A chemical approach to controlling the work function of few‐layer graphene is investigated. Graphene films are synthesized on Cu foil by chemical vapor deposition. Six metal chlorides, AuCl3, IrCl3, MoCl3, OsCl3, PdCl2, and RhCl3, are used as dopants. The sheet resistance of the doped graphene decreases from 1100 Ω/sq to ≈500–700 Ω/sq and its transmittance at 550 nm also decreases from 96.7% to 93% for 20 mM AuCl3 due to the formation of metal particles. The sheet resistance and transmittance are reduced with increasing metal chloride concentration. The G peak in the Raman spectra is shifted to a higher wavenumber after metal chloride doping, which indicates a charge transfer from graphene to metal ions. The intensity ratio of ICC/IC−C increases with doping, indicating an electron transfer from graphene sheets to metal ions. Ultraviolet photoemission spectroscopy data shows that the work function of graphene increases from 4.2 eV to 5.0, 4.9, 4.8, 4.68, 5.0, and 5.14 eV for the graphene with 20 mM AuCl3, IrCl3, MoCl3, OsCl3, PdCl2, and RhCl3, respectively. It is considered that spontaneous charge transfer occurs from the specific energy level of graphene to the metal ions, thus increasing the work function.

Wafer-scale n-MoS2/p-Si photocathodes with high hydrogen evolution reaction activities are demonstrated.

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.