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Diphenyl sulfide is connected with one or two thioanisoles, in a system initially prepared to learn about the electronic charge transport through it. Unexpectedly, we observed a remarkably high conductance, which is proved to arise from the S atom of the diphenyl sulfide (featuring superior chemical stability) involved in the formed molecular junction. This work provides a new anchor to fabricate reliable and reproducible molecular junctions. [ABSTRACT FROM AUTHOR]

Persistent luminescence (PersL), characterized by continuous light emission after cessation of excitation, finds enormous applications in night displays, optoelectronics, and biomedicine. Despite the long history and a recent renaissance of PersL research, there still lacks a versatile approach for deliberate control over PersL in a single material system, which is crucial for mechanistic understanding and rational design of PersL. Herein, a strategy for the systematic tuning of PersL in the wavelength, time, and temperature domains in a wurtzite CaZnOS crystal, based on synergistic defect engineering (i.e., simultaneous control of activator/trap states and trap‐filling process) through combinatorial doping in the double cationic sites, is presented. This design principle can be harnessed to produce PersL in a vast collection of emitters with distinct electronic transitions, including ns2‐nsnp, 3d–3d, 4f–4f, and donor–acceptor recombination, which is inaccessible to the existing material systems. The results highlight that the strategy of synergistic defect engineering can provide unprecedented PersL properties with stimulus‐responsive features for information encryption and photoexcitation‐free optical thermometry. [ABSTRACT FROM AUTHOR]

We studied a method to prepare a novel titanium dioxide (TiO2) composite photocatalyst, starting from improving the separation efficiency of photogenerated electrons and holes. Regular TiO2 microspheres were prepared by sol-gel method and loaded onto Ti3C2Tx (Mxene). The high electrical conductivity of Mxene was utilized to transfer photogenerated electrons quickly and effectively prevent their recombination. By adjusting the addition amount of Mxene, the hydrogen production efficiency of the sample was greatly improved, and the maximum efficiency reached 135.2 µmol·g−1·h−1, which was twice that of pure TiO2. The nanocomposites were characterized by XRD, PL, TEM and SEM analysis and electrochemical methods, and the test proved that the improvement of hydrogen production efficiency was caused by the improvement of the separation efficiency of photogenerated electrons and holes. This work demonstrates the application of Mxene as a catalyst to improve efficiency and broadens the application prospects of Mxene. [ABSTRACT FROM AUTHOR]

The search for color‐tunable, efficient, and robust scintillators plays a vital role in the development of modern X‐ray radiography. The radioluminescence tuning of copper iodide cluster scintillators in the entire visible region by bandgap engineering is herein reported. The bandgap engineering benefits from the fact that the conduction band minimum and valence band maximum of copper iodide cluster crystals are contributed by atomic orbitals from the inorganic core and organic ligand components, respectively. In addition to high scintillation performance, the as‐prepared crystalline copper iodide cluster solids exhibit remarkable resistance toward both moisture and X‐ray irradiation. These features allow copper iodide cluster scintillators to show particular attractiveness for low‐dose X‐ray radiography with a detection limit of 55 nGy s−1, a value ≈100 times lower than a standard dosage for X‐ray examinations. The results suggest that optimizing both inorganic core and organic ligand for the building blocks of metal halide cluster crystals may provide new opportunities for a new generation of high‐performance scintillation materials. [ABSTRACT FROM AUTHOR]

Quaternary oxysulfide semiconductors with a formula of MZnOS (M = Ca, Sr, or Ba) have recently attracted a lot of attention as host materials for luminescent dopant ions. Among these oxysulfides, the BaZnOS remains largely unexplored for luminescence in comparison with the CaZnOS and SrZnOS counterparts. This study describes a highly efficient and repeatable synthesis of BaZnOS microcrystals comprising a variety of lanthanide and transition metal ions. Spectroscopic investigations reveal a host‐sensitized luminescence in the dopant ions that display distinct temperature dependence of emission intensity. Accordingly, a class of BaZnOS crystals doubly doped with Pr3+ and Mn2+ is further constructed, which shows a sensitive switching of luminescence color in response to temperature change. The advances in these luminescent nanomaterials offer exciting opportunities for temperature sensing (relative sensitivity of 8.58% K–1) and electrochromism applications. [ABSTRACT FROM AUTHOR]

DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Several machine learning-based 4mC predictors have been proposed in the past 3 years, although their performance is unsatisfactory. Deep learning is a promising technique for the development of more accurate 4mC site predictions. In this work, we propose a deep learning-based approach, called DeepTorrent, for improved prediction of 4mC sites from DNA sequences. It combines four different feature encoding schemes to encode raw DNA sequences and employs multi-layer convolutional neural networks with an inception module integrated with bidirectional long short-term memory to effectively learn the higher-order feature representations. Dimension reduction and concatenated feature maps from the filters of different sizes are then applied to the inception module. In addition, an attention mechanism and transfer learning techniques are also employed to train the robust predictor. Extensive benchmarking experiments demonstrate that DeepTorrent significantly improves the performance of 4mC site prediction compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Motivation Proteases are enzymes that cleave target substrate proteins by catalyzing the hydrolysis of peptide bonds between specific amino acids. While the functional proteolysis regulated by proteases plays a central role in the 'life and death' cellular processes, many of the corresponding substrates and their cleavage sites were not found yet. Availability of accurate predictors of the substrates and cleavage sites would facilitate understanding of proteases' functions and physiological roles. Deep learning is a promising approach for the development of accurate predictors of substrate cleavage events. Results We propose DeepCleave, the first deep learning-based predictor of protease-specific substrates and cleavage sites. DeepCleave uses protein substrate sequence data as input and employs convolutional neural networks with transfer learning to train accurate predictive models. High predictive performance of our models stems from the use of high-quality cleavage site features extracted from the substrate sequences through the deep learning process, and the application of transfer learning, multiple kernels and attention layer in the design of the deep network. Empirical tests against several related state-of-the-art methods demonstrate that DeepCleave outperforms these methods in predicting caspase and matrix metalloprotease substrate-cleavage sites. Availability and implementation The DeepCleave webserver and source code are freely available at http://deepcleave.erc.monash.edu/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]