富勒烯负离子的自聚行为

The pure buckminsterfullerene sample was ablated by pulsed laser beam on a home-made apparatus. From the recorded time-of-flight mass spectra, it is found that,like the positive ions, the larger negative carbon cluster ions could also be formed from aggregation of buckminsterfullerene. The sizes of the cluster ions with highest signal intensities among each polymerized group of ions axe less than exact tfors of 60 and are related to the expermental conditions. The sizes of some cluster boons are odd numbers,so they must have non-fullerene structures.

A new association state of solutes in nanoconfined aqueous solutions

Recently, we have found a reversible transition between the dispersion and aggregation states of solute molecules in aqueous solutions confined in nanoscale geometry, where solutes exhibit distinct behavior in a new association state from that in the dispersion and aggregation states observed usually in macroscopic systems. However, it remains unknown whether this new association state of solute molecules found in nanoconfined systems would vanish with the system size increasing and approaching the macroscopic scale. Here, we achieve the phase diagram of solute association states by making the analyses of Gibbs free energy of solutes in nanoconfined aqueous solutions in detail. In the phase diagram, we observe a closed regime with a finite system size of nanoconfined aqueous solutions and a solute concentration range, only in which there exists the new association state of solutes with the reversible transition between the aggregation and dispersion states, and there indeed exists an upper limit of the system size for the new association state, around several tens nanometers. These findings regarding the intimate connection between the system size and the solute association behavior provides the comprehensive understanding of the association dynamics of solutes in nanoconfined environment.

Adaptive Clustering Algorithm by Ants＇ Optimization

Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the previous clustering approaches such as K-means, the main advantage of ant-based clustering algorithms is that no additional information is needed, such as the initial partitioning of the data or the number of clusters. In this paper, we present an adaptive ant clustering algorithm ACAD. The algorithm uses a digraph where the vertexes represent the data to be clustered. The weighted edges represent the acceptance rate between the two data it connected. The pheromone on the edges is adaptively updated by the ants passing it. Some edges with less pheromone are progressively removed under a threshold in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that ACAD is conceptually simpler, more efficient and more robust than previous research such as the classical K-means clustering algorithm and LF algorithm which.is also based on ACO