Manganese-doped cesium lead chloride (CsPbCl3) perovskite nanocrystals (NCs) have recently garnered attention because of their unique magneto-optical properties, giving them potential in a variety of optoelectronic applications. One common method to dope Mn2+ into host CsPbCl3 NCs is through a postsynthetic ion exchange reaction. However, most ion exchange strategies utilize a Mn2+-containing precursor solution, which adds limitations to the reaction due to compatibility and stability issues. Here, we report a new method of cation exchange in CsPbCl3 NCs where Pb2+ cations are partially replaced by Mn2+ cations using a solid Mn2+–precursor source, resulting in a quasi-solid-solid cation exchange at ambient conditions. The ability to perform the cation exchange without the addition of any external solvents allowed for a systematic study on the NC doping. The reaction takes place at the interface between the Mn2+-containing solid precursor and the NC surface. Electron paramagnetic resonance and optical characterizations including a shortened Mn2+ photoluminescence lifetime immediately following the exchange indicated initial heterogeneous doping with the Mn2+ dopants localized on the NC surface. Spatial distribution of dopants within the NCs is observed by inward diffusion over time. Additionally, dopant concentration can be controlled through engineering starting ligand compositions, which not only changes the ligands present at the Mn2+–precursor–NC interface but also leads to varying degrees of precursor activation. This study not only provides a clean and facile doping method without the need for additional solvents but also a cation exchange strategy which can be closely studied to improve the understanding of doping processes at the molecular level in perovskite NC systems.

We generalize the notion of λ-superstrings, presented in a previous paper, to the notion of weighted λ-superstrings. This generalization entails an important improvement in the applications to vaccine designs, as it allows epitopes to be weighted by their immunogenicities. Motivated by these potential applications of constructing short weighted λ-superstrings to vaccine design, we approach this problem in two ways. First, we formalize the problem as a combinatorial optimization problem (in fact, as two polynomially equivalent problems) and develop an integer programming (IP) formulation for solving it optimally. Second, we describe a model that also takes into account good pairwise alignments of the obtained superstring with the input strings, and present a genetic algorithm that solves the problem approximately. We apply both algorithms to a set of 169 strings corresponding to the Nef protein taken from patiens infected with HIV-1. In the IP-based algorithm, we take the epitopes and the estimation of the immunogenicities from databases of experimental epitopes. In the genetic algorithm we take as candidate epitopes all 9-mers present in the 169 strings and estimate their immunogenicities using a public bioinformatics tool. Finally, we used several bioinformatic tools to evaluate the properties of the candidates generated by our method, which indicated that we can score high immunogenic λ-superstrings that at the same time present similar conformations to the Nef virus proteins.

This work addresses a real-world adjustment of economic models where the application of robust and global optimization techniques is required. The problem dealt with is the search for a set of parameters to calculate the reported claim amount. Several functions are proposed to obtain the reported claim amount, and a multi-objective optimization procedure is used to obtain parameters using real data and to decide the best function to approximate the reported claim amount. Using this function, insurance companies negotiate the underlying contract—that is, the catastrophic loss ratio defined from the total reported claim amount. They are associated with catastrophes that occurred during the loss period and declared until the development period expired. The suitability of different techniques coming from evolutionary computation (EC) to solve this problem is explored, contrasting the performance achieved with recent proposals of multi-objective evolutionary algorithms (MOEAs). Results show the advantages of MOEAs in the proposal in terms of effectiveness and completeness in searching for solutions, compared with particular solutions of classical EC approaches (using an aggregation operator) in problems with real data.

No abstract