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Abstract In this manuscript, we report the first demonstration of controlled helicity in extended graphene nanoribbons (GNRs). We present a wealth of new graphene nanoribbons that are a direct consequence of the high‐yielding and robust synthetic method revealed in this study. We created a series of defect‐free, ultralong, chiral cove‐edged graphene nanoribbons where helical twisting of the graphene nanoribbon backbone is tuned through functionalization with chiral side chains.S‐configured point chiral centers in the side chains transfer their chiral information to induce a helically chiral, right‐handed twist in the graphene nanoribbon. As the backbone is extended, these helically twisted graphene nanoribbons exhibit a substantial increase in their circular dichroic response. The longest variant synthesized consists of an average of 268 linearly fused rings, reaching 65 nm in average length with nearly 10 full end‐to‐end helical rotations. The structure exhibits an extraordinary |Δε| value of 6780 M⁻¹cm⁻¹at 550 nm—the highest recorded for an organic molecule in the visible wavelength range. This new chiroptic material acts as room‐temperature spin filters in thin films due to its chirality‐induced spin selectivity. 

This paper studies a multi-robot visibility-based pursuit-evasion problem in which a group of pursuer robots are tasked with detecting an evader within a two dimensional polygonal environment. The primary contribution is a novel formulation of the pursuit-evasion problem that modifies the pursuers' objective by requiring that the evader still be detected, even in spite of the failure of any single pursuer robot. This novel constraint, whereby two pursuers are required to detect an evader, has the benefit of providing redundancy to the search, should any member of the team become unresponsive, suffer temporary sensor disruption/failure, or otherwise become incapacitated. Existing methods, even those that are designed to respond to failures, rely on the pursuers to replan and update their search pattern to handle such occurrences. In contrast, the proposed formulation produces plans that are inherently tolerant of some level of disturbance. Building upon this new formulation, we introduce an augmented data structure for encoding the problem state and a novel sampling technique to ensure that the generated plans are robust to failures of any single pursuer robot. An implementation and simulation results illustrating the effectiveness of this approach are described.