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The vibrational modes of twisted bilayer graphene (tBLG) are computed and analyzed for a series of 692 twisting angle values in the [0, 30°] range. To help explore new combinations of techniques not available in existing software, a new code is written specialized to meet the unique demands of this problem. Details of the implementation are discussed in depth, particularly in the context of other existing software solutions. In broad, the structures are relaxed using the conjugate gradient method, and then phonon normal modes are computed in the harmonic approximation using a new code optimized for large structures, modeling energy with the classical second-generation reactive empirical bond order potential (REBO) with a Kolmogorov/Crespi registry-dependent term. The structure can then be further optimized as necessary using a novel technique based on the computed phonon modes. With this, a database is constructed with the vibrational normal mode frequencies and non-resonant Raman spectra for all of the structures. When nonlinear machine learning models are applied to the dataset to predict twist angle from Raman spectra, they are found to be robust to noise and make successful predictions during cross-validation despite the spectra lacking many of the key features that have previously been identified as possible fingerprints of twist angle. This is promising evidence for the viability of creating a black box model mapping experimental spectra to twist angle using supervised machine learning. Additionally, by unfolding the phonon modes onto the first Brillouin zone (FBZ) of a single layer, it becomes viable to track the evolution of the phonon modes as a function of twist angle, and splitting of bands around the M and K points is observed which is attributed to phonon scattering by the network of solitons that arises during relaxation. The standalone Python script written to perform this unfolding will be suitable for other future work involving band structures on extremely large supercells.

The vibrational properties of twisted bilayer graphene (tBLG) show complex features, due to the intricate energy landscape of its low-symmetry configurations. A machine learning-based approach is developed to provide a continuous model between the twist angle and the simulated Raman spectra of tBLGs. Extracting the structural information of the twist angle from Raman spectra corresponds to solving a complicated inverse problem. Once trained, the machine learning regressors (MLRs) quickly provide predictions without human bias and with an average of 98% of the data variance being explained by the model. The significant spectral features learned by MLRs are analyzed revealing the intensity profile near the calculated G-band to be the most important feature. The trained models are tested on noise-containing test data demonstrating their robustness. The transferability of the present models to experimental Raman spectra is discussed in the context of validation of the level of theory used for the construction of the analyzed database. This work serves as a proof of concept that machine-learning analysis is a potentially powerful tool for interpretation of Raman spectra of tBLG and other 2D materials.

The importance of phonons in the strong correlation phenomena observed in twisted bilayer graphene (TBG) at the so-called magic-angle is under debate. Here we apply gate-dependent micro-Raman spectroscopy to monitor the G band linewidth in TBG devices of twist angles \\(=0^\\), \\( 1.1^\\) (magic-angle) and \\( 7^\\) (large angle). The results show a broad and p/n-asymmetric doping behavior at the magic-angle, in clear contrast to the behavior observed in twist angles above and below. Atomistic modeling reproduces the experimental observations, revealing how the unique electronic structure of magic-angle TBGs influences the electron-phonon coupling and, consequently, the G band linewidth. Our findings indicate a connection between electron-phonon coupling and experimental observations of strongly correlated phenomena in magic-angle TBG.

The phonon spectra of twisted bilayer graphene (tBLG) are analyzed for a series of 692 twisting angle values in the \\([0,30]\\) range. The evolution of the phonon bandstructure as a function of twist angle is examined using a band unfolding scheme where the large number of phonon modes computed at the \\(\\) point for the large moiré tBLG supercells are unfolded onto the Brillouin Zone (BZ) of one of the two constituent layers. In addition to changes to the low-frequency breathing and shear modes, a series of well-defined side-bands around high-symmetry points of the extended BZ emerge due to the twist angle-dependent structural relaxation. The results are rationalized by introducing a nearly-free-phonon model that highlights the central role played by solitons in the description of the new phonon branches, which are particularly pronounced for structures with small twist angles, below a buckling angle \\(_ B 3.75\\).

The electronic, optical and magnetic properties of graphene nanoribbons (GNRs) can be engineered by controlling their edge structure and width with atomic precision through bottom-up fabrication based on molecular precursors. This approach offers a unique platform for all-carbon electronic devices but requires careful optimization of the growth conditions to match structural requirements for successful device integration, with GNR length being the most critical parameter. In this work, we study the growth, characterization, and device integration of 5-atom wide armchair GNRs (5-AGNRs), which are expected to have an optimal band gap as active material in switching devices. 5-AGNRs are obtained via on-surface synthesis under ultra-high vacuum conditions from Br- and I-substituted precursors. We show that the use of I-substituted precursors and the optimization of the initial precursor coverage quintupled the average 5-AGNR length. This significant length increase allowed us to integrate 5-AGNRs into devices and to realize the first field-effect transistor based on narrow bandgap AGNRs that shows switching behavior at room temperature. Our study highlights that optimized growth protocols can successfully bridge between the sub-nanometer scale, where atomic precision is needed to control the electronic properties, and the scale of tens of nanometers relevant for successful device integration of GNRs.

