Strain engineering is a promising way to tune the electrical, electrochemical, magnetic, and optical properties of 2D materials, with the potential to achieve high‐performance 2D‐material‐based devices ultimately. This review discusses the experimental and theoretical results from recent advances in the strain engineering of 2D materials. Some novel methods to induce strain are summarized and then the tunable electrical and optical/optoelectronic properties of 2D materials via strain engineering are highlighted, including particularly the previously less‐discussed strain tuning of superconducting, magnetic, and electrochemical properties. Also, future perspectives of strain engineering are given for its potential applications in functional devices. The state of the survey presents the ever‐increasing advantages and popularity of strain engineering for tuning properties of 2D materials. Suggestions and insights for further research and applications in optical, electronic, and spintronic devices are provided.

Polyphenols are the most important phytochemicals in our diets and have received great attention due to their broad benefits for human health by suppressing oxidative stress and playing a protective role in preventing different pathologies such as cardiovascular disease, cancer, diabetes, and obesity. The stability of polyphenols depends on their environments of processing and storage, such as pH and temperature. A wide range of technologies has been developed to stabilize polyphenols during processing. This review will provide an overview of the stability of polyphenols in relation to their structure, the factors impacting the stability of polyphenols, the new products deriving from unstable polyphenols, and the effect of a series of technologies for the stabilization of polyphenols, such as chemical modification, nanotechnology, lyophilization, encapsulation, cold plasma treatment, polyphenol–protein interaction, and emulsion as a means of improving stability. Finally, the effects of cooking and storage on the stability of polyphenols were discussed.

With the development of Raman spectroscopy and the expansion of its application domains, conventional methods for spectral data analysis have manifested many limitations. Exploring new approaches to facilitate Raman spectroscopy and analysis has become an area of intensifying focus for research. It has been demonstrated that machine learning techniques can more efficiently extract valuable information from spectral data, creating unprecedented opportunities for analytical science. This paper outlines traditional and more recently developed statistical methods that are commonly used in machine learning (ML) and ML‐algorithms for different Raman spectroscopy‐based classification and recognition applications. The methods include Principal Component Analysis, K‐Nearest Neighbor, Random Forest, and Support Vector Machine, as well as neural network‐based deep learning algorithms such as Artificial Neural Networks, Convolutional Neural Networks, etc. The bulk of the review is dedicated to the research advances in machine learning applied to Raman spectroscopy from several fields, including material science, biomedical applications, food science, and others, which reached impressive levels of analytical accuracy. The combination of Raman spectroscopy and machine learning offers unprecedented opportunities to achieve high throughput and fast identification in many of these application fields. The limitations of current studies are also discussed and perspectives on future research are provided.

Ferroelectrics are essential in memory devices for multi-bit storage and high-density integration. Ferroelectricity mainly exists in compounds but rare in single-element materials due to their lack of spontaneous polarization in the latter. However, we report a room-temperature ferroelectricity in quasi-one-dimensional Te nanowires. Piezoelectric characteristics, ferroelectric loops and domain reversals are clearly observed. We attribute the ferroelectricity to the ion displacement created by the interlayer interaction between lone-pair electrons. Ferroelectric polarization can induce a strong field effect on the transport along the Te chain, giving rise to a self-gated ferroelectric field-effect transistor. By utilizing ferroelectric Te nanowire as channel, the device exhibits high mobility (~220 cm²·V⁻¹·s⁻¹), continuous-variable resistive states can be observed with long-term retention (>10⁵ s), fast speed (1.92 TB/cm²). Our work provides opportunities for single-element ferroelectrics and advances practical applications such as ultrahigh-density data storage and computing-in-memory devices.