Iridium-catalysed arylation of C-H bonds enabled by oxidatively induced reductive elimination

Direct arylation of C-H bonds is in principle a powerful way of preparing value-added molecules that contain carbon-aryl fragments. Unfortunately, currently available synthetic methods are not sufficiently effective to be practical alternatives to conventional cross-coupling reactions. We propose that the main problem lies in the late portion of the catalytic cycle where reductive elimination gives the desired carbon-aryl bond. Accordingly, we have developed a strategy where the Ir(III) centre of the key intermediate is first oxidized to Ir(IV). Density functional theory calculations indicate that the barrier to reductive elimination is reduced by nearly 19 kcal mol-1 for this oxidized complex compared with that of its Ir(III) counterpart. Various experiments confirm this prediction, affording a new methodology capable of directly arylating C-H bonds at room temperature with a broad substrate scope and in good yields. This work highlights how the oxidation states of intermediates can be targeted deliberately to catalyse an otherwise impossible reaction. ©2018 Macmillan Publishers Limited, part of Springer Nature. (c) All rights reserve

Computational Population Biology: Linking the inner and outer worlds of organisms.

Computationally complex systems models are needed to advance research and implement policy in theoretical and applied population biology. Difference and differential equations used to build lumped dynamic models (LDMs) may have the advantage of clarity, but are limited in their inability to include fine-scale spatial information and individual-specific physical, physiological, immunological, neural and behavioral states. Current formulations of agent-based models (ABMs) are too idiosyncratic and freewheeling to provide a general, coherent framework for dynamically linking the inner and outer worlds of organisms. Here I propose principles for a general, modular, hierarchically scalable, framework for building computational population models (CPMs) designed to treat the inner world of individual agents as complex dynamical systems that take information from their spatially detailed outer worlds to drive the dynamic inner worlds of these agents, simulate their ecology and the evolutionary pathways of their progeny. All the modeling elements are in place, although improvements in software technology will be helpful; but most of all we need a cultural shift in the way population biologists communicate and share model components and the models themselves, fit, test, refute, and refine models, to make the progress needed to meet the ecosystems management challenges posed by global change biology