Atomic Insights into Synergistic Nitroarene Hydrogenation over Nanodiamond-Supported Pt1 -Fe1 Dual-Single-Atom Catalyst.

Fundamental understanding of the synergistic effect of bimetallic catalysts is of extreme significance in heterogeneous catalysis, but a great challenge lies in the precise construction of uniform dual-metal sites. Here, we develop a novel method for constructing Pt1 -Fe1 /ND dual-single-atom catalyst, by anchoring Pt single atoms on Fe1 -N4 sites decorating a nanodiamond (ND) surface. Using this catalyst, the synergy of nitroarenes selective hydrogenation is revealed. In detail, hydrogen is activated on the Pt1 -Fe1 dual site and the nitro group is strongly adsorbed on the Fe1 site via a vertical configuration for subsequent hydrogenation. Such synergistic effect decreases the activation energy and results in an unprecedented catalytic performance (3.1 s-1 turnover frequency, ca. 100 % selectivity, 24 types of substrates). Our findings advance the applications of dual-single-atom catalysts in selective hydrogenations and open up a new way to explore the nature of synergistic catalysis at the atomic level.

A spectral learning path for simultaneous multi-parameter detection of water quality.

Water quality parameters (WQP) are the most intuitive indicators of the environmental quality of water body. Due to the complexity and variability of the chemical environment of water body, simple and rapid detection of multiple parameters of water quality becomes a difficult task. In this paper, spectral images (named SPIs) and deep learning (DL) techniques were combined to construct an intelligent method for WQP detection. A novel spectroscopic instrument was used to obtain SPIs, which were converted into feature images of water chemistry and then combined with deep convolutional neural networks (CNNs) to train models and predict WQP. The results showed that the method of combining SPIs and DL has high accuracy and stability, and good prediction results with average relative error of each parameter (anions and cations, TOC, TP, TN, NO3--N, NH3-N) at 1.3%, coefficient of determination (R2) of 0.996, root mean square error (RMSE) of 0.1, residual prediction deviation (RPD) of 16.2, and mean absolute error (MAE) of 0.067. The method can achieve rapid and accurate detection of high-dimensional water quality multi-parameters, and has the advantages of simple pre-processing and low cost. It can be applied not only to the intelligent detection of environmental waters, but also has the potential to be applied in chemical, biological and medical fields.

Image learning to accurately identify complex mixture components.

The study of complex mixtures is very important for exploring the evolution of natural phenomena, but the complexity of the mixtures greatly increases the difficulty of material information extraction. Image perception-based machine-learning techniques have the ability to cope with this problem in a data-driven way. Herein, we report a 2D-spectral imaging method to collect matter information from mixture components, and the obtained feature images can be easily provided to deep convolutional neural networks (CNNs) for establishing a spectral network. The results demonstrated that a single CNN trained end-to-end from the proposed images can directly accomplish synchronous measurement of multi-component samples using only raw pixels as inputs. Our strategy has some innate advantages, such as fast data acquisition, low cost, and simple chemical treatment, suggesting that it can be extensively applied in many fields, including environmental science, biology, medicine, and chemistry.

Machine learning for total organic carbon analysis of environmental water samples using high-throughput colorimetric sensors.

Due to the complexity of nonlinear reactions, the analysis of environmental samples often relies on expensive equipment as well as tedious and time-consuming experimental procedures. Currently, the efficient machine learning (ML) strategy based on big data offers some new insights for the analysis of complex components in the environmental field. In this study, ML was applied for the analysis of total organic carbon (TOC). We prepared a special colorimetric sensor (c-sensor) by inkjet printing. The sensor reacted with water samples in a high-throughput process, producing characteristic patterns to map TOC information in water samples. To quickly acquire TOC information on c-sensors, a ML model was proposed to describe the relationship between the c-sensor and TOC value. According to this study, the c-sensor and ML can be effectively applied to TOC information analysis of environmental water samples, which provides convenience for environmental research. It is foreseeable that ML has a broad prospect of application in environmental research.

A 1,8-naphthalimide-[12]aneN3 derivative for efficient Cu2+ recognition, lysosome staining and siRNA delivery.

