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In this work, we investigate trion dynamics occurring at the heterojunction between organometallic molecules and a monolayer transition metal dichalcogenide (TMD) with transient electronic sum frequency generation (tr‐ESFG) spectroscopy. By pumping at 2.4 eV with laser pulses, we have observed an ultrafast hole transfer, succeeded by the emergence of charge‐transfer trions. This observation is facilitated by the cancellation of ground state bleach and stimulated emission signals due to their opposite phases, making tr‐ESFG especially sensitive to the trion formation dynamics. The presence of charge‐transfer trion at molecular functionalized TMD monolayers suggests the potential for engineering the local electronic structures and dynamics of specific locations on TMDs and offers a potential for transferring unique electronic attributes of TMD to the molecular layers.

 

Transition metal dichalcogenide (TMD) moiré superlattices have emerged as a significant area of study in condensed matter physics. Thanks to their superior optical properties, tunable electronic band structure, strong Coulomb interactions, and quenched electron kinetic energy, they offer exciting avenues to explore correlated quantum phenomena, topological properties, and light–matter interactions. In recent years, scanning tunneling microscopy (STM) has made significant impacts on the study of these fields by enabling intrinsic surface visualization and spectroscopic measurements with unprecedented atomic scale detail. Here, we spotlight the key findings and innovative developments in imaging and characterization of TMD heterostructures via STM, from its initial implementation on the in situ grown sample to the latest photocurrent tunneling microscopy. The evolution in sample design, progressing from a conductive to an insulating substrate, has not only expanded our control over TMD moiré superlattices but also promoted an understanding of their structures and strongly correlated properties, such as the structural reconstruction and formation of generalized two-dimensional Wigner crystal states. In addition to highlighting recent advancements, we outline upcoming challenges, suggest the direction of future research, and advocate for the versatile use of STM to further comprehend and manipulate the quantum dynamics in TMD moiré superlattices. 

In this Letter, we report that the fourth-order interatomic force constants (4th-IFCs) are significantly sensitive to the energy surface roughness of exchange-correlation (XC) functionals in density functional theory calculations. This sensitivity, which is insignificant for the second- (2nd-) and third-order (3rd-) IFCs, varies for different functionals in different materials and can cause misprediction of thermal conductivity by several times of magnitude. As a result, when calculating the 4th-IFCs using the finite difference method, the atomic displacement needs to be taken large enough to overcome the energy surface roughness, in order to accurately predict phonon lifetime and thermal conductivity. We demonstrate this phenomenon on a benchmark material (Si), a high-thermal conductivity material (BAs), and a low thermal conductivity material (NaCl). For Si, we find that the LDA, PBE, and PBEsol XC functionals are all smooth to the 2nd- and 3rd-IFCs but all rough to the 4th-IFCs. This roughness can lead to a prediction of nearly one order of magnitude lower thermal conductivity. For BAs, all three functionals are smooth to the 2nd- and 3rd-IFCs, and only the PBEsol XC functional is rough for the 4th-IFCs, which leads to a 40% underestimation of thermal conductivity. For NaCl, all functionals are smooth to the 2nd- and 3rd-IFCs but rough to the 4th-IFCs, leading to a 70% underprediction of thermal conductivity at room temperature. With these observations, we provide general guidance on the calculation of 4th-IFCs for an accurate thermal conductivity prediction.

 

This paper presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user-interfaces, AR/VR applications, affective computing, accessibility, etc. While conventional vision-based solutions break down under poor lighting, occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions, while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form-factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response (CFR) of the inner ear, ultimately resulting in tracking of the facial motion. A transformer based machine learning (ML) model is designed to exploit spectral and temporal relationships in the ultrasonic CFR data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer based ML model runs on smartphone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form-factors. 

Silicon nitride (Si3N4) is a promising substrate for high-power electronics due to its superior mechanical properties and potential outstanding thermal conductivity (κ). As experiments keep pushing the upper limit of κ of Si3N4, it is believed that it can reach 450 W/mK, similar to SiC, based on classical models and molecular dynamics simulations. In this work, we reveal from first principles that the theoretical κ upper limits of β-Si3N4 are only 169 and 57 W/mK along the c and a axes at room temperature, respectively. Those of α-Si3N4 are about 116 and 87 W/mK, respectively. The predicted temperature-dependent κ matches well with the highest available experimental data, which supports the accuracy of our calculations, and suggests that the κ upper limit of Si3N4 has already been reached in the experiment. Compared to other promising semiconductors (e.g., SiC, AlN, and GaN), Si3N4 has a much lower κ than expected even though the chemical bonding and mechanical strengths are close or even stronger. We find the underlying reason is that Si3N4 has much lower phonon lifetimes and mean free paths (<0.5 μm) due to the larger three-phonon scattering phase space and stronger anharmonicity. Interestingly, we find that the larger unit cell (with more basis atoms) that leads to a smaller fraction of acoustic phonons is not the reason for lower κ. Grain size-dependent κ indicates that the grain boundary scattering plays a negligible role in most experimental samples. This work clarifies the theoretical κ upper limits of Si3N4 and can guide experimental research.