Owing to the strong affinity of thiols to Au and Ag, they are often employed to modify the surfaces of nanoparticles (NPs). Recently, these strong ligand-interface interactions have been employed to control NP growth, and this technique has emerged as a unique modulation strategy for creating unconventional plasmonic hybrid nanostructures. In these systems, the roles of the non-mercapto components of the thiol molecules and their structures are still unknown. Therefore, we herein present our investigation into this phenomenon. Primary amino (–NH2) groups in thiols are found to play a key role in regulating growth kinetics, i.e., in accelerating Ag deposition on Au NPs. The–NH2 groups are thought to bring Ag ions to the particle surface by coordinating to them, and thereby assist their reduction. The effect of molecular structure is non-trivial and thus provides the possibility of selective thiol detection. Based on the dependence of kinetic modulation on the non-mercapto components and molecular structures of molecules, we demonstrate the highly sensitive and specific detection of cysteine (limit of detection: 6 nM) in a mixture of 19 natural amino acids based on Ag growth on Au nanospheres. In addition, based on this modulation effect, we reveal the entrapping of chiral thiols within the growth layer through their plasmonic circular dichroism (PCD) responses. We believe that thiol-based growth regulation has great potential for creating plasmonic nanostructures with novel functionalities.
We develop the complex scaling method within the relativistic framework by expanding the Dirac spinors in the complete set of eigensolutions of a harmonic oscillator potential, and present the theoretical formalism of describing the discrete bound and resonant states on the same footing. Based on a well established and frequently used model, we demonstrate the utility and applicability of the extended method and examine the stability of the results with respect to the variations of the parameters of the model. Satisfactory agreements are found for all the calculated results in comparison with some other calculations in references. Especially, the present calculation in the nonrelativistic limit gives a consistent result with that in the nonrelativistic calculation. 相似文献
Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time. 相似文献