Many computational models have been developed to predict the rates of atomic displacements in two-dimensional (2D) materials under electron beam irradiation. However, these models often drastically underestimate the displacement rates in 2D insulators, in which beam-induced electronic excitations can reduce the binding energies of the irradiated atoms. This bond softening leads to a qualitative disagreement between theory and experiment, in that substantial sputtering is experimentally observed at beam energies deemed far to small to drive atomic dislocation by many current models. To address these theoretical shortcomings, this paper develops a first-principles method to calculate the probability of beam-induced electronic excitations by coupling quantum electrodynamics (QED) scattering amplitudes to density functional theory (DFT) single-particle orbitals. The presented theory then explicitly considers the effect of these electronic excitations on the sputtering cross section. Applying this method to 2D hexagonal BN and MoS\\(_2\\) significantly increases their calculated sputtering cross sections and correctly yields appreciable sputtering rates at beam energies previously predicted to leave the crystals intact. The proposed QED-DFT approach can be easily extended to describe a rich variety of beam-driven phenomena in any crystalline material.

A low twist angle between the two stacked crystal networks in bilayer graphene enables self-organized lattice reconstruction with the formation of a periodic domain. This superlattice modulates the vibrational and electronic structures, imposing new rules for electron-phonon coupling and the eventual observation of strong correlation and superconductivity. Direct optical images of the crystal superlattice in reconstructed twisted bilayer graphene are reported here, generated by the inelastic scattering of light in a nano-Raman spectroscope. The observation of the crystallographic structure with visible light is made possible due to lattice dynamics localization, the images resembling spectral variations caused by the presence of strain solitons and topological points. The results are rationalized by a nearly-free-phonon model and electronic calculations that highlight the relevance of solitons and topological points, particularly pronounced for structures with small twist angles. We anticipate our discovery to play a role in understanding Jahn-Teller effects and electronic Cooper pairing, among many other important phonon-related effects, and it may be useful for characterizing devices in the most prominent platform for the field of twistronics.

Using gas chromatography/mass spectrometry, we measured lipid-adjusted serum levels for all 2,3,7,8-substituted dioxins and furans, and four coplanar polychlorinated biphenyls in 62 workers with chlorophenol exposure and 36 workers without chlorophenol exposures working at the same plant during the same time. We oversampled among workers diagnosed with chloracne. Mean dioxin background levels from 36 nonchlorophenol workers were estimated as 6.0 parts-per-trillion (ppt) for 2,3,7,8 tetrachlorodibenzo- p -dioxin (2,3,7,8-TCDD) and 67.5 ppt for 1,2,3,4,6,7,8 heptachlorodibenzo- p -dioxin (Hepta-CDD). We found different dioxin and furan profiles for trichlorophenol and pentachlorophenol (PCP) workers. Among trichlorophenol workers with chloracne, we found 2,3,7,8-TCDD (mean=30.5 ppt) above background levels and among PCP workers with chloracne, we found high levels of Hepta-CDD (mean=312.5 ppt) and other higher chlorinated dioxins and furans. Cumulative exposure estimates for dioxins for both 2,3,7,8-TCDD and the higher chlorinated dioxins created in the early 1980s for our epidemiology studies were highly correlated with serum dioxin levels when age and body mass index were taken into account. While workers previously diagnosed with chloracne had high serum dioxin levels, some workers without diagnosed chloracne also had high levels. Among tradesworkers with plant-wide responsibilities, we observed serum dioxins and PCB levels higher than background indicating workplace exposures. We estimate that the mean level of 2,3,7,8-TCDD present in the serum of workers on the date workplace exposure terminated was 267 ppt (ranging from 8 to 1184 ppt) assuming a 9-year half-life, 582 ppt (ranging from 10 to 2,641 ppt) assuming a 7-year half-life, and 1928 ppt (ranging from 22 to 17,847) when a toxicokinetic model is used. We conclude that our findings are consistent with other studies reporting high serum dioxin levels among chlorophenol workers after occupational exposures.