The infrar-red spectra of gaseous HC=CCHO, DC=CCHO, HC=CCDO and the Raman spectrum of liquid HC=CCHO has been measured and analysed to give the normal vibrational frequencies of the molecule in the electronic ground state. The electronic band spectrum of propynal with origin at 4144 A has been photographed under low resolution in both emission and absorption and analysed in terms of the vibrational frequencies associated with the combining electronic states. The origin band for both HC=CCHO and DC=CCHO has been photographed under high resolution and a partial rotational analysis is presented for this band. The transition responsible for the electronic spectrum has been identified as being of n ->3pi*, 3A"<- 1A' type, and the observed spectrum confirms the theoretical predictions as to the structure that results from a transition of this type. / Thesis / Doctor of Philosophy (PhD)

Adsorbed carbon monoxide is utilized as a double layer probe molecule because of its strong absorption in infrared region and because of the high sensitivity of the carbon-oxygen bond to changes in the environment local to the electrode surface. Potential Difference Infrared Spectroscopy was used to investigate the structural behavior of CO adsorbed on a platinum electrode. Carbon monoxide was found to be exclusively linear-bonded on platinum electrode in the presence of tetra-n alkylammonium perchlorate/acetonitrile. No bridge-bonded species were observed. It was also found that the IR peak position of adsorbed CO is linearly dependent upon applied electrode potential, in agreement with Electrochemical Stark effect. The Stark tuning rate of adsorbed CO was determined to be inversely proportional to electrolyte cation size. This quantitative relationship between the Stark tuning rate and cation size is the first time that this has been experimentally demonstrated. Statistical treatment proved that surface coverage influences the rate of infrared peak position shift. The effect of surface coverage upon the conformation of tetra-n-octylammonium cation was also observed. Data suggested that tetra-n-octylammonium cation changes its conformation with surface coverage / Master of Science

A 2 to 55 micron spectrum was obtained for sulfur in its three thermodynamic phases and at three temperatures covering a range of 320%. A temperature analysis was performed in order to identify the three infrared active fundamental vibrations in the