In this paper a system consisting of two electron spins has been prepared initially in a singlet state using the chemical compass model is considered. It is assumed that each electron spin interacts symmetrically and/or asymmetrically with its respective private nuclear environment in the presence of an external magnetic field. We discussed the effect of the interaction parameters and the external magnetic field on some quantifiers of quantum correlations as entanglement, coherence, Bell inequality, as well as the steerability inequality. It is shown that within a certain range of external magnetic fields, the quantum coherence and entanglement behave similarly. The Bell and the steerable inequalities predicted a similar behavior for symmetric and asymmetric interactions. Moreover, as one increases the external magnetic field, the lower bounds of both inequalities have improved. The usefulness of using the spin state as quantum channel to teleport a two-qubit system has examined where the Bell inequality could be above its classical bounds by controlling the interaction parameters. It is shown that by tuning the coupling parameters the fidelity of the teleported state exceeds the classical bounds, as well as the long-lived stationary fidelity could be achieved during the interaction time.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1103/PhysRevE.109.034101 | DOI Listing |
Nano Lett
January 2025
School of Microelectronics, University of Science and Technology of China, Hefei 230026, People's Republic of China.
Spin-orbit torque (SOT) is widely considered to be a fast and robust writing scheme for magnetic random-access memories (MRAMs). However, the requirements of field-free switching and high switching efficiency are often incompatible in SOT devices, placing a critical challenge on its improvement. Here we propose that by utilizing biaxial systems the dilemma between high-efficiency and external-field-free SOT switching can be solved intrinsically.
View Article and Find Full Text PDFEur Biophys J
January 2025
Faculty of Sciences, P. J. Šafárik University, Košice, Slovakia.
X-ray crystallography has tremendously served structural biology by routinely providing high-resolution 3D structures of macromolecules. The extent of information encoded in the X-ray crystallography is proportional to which resolution the crystals diffract and the structure can be refined to. Therefore, there is a continuous effort to obtain high-quality crystals, especially for those proteins, which are considered difficult to crystallize into high-quality protein crystals of suitable sizes for X-ray crystallography.
View Article and Find Full Text PDFSmall
January 2025
The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, 324000, China.
In cancer research and personalized medicine, mesoporous silica nanoparticles (MSNs) have emerged as a significant breakthrough in both cancer treatment and diagnosis. MSNs offer targeted drug delivery, enhancing therapeutic effectiveness while minimizing adverse effects on healthy cells. Due to their unique characteristics, MSNs provide targeted drug delivery, maximizing therapeutic effectiveness with minimal adverse effects on healthy cells.
View Article and Find Full Text PDFFront Bioeng Biotechnol
January 2025
Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), National Research Council (CNR), Milan, Italy.
Minimally invasive medical treatments for peripheral nerve stimulation are critically needed to minimize surgical risks, enhance the precision of therapeutic interventions, and reduce patient recovery time. Magnetoelectric nanoparticles (MENPs), known for their unique ability to respond to both magnetic and electric fields, offer promising potential for precision medicine due to their dual tunable functionality. In this study a multi-physics modeling of the MENPs was performed, assessing their capability to be targeted through external magnetic fields and become electrically activated.
View Article and Find Full Text PDFObjective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.
Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!