Converting the Bulk Transition Metal Dichalcogenides Crystal into Stacked Monolayers via Ethylenediamine Intercalation.

We report the synthesis of ethylenediamine-intercalated NbSe2 and Li-ethylenediamine-intercalated MoSe2 single crystals with increased interlayer distances and their electronic structures measured by means of angle-resolved photoemission spectroscopy (ARPES). X-ray diffraction patterns and transmission electron microscopy images confirm the successful intercalation and an increase in the interlayer distance. ARPES measurement reveals that intercalated NbSe2 shows an electronic structure almost identical to that of monolayer NbSe2. Intercalated MoSe2 also returns the characteristic feature of the monolayer electronic structure, a direct band gap, which generates sizable photoluminescence even in the bulk form. Our results demonstrate that the properties and phenomena of the monolayer transition metal dichalcogenides can be achieved with large-scale bulk samples by blocking the interlayer interaction through intercalation.

Quantum electron liquid and its possible phase transition.

Purely quantum electron systems exhibit intriguing correlated electronic phases by virtue of quantum fluctuations in addition to electron-electron interactions. To realize such quantum electron systems, a key ingredient is dense electrons decoupled from other degrees of freedom. Here, we report the discovery of a pure quantum electron liquid that spreads up to ~3 Å in a vacuum on the surface of an electride crystal. Its extremely high electron density and weak hybridization with buried atomic orbitals show the quantum and pure nature of the electrons, which exhibit a polarized liquid phase, as demonstrated by our spin-dependent measurement. Furthermore, upon enhancing the electron correlation strength, the dynamics of the quantum electrons change to that of a non-Fermi liquid along with an anomalous band deformation, suggestive of a transition to a hexatic liquid crystal phase. Our findings develop the frontier of quantum electron systems and serve as a platform for exploring correlated electronic phases in a pure fashion.

Deep learning-based statistical noise reduction for multidimensional spectral data.

In spectroscopic experiments, data acquisition in multi-dimensional phase space may require long acquisition time, owing to the large phase space volume to be covered. In such a case, the limited time available for data acquisition can be a serious constraint for experiments in which multidimensional spectral data are acquired. Here, taking angle-resolved photoemission spectroscopy (ARPES) as an example, we demonstrate a denoising method that utilizes deep learning as an intelligent way to overcome the constraint. With readily available ARPES data and random generation of training datasets, we successfully trained the denoising neural network without overfitting. The denoising neural network can remove the noise in the data while preserving its intrinsic information. We show that the denoising neural network allows us to perform a similar level of second-derivative and line shape analysis on data taken with two orders of magnitude less acquisition time. The importance of our method lies in its applicability to any multidimensional spectral data that are susceptible to statistical noise.