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Abstract We report the electrochemically switchable reactivity of (salfen)Al(OⁱPr) (salfen = 1,1′‐di(2,4‐bis‐tert‐butyl‐salicylimino)ferrocene) toward the ring‐opening polymerization of various cyclic esters, ethers, and carbonates. Using a recently developed electrochemical system comprised of an H‐cell and a glassy carbon working electrode, an applied potential can alternate between the two redox states of the catalyst and alter monomer incorporation during ring‐opening polymerization. We discuss differences in activity and control under electrochemical conditions compared to previously studied chemical redox methods and discuss the necessity of a redox switch during certain copolymerization reactions. 

Abstract Electrochemically controlled redox-switchable polymerization uses an electric potential to bias the monomer selectivity of a catalyst. Many ferrocene-appended catalysts can exist in two oxidation states, a neutral reduced state and an oxidized cationic state. Electrochemical generation of the oxidized cationic state produces a charged species whose counteranion is determined by the identity of the supporting electrolyte anion. Herein, the role the counteranion has on monomer selectivity and polymerization kinetics is investigated. Minimal differences in monomer selectivity in the reduced state was found, however, in the oxidized state, the coordinating ability of the counteranion greatly influenced the rate of polymerization. How activity differences governed by the choice of electrolyte can be utilized to access desired diblock copolymers is also described. 

Objective: Despite advances in human-machine interface design, we lack the ability to give people precise and fast control over high degree of freedom (DOF) systems, like robotic limbs. Attempts to improve control often focus on the static map that links user input to device commands; hypothesizing that the user’s skill acquisition can be improved by finding an intuitive map. Here we investigate what map features affect skill acquisition. Methods: Each of our 36 participants used one of three maps that translated their 19-dimensional finger movement into the 5 robot joints and used the robot to pick up and move objects. The maps were each constructed to maximize a different control principle to reveal what features are most critical for user performance. 1) Principal Components Analysis to maximize the linear capture of finger variance, 2) our novel Egalitarian Principal Components Analysis to maximize the equality of variance captured by each component and 3) a Nonlinear Autoencoder to achieve both high variance capture and less biased variance allocation across latent dimensions Results: Despite large differences in the mapping structures there were no significant differences in group performance. Conclusion: Participants’ natural aptitude had a far greater effect on performance than the map. Significance: Robot-user interfaces are becoming increasingly common and require new designs to make them easier to operate. Here we show that optimizing the map may not be the appropriate target to improve operator skill. Therefore, further efforts should focus on other aspects of the robot-user-interface such as feedback or learning environment.