Multifunctional Applications of Ionic Liquids in Polymer Materials: A Brief Review.

As a new generation of green media and functional materials, ionic liquids (ILs) have been extensively investigated in scientific and industrial communities, which have found numerous ap-plications in polymeric materials. On the one hand, much of the research has determined that ILs can be applied to modify polymers which use nanofillers such as carbon black, silica, graphene oxide, multi-walled carbon nanotubes, etc., toward the fabrication of high-performance polymer composites. On the other hand, ILs were extensively reported to be utilized to fabricate polymeric materials with improved thermal stability, thermal and electrical conductivity, etc. Despite substantial progress in these areas, summary and discussion of state-of-the-art functionalities and underlying mechanisms of ILs are still inadequate. In this review, a comprehensive introduction of various fillers modified by ILs precedes a systematic summary of the multifunctional applications of ILs in polymeric materials, emphasizing the effect on vulcanization, thermal stability, electrical and thermal conductivity, selective permeability, electromagnetic shielding, piezoresistive sensitivity and electrochemical activity. Overall, this review in this area is intended to provide a fundamental understanding of ILs within a polymer context based on advantages and disadvantages, to help researchers expand ideas on the promising applications of ILs in polymer fabrication with enormous potential.

A Network Enhancement Method to Identify Spurious Drug-Drug Interactions.

As medical safety and drug regulation gain heightened attention, the detection of spurious drug-drug interactions (DDI) has become key in healthcare. Although current research using graph neural networks (GNNs) to predict DDI has shown impressive results, it often fails to account for false DDI in the constructed DDI networks. Such inaccuracies caused by data errors, false alarms, or incorrect drug details can skew the network's structure and hinder the accuracy of GNN-based predictions. To tackle this challenge, we propose ANSM, a network-enhancement method specifically designed to identify and attenuate spurious links between drugs for ensuring the accuracy of DDI networks. ANSM integrates three key components: the feature extractor, the network optimizer, and the discriminative classifier. The feature extractor captures local structural features from drug node pairs, while the network optimizer leverages network information to improve feature extraction and reduce the impact of spurious DDI links. The discriminative classifier then identifies potential spurious links. Experimental results demonstrate that ANSM outperforms state-of-the-art methods in identifying spurious DDI.

Multitype Perception Method for Drug-Target Interaction Prediction.

With the growing popularity of artificial intelligence in drug discovery, many deep-learning technologies have been used to automatically predict unknown drug-target interactions (DTIs). A unique challenge in using these technologies to predict DTI is fully exploiting the knowledge diversity across different interaction types, such as drug-drug, drug-target, drug-enzyme, drug-path, and drug-structure types. Unfortunately, existing methods tend to learn the specifical knowledge on each interaction type and they usually ignore the knowledge diversity across different interaction types. Therefore, we propose a multitype perception method (MPM) for DTI prediction by exploiting knowledge diversity across different link types. The method consists of two main components: a type perceptor and a multitype predictor. The type perceptor learns distinguished edge representations by retaining the specifical features across different interaction types; this maximizes the prediction performance for each interaction type. The multitype predictor calculates the type similarity between the type perceptor and predicted interactions, and the domain gate module is reconstructed to assign an adaptive weight to each type perceptor. Extensive experiments demonstrate that our proposed MPM outperforms the state-of-the-art methods in DTI prediction.

Al-Doped MoSe2 Monolayer as a Promising Biosensor for Exhaled Breath Analysis: A DFT Study.

Exhaled breath analysis by nanosensors is a workable and rapid manner to diagnose lung cancer in the early stage. In this paper, we proposed Al-doped MoSe2 (Al-MoSe2) as a promising biosensor for sensing three typically exhaled volatile organic compounds (VOCs) of lung cancer, namely, C3H4O, C3H6O, and C5H8, using the density functional theory (DFT) method. Single Al atom is doped on the Se-vacancy site of the MoSe2 surface, which behaves as an electron-donor and enhances the electrical conductivity of the nanosystem. The adsorption and desorption performances, electronic behavior, and the thermostability of the Al-MoSe2 monolayer are conducted to fully understand its physicochemical properties as a sensing material. The results indicate that the Al-MoSe2 monolayer shows admirable sensing performances with C3H4O, C3H6O, and C5H8 with responses of -85.7, -95.6, and -96.3%, respectively. Also, the desirable adsorption performance and the thermostability endow with the Al-MoSe2 monolayer with good sensing and desorbing behaviors for the recycle detection of three VOCs. We are hopeful that the results in this paper could provide some guidance to the experimentalists fulfilling their exploration in the practical application, which can also broaden the exploration of transition-metal dichalcogenides (TMDs) in more fields as well.

Rh-doped MoTe2 Monolayer as a Promising Candidate for Sensing and Scavenging SF6 Decomposed Species: a DFT Study.

In this work, the adsorption and sensing behaviors of Rh-doped MoTe2 (Rh-MoTe2) monolayer upon SO2, SOF2, and SO2F2 are investigated using first-principles theory, wherein the Rh doping behavior on the pure MoTe2 surface is included as well. Results indicate that TMo is the preferred Rh doping site with Eb of - 2.69 eV, and on the Rh-MoTe2 surface, SO2 and SO2F2 are identified as chemisorption with Ead of - 2.12 and - 1.65 eV, respectively, while SOF2 is physically adsorbed with Ead of - 0.46 eV. The DOS analysis verifies the adsorption performance and illustrates the electronic behavior of Rh doping on gas adsorption. Band structure and frontier molecular orbital analysis provide the basic sensing mechanism of Rh-MoTe2 monolayer as a resistance-type sensor. The recovery behavior supports the potential of Rh-doped surface as a reusable SO2 sensor and suggests its exploration as a gas scavenger for removal of SO2F2 in SF6 insulation devices. The dielectric function manifests that Rh-MoTe2 monolayer is a promising optical sensor for selective detection of three gases. This work is beneficial to explore Rh-MoTe2 monolayer as a sensing material or a gas adsorbent to guarantee the safe operation of SF6 insulation devices in an easy and high-efficiency manner.