Transition-Metal-Free Multiple Functionalization of Piperidines to 4-Substituted and 3,4-Disubstituted 2-Piperidinones

Remote and multiple functionalization of piperidines without the use of transition-metal catalysts and elaborate directing groups is one of the major challenges in organic synthesis. Herein is reported an unprecedented two-step protocol that enables the multiple functionalization of piperidines to either 4-substituted or trans-3,4-disubstituted 2-piperidones. First, by exploiting the duality of TEMPO reactivity, which under oxidative and thermal conditions fluctuates between cationic and persistent-radical form, a novel multiple C(sp³)-H oxidation of piperidines to α,β-unsaturated 2-piperidones was developed. Second, the intrinsic low reactivity of the unsaturated piperidones toward conjugated Grignard additions was overcome by using trimethylsilyl chloride (TMSCl) as Lewis acid. Subsequently, conjugated Grignard addition/electrophilic trapping protocol provided substituted 2-piperidone intermediates, some of which were then transformed into pharmaceutical alkaloids.     

Interaction of H2 and CO with carbided and nitrided Ni(100)

A comparison is made of the chemisorption behavior of H₂ and CO over a carbided and nitrided Ni(100) surface. These results together with previous results for S and Cl support the argument that adatom electronegativity rather than radius is primarily responsible for modification of chemisorption behavior.     

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.     

Vibrationally resolved K-shell photoionization cross sections of methane

We use an extension of the static-exchange density functional theory (DFT) method, previously reported in [E. Plésiat et al., Phys. Rev. A 2, 023409 (2012), E. Plésiat, P. Decleva, F. Martín, Phys. Chem. Chem. Phys. 31, 10853 (2012)], to evaluate vibrationally resolved (total and angular) K-shell photoelectron cross sections of methane. The calculated cross sections are in very good agreement with the existing experimental measurements at low photoelectron energies. We show that, in contrast with the rich interference patterns previously observed in molecular frame C(1s) photoelectron angular distributions of methane at both low and high photoelectron energy, no interference effects are observed in the calculated β parameters, even at high photon energies.     

Multi-objective Bayesian Artificial Immune System: Empirical Evaluation and Comparative Analyses

Recently, we have proposed a Multi-Objective Bayesian Artificial Immune System (MOBAIS) to deal effectively with building blocks (high-quality partial solutions coded in the solution vector) in combinatorial multi-objective problems. By replacing the mutation and cloning operators with a probabilistic model, more specifically a Bayesian network representing the joint distribution of promising solutions, MOBAIS takes into account the relationships among the variables of the problem, avoiding the disruption of already obtained high-quality partial solutions. The preliminary results have indicated that our proposal is able to properly build the Pareto front. Motivated by this scenario, this paper better formalizes the proposal and investigates its usefulness on more challenging problems. In addition, an important enhancement regarding the Bayesian network learning was incorporated into the algorithm in order to speed up its execution. To conclude, we compare MOBAIS with state-of-the-art algorithms taking into account quantitative aspects of the Pareto front found by the algorithms. MOBAIS outperforms the contenders in terms of the quality of the obtained solutions and requires an amount of computational resource inferior or compatible with the contenders.