资源环境科技发展态势分析平台

Precise protein sequencing and folding are believed to generate the structure and chemical diversity of natural channels(1,2), both of which are essential to synthetically achieve proton transport performance comparable to that seen in natural systems. Geometrically defined channels have been fabricated using peptides, DNAs, carbon nanotubes, sequence-defined polymers and organic frameworks(3-13). However, none of these channels rivals the performance observed in their natural counterparts. Here we show that without forming an atomically structured channel, four-monomer-based random heteropolymers (RHPs)(14) can mimic membrane proteins and exhibit selective proton transport across lipid bilayers at a rate similar to those of natural proton channels. Statistical control over the monomer distribution in an RHP leads to segmental heterogeneity in hydrophobicity, which facilitates the insertion of single RHPs into the lipid bilayers. It also results in bilayer-spanning segments containing polar monomers that promote the formation of hydrogen-bonded chains(15,16) for proton transport. Our study demonstrates the importance of the adaptability that is enabled by statistical similarity among RHP chains and of the modularity provided by the chemical diversity of monomers, to achieve uniform behaviour in heterogeneous systems. Our results also validate statistical randomness as an unexplored approach to realize protein-like behaviour at the single-polymer-chain level in a predictable manner.

The rapid increase in global energy demand and the need to replace carbon dioxide (CO2)-emitting fossil fuels with renewable sources have driven interest in chemical storage of intermittent solar and wind energy(1,2). Particularly attractive is the electrochemical reduction of CO2 to chemical feedstocks, which uses both CO2 and renewable energy(3-8). Copper has been the predominant electrocatalyst for this reaction when aiming for more valuable multi-carbon products(9-16), and process improvements have been particularly notable when targeting ethylene. However, the energy efficiency and productivity (current density) achieved so far still fall below the values required to produce ethylene at cost-competitive prices. Here we describe Cu-Al electrocatalysts, identified using density functional theory calculations in combination with active machine learning, that efficiently reduce CO2 to ethylene with the highest Faradaic efficiency reported so far. This Faradaic efficiency of over 80 per cent (compared to about 66 per cent for pure Cu) is achieved at a current density of 400 milliamperes per square centimetre (at 1.5 volts versus a reversible hydrogen electrode) and a cathodic-side (half-cell) ethylene power conversion efficiency of 55 +/- 2 per cent at 150 milliamperes per square centimetre. We perform computational studies that suggest that the Cu-Al alloys provide multiple sites and surface orientations with near-optimal CO binding for both efficient and selective CO2 reduction(17). Furthermore, in situ X-ray absorption measurements reveal that Cu and Al enable a favourable Cu coordination environment that enhances C-C dimerization. These findings illustrate the value of computation and machine learning in guiding the experimental exploration of multi-metallic systems that go beyond the limitations of conventional single-metal electrocatalysts.

Functionalization of the beta-C-H bonds of aliphatic acids is emerging as a valuable synthetic disconnection that complements a wide range of conjugate addition reactions(1-5). Despite efforts for beta-C-H functionalization in carbon-carbon and carbon-heteroatom bond-forming reactions, these have numerous crucial limitations, especially for industrial-scale applications, including lack of mono-selectivity, use of expensive oxidants and limited scope(6-13). Notably, the majority of these reactions are incompatible with free aliphatic acids without exogenous directing groups. Considering the challenge of developing C-H activation reactions, it is not surprising that achieving different transformations requires independent catalyst design and directing group optimizations in each case. Here we report a Pd-catalysed beta-C(sp(3))-H lactonization of aliphatic acids enabled by a mono-N-protected beta-amino acid ligand. The highly strained and reactive beta-lactone products are versatile linchpins for the mono-selective installation of diverse alkyl, alkenyl, aryl, alkynyl, fluoro, hydroxyl and amino groups at the beta position of the parent acid, thus providing a route to many carboxylic acids. The use of inexpensive tert-butyl hydrogen peroxide as the oxidant to promote the desired selective reductive elimination from the Pd(IV) centre, as well as the ease of product purification without column chromatography, render this reaction amenable to tonne-scale manufacturing.