A Combined Spectroscopic and Theoretical Study on a Ruthenium Complex Featuring a π-Extended dppz Ligand for Light-Driven Accumulation of Multiple Reducing Equivalents

The design of photoactive systems capable of storing and relaying multiple electrons is highly demanded in the field of artificial photosynthesis, where transformations of interest rely on multielectronic redox processes. The photophysical properties of the ruthenium photosensitizer [(bpy)₂Ru( oxim-dppqp )]²⁺ ( Ru ), storing two electrons coupled to two protons on the π-extended oxim-dppqp ligand under light-driven conditions, are investigated by means of excitation wavelength-dependent resonance Raman and transient absorption spectroscopies, in combination with time-dependent density functional theory; the results are discussed in comparison to the parent [(bpy)₂Ru(dppz)]²⁺ and [(bpy)₂Ru( oxo-dppqp )]²⁺ complexes. In addition, this study provides in-depth insights on the impact of protonation or of accumulation of multiple reducing equivalents on the reactive excited states.     

Computational modeling of Hg/Ni ions separation via MOF/LDH nanocomposite: Machine learning based modeling

Nowadays, sustainable supplement of water has recently been identified as a vital necessity due to the existence of limited drinkable water sources. To do this, various techniques are being developed to remove various types of pollutants from water/wastewater sources. Adsorption of common water pollutants using nanocomposite materials has been of great popularity in recent years due to its high efficiency. This paper aims to develop various models based on machine learning approach to study their efficiency on predicting the experimentally measured results of Hg/Ni ions removal from water sources. To do this, this study attempts regression on a small data set using two parameters as inputs and two parameters as outputs. In this dataset, the inputs are Ion and C0, and the outputs are Ce and Qe. AdaBoost (Adaptive Boosting), a well-known ensemble method, was applied on top of three different models, including Decision Tree Regression (DT), Gaussian Process Regression (GPR), and Linear Regression (LR). After fine-tuning their hyper-parameters, the optimized model was evaluated through various metrics. For example, the R² for ADA + GPR model has a score of 0.998 for Ce and 0.999 for Qe as the best model among these three models. This model in RMSE is the best and illustrates 0.1512 and 1.490 for Ce and Qe as error. Eventually, ADA + GPR has been selected as the optimized model with optimized dataset: (Ion = Ni, C0 = 250, Ce = 206.0). But for Qe, different amounts are illustrated: (Ion = Hg, C0 = 106.7, Ce = 577.35)     

Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: Recent advances and prospects

Environmental pollution and its drastic effects on human and animal health have urged governments to implement strict policies to minimize damage. The first step in applying such policies is to find reliable methods to detect pollution in various media, including water, food, soil, and air. In this regard, various approaches such as spectrophotometric, chromatographic, and electrochemical techniques have been proposed. To overcome the limitations associated with conventional analytical methods, microfluidic devices have emerged as sensitive technologies capable of generating high content information during the past few years. The passage of contaminant samples through the microfluidic channels provides essential details about the whole environment after detection by the detector. In the meantime, artificial intelligence is an ideal means to identify, classify, characterize, and even predict the data obtained from microfluidic systems. The development of microfluidic devices with integrated machine learning and artificial intelligence is promising for the development of next-generation monitoring systems. Combination of the two systems ensures time efficient setups with easy operation. This review article is dedicated to the recent developments in microfluidic chips coupled with artificial intelligence technology for the evolution of more convenient pollution monitoring systems.     

Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

将铂(Ⅱ)-四(4-羧基苯基)卟啉组装到光捕获金属有机框架中增强可见光驱动的产氢效能

氢气析出反应的分子催化剂因能够将其整合到用于光催化水分解的光捕集复合物中而受到广泛关注.研究者期望通过构建吸光网络,提高分子催化剂的光催化产氢效能.本文报道了以[(TCPP)Pt^Ⅱ][TCPP=meso-四(4-羧基苯基)卟啉]络合物作为光催化产氢的分子催化剂.采用氯冉酸(CA)作为电子牺牲剂可以很好地稳定光催化剂,使CA被氧化为[CA-H·]自由基.当使用三乙醇胺作为电子牺牲剂时,[(TCPP)Pt^Ⅱ]分解形成Pt纳米颗粒.电化学循环伏安实验结果表明,光催化产氢的第一步是质子偶联电子转移,以获得[(TCPP)Pt+H]0.然而,第二个电子转移-1.02 V的氧化还原电位不随添加三氟乙酸而位移,表明该电子转移未与质子转移耦合,得到[(TCPP)Pt+H]-.此外,第二次电子转移的峰值处产生催化波,表明氢气是由[(TCPP)Pt+H]-的质子化生成,然后再生[(TCPP)Pt^Ⅱ]并释放氢气.密度泛函理论计算结果表明,[(TCPP)Pt^Ⅱ]分子催化剂光催化产氢的机理可能先经过质子耦合电子转移反应,形成[(TCPP)Pt^Ⅰ]-NH,然后依次经过电子注入和质子化形成[(TCPP)Pt^Ⅱ-H]-NH中间体,最终释放H2.由于整个催化循环过程涉及多个电子的注入,光捕获网络的引入有助于提供多个光电子.因此,本文通过将[(TCPP)Pt^Ⅱ]掺杂生长到主要由[(TCPP)Zn^Ⅱ]构筑的金属有机框架中,构筑了与分子催化剂连接的光捕获网络,从而将其活性提高了约830倍.纳秒瞬态吸收光谱和时间分辨的磷光光谱表明,向[(TCPP)Pt^Ⅱ]均相溶液中加入氯冉酸会因电荷转移而缩短3[(TCPP)Pt^Ⅱ]*寿命.同样现象在金属有机框架体系中也被观察到.然而,在磷光猝灭后,瞬态吸收光谱观察到均相溶液中[(TCPP)Pt+H]0及[CA-H·]自由基信号迅速衰减,在微秒时间尺度上衰减为0,表明大部分还原的[(TCPP)Pt+H]0迅速与氧化后的[CA-H·]复合,限制了光催化氢气析出的光量子效率.然而,在金属有机框架体系中,磷光猝灭后纳秒瞬态吸收光谱在较长时间尺度观察到残留吸收带,表明随后消耗CA,向反应体系中注入电子,推动了反应的完成.本文研究了能量转移对光催化H2析出的影响,并强调了光捕获网络在多电子注入中的重要性.     

过渡金属电催化剂在硅基光阳极分解水产氧中的作用

光电化学分解水可将太阳能转换为绿色的氢能,为目前的能源危机和环境问题提供了一种理想的解决方案.在分解水反应中,涉及四空穴过程的产氧半反应是制约性能的关键步骤,往往需要在半导体表面沉积电催化剂以加速产氧反应动力学.因此,全面理解电催化剂在光电化学分解水体系中的作用至关重要.在目前的产氧电催化剂中,过渡金属羟基氧化物电催化剂(MOOH,M=Fe,Co,Ni)因其环保、廉价、高效以及稳定的特性,已被广泛用于半导体光阳极分解水器件中.而且,MOOH可用简单的电沉积方法沉积在光电极表面,易于大面积制备.然而,电沉积法制备的MOOH具有复杂的结构,对其作用机制的全面理解更加困难.因此,本文以电沉积MOOH修饰的硅基光阳极(n⁺p-Si/SiO_x/Fe/FeOx/MOOH)作为模型,研究了不同电催化剂对硅光阳极光电化学产氧性能的影响.实验发现电催化剂的界面优化在电催化剂修饰的光电极中发挥着重要作用,这是因为优化的界面可以提升界面电荷传输,提供更多的催化反应活性位点以及更高的本征催化活性,从而更有利于光解水性能的提升.该项研究揭示了电催化剂在光解水器件中的作用,并为今后高效光解水器件的设计提供了一定指导.首先在多晶n⁺p-Si基底上热蒸镀了一层30 nm的金属Fe膜,并通过电化学活化将Fe膜表面转换为FeOx得到Fe/FeOx(记作aFe)界面层,然后利用电沉积方法制备MOOH表面修饰层,最终得到n⁺p-Si/SiO_x/aFe:MOOH光阳极.X射线光电子能谱、拉曼光谱以及扫描电子显微镜表面元素成像的表征结果均证实电极表面由于界面层金属Fe元素的掺杂而形成了Fe1-xNixOOH.在模拟太阳光下用于光解水产氧时,n⁺p-Si/SiO_x/aFe:NiOOH电极的起始电位为~1.01 VRHE(相对于可逆氢电极的电势),在1.23 VRHE下的光电流为38.82 mA cm^-2,显著优于n⁺p-Si/SiO_x/aFe、n⁺p-Si/SiO_x/aFe:FeOOH以及n⁺p-Si/SiO_x/aFe:CoOOH三个对比样品,且其稳定性达到75 h.另外,我们发现n⁺p-Si/SiO_x/aFe:MOOH电极的光电化学产氧性能均显著高于n⁺p-Si/SiO_x/aFe电极,且p++-Si/SiO_x/aFe:MOOH的电催化产氧性能也高于p++-Si/SiO_x/MOOH,不仅证明了aFe界面层对Si与MOOH层之间的界面接触作用的有效调控,而且表明双电催化剂体系(aFe:MOOH)的电催化产氧活性高于单电催化剂(MOOH).热力学分析表明,n⁺p-Si/SiO_x/aFe:MOOH光阳极的光电压大小与其光解水产氧性能并不一致,从而排除了热力学因素对性能的关键影响.进一步从塔菲尔斜率、电化学活性表面积和电化学阻抗谱对各电极的动力学进行了分析,证明了动力学因素在上述光阳极产氧性能中的主导作用.同时发现,由于aFe:NiOOH双电催化剂具有更高的本征电催化产氧性能,提供了更多的表面活性位点以及更有效地促进了光生载流子的传输,对动力学的提升效果更显著,从而使n⁺p-Si/SiO_x/aFe:NiOOH光阳极表现出最高的光解水产氧性能.     

锂金属负极人造保护膜的研究进展

金属锂因其具有极高的理论容量(3860 mAh·g^?1)、最低的电极电位(?3.04 V vs.标准氢电极)和低的密度(0.534 g·cm^?3),被认为是最具潜力的负极材料。但循环过程中不可控的枝晶生长及不稳定的固体电解质相界面膜所引起的安全隐患和电池库伦效率低等问题严重阻碍了锂金属负极的发展。通过在电极表面构建人造保护膜可以有效调控锂离子沉积行为,因此人造保护膜的构建是一种简单高效抑制锂枝晶生长的策略。本综述将从聚合物保护膜、无机保护膜、有机-无机复合保护膜和合金保护膜总结了人造保护膜的构建方法、抑制锂枝晶生长机理,为促进高比能锂金属电池的商业化应用提供借鉴参考作用。     

Uncertain multiobjective traveling salesman problem

Traveling salesman problem is a fundamental combinatorial optimization model studied in the operations research community for nearly half a century, yet there is surprisingly little literature that addresses uncertainty and multiple objectives in it. A novel TSP variation, called uncertain multiobjective TSP (UMTSP) with uncertain variables on the arc, is proposed in this paper on the basis of uncertainty theory, and a new solution approach named uncertain approach is applied to obtain Pareto efficient route in UMTSP. Considering the uncertain and combinatorial nature of UMTSP, a new ABC algorithm inserted with reverse operator, crossover operator and mutation operator is designed to this problem, which outperforms other algorithms through the performance comparison on three benchmark TSPs. Finally, a new benchmark UMTSP case study is presented to illustrate the construction and solution of UMTSP, which shows that the optimal route in deterministic TSP can be a poor route in UMTSP.     

Evolution of Artificial Base Pairs with Hydrogen Bond Complementarity

Artificial base pairs,from the perspective of synthetic biology,are designed to contain the features of modularity,orthogonality,and manipulability.And the development of artificial base pairs has beat endowed with responsibility to understand the biological process,improve the recognition capacity and stability of aptamers,and develop the nucleoside drugs,diagnosis,and drag delivery.In this review,we first gave a concise introduction of artificial base pairs based on their interaction modes including alternative hydrogen bonding,hydrophobic interaction,and metal coordination.Then we displayed the detailed information of artificial base pairs with hydrogen bonding interaction,and analyzed how the changes of their structures affect their functions.Subsequently,we highlighted the applications of functional artificial base pairs in aptamer discovery,diagnosis,and drug delivery.Finally,an insight into the remaining challenges and future perspective of the artificial bases was provided.