Ti₃C₂T_<i>x</i> MXene Nanosheet-Based Probe for Ion Fluorescence and Visual Detection of Ag⁺ in Aqueous Solution and Living Cells

In this study, a naked-eye visible and fluorescence-on Ag+ probe based on two-dimensional material MXene was successfully constructed. This study investigated the π–π and dispersive interactions between MXene and rhodamine-6G, and the reduction and growth of silver ions on the MXene surface. The characterization by transmission electron microscopy and X-ray photoelectron spectroscopy confirmed our conjecture about this process. This probe shows competent highly selective real-time detection of Ag+ in aqueous solution with a 0.035 μM detection limit, which is also capable of Ag+ sensing in living cells. Comparing the fluorescence response of the probe to silver ions in the presence of other metal ions, it is confirmed that the probe has good selectivity to silver ions. In particular, this probe can also be carried out to detect and track Ag+ levels in living cells. The discoveries provide fresh insights into the detection and analysis of silver ions in environmental and biological samples. Meanwhile, this method also provides ideas for constructing MXene-based sensing platforms

Soft, Tough, and Thermally Conductive Elastomer Composites by Constructing a Curled Conformation

Filled elastomer composites have gained significant attention due to their ability to undergo large-strain reversible deformations with minimal force. However, achieving the desired functionality, such as high thermal conductivity, often requires ultrahigh filler loadings (above 50%). Unfortunately, excessive filler loading compromises the softness and toughness of the composites due to the prevalence of trapped entanglements. To address this challenge, a simple solvent-thermal design strategy is reported to optimize the balance among Young’s modulus, stretchability, and toughness in highly filled elastomer composites. This is realized by the curled conformation formed by the disentangling of the excessively entangled polymer chains and by better mixing of the BN filler and the polymer matrix. The released trapped entanglement can effectively reduce the Young’s modulus (2.80 MPa) of the C-PDMS/60 wt % BN elastomer composites, and the strong unfolding and stretching ability of the curled conformation also endows it with excellent stretchability (∼492%), thus achieving high toughness (∼2.80 MJ m–3). Additionally, the better mixing ability allows the C-PDMS/60 wt % BN elastomer composites to be compounded with the high BN filler loading (60 wt %), thus achieving high thermal conductivity (1.65 W m–1 K–1). The comprehensive performance of the C-PDMS/60 wt % BN demonstrates remarkable advancements in highly filled elastomer composites. Leveraging these favorable characteristics, the curled PDMS/BN elastomer composites can serve as effective thermal interface materials for efficient heat dissipation and hold great potential for applications in the field of flexible electronics

Synergistic Effect of Multifunctional Layered Double Hydroxide-Based Hybrids and Modified Phosphagen with an Active Amino Group for Enhancing the Smoke Suppression and Flame Retardancy of Epoxy

To improve the fire hazard of epoxy resin (EP), phosphomolybdate (PMoA), as a classical Keggin cluster, was successfully intercalated into Mg, Al, and Zn layered double hydrotalcite (LDH) by the reconstruction method, and it was denoted as MgAlZn-LDH-PMoA. The structure and morphology of MgAlZn-LDH-PMoA were characterized by X-ray diffraction and Fourier transform infrared spectroscopy. Subsequently, hexa­(4-aminophenoxy)­cyclotriphosphazene (HACP) was prepared and characterized as a high-performance organic flame retardant, which is rich in flame elements phosphorus and nitrogen. The synergistic effects of MgAlZn-LDH-PMoA and HACP on the fire safety of EP composites loaded with different amounts of flame retardant hybrids were studied in detail. Thermogravimetric analysis showed that the char residue of these EP composites increased significantly. Compared with the EP matrix filled with only MgAlZn-LDH-PMoA or HACP, the incorporation of MgAlZn-LDH-PMoA and HACP had a synergistic effect on promoting char formation of EP composites. Particularly, the char yield of EP7 is as high as 29.0%. Furthermore, the synergistic effects of incorporation of MgAlZn-LDH-PMoA with HACP were investigated using the cone calorimeter combustion tests. The results showed that the total heat release and peak heat release rate of the EP composites remarkably declined by 35.2 and 50.9%, respectively, with a loading of 7 wt % hybrid flame retardant. Moreover, the hybrid flame retardants also showed an obvious inhibitory effect on the total smoke production and the release of toxic CO gas. The detailed analysis of the residual char indicated that the main mechanism for improving the flame retardancy and smoke suppression performance is due to both the catalytic carbonization of MgAlZn-LDH-PMoA and phosphoric acid compounds and physical barrier function of the char layer. In addition, the molybdenum oxides produced from [PMo12O40]3– during combustion can not only increase the yield and compactness of the char layer but also reduce the release of CO through a redox reaction, which has important application value to reduce the fire hazard

Novel Synthesis of Birnessite-Type MnO₂ Nanostructure for Water Treatment and Electrochemical Capacitor

We report for the first time a novel and rapid (1 h) synthesis method for birnessite-type MnO₂ nanostructures via a polyol-reflux process through oxidizing MnCl₂ with H₂O₂ under basic conditions in the presence of polyvinylpyrrolidone (PVP). Influencing factors such as the dosage of reactants and the reaction times are systematically investigated. The molar ratios of OH/Mn played an important role in the formation of birnessite-type MnO₂ nanostructure with good crystallinity and ordered 3D nanostructures. A possible formation mechanism for the nanostructure was proposed. The flower-like birnessite-type MnO₂ nanostructure is composed of nanosheets with an average diameter ca. 300–500 nm and shows mesoporous characteristics with a pore diameter of 20 nm. Due to its unique mesoporous structure, the birnessite-type MnO₂ exhibits excellent ability to remove organic pollutants (Ponceau 2R) and shows a potential application as an electrochemical capacitor

Fast Self-Healing and Self-Cleaning Anticorrosion Coating Based on Dynamic Reversible Imine and Multiple Hydrogen Bonds

Anticorrosive coatings are extensively investigated as a potential solution to prevent or at least retard metal corrosion occurrence. However, the actual breakthrough is still hampered by the risk of barrier properties loss because of the local failure of the coating. Self-healing coatings can effectively repair microcracks, but outstanding self-healing behavior is always accompanied by poor self-cleaning ability. Herein, we report a series of poly­(dimethylsiloxane) (PDMS) modified with a terephthalic aldehyde (TA)-polyurea (PDMS-TA-PUa) copolymer with a double reversible dynamic bond crosslinking network structure. The PDMS-TA-PUa coating exhibits fast and re-recycled self-healing behavior that heals cracks within 40–50 min at room temperature. The fast self-healing property is attributed to the dynamic nature of the imine bonds and hydrogen bonds in polymer networks. The PDMS-TA-PUa coating also shows great self-cleaning and anticorrosive ability, due to high hydrophobic, low surface energy, and high corrosion potential. Our work gives an insight into the design and preparation of multifunctional coating material with excellent anticorrosion performance, fast self-healing, and self-cleaning properties

Data_Sheet_1_Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine.docx

Depression is a common mental disorder that seriously affects patients’ social function and daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this study, we used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and measured the whole brain EEG signals and forehead hemodynamic signals from 25 depression patients and 30 healthy subjects during the resting state. On one hand, we explored the EEG brain functional network properties, and found that the clustering coefficient and local efficiency of the delta and theta bands in patients were significantly higher than those in normal subjects. On the other hand, we extracted brain network properties, asymmetry, and brain oxygen entropy as alternative features, used a data-driven automated method to select features, and established a support vector machine model for automatic depression classification. The results showed the classification accuracy was 81.8% when using EEG features alone and increased to 92.7% when using hybrid EEG and fNIRS features. The brain network local efficiency in the delta band, hemispheric asymmetry in the theta band and brain oxygen sample entropy features differed significantly between the two groups (p < 0.05) and showed high depression distinguishing ability indicating that they may be effective biological markers for identifying depression. EEG, fNIRS and machine learning constitute an effective method for classifying depression at the individual level.</p