Frequency locked micro disk resonator for real time and precise monitoring of refractive index.

We experimentally demonstrate a frequency modulation locked servo loop, locked to a resonance line of an on-chip microdisk resonator in a silicon nitride platform. By using this approach, we demonstrate real-time monitoring of refractive index variations with a precision approaching 10(-7) RIU, using a moderate Q factor of 10(4). The approach can be applied for intensity independent, dynamic and precise index of refraction monitoring for biosensing applications.

Magnetic property and interlayer segregation in spin valve metal multilayers

The experimental results show that the exchange coupling field of NiFe/FeMn for Ta/ NiFe/FeMn/Ta multilayers is higher than that for the spin valve multilayers Ta/NiFe/Cu/NiFe/FeMn/ Ta. In order to find out the reason, the composition and chemical states at the surfaces of Ta(12nm)/ NiFe(7nm), Ta(12nm)/NiFe(7nm)/Cu(4nm) and Ta(12nm)/NiFe(7nm)/Cu(3nm)/NiFe(5nm) were studied using the X-ray photoelectron spectroscopy (XPS). The results show that no elements from lower layers float out or segregate to the surface for the first and second samples. However, Cu atoms segregate to the surface of Ta(12nm)/NiFe(7nm)/Cu(3nm)/NiFe(5nm) multilayers, i.e. Cu atoms segregate to the NiFe/FeMn interface for Ta/NiFe/Cu/NiFe/FeMn/Ta multilayers. We believe that the presence of Cu atoms at the interface of NiFe/FeMn is one of the important factors causing the exchange coupling field of Ta/NiFe/FeMn/Ta multilayers to be higher than that of Ta/NiFe/Cu/NiFe/ FeMn/Ta multilayers.

Magnetic property and interface structure of Ta/NiO/NiFe/Ta

Ta/NiO/NiFe/Ta multilayers, utilizing Ta as buffer layer, were prepared by rf reactive and de magnetron sputtering. The exchange coupling field between NiO and NiFe reached a maximum value of 9.6x10(3) A/m at a NiO film thickness of 50 nm. The composition and chemical states at interface region of Ta/NiO/Ta were studied by using the X-ray photoelectron spectroscopy (XPS) and peak decomposition technique. The results show that there is an "intermixing layer" at the Ta/NiO land NiO/Ta) interface due to a thermodynamically favorable reaction 2Ta + 5NiO = 5Ni + Ta2O5. This interface reaction has a great effect on exchange coupling. The thickness of Ni+NiO estimated by XPS depth. profiles is about 8-10 nm.

Reduction property and catalytic activity of Ce1-XNiXO2 mixed oxide catalysts for CH4 oxidation

Ce1-XNiXO2 oxides with X varying from 0.05 to 0.5 were prepared by different methods and characterized by XRD and TPR techniques. Ce(0.7)Mi(0.3)O(2) sample prepared by sol-gel method shows the highest reducibility and the highest catalytic activity for methane combustion. Three kinds of Ni phases co-exist in the Ce1-XNiXO2 catalysts prepared by sol-gel method: (i) aggregated NiO on the support CeO2, (ii) highly dispersed NiO with strong interaction with CeO2 and (iii) Ni atoms incorporated into CeO2 lattice. The distribution of different Ni species strongly depends on the preparation methods. The highly dispersed NiO shows the highest activity for methane combustion. The NiO aggregated on the support CeO2 shows lower catalytic activity for methane combustion, while the least catalytic activity is found for the Ni species incorporated into CeO2. Any oxygen vacancy formed in CeO2 lattice due to the incorporating of Ni atoms adsorbs and activates the molecular oxygen to form active oxygen species. So the highest catalytic activity for methane combustion on Ce0.7Ni0.3O2 catalyst is attributed not only to the highly dispersed Ni species but also to the more active oxygen species formed. (C) 2002 Elsevier Science B.V. All rights reserved.

Chemical bond characteristics, thermal expansion property and compressibility of AR(2)O(4) (A = Ca, Sr, Ba; R = rare earths)

Theoretical researches were performed on the CaFe2O4-type binary rare earth oxides AR(2)O(4) (A = Ca, Sr, Ba; R = rare earths) by using chemical bond theory of dielectric description. The chemical bond properties of these crystals were explored, and then the thermal expansion property and compressibility were studied. The theoretical values of linear thermal expansion coefficient (LTEC) and bulk modulus were presented. The calculations revealed that the LTECs and the bulk moduli do have linear relationship with the ionic radii of the rare earths. In the cases of Sc and Y, both the LTEC and bulk modulus values are larger than the lanthanide series. We attribute this to the difference in the electronic configuration between Sc (Y) and lanthanide series. For SrY2O4 and BaY2O4 crystals, the theoretical values of LTEC and bulk modulus agree well with experimental ones.

Oligoaniline-Functionalized terpyridine ligands and their ruthenium(II) complexes: synthesis, spectroscopic property and redox behaviors

A series of oligoaniline-functionalized mono- and bis-topic terpyridine ligands, i.e. C6H5[N(R)C6H4](n)TPY (R = H, butyl, tert-butyloxycarbonyl; n = 1-4; TPY = 2,2':6',2"-terpyridyl) and TPYC6H4[N(R)C6H4](m)TPY (R = H, tert-butyloxycarbonyl; m = 2, 4), and the corresponding monoand bis-nuclear ruthenium(II) complexes have been synthesized and verified. The spectroscopic results indicate that two kinds of pi-pi* transitions from TPY and oligoaniline fragments of ligands strongly shift to lower energy, and the metal-to-ligand charge-transfer transition ((MLCT)-M-1) bands of all obtained complexes are considerably red-shifted (Delta lambda(max) = 22-64 nm) and their intensities become much more intense (approximately 4-6 times), compared with those of the reported complex [Ru(TPY)(2)](2+). Moreover, the spectroscopic properties of the ligands and complexes with longer oligoaniline units (n = 3, 4) are markedly influenced by the external stimulus, such as the oxidation and proton acid doping.

Synthesis and Characterization of Hyperbranched Poly(ether amide)s with Thermoresponsive Property and Unexpected Strong Blue Photoluminescence

A facile and efficient strategy for the syntheses of novel hyperbranched poly(ether amide)s (HPEA) from multihydroxyl primary amines and (meth)acryloyl chloride has been developed. The chemical structures of the HPEAs were confirmed by IR and NMR spectra. Analyses of SEC (size exclusion chromatography) and viscosity characterizations revealed the highly branched structures of the polymers obtained. The resultant hyperbranched polymers contain abundant hydroxyl groups. The thermoresponsive property was obtained from in situ surface modification of abundant OH end groups with N-isopropylacrylamide (NIPAAm). The study oil temperature-dependent characteristics has revealed that NIPAAm-g-HPEA exhibits an adjustable lower critical solution temperature (LCST) of about 34-42 degrees C depending on the grafting degree. More interestingly, the work provided an interesting phenomenon where the HPEA backbones exhibited strong blue photoluminescence.

Barrier property and structure of acrylonitrile/acrylic copolymers

A series of acrylonitrile (AN) copolymers with methyl acrylate (MA) or ethyl acrylate (EA) as comonomer (5-23 wt%) was prepared by free-radical copolymerisation. The permeability coefficients of the copolymers to oxygen and carbon dioxide were measured at 1.0 MPa and at 30 degrees C, and those to water vapor also measured at 100% relative humidity and at 30 degrees C. All the AN/acrylic copolymers are semicrystalline. As the acrylate content increase, the permeability coefficients of the copolymers to oxygen and carbon dioxide are increased progressively, but those to water vapor are decreased progressively. The gas permeability coefficients of the polymers were correlated with free-volume fractions or the ratio of free volume to cohesive energy.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.