The Stereospecific Ligand Exchange at a Pseudo‐Benzylic T‐4 Iridium Centre in Planar‐Chiral Cycloiridium (η6‐Arene)tricarbonylchromium Complexes

The stereospecificity of ligand exchange at the Ir^III centre of a cycloiridium arenetricarbonylchromium complex has been established experimentally by various analytical methods as well as by X‐ray diffraction structural analysis and computational investigations. Two new cases of phenyl and methyl iridium(III) complexes have been prepared by reaction of ( , )‐chlorido{2‐[(tricarbonyl)(η⁶‐phenylene‐κC^1′)chromium(0)]pyridine‐κN}(pentamethylcyclopentadienyl)iridium(III) with PhMgBr and MeMgBr. The determining influence of electrostatic repulsion has been established by means of density functional theory at the Becke–Perdew/TZP(ZORA) level by using, among other means, energy partitioning analysis. It is also shown that the Cr(CO)₃ fragment is likely to ease the ionic cleavage of the Ir? Cl bond in chlorido cycloiridium tricarbonylchromium complexes in a way similar to that already established for the solvolysis of benzyl halide complexes, that is, through a direct interaction of the Cr⁰ centre with the cationic Ir^III centre.     

Contents of Volume 56

Volume Contents

Contents of Volume 56     

Uncertainty in aflatoxin B<Subscript>1</Subscript> analysis using information from proficiency tests

We describe how to calculate uncertainty in the determination of aflatoxin B₁ in nuts and maize using an HPLC method with prederivatisation with trifluoroacetic acid and fluorescence detection. The uncertainty is easily calculated using the information obtained from the participation in several proficiency tests. All the sources of uncertainty are grouped in two terms: (1) matrix variability, intermediate precision and sample heterogeneity, and (2) method concordance. This methodology has the advantage that no extra work needs to be done because all the information to calculate uncertainty comes from proficiency tests. The uncertainty values were calculated using samples whose concentration ranged between 2 and 20 g L^–1. The relative standard uncertainty computed for aflatoxin B₁ was 16.3%.     

Synthesis and X-ray crystal structure of [2-(phosphinomethyl)ferrocenyl]diphenylphosphine

[2-(Phosphinomethyl)ferrocenyl]diphenylphosphine 2, is an air stable primary phosphine bearing a 1,2-disubstituted ferrocene framework, which has been prepared by reduction of the corresponding phosphonate. Confirmation of its structure has been obtained by X-ray single-crystals diffraction analysis. Despite its high stability toward oxidation, phosphine 2 still displays a normal coordinative behaviour toward [(p-cymene)RuCl₂]₂. The expected (p-cymene)RuCl₂(phosphine) complex is formed by coordination of the primary phosphine function, while the conceivably competitive complexation of the PPh₂ group was not observed.     

Tuning the optical properties of Prussian blue-like complexes

The rational design of the organic ligand allows to finely tune the degree of delocalization of mixed-valence Prussian blue-like molecules.     

Synthesis of phosphole-2,5-dicarboxylic acids via a [1,5]-shift of carbon dioxide around the phosphole nucleus

A new methodology for the synthesis of 1-P-functionalized phosphole-2,5-dicarboxylic acids is described. The carboxylic group is introduced through a two-step sequence which involves a preliminary attack of the phospholide unit onto CO(2) followed by a [1,5]-shift reaction of the CO(2)Li group around the phosphole nucleus. This approach was extended to the preparation of a new type of bidentate ligands. [reaction: see text]     

Computational models of music perception and cognition I: The perceptual and cognitive processing chain

We present a review on perception and cognition models designed for or applicable to music. An emphasis is put on computational implementations. We include findings from different disciplines: neuroscience, psychology, cognitive science, artificial intelligence, and musicology. The article summarizes the methodology that these disciplines use to approach the phenomena of music understanding, the localization of musical processes in the brain, and the flow of cognitive operations involved in turning physical signals into musical symbols, going from the transducers to the memory systems of the brain. We discuss formal models developed to emulate, explain and predict phenomena involved in early auditory processing, pitch processing, grouping, source separation, and music structure computation. We cover generic computational architectures of attention, memory, and expectation that can be instantiated and tuned to deal with specific musical phenomena. Criteria for the evaluation of such models are presented and discussed. Thereby, we lay out the general framework that provides the basis for the discussion of domain-specific music models in Part II.