Development of multifunctional compounds as both fluorescence probes and non-viral vectors is still difficult till date. It is necessary to overcome many hurdles such as the balance of hydrophilic and hydrophobic moieties, binding affinity between multifunctional compound and targeting substrate, the cytotoxicity of multifunctional compound, and so on. In this work, the performances of compound 1 on Cu2+ recognition, lysosome staining and siRNA (small interfering RNA) delivery were investigated. It was found that compound 1 exhibited high selectivity and sensitivity toward Cu2+ in aqueous solutions. The fluorescence emission of 1 was quenched by a factor of 42-fold in the presence of Cu2+ ions. Even in the common pure organic solutions, still more than 8-fold fluorescence quenching was achieved. Due to its high sensitivity to the pH, the complex of 1-Cu was also successfully applied in selective staining of lysosome in HeLa cells. Furthermore, cellular uptake experiment revealed that compound 1 showed good RNA delivery ability in HeLa, HepG2, U2Os and MC3T3-E1 cells, and its performance was better than commercial agents lipofectamine 2000 and 25 kDa PEI (Polyethylenimine). The RNA interference effect mediated by compound 1 was further evaluated by real-time fluorescent quantitative PCR experiment. Compound 1 showed much higher transfection efficacy than lipofectamine 2000 in MC3T3-E1 cells. Our study demonstrated that 1,8-naphthalimide- [12]aneN3 compound 1 with low cytotoxicity, high specificity towards Cu2+ and lysosome, high transfection efficacy, and low cost is an efficient multifunctional material both in molecular recognition and gene delivery.

1,8-Naphthalimide-Based Multifunctional Compounds as Cu2+ Probes, Lysosome Staining Agents, and Non-viral Vectors.

A series of multifunctional compounds (MFCs) 1a-1d based on 1,8-naphthalimide moiety were designed and synthesized. Due to the good fluorescence property and nucleic acid binding ability of 1,8-naphthalimide, these MFCs were applied in Cu2+ ion recognition, lysosome staining as well as RNA delivery. It was found that these MFCs exhibited highly selective fluorescence turn-off for Cu2+ in aqueous solution. The fluorescence emission of 1a-1d was quenched by a factor of 116-, 20-, 12-, and 14-fold in the presence of Cu2+ ions, respectively. Most importantly, 1a-Cu and 1b-Cu could be used as imaging reagents for detection of lysosome in live human cervical cancer cells (HeLa) using fluorescence microscopy. Furthermore, in order to evaluate the RNA delivery ability of 1a-1d, cellular uptake experiments were performed in HeLa, HepG2, U2Os, and MC3T3-E1 cell lines. The results showed that all the materials could deliver Cy5-labled RNA into the targeted cells. Among them, compound 1d modified with long hydrophobic chain exhibited the best RNA delivery efficiency in the four tested cell lines, and the performance was far better than lipofectamine 2000 and 25 kDa PEI, indicating the potential application in non-viral vectors.

Structure-activity relationship of novel low-generation dendrimers for gene delivery.

Structure-activity relationship (SAR) studies are very critical to design ideal gene vectors for gene delivery. However, It is difficult to obtain SAR information of low-generation dendrimers due to the lack of easy structural modification ways. Here, we synthesized a novel family of rigid aromatic backbone-based low-generation polyamidoamine (PAMAM) dendrimers. According to the number of primary amines, they were divided into two types: four-amine-containing PAMAM (DL1-DL5) and eight-amine-containing PAMAM (DL6-DL10). Due to the introduction of a rigid aromatic backbone, the low-generation PAMAM could be modified easier by different hydrophobic aliphatic chains. Several assays were used to study the interactions of the PAMAM dendrimers with plasmid DNA, and the results revealed that they not only had good DNA binding ability but also could efficiently condense DNA into spherical-shaped nanoparticles with suitable sizes and zeta potentials. The SAR studies indicated that the gene-transfection efficiency of the synthesized materials depended on not only the structure of their hydrophobic chains but also the number of primary amines. It was found that four-amine-containing PAMAM prepared from oleylamine (DL5) gave the best transfection efficiency, which was 3 times higher than that of lipofectamine 2000 in HEK293 cells. The cellular uptake mechanism mediated by DL5 was further investigated, and the results indicated that DL5/DNA complexes entered the cells mainly via caveolae and clathrin-mediated endocytosis. In addition, these low-generation PAMAMs modified with a single hydrophobic tail showed lower toxicity than lipofectamine 2000 in MC3T3-E1, MG63, HeLa, and HEK293 cells. These results reveal that such a type of low-generation polyamidoamines might be promising non-viral gene vectors, and also give us clues for the design of safe and high-efficiency gene vectors.