RESUMEN
Twisted bilayer graphene is created by slightly rotating the two crystal networks in bilayer graphene with respect to each other. For small twist angles, the material undergoes a self-organized lattice reconstruction, leading to the formation of a periodically repeated domain1-3. The resulting superlattice modulates the vibrational3,4 and electronic5,6 structures within the material, leading to changes in the behaviour of electron-phonon coupling7,8 and to the observation of strong correlations and superconductivity9. However, accessing these modulations and understanding the related effects are challenging, because the modulations are too small for experimental techniques to accurately resolve the relevant energy levels and too large for theoretical models to properly describe the localized effects. Here we report hyperspectral optical images, generated by a nano-Raman spectroscope10, of the crystal superlattice in reconstructed (low-angle) twisted bilayer graphene. Observations of the crystallographic structure with visible light are made possible by the nano-Raman technique, which reveals the localization of lattice dynamics, with the presence of strain solitons and topological points1 causing detectable spectral variations. The results are rationalized by an atomistic model that enables evaluation of the local density of the electronic and vibrational states of the superlattice. This evaluation highlights the relevance of solitons and topological points for the vibrational and electronic properties of the structures, particularly for small twist angles. Our results are an important step towards understanding phonon-related effects at atomic and nanometric scales, such as Jahn-Teller effects11 and electronic Cooper pairing12-14, and may help to improve device characterization15 in the context of the rapidly developing field of twistronics16.
RESUMEN
In this work, a conductive ink based on microfibrillated cellulose (MFC) and multiwalled carbon nanotubes (MWCNTs) was used to produce transducers for rapid liquid identification. The transducers are simple resistive devices that can be easily fabricated by scalable printing techniques. We monitored the electrical response due to the interaction between a given liquid with the carbon nanotube-cellulose film over time. Using principal component analysis of the electrical response, we were able to extract robust data to differentiate between the liquids. We show that the proposed liquid sensor can classify different liquids, including organic solvents (acetone, chloroform, and different alcohols) and is also able to differentiate low concentrations of glycerin in water (10-100 ppm). We have also investigated the influence of two important properties of the liquids, namely dielectric constant and vapor pressure, on the transduction of the MFC-MWCNT sensors. These results were corroborated by independent heat flow measurements (thermogravimetric analysis). The proposed MFC-MWCNT sensor platform may help paving the way to rapid, inexpensive, and robust liquid analysis and identification.