The active sites in different TS-1 zeolites for propylene epoxidation studied by ultraviolet resonance Raman and ultraviolet visible absorption spectroscopies

The titanium species in four kinds of titanium-containing MFI zeolites have been studied by ultraviolet (UV)-Raman and ultraviolet visible (UV-Vis) absorption spectroscopies and by the epoxidation of propylene with diluted H2O2 solution (30%). UV-Raman spectroscopy is proved to be a suitable means to estimate qualitatively the framework titanium in TS-l zeolites. Based on the comparison of the relative intensity ratio I-1125/I-380 of UV-Raman spectra, the TS-1(conv.) sample synthesized hydrothermally by the conventional procedure shows the highest amount of framework titanium. UV-Vis spectroscopy reveals that besides minor anatase. titanium species are mainly tetrahydrally coordinated into the framework for TS-l(conv.) or the Ti-ZSM-5 sample prepared by gas-solid reaction between deboronated B-ZSM-5 and TiCl4 vapor at elevated temperatures. For the TS-1(org.) and TS-1(inorg.) samples synthesized hydrothermally using tetrapropylammonium bromide (TPABr) as template and tetrabutylorthotitanite (TBOT) and TiCl3 as titanium source, respectively, the presence of mononuclear and isolated TiOx species which are proposed to bond to the zeolite extraframework is observed. In addition to the framework titanium species, these isolated TiOx species are assumed to be also active for propylene epoxidation.

Direct spectroscopic evidence for vanadium species in V-MCM-41 molecular sieve characterized by UV resonance Raman spectroscopy

Vanadium species in tetrahedral and octahedral coordination in V-MCM-41 molecular sieve are characterized by UV resonance Raman bands at 1070 and 930 cm(-1) respectively.

Identifying framework titanium in TS-1 zeolite by UV resonance Raman spectroscopy

Framework titanium in Ti-silicalite-1 (TS-1) zeolite was selectively identified by its resonance Raman bands using ultraviolet (W) Raman spectroscopy. Raman spectra of the TS-1 and silicalite-1 zeolites were obtained and compared using continuous wave laser lines at 244, 325, and 488 nm as the excitation sources. It was only with the excitation at 244 nm that resonance enhanced Raman bands at 490, 530, and 1125 cm(-1) appeared exclusively for the TS-1 zeolite. Furthermore, these bands increased in intensity with the crystallization time of the TS-1 zeolite. The Raman bands at 490, 530, and 1125 cm(-1) are identified as the framework titanium species because they only appeared when the laser excites the charge-transfer transition of the framework titanium species in the TS-1. No resonance Raman enhancement was detected for the bands of silicalite-1 zeolite and for the band at 960 cm(-1) of TS-1 with any of the excitation sources ranging from the visible tb UV regions. This approach can be applicable for the identification of other transition metal ions substituted in the framework of a zeolite or any other molecular sieve.

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378)

Extracted Memory from Temporal Patterns Using Adaptive Resonance and Recurrent Network

We can recognize objects through receiving continuously huge temporal information including redundancy and noise, and can memorize them. This paper proposes a neural network model which extracts pre-recognized patterns from temporally sequential patterns which include redundancy, and memorizes the patterns temporarily. This model consists of an adaptive resonance system and a recurrent time-delay network. The extraction is executed by the matching mechanism of the adaptive resonance system, and the temporal information is processed and stored by the recurrent network. Simple simulations are examined to exemplify the property of extraction.

Fuzzy ART: An Adaptive Resonance Algorithm for Rapid, Stable Classification of Analog Patterns

The Fuzzy ART system introduced herein incorporates computations from fuzzy set theory into ART 1. For example, the intersection (n) operator used in ART 1 learning is replaced by the MIN operator (A) of fuzzy set theory. Fuzzy ART reduces to ART 1 in response to binary input vectors, but can also learn stable categories in response to analog input vectors. In particular, the MIN operator reduces to the intersection operator in the binary case. Learning is stable because all adaptive weights can only decrease in time. A preprocessing step, called complement coding, uses on-cell and off-cell responses to prevent category proliferation. Complement coding normalizes input vectors while preserving the amplitudes of individual feature activations.