Graphene is attracting much attention in condensed matter physics and material science in the two-dimensional(2D) system due to its special structure, and mechanical and electronic properties. However, the lack of electronic bandgap and uncontrollable phase structure greatly limit its application in semiconductors, such as power conversion devices, optoelectronic devices, transistors, etc. During the past few decades, 2D transition metal dichalcogenides (TMDs) with much more phase structures have attracted intensive research interest in fundamental studies and practical applications for energy storage, as catalysts, and in piezoelectricity, energy harvesting, electronics, optoelectronic, and spintronics. The controllable phase transition also provides another degree of freedom to pave the way for more novel devices. In this review, we introduce the abundant phase structures of 2D-TMDs, including 2H, 1T, 1T’ and charge density waves, and highlight the corresponding attractive properties and applications of each phase. In addition, all the possible methods to trigger the phase transition in TMDs are systematically introduced, including strain engineering, electron doping, alloying, thermal, electric field, and chemical absorption. Finally, the outlook of future opportunities in TMD phase transitions and the corresponding challenges, including both the synthesis and applications, are also addressed.

Explainable recommender systems ( ERS ) aim to enhance users’ trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the recommendations, fostering a sense of confidence and reliability in the system’s outputs. Generally, the explanations are presented in a familiar and intuitive way, which is in the form of natural language, thus enhancing their accessibility to users. Recently, there has been an increasing focus on leveraging reviews as a valuable source of rich information in both modeling user-item preferences and generating textual interpretations, which can be performed simultaneously in a multi-task framework. Despite the progress made in these review-based recommendation systems, the integration of implicit feedback derived from user-item interactions and user-written text reviews has yet to be fully explored. To fill this gap, we propose a model named SERMON (A s pect-enhanced E xplainable R ecommendation with M ulti-modal C o ntrast Lear n ing). Our model explores the application of multimodal contrastive learning to facilitate reciprocal learning across two modalities, thereby enhancing the modeling of user preferences. Moreover, our model incorporates the aspect information extracted from the review, which provides two significant enhancements to our tasks. Firstly, the quality of the generated explanations is improved by incorporating the aspect characteristics into the explanations generated by a pre-trained model with controlled textual generation ability. Secondly, the commonly used user-item interactions are transformed into user-item-aspect interactions, which we refer to as interaction triple, resulting in a more nuanced representation of user preference. To validate the effectiveness of our model, we conduct extensive experiments on three real-world datasets. The experimental results show that our model outperforms state-of-the-art baselines, with a 2.0% improvement in prediction accuracy and a substantial 24.5% enhancement in explanation quality for the TripAdvisor dataset.

In this paper, we use 2D PbSe to design a gas sensor to monitor the presence of SO₂ and Cl₂. The first principle calculation shows that our scheme is feasible.

Using first-principles theory, we investigated the stability and feasibility on Mn-doped SnTe monolayer and the interactions of Mn-doped SnTe monolayer (Mn-SnTe) for the adsorption of toxic gases H₂S, SO₂, NO, NO₂ and Cl₂. The results show that the Mn-SnTe monolayer has a weak interaction for H₂S and strong adsorption energies of −1.840, −4.123, −2.954 and −3.578 eV for SO₂, NO, NO₂ and Cl₂, respectively. In addition, due to the high sensitivity of Mn-SnTe monolayer to adsorb SO₂ and Cl₂, the bandgaps of the developed systems are reduced by about 79.96% and 100%, respectively. The results indicate that the Mn-SnTe monolayer has a high sensitivity for the detection of SO₂ and Cl₂ gases. Our calculations provide a theoretical basis for the development of Mn-SnTe monolayer for potential applications as sensors or scavengers for toxic gases.

The adsorption behaviors of gas molecules such as Cl₂, CO, NO, NO₂ and SO₂ on MoSSe/GaN, as well as their adsorption energies, charge transfer and adsorption distances are investigated based on the first-principles calculation. The large adsorption energies of Cl₂, NO, NO₂ and SO₂ on MoSSe/GaN indicate that the gas molecules have strong interaction forces with MoSSe/GaN. Probing the changes in the electronic structure, the adsorption of NO and NO₂ on MoSSe/GaN introduces magnetism into their systems and reduces the band gaps. The magnetism mainly originates from the O atoms in NO and NO₂, while the decrease of the band gap is due to the hybridization of orbitals near the bottom of the conduction band and the top of the valence band with the p orbitals of Ga atoms in MoSSe/GaN. This suggests that MoSSe/GaN can be applied to detect NO and NO₂ gas molecules and has potential as a semiconductor gas sensor for these gases.