alkyl chain types and halogen ions in ammonium salts. Branched‐alkyl chain ammonium salts demonstrated superior passivation effects on V_Pband V_FAdefects in perovskite films compared to linear‐alkyl chain salts, while exhibiting similar passivation effects for V_Idefects. PSCs passivated with tert‐OAI achieved an impressive efficiency of 25.49%, with a V_ocof 1.19 V, a J_scof 25.40 mA cm⁻², and an FF of 84.34%. This work highlights a targeted ammonium salt passivation strategy tailored to address different defect types in perovskite films, accounting for variations in perovskite composition and fabrication environments.