Multinuclear magnetic resonance studies of five silver(I) trinuclear pyrazolate complexes

Structural Chemistry - Experimental multinuclear determination of the chemical shifts, especially 15N and 109Ag, of five silver(I) trinuclear pyrazolate complexes, (PzAg)3, coupled with ZORA...     

Towards the Rational Design of Efficient Organic Structure‐Directing Agents for Zeolite Synthesis

Zeolites are crystalline microporous materials with application in diverse fields, especially in catalysis. The ability to prepare zeolites with targeted physicochemical properties for a specific catalytic application is a matter of great interest, because it allows the efficiency of the entire chemical process to be increased (higher product yields, lower undesired by‐products, less energy consumption, and cost savings, etc). Nevertheless, directing the zeolite crystallization towards the material with the desired framework topology, crystal size, or chemical composition is not an easy task, since several variables influence the nucleation and crystallization processes. The combination of accumulated knowledge, rationalization, and innovation has allowed the synthesis of unique zeolitic structures in the last few years. This is especially true in terms of the design of organic and inorganic structure‐directing agents (SDAs). In this Minireview we will present the rationale we have followed in our studies to synthesize new zeolite structures, while putting this in perspective with the advances made by other researchers of the zeolite community.     

Use of capillary electrophoresis for characterisation of vinyl‐terminated Au nanoprisms and nanooctahedra

It is described a simple, rapid and efficient methodology to characterise and separate gold nanoprisms and nanooctahedra by capillary electrophoresis. This technique is suitable to distinguish between morphologies and it can be used as a powerful separation tool after a customised synthesis of both structures. This synthesis was carried out by amending two parameters, temperature and pH, and a sharp decrease was found in nanotriangles when temperature was increased from 70 up to 95°C. However, when the synthesis was performed at a given temperature, an increase in pH did not promote an important change in isolation of any structure until pH = 9.5, critical in the final morphology of the nanoparticle. Gold nanoprisms and nanooctahedra were successfully separated by capillary electrophoresis according to differences in charge‐to‐mass ratio of the morphologies. Final particle morphology was confirmed by transmission electron microscopy analysis. Under optimal working conditions, a mixture containing both shapes of gold nanoparticles was initially injected and two major peaks were obtained for each structure. Capillary electrophoresis allowed to study pH and temperature influence on both morphologies. It was inferred that the ratio between triangles and octahedra decreased to a great extent when increasing both temperature and pH.     

Recent Developments in Flow Injection/Sequential Injection Liquid-Liquid Extraction for Atomic Spectrometric Determination of Metals and Metalloids

Abstract

This review aims to provide a critical overview of automated flow injection and sequential injection liquid-liquid extraction for preconcentration and/or separation of ultra-trace metal and metalloid species hyphenated with atomic spectrometric detection systems, including some new trends and applications in the subbranches of cloud point extraction (CPE), wetting film extraction (WFE), supported liquid membrane extraction (SLME), extraction chromatography (EChr), and liquid-phase microextraction (LPME) techniques. The analytical performance of flow-injection/sequential injection liquid-liquid extraction methods is markedly affected by the components of the flow network such as segmentor, extraction coil, and phase separator. Thus, an overall presentation of system components along with some novel strategies for interface with atomic spectrometers is discussed and exemplified with selected applications.     

Learning recursive probability trees from probabilistic potentials

A Recursive Probability Tree (RPT) is a data structure for representing the potentials involved in Probabilistic Graphical Models (PGMs). This structure is developed with the aim of capturing some types of independencies that cannot be represented with previous structures. This capability leads to improvements in memory space and computation time during inference. This paper describes a learning algorithm for building RPTs from probability distributions. The experimental analysis shows the proper behavior of the algorithm: it produces RPTs encoding good approximations of the original probability distributions.