A parallel implementation for parameter estimation with implicit models

Parameter estimation and data regression represent special classes of optimization problems. Often, nonlinear programming methods can be tailored to take advantage of the least squares, or more generally, the maximum likelihood, objective function. In previous studies we have developed large-scale nonlinear programming methods that are based on tailored quasi-Newton updating strategies and matrix decomposition of the process model. The resulting algorithms converge in a reduced space of the parameters while simultaneously converging the process model. Moreover, tailoring the method to least squares functions leads to significant improvements in the performance of the algorithm. These approaches can be very efficient for both explicit and implicit models (i.e. problems with small and large degrees of freedom, respectively). In the latter case, degrees of freedom are proportional to a potential large number of data sets. Applications of this case include errors-in-all-variables estimation, data reconciliation and identification of process parameters. To deal with this structure, we apply a decomposition approach that performs a quadratic programming factorization for each data set. Because these are small components of large problems, an efficient and reliable algorithm results. These methods have generally been implemented on standard von Neumann architectures and few studies exist that exploit the parallelism of nonlinear programming algorithms. It is therefore interesting to note that for implicit model parameter estimation and related process applications, this approach can be quite amenable to parallel computation, because the major cost occurs in matrix decompositions for each data set. Here we describe an implementation of this approach on the Alliant FX/8 parallel computer at the Advanced Computing Research Facility at Argone National Laboratory. Special attention is paid to the architecture of this machine and its effect on the performance of the algorithm. This approach is demonstrated on five small, undetermined regression problems as well as a larger process example for simultaneous data reconciliation and parameter estimation.     

Reduced graphene oxide conjugated Cu2O nanowire mesocrystals for high-performance NO2 gas sensor

Reduced graphene oxide (rGO)-conjugated Cu(2)O nanowire mesocrystals were formed by nonclassical crystallization in the presence of GO and o-anisidine under hydrothermal conditions. The resultant mesocrystals are comprised of highly anisotropic nanowires as building blocks and possess a distinct octahedral morphology with eight {111} equivalent crystal faces. The mechanisms underlying the sequential formation of the mesocrystals are as follows: first, GO-promoted agglomeration of amorphous spherical Cu(2)O nanoparticles at the initial stage, leading to the transition of growth mechanism from conventional ion-by-ion growth to particle-mediated crystallization; second, the evolution of the amorphous microspheres into hierarchical structure, and finally to nanowire mesocrystals through mesoscale transformation, where Ostwald ripening is responsible for the growth of the nanowire building blocks; third, large-scale self-organization of the mesocrystals and the reduction of GO (at high GO concentration) occur simultaneously, resulting in an integrated hybrid architecture where porous three-dimensional (3D) framework structures interspersed among two-dimensional (2D) rGO sheets. Interestingly, "super-mesocrystals" formed by 3D oriented attachment of mesocrystals are also formed judging from the voided Sierpinski polyhedrons observed. Furthermore, the interior nanowire architecture of these mesocrystals can be kinetically controlled by careful variation of growth conditions. Owing to high specific surface area and improved conductivity, the rGO-Cu(2)O mesocrystals achieved a higher sensitivity toward NO(2) at room temperature, surpassing the performance of standalone systems of Cu(2)O nanowires networks and rGO sheets. The unique characteristics of rGO-Cu(2)O mesocrystal point to its promising applications in ultrasensitive environmental sensors.     

Effects of metal salts on polymerization. Part IV. Polymerization of N-vinylcarbazole initiated by oxidizing metal nitrates

The polymerization of N-vinylcarbazole (VCZ) in ethylene dichloride, acetone, benzene, and dioxane with cupric nitrate, ferric nitrate, and ceric ammonium nitrate as catalyst was studied. In all cases the polymerization seemed to be of a cationic nature, judged by copolymerization with styrene. Electron spin resonance (ESR) spectroscopy was made for the polymerization system and also for a system containing N-ethylcarbazole instead of VCZ. Singlet ESR spectra were observed for all systems containing ceric salt and for some systems containing ferric salt but not for systems containing cupric salt. The ESR spectra indicated the formation of an ion radical by electron transfer between the oxidizing metal salt and the carbazole derivatives. Mechanisms of initiation other than electron transfer were less likely, and it was concluded that the initiation process was most likely to be of the electron transfer type.     

Graphical analysis of styrenated linseed oils

A graphical method for the analysis of styrenated linseed oils prepared by reaction of linseed oil with gaseous styrene is described. The method allows détermination of the percentage of styrene from refractive index and density.     

Rearrangements of the TMS derivatives of acylglycines

The investigation of a series of 18 acylglycines as their mono- and diTMS derivatives has revealed a set of common ions at [M–28]⁺˙, [M–43]⁺, [M–44]⁺˙ and [M–59]⁺. These ions are difficult to explain on the basis of simple cleavage of the molecular ion and were shown to be the result of rearrangement processes. The evidence for the rearrangements was derived from studies using deuterium and carbon-13 labeling as well as high resolution mass spectrometry. The losses from the molecular ion were shown to be a combination of CH₃ from the TMS moiety and CO or CO₂ from the carbonyl of the glycine portion of the molecules. An understanding of these rearrangements should aid others in the interpretation of acylglycines of unknown structure.