Directly Unveiling the Energy Transfer Dynamics between Alq3 Molecules and Si by Ultrafast Optical Pump-Probe Spectroscopy.

The energy transfer (ET) between organic molecules and semiconductors is a crucial mechanism for enhancing the performance of semiconductor-based optoelectronic devices, but it remains undiscovered. Here, ultrafast optical pump-probe spectroscopy was utilized to directly reveal the ET between organic Alq3 molecules and Si semiconductors. Ultrathin SiO2 dielectric layers with a thickness of 3.2-10.8 nm were inserted between Alq3 and Si to prevent charge transfer. By means of the ET from Alq3 to Si, the SiO2 thickness-dependent relaxation dynamics of photoexcited carriers in Si have been unambiguously observed on the transient reflectivity change (ΔR/R) spectra, especially for the relaxation process on a time scale of 200-350 ps. In addition, these findings also agree with the results of our calculation in a model of long-range dipole-dipole interactions, which provides critical information for developing future optoelectronic devices.

Enhanced Superconductivity and Rashba Effect in a Buckled Plumbene-Au Kagome Superstructure.

Plumbene, with a structure similar to graphene, is expected to possess a strong spin-orbit coupling and thus enhances its superconducting critical temperature (Tc ). In this work, a buckled plumbene-Au Kagome superstructure grown by depositing Au on Pb(111) is investigated. The superconducting gap monitored by temperature-dependent scanning tunneling microscopy/spectroscopy shows that the buckled plumbene-Au Kagome superstructure not only has an enhanced Tc with respect to that of a monolayer Pb but also possesses a higher value than what owned by a bulk Pb substrate. By combining angle-resolved photoemission spectroscopy with density functional theory, the monolayer Au-intercalated low-buckled plumbene sandwiched between the top Au Kagome layer and the bottom Pb(111) substrate is confirmed and the electron-phonon coupling-enhanced superconductivity is revealed. This work demonstrates that a buckled plumbene-Au Kagome superstructure can enhance superconducting Tc and Rashba effect, effectively triggering the novel properties of a plumbene.

Deep learning based atomic defect detection framework for two-dimensional materials.

Defects to popular two-dimensional (2D) transition metal dichalcogenides (TMDs) seriously lower the efficiency of field-effect transistor (FET) and depress the development of 2D materials. These atomic defects are mainly identified and researched by scanning tunneling microscope (STM) because it can provide precise measurement without harming the samples. The long analysis time of STM for locating defects in images has been solved by combining feature detection with convolutional neural networks (CNN). However, the low signal-noise ratio, insufficient data, and a large amount of TMDs members make the automatic defect detection system hard to be applied. In this study, we propose a deep learning-based atomic defect detection framework (DL-ADD) to efficiently detect atomic defects in molybdenum disulfide (MoS2) and generalize the model for defect detection in other TMD materials. We design DL-ADD with data augmentation, color preprocessing, noise filtering, and a detection model to improve detection quality. The DL-ADD provides precise detection in MoS2 (F2-scores is 0.86 on average) and good generality to WS2 (F2-scores is 0.89 on average).