Enhanced Lithium Storage Performance: Dual-Modified Electrospun Si@MnO@CNFs Composites for Advanced Anodes.

Due to its many benefits, including high specific capacity, low voltage plateau, and plentiful supplies, silicon-based anode materials are a strong contender to replace graphite anodes. However, silicon has drawbacks such as poor electrical conductivity, abrupt volume changes during the discharge process, and continuous growth of the solid electrolyte interfacial (SEI) film during cycling, which would cause the electrode capacity to degrade quickly. Coating the silicon's exterior with carbon or metal oxide is a popular method to resolve the above-mentioned problems. In light of those above, the liquid-phase approach and electrostatic spinning technique were used in this work to create Si@MnO@CNFs bilayer-coated silicon-based anode materials. Because of the well-thought-out design, MnO and C bilaterally coat the silicon nanoparticles, significantly reducing their volume effect during cycling. Furthermore, manganese oxide has outstanding electrochemical kinetics and an excellent theoretical capacity. The carbon nanofibers' outermost layer increases the material's conductivity and stabilizes the composite material's structure, reducing the volume effect. After 1100 cycles at 2 A g-1, the composite anode material prepared in this work can still maintain a high capacity of 994.4 mAh g-1. This study offers an unusual combination of silicon and MnO that might set the way for the application of silicon-based composites in lithium-ion batteries.

Diagnosis and classification of intestinal diseases with urine by surface-enhanced Raman spectroscopy.

Intestinal Disease (ID) is often characterized by clinical symptoms such as malabsorption, intestinal dysfunction, and injury. If treatment is not timely, it will increase the risk of cancer. Early diagnosis of ID is the key to cure it. There are certain limitations of the conventional diagnostic methods, such as low sensitivity and specificity. Therefore, development of a highly sensitive, non-invasive diagnostic method for ID is extremely important. Urine samples are easier to collect and more sensitive to changes in biomolecules than other pathological diagnostic samples such as tissue and blood. In this paper, a diagnostic method of ID with urine by surface-enhanced Raman spectroscopy (SERS) is proposed. A classification model between ID patients and healthy controls (HC) and a classification model between different pathological types of ID (i.e., benign intestinal disease (BID) and colorectal cancer (CRC)) are established. Here, 830 urine samples, including 100 HC, 443 BID, and 287 CRC, were investigated by SERS. The ID/HC classification model was developed by analyzing the SERS spectra of 150 ID and 100 HC, while BID/CRC classification model was built with 300 BID and 150 CRC patients by principal component analysis (PCA)-support vector machines (SVM). The two established models were internally verified by leave-one-out-cross-validation (LOOCV). Finally, the BID/CRC classification model was further evaluated by 143 BID and 137 CRC patients as an external test set. It shows that the accuracy of the classification model validated by the LOOCV for ID/HC and BID/CRC is 86.4% and 85.56%, respectively. And the accuracy of the BID/CRC classification model with external test set is 82.14%. It shows that high accuracy can be achieved with these two established classification models. It indicates that ID patients in the general population can be identified and BID and CRC patients can be further classified with measuring urine by SERS. It shows that the proposed diagnostic method and established classification models provide valuable information for clinicians to early diagnose ID patients and analyze different stages of ID.

Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process.

This article studies the performance monitoring problem for the potassium chloride flotation process, which is a critical component of potassium fertilizer processing. To address its froth image segmentation problem, this article proposes a multiscale feature extraction and fusion network (MsFEFNet) to overcome the multiscale and weak edge characteristics of potassium chloride flotation froth images. MsFEFNet performs simultaneous feature extraction at multiple image scales and automatically learns spatial information of interest at each scale to achieve efficient multiscale information fusion. In addition, the potassium chloride flotation process is a multistage dynamic process with massive unlabeled data. To overcome its dynamic time-varying and working condition spatial similarity characteristics, a semi-supervised froth-grade prediction model based on a temporal-spatial neighborhood learning network combined with Mean Teacher (MT-TSNLNet) is proposed. MT-TSNLNet designs a new objective function for learning the temporal-spatial neighborhood structure of data. The introduction of Mean Teacher can further utilize unlabeled data to promote the proposed prediction model to better track the concentrate grade. To verify the effectiveness of the proposed MsFEFNet and MT-TSNLNet, froth image segmentation and grade prediction experiments are performed on a real-world potassium chloride flotation process dataset.

Post-Quantum Security: Opportunities and Challenges.

Cryptography is very essential in our daily life, not only for confidentiality of information, but also for information integrity verification, non-repudiation, authentication, and other aspects. In modern society, cryptography is widely used; everything from personal life to national security is inseparable from it. With the emergence of quantum computing, traditional encryption methods are at risk of being cracked. People are beginning to explore methods for defending against quantum computer attacks. Among the methods currently developed, quantum key distribution is a technology that uses the principles of quantum mechanics to distribute keys. Post-quantum encryption algorithms are encryption methods that rely on mathematical challenges that quantum computers cannot solve quickly to ensure security. In this study, an integrated review of post-quantum encryption algorithms is conducted from the perspective of traditional cryptography. First, the concept and development background of post-quantum encryption are introduced. Then, the post-quantum encryption algorithm Kyber is studied. Finally, the achievements, difficulties and outstanding problems in this emerging field are summarized, and some predictions for the future are made.

The Effect of Initial Grain Size on the Nanocrystallization of AZ31 Mg Alloy during Rotary Swaging.

Nanograins were obtained in the AZ31 Mg alloy bars with different initial grain sizes via cold rotary swaging. Microstructure evolution during deformation was investigated through electron backscatter diffraction analysis and transmission electron microscopy studies. The results indicate that initial grain size had little effect on the mechanism of grain refinement during swaging. The nanocrystallization process of the alloys with different initial grain sizes included extensive twinning followed by the further refinement of the twin lamellae through the formation of massive dislocation arrays. However, as the initial grain size decreased, the formation rate of nanograins increased, resulting in a higher degree of nanocrystallization after the same swaging pass. The mean grain size and yield strength of the sample with the smallest initial grain size were about 91 nm and 489 MPa, respectively. The slower rate and lower degree of nanocrystallization in the alloy with a larger initial grain size were mainly attributed to the less grain boundary areas and higher activity of twinning.