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We elucidate a previously unknown synthesis pathway that leads to polymeric nanospheres, orientation-controlled microgels, or microspheroids via single-step polymerization of divinylbenzene (DVB) using initiated chemical vapor deposition (iCVD) in liquid crystals (LC). iCVD supplies vapor-phase reactants continuously, avoiding the critical limitation of reactant-induced disruption of LC structure that has plagued past LC-templated polymerization processes. LC is leveraged as a real-time display of the polymerization conditions and particle emergence, captured using an in situ long–focal range microscope. Detailed image analysis unravels key LC-guided mechanisms during polymerization. pDVB forms nanospheres due to poor solubilization by nematic LC. The nanospheres partition to the LC-solid interface and further assemble into microgel clusters whose orientation is guided by the LC molecular alignment. On further polymerization, microgel clusters transition to microspheroids that resemble liquid drops. We identify key energetic factors that guide trajectories along the synthesis pathway, providing the fundamental basis of a framework for engineering particle synthesis with shape control. 

Size-controlled polymer nanodomes (PNDs) benefit a broad cross-section of existing and emerging technologies. Condensed droplet polymerization (CDP) is a vacuum-based synthesis technology that produces PNDs from monomer precursors in a single step. However, the effect of synthesis and processing conditions on the PND size distribution remains elusive. Towards size distribution control, we report the effect of substrate temperature, on which monomer droplets condense, on the size distribution of PNDs. We take a reductionist approach and operate the CDP under batch mode to match the conditions commonly used in condensation research. Notably, despite the rich knowledge base in dropwise condensation, the behavior of nonpolar liquids like a common monomer, i.e., 2-hydroxyethyl methacrylate (HEMA), is not well understood. We bridge that gap by demonstrating that dropwise condensation of HEMA follows a two-stage growth process. Early-stage growth is dominated by drop nucleation and growth, giving rise to relatively uniform sizes with a lognormal distribution, whereas late-stage growth is dominated by the combined effect of drop coalescence and renucleation, leading to a bimodal size distribution. This new framework for understanding the PND size distribution enables an unprecedented population of PNDs. Their controlled size distribution has the potential to enable programmable properties for emergent materials. 

Abstract Amphiphilic copolymers (AP) represent a class of novel antibiofouling materials whose chemistry and composition can be tuned to optimize their performance. However, the enormous chemistry‐composition design space associated with AP makes their performance optimization laborious; it is not experimentally feasible to assess and validate all possible AP compositions even with the use of rapid screening methodologies. To address this constraint, a robust model development paradigm is reported, yielding a versatile machine learning approach that accurately predicts biofilm formation by Pseudomonas aeruginosa on a library of AP. The model excels in extracting underlying patterns in a “pooled” dataset from various experimental sources, thereby expanding the design space accessible to the model to a much larger selection of AP chemistries and compositions. The model is used to screen virtual libraries of AP for identification of best‐performing candidates for experimental validation. Initiated chemical vapor deposition is used for the precision synthesis of the model‐selected AP chemistries and compositions for validation at solid–liquid interface (often used in conventional antifouling studies) as well as the air–liquid–solid triple interface. Despite the vastly different growth conditions, the model successfully identifies the best‐performing AP for biofilm inhibition at the triple interface.