No abstract

The stabilization of transition metals as isolated centres on suitably tailored carriers with high density is crucial to exploit the technical potential of single-atom heterogeneous catalysts, enabling their maximized productivity in industrial reactors. Wet-chemical methods are best suited for practical applications due to their amenability to scale up. However, achieving single-atom dispersions at metal contents above 2 wt.% remains challenging. We introduce a versatile approach combining impregnation and two-step annealing to synthesize ultra-high-density single-atom catalysts (UHD-SACs) with unprecedented metal contents up to 23 wt.% for 15 metals on chemically-distinct carriers. Translation to an automated protocol demonstrates its robustness and provides a path to explore virtually unlimited libraries of mono or multimetallic catalysts. At the molecular level, characterization of the synthesis mechanism through experiments and simulations shows that controlling the bonding of metal precursors with the carrier via stepwise ligand removal prevents their thermally-induced aggregation into nanoparticles, ensuring atomic dispersion in the resulting UHD-SACs. The catalytic bene ts of UHD-SACs are demonstrated for the electrochemical reduction of CO 2 to CO over NiN 4 motifs on carbon.

Two-dimensional ferroelectrics is attractive for synaptic device applications because of its low power consumption and amenability to high-density device integration. Here, we demonstrate that tin monosulfide (SnS) films less than 6 nm thick show optimum performance as a semiconductor channel in an in-plane ferroelectric analogue synaptic device, whereas thicker films have a much poorer ferroelectric response due to screening effects by a higher concentration of charge carriers. The SnS ferroelectric device exhibits synaptic behaviors with highly stable room-temperature operation, high linearity in potentiation/depression, long retention, and low cycle-to-cycle/device-to-device variations. The simulated device based on ferroelectric SnS achieves ∼92.1% pattern recognition accuracy in an artificial neural network simulation. By switching the ferroelectric domains partially, multilevel conductance states and the conductance ratio can be obtained, achieving high pattern recognition accuracy.

This review highlights the atomically-precise on-surface synthesis, topological and electronic structure characterization of open-shell graphene nanostructure, in combined with in-depth discussion on the mechanisms behind the π-magnetism.

No abstract

Controllable synthesis of single atom catalysts (SACs) with high loading remains challenging due to the aggregation tendency of metal atoms as the surface coverage increases. Here we report the synthesis of graphene supported cobalt SACs (Co1/G) with a tuneable high loading by atomic layer deposition. Ozone treatment of the graphene support not only eliminates the undesirable ligands of the pre-deposited metal precursors, but also regenerates active sites for the precise tuning of the density of Co atoms. The Co1/G SACs also demonstrate exceptional activity and high selectivity for the hydrogenation of nitroarenes to produce azoxy aromatic compounds, attributable to the formation of a coordinatively unsaturated and positively charged catalytically active center (Co–O–C) arising from the proximal-atom induced partial depletion of the 3d Co orbitals. Our findings pave the way for the precise engineering of the metal loading in a variety of SACs for superior catalytic activities.

The efficient utilization of near‐infrared (NIR) light for photocatalytic hydrogen generation is vitally important to both solar hydrogen energy and hydrogen medicine, but remains a challenge at present, owing to the strict requirement of the semiconductor for high NIR responsiveness, narrow bandgap, and suitable redox potentials. Here, an NIR‐active carbon/potassium‐doped red polymeric carbon nitride (RPCN) is achieved for by using a similar‐structure dopant as the melamine (C3H6N6) precursor with the solid KCl. The homogeneous and high incorporation of carbon and potassium remarkably narrows the bandgap of carbon nitride (1.7 eV) and endows RPCN with a high NIR‐photocatalytic activity for H2 evolution from water at the rate of 140 µmol h−1 g−1 under NIR irradiation (700 nm ≤ λ ≤ 780 nm), and the apparent quantum efficiency is high as 0.84% at 700 ± 10 nm (and 13% at 500 ± 10 nm). A proof‐of‐concept experiment on a tumor‐bearing mouse model verifies RPCN as being capable of intratumoral NIR‐photocatalytic hydrogen generation and simultaneous glutathione deprivation for safe and high‐efficacy drug‐free cancer therapy. The results shed light on designing efficient photocatalysts to capture the full spectrum of solar energy, and also pioneer a new pathway to develop NIR photocatalysts for hydrogen therapy of major diseases.

Recently, in-sensor computing with individual sensors or multiple connected sensors directly processing information has been proposed to improve energy, area, and time efficiency of artificial intelligence systems. Current investigations mainly focus on a single sensory processing such as auditory, visual, tactile, olfactory, and so on. However, a human perception system can sense and process different types of information with a complex environment and small perceptive field simultaneously. For example, the recognition accuracy of human eyes is highly affected by the environment such as extremely low or high relative humidity (RH). Here, a multi-modal MXene-ZnO memristor that combines visual data sensing, RH sensing, and pre-processing functions to emulate the unique environmental adaptive behavior of the human eye is designed and constructed. The multi-field controlled resistive switching of the MXene-ZnO memristor is originated from the photon-/protons-regulated formation of oxygen vacancies filaments. Finally, in-sensor computing with a MXene-ZnO memristor functioning as both filter to preprocess the information and synapse to implement a weight updating process with different humidity adaptability has been demonstrated. Multimodal in-sensor computing provides the potential to reduce the underlying circuitry complexity of the traditional neuromorphic visual system and contributes to the development of intelligence in device-level implementations.