Electronic and magnetic properties of twisted graphene nanoribbon and M\"obius strips: first-principles calculations

The geometrical, electronic, and magnetic properties of twisted zigzag-edged graphene nanoribbons (ZGNRs) and novel graphene M\"obius strips (GMS) are systematically investigated using first-principles density functional calculations. The structures of ZGNRs and GMS are optimized, and their stabilities are examined. The molecular energy levels and the spin polarized density of states are calculated. It is found that for twisted ZGNRs, the atomic bonding energy decreases quadratically with the increase of the twisted angle, and the HOMO-LUMO gap are varying in a sine-like behavior with the twisted angle. The calculated spin densities reveal that the ZGNRs and GMS have antiferromagnetic ground states, which persist during the twisting. The spin flips on the zigzag edges of GMS are observed at some positions.Comment: 22 pages,6 figure

Octagraphene as a Versatile Carbon Atomic Sheet for Novel Nanotubes, Unconventional Fullerenes and Hydrogen Storage

We study a versatile structurally favorable periodic $sp^2$-bonded carbon atomic planar sheet with $C_{4v}$ symmetry by means of the first-principles calculations. This carbon allotrope is composed of carbon octagons and squares with two bond lengths and is thus dubbed as octagraphene. It is a semimetal with the Fermi surface consisting of one hole and one electron pocket, whose low-energy physics can be well described by a tight-binding model of $\pi$-electrons. Its Young's modulus, breaking strength and Poisson's ratio are obtained to be 306 $N/m$, 34.4 $N/m$ and 0.13, respectively, which are close to those of graphene. The novel sawtooth and armchair carbon nanotubes as well as unconventional fullerenes can also be constructed from octagraphene. It is found that the Ti-absorbed octagraphene can be allowed for hydrogen storage with capacity around 7.76 wt%

Bayesian Inference Federated Learning for Heart Rate Prediction

The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable devices measure heart rate, which accurately reflects the intensity of physical exercise. Therefore, heart rate prediction from wearable devices benefits users with optimization of the training process. Conventionally, Cloud collects user data from wearable devices and conducts inference. However, this paradigm introduces significant privacy concerns. Federated learning is an emerging paradigm that enhances user privacy by remaining the majority of personal data on users’ devices. In this paper, we propose a statistically sound, Bayesian inference federated learning for heart rate prediction with autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices.Peer reviewe

Multi-energy X-ray linear-array detector enabled by the side-illuminated metal halide scintillator

Conventional scintillator-based X-ray imaging typically captures the full spectral of X-ray photons without distinguishing their energy. However, the absence of X-ray spectral information often results in insufficient image contrast, particularly for substances possessing similar atomic numbers and densities. In this study, we present an innovative multi-energy X-ray linear-array detector that leverages side-illuminated X-ray scintillation using emerging metal halide Cs3Cu2I5. The negligible self-absorption characteristic not only improves the scintillation output but is also beneficial for improving the energy resolution for the side-illuminated scintillation scenarios. By exploiting Beer's law, which governs the absorption of X-ray photons with different energies, the incident X-ray spectral can be reconstructed by analyzing the distribution of scintillation intensity when the scintillator is illuminated from the side. The relative error between the reconstructed and measured X-ray spectral was less than 5.63 %. Our method offers an additional energy-resolving capability for X-ray linear-array detectors commonly used in computed tomography (CT) imaging setups, surpassing the capabilities of conventional energy-integration approaches, all without requiring extra hardware components. A proof-of-concept multi-energy CT imaging system featuring eight energy channels was successfully implemented. This study presents a simple and efficient strategy for achieving multi-energy X-ray detection and CT imaging based on emerging metal halides