A single-step procedure for the preparation of palladium nanoparticles and a phosphine-functionalized support as catalyst for Suzuki cross-coupling reactions

We here report the preparation of supported palladium nanoparticles (NPs) stabilized by pendant phosphine groups by reacting a palladium complex containing the ligand 2-(diphenylphosphino)benzaldehyde with an amino-functionalized silica surface The Pd nanocatalyst is active for Suzuki cross-coupling reaction avoiding any addition of other sources of phosphine ligands The Pd intermediates and Pd NPs were characterized by solid-state nuclear magnetic resonance and transmission electron microscopy techniques The synthetic method was also applied to prepare magnetically recoverable Pd NPs leading to a catalyst that could be reused for up to 10 recycles In summary we gathered the advantages of heterogeneous catalysis magnetic separation and enhanced catalytic activity of palladium promoted by phosphine ligands to synthesize a new catalyst for Suzuki cross-coupling reactions The Pd NP catalyst prepared on the phosphine-functionalized support was more active and selective than a similar Pd NP catalyst prepared on an amino-functionalized support (C) 2010 Elsevier Inc All rights reserved

Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.