Search for: All records

Abstract Electrophilic aromatic substitution reactions are of profound importance for the synthesis of biologically active compounds and other advanced materials. They represent an important means to activate specific aromatic C–H bonds without requiring transition-metal catalysts. Surprisingly, few stereoselective variants are known for electrophilic aromatic substitutions, which limits the utility of these classical reactions for stereoselective synthesis. While many electrophilic aromatic substitutions lead to achiral products (due to the planar nature of aromatic rings), there are important examples where chiral products are produced, including desymmetrization reactions of aromatic cyclophanes and of prochiral substrates with multiple aromatic rings. This Synpacts article now illustrates how chiral arms, when placed precisely above and underneath delocalized carbocations, can act as chiral auxiliaries to convert classical electrophilic aromatic substitution reactions into powerful diastereo- and enantioselective transformations. 

We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21 degrees in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects. 

This work presents the first transition metal-free synthesis of oxygen-linked aromatic polymers by integrating iterative exponential polymer growth (IEG) with nucleophilic aromatic substitution (S N Ar) reactions. Our approach applies methyl sulfones as the leaving groups, which eliminate the need for a transition metal catalyst, while also providing flexibility in functionality and configuration of the building blocks used. As indicated by 1) 1 H- 1 H NOESY NMR spectroscopy, 2) single-crystal X-ray crystallography, and 3) density functional theory (DFT) calculations, the unimolecular polymers obtained are folded by nonclassical hydrogen bonds formed between the oxygens of the electron-rich aromatic rings and the positively polarized C–H bonds of the electron-poor pyrimidine functions. Our results not only introduce a transition metal-free synthetic methodology to access precision polymers but also demonstrate how interactions between relatively small, neutral aromatic units in the polymers can be utilized as new supramolecular interaction pairs to control the folding of precision macromolecules. 

Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy. Despite this observation, few HRI methods are capable of efficiently performing inference for multimodal systems. In this work, we introduce a reformulation of Interaction Primitives which allows for learning from demonstration of interaction tasks, while also gracefully handling nonlinearities inherent to multimodal inference in such scenarios. We also empirically show that our method results in more accurate, more robust, and faster inference than standard Interaction Primitives and other common methods in challenging HRI scenarios.