Electroreduction of CO2 into useful fuels, especially if driven by renewable energy, represents a potentially 'clean' strategy for replacing fossil feedstocks and dealing with increasing CO2 emissions and their adverse effects on climate. The critical bottleneck lies in activating CO2 into the CO2(•-) radical anion or other intermediates that can be converted further, as the activation usually requires impractically high overpotentials. Recently, electrocatalysts based on oxide-derived metal nanostructures have been shown to enable CO2 reduction at low overpotentials. However, it remains unclear how the electrocatalytic activity of these metals is influenced by their native oxides, mainly because microstructural features such as interfaces and defects influence CO2 reduction activity yet are difficult to control. To evaluate the role of the two different catalytic sites, here we fabricate two kinds of four-atom-thick layers: pure cobalt metal, and co-existing domains of cobalt metal and cobalt oxide. Cobalt mainly produces formate (HCOO(-)) during CO2 electroreduction; we find that surface cobalt atoms of the atomically thin layers have higher intrinsic activity and selectivity towards formate production, at lower overpotentials, than do surface cobalt atoms on bulk samples. Partial oxidation of the atomic layers further increases their intrinsic activity, allowing us to realize stable current densities of about 10 milliamperes per square centimetre over 40 hours, with approximately 90 per cent formate selectivity at an overpotential of only 0.24 volts, which outperforms previously reported metal or metal oxide electrodes evaluated under comparable conditions. The correct morphology and oxidation state can thus transform a material from one considered nearly non-catalytic for the CO2 electroreduction reaction into an active catalyst. These findings point to new opportunities for manipulating and improving the CO2 electroreduction properties of metal systems, especially once the influence of both the atomic-scale structure and the presence of oxide are mechanistically better understood.

We report an anionic solid solution process that induces frustrated magnetic structures within two-dimensional transition metal chalcogenides, which leads to huge negative magnetoresistance effects. Ultrathin nanosheets of TiTe(2-x)I(x) solid solutions, which are a new class of inorganic two-dimensional magnetic material, exhibit negative magnetoresistance with a value of up to -85%, due to the spin-dependent scattering effects of local Ti(3+) 3d(1) moments that are antiferromagnetically coupled. Moreover, TiTe(2-x)I(x) serials show unique transport behaviors with continuous evolution from metallic to semiconducting states. We anticipate that anionic doping will be a powerful tool for optimizing the intrinsic physical properties of two-dimensional transition metal chalcogenide system.

Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of generating such examples are typically driven by local heuristic rules that are agnostic to the context, often resulting in unnatural and ungrammatical outputs. This paper presents CLARE, a ContextuaLized AdversaRial Example generation model that produces fluent and grammatical outputs through a mask-then-infill procedure. CLARE builds on a pre-trained masked language model and modifies the inputs in a contextaware manner. We propose three contextualized perturbations, Replace, Insert and Merge, that allow for generating outputs of varied lengths. CLARE can flexibly combine these perturbations and apply them at any position in the inputs, and is thus able to attack the victim model more effectively with fewer edits. Extensive experiments and human evaluation demonstrate that CLARE outperforms the baselines in terms of attack success rate, textual similarity, fluency and grammaticality.