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Porous and amorphous materials are typically not employed for photocatalytic purposes, like CO2 photoreduction, as their high number of defects can lead to low charge mobility and favour bulk electron–hole recombination. Yet, with a disordered nature can come porosity, which in turn promotes catalyst/reactant interactions and fast charge transfer to reactants. Here, we demonstrate that moving from h-BN, a well-known crystalline insulator, to amorphous BN, we create a semiconductor, which is able to photoreduce CO2 in the gas/solid phase, under both UV-vis and pure visible light and ambient conditions, without the need for cocatalysts. The material selectively produces CO and maintains its photocatalytic stability over several catalytic cycles. The performance of this un-optimized material is on par with that of TiO2, the benchmark in the field. For the first time, we map out experimentally the band edges of porous BN on the absolute energy scale vs. vacuum to provide fundamental insight into the reaction mechanism. Owing to the chemical and structural tunability of porous BN, these findings highlight the potential of porous BN-based structures for photocatalysis particularly solar fuel production.

We present significantly improved one-to-one routing algorithms in the datacenter networks HCN and View the MathML source in that our routing algorithms result in much shorter paths when compared with existing routing algorithms. We also present a much tighter analysis of HCN and View the MathML source by observing that there is a very close relationship between the datacenter networks HCN and the interconnection networks known as WK-recursive networks. We use existing results concerning WK-recursive networks to prove the optimality of our new routing algorithm for HCN and also to significantly aid the implementation of our routing algorithms in both HCN and View the MathML source. Furthermore, we empirically evaluate our new routing algorithms for View the MathML source, against existing ones, across a range of metrics relating to path-length, throughput, and latency for the traffic patterns all-to-one, bisection, butterfly, hot-region, many-all-to-all, and uniform-random, and we also study the completion times of workloads relating to MapReduce, stencil and sweep, and unstructured applications. Not only do our results significantly improve routing in our datacenter networks for all of the different scenarios considered but they also emphasize that existing theoretical research can impact upon modern computational platforms.