Structural and theoretical basis for ligand exchange on thiolate monolayer protected gold nanoclusters

Ligand exchange reactions are widely used for imparting new functionality on or integrating nanoparticles into devices. Thiolate-for-thiolate ligand exchange in monolayer protected gold nanoclusters has been used for over a decade; however, a firm structural basis of this reaction has been lacking. Herein, we present the first single-crystal X-ray structure of a partially exchanged Au(102)(p-MBA)(40)(p-BBT)(4) (p-MBA = para-mercaptobenzoic acid, p-BBT = para-bromobenzene thiol) with p-BBT as the incoming ligand. The crystal structure shows that 2 of the 22 symmetry-unique p-MBA ligand sites are partially exchanged to p-BBT under the initial fast kinetics in a 5 min timescale exchange reaction. Each of these ligand-binding sites is bonded to a different solvent-exposed Au atom, suggesting an associative mechanism for the initial ligand exchange. Density functional theory calculations modeling both thiol and thiolate incoming ligands postulate a mechanistic pathway for thiol-based ligand exchange. The discrete modification of a small set of ligand binding sites suggests Au(102)(p-MBA)(44) as a powerful platform for surface chemical engineering.     

The room temperature oxidation of porous silicon

The room temperature oxidation of porous silicon was studied using isothermal methods. The oxidation was found to depend on the type of the porous silicon. The microcalorimetric signals from the oxidation of the p⁺- and n-type porous silicon in dry air were different. In humid air the signals from the oxidation could not be distinguished from the strong signal due to adsorption of water vapour, but when the samples were placed in water similar differences were observed. The reason for differences in reactions is discussed. The oxidation in different liquids was also studied. The signal from reactions in methanol and ethanol were found to be 100 times higher than in water. In FTIR studies the reaction gas produced by reactions between alcohols and the porous silicon, silane (SiH₄) was found in the gas. Traces of SiOCH₃ and SiOC₂H₅ groups were also found in FTIR spectra indicating Si---O---C_xH_y passivation of the surface.     

Artificial pinning in thick YBCO films: Pinning potential and c-axis correlation

We have introduced artificial pinning centres in thick (>1 μm) YBCO films grown by Pulsed Laser Deposition using substrate decoration, quasi-multilayers, and target doping approaches. We have found that the frequency dependence of critical current density is consistent with a logarithmic dependence of pinning potential on current density. For most of materials used as nano-dots, artificially-induced pinning centres have a larger potential than natural ones. From angle-dependent in-field transport measurements and from Transmission Electron Microscopy we have found evidence of c-axis correlated pinning centres.     

Novel Bioreversible Prodrugs of Clodronate

Abstract

Clodronate (1) belongs to family of bisphosphonates which are used to inhibit mineralization of soft tissues as well as bone formation and resorption disorders.[I] The clinical use of bisphosphonates is limited due to their poor bióvailability which is mostly attributed to their hydrophilic structure. More lipophilic molecules are obtained if some or all of the anionic sites are converted to covalent bonds. We herein report a new prodrug serie for clodronate: the anhydrides 2 and 3. Previously we have reported that the simple alkyl esters 4 do not release clodronate via chemical or enzymatic hydrolysis.[2] The prepared new derivatives are stable in water (pH 5–7.5) but clodronate isrelased rapidly with enzymatic hydrolysis.     

Target training with soft computing tools

The personal capabilities and intentions of employees indicate their performance within their organization. It is important for the organization to capture this kind of tacit knowledge since the workforce are the true experts in perceiving the organization's current reality and evaluating which assets require development – including themselves as knowledge assets. The collective inner voice of the workforce helps the organization's management to steer the company and its assets in a sustainable direction.This article presents how the collective inner voice of the workforce can be captured and how it can be used for the benefit of the organization and its employees. The objective is to support individuals’ personal aspirations, as well as to save the money, time and resources that an organization spends on personnel training.The focus of this article is on demonstrating a possible soft-computing method used for competency simulation. The process starts with a linguistic self-evaluation conducted by employees, where individuals’ own perception of current and target competence levels is captured. The self-evaluation is conducted with the help of fuzzy logic. Clusters are formed from the result dataset using an unsupervised neural network clustering method: self-organizing maps. A demonstrator tool is then used to perform a “what-if” type of analysis/simulation on the clusters in the results. With the demonstrator tool, employees can roughly test the impact of alternative training scenarios for themselves. For individuals this may open up new directions for self-development, and for organizations this may allow the efficient use of training resources. We tested the approach with a dataset from a real human resource development project among nuclear power plant operators.The case study reveals the potential of soft-computing based collective competency simulation as one part of personnel development projects in the future. Yet the techniques and the demonstrator tool used in this experiment are far from being products that employees could easily use as part of their training project. Possible benefits of the proposed approach are demonstrated in this article.     

JADE for Tensor-Valued Observations

Independent component analysis is a standard tool in modern data analysis and numerous different techniques for applying it exist. The standard methods however quickly lose their effectiveness when the data are made up of structures of higher order than vectors, namely, matrices or tensors (e.g., images or videos), being unable to handle the high amounts of noise. Recently, an extension of the classic fourth-order blind identification (FOBI) specially suited for tensor-valued observations was proposed and showed to outperform its vector version for tensor data. In this article, we extend another popular independent component analysis method, the joint approximate diagonalization of eigen-matrices (JADE), for tensor observations. In addition to the theoretical background, we also provide the asymptotic properties of the proposed estimator and use both simulations and real data to show its usefulness and superiority over its competitors. Supplementary material including the proofs of the theorems and the codes for running the simulations and the real data example are available online.