Exploring the interaction between two neighbouring monomers has great potential to significantly raise the performance and deepen the mechanistic understanding of heterogeneous catalysis. Herein, we demonstrate that the synergetic interaction between neighbouring Pt monomers on MoS greatly enhanced the CO hydrogenation catalytic activity and reduced the activation energy relative to isolated monomers. Neighbouring Pt monomers were achieved by increasing the Pt mass loading up to 7.5% while maintaining the atomic dispersion of Pt. Mechanistic studies reveal that neighbouring Pt monomers not only worked in synergy to vary the reaction barrier, but also underwent distinct reaction paths compared with isolated monomers. Isolated Pt monomers favour the conversion of CO into methanol without the formation of formic acid, whereas CO is hydrogenated stepwise into formic acid and methanol for neighbouring Pt monomers. The discovery of the synergetic interaction between neighbouring monomers may create a new path for manipulating catalytic properties.

Disulfide bonds have been widely used to develop reduction-responsive drug-delivery systems (DDS) for cancer therapy. We propose that disulfide bonds might be also used as an oxidation-responsive linkage just like thioether bonds, which can be oxidized to hydrophilic sulfoxide or sulphone in the presence of oxidation stimuli. To test our hypothesis, we design three novel paclitaxel-citronellol conjugates linked via different lengths of disulfide-bond-containing carbon chain. The prodrugs can self-assemble into uniform-size nanoparticles with impressively high drug loading (>55%). As expected, the disulfide-bond-bridged prodrug nanoparticles show redox dual-responsive drug release. More interestingly, the position of disulfide bonds in the carbon chain linkage has profound impacts on the redox dual responsiveness, thereby affecting the drug release, cytotoxicity, pharmacokinetics, biodistribution, and in vivo antitumor efficacy of prodrug nanoassemblies. The redox dual-responsive mechanism is elucidated, and how the position of disulfide bonds in the carbon chain affects the redox dual responsiveness and antitumor efficiency of prodrug nanoassemblies is also clarified. Our findings give new insight into the stimuli responsiveness of disulfide bonds and provide a good foundation for the development of novel redox dual-responsive DDS for cancer therapy.

Rh-based heterogeneous catalysts generally have limited selectivity relative to their homogeneous counterparts in hydroformylation reactions despite of the convenience of catalyst separation in heterogeneous catalysis. Here, we develop CoO-supported Rh single-atom catalysts (Rh/CoO) with remarkable activity and selectivity towards propene hydroformylation. By increasing Rh mass loading, isolated Rh atoms switch to aggregated clusters of different atomicity. During the hydroformylation, Rh/CoO achieves the optimal selectivity of 94.4% for butyraldehyde and the highest turnover frequency number of 2,065 h−1 among the obtained atomic-scale Rh-based catalysts. Mechanistic studies reveal that a structural reconstruction of Rh single atoms in Rh/CoO occurs during the catalytic process, facilitating the adsorption and activation of reactants. In kinetic view, linear products are determined as the dominating products by analysing reaction paths deriving from the two most stable co-adsorbed configurations. As a bridge of homogeneous and heterogeneous catalysis, single-atom catalysts can be potentially applied in other industrial reactions.

Tumor cells are characterized as redox-heterogeneous intracellular microenvironment due to the simultaneous overproduction of reactive oxygen species and glutathione. Rational design of redox-responsive drug delivery systems is a promising prospect for efficient cancer therapy. Herein, six paclitaxel-citronellol conjugates are synthesized using either thioether bond, disulfide bond, selenoether bond, diselenide bond, carbon bond or carbon-carbon bond as linkages. These prodrugs can self-assemble into uniform nanoparticles with ultrahigh drug-loading capacity. Interestingly, sulfur/selenium/carbon bonds significantly affect the efficiency of prodrug nanoassemblies. The bond angles/dihedral angles impact the self-assembly, stability and pharmacokinetics. The redox-responsivity of sulfur/selenium/carbon bonds has remarkable influence on drug release and cytotoxicity. Moreover, selenoether/diselenide bond possess unique ability to produce reactive oxygen species, which further improve the cytotoxicity of these prodrugs. Our findings give deep insight into the impact of chemical linkages on prodrug nanoassemblies and provide strategies to the rational design of redox-responsive drug delivery systems for cancer therapy.

Deepfakes are a recent off-the-shelf manipulation technique that allows anyone to swap two identities in a single video. In addition to Deepfakes, a variety of GANbased face swapping methods have also been published with accompanying code. To counter this emerging threat, we have constructed an extremely large face swap video dataset to enable the training of detection models, and organized the accompanying DeepFake Detection Challenge (DFDC) Kaggle competition. Importantly, all recorded subjects agreed to participate in and have their likenesses modified during the construction of the face-swapped dataset.The DFDC dataset is by far the largest currentlyand publicly-available face swap video dataset, with over 100,000 total clips sourced from 3,426 paid actors, produced with several Deepfake, GAN-based, and non-learned methods. In addition to describing the methods used to construct the dataset, we provide a detailed analysis of the top submissions from the Kaggle contest. We show although Deepfake detection is extremely difficult and still an unsolved problem, a Deepfake detection model trained only on the DFDC can generalize to real "in-the-wild" Deepfake videos, and such a model can be a valuable analysis tool when analyzing potentially Deepfaked videos. Training, validation and testing corpuses can be downloaded from https://ai.facebook.com/datasets/dfdc.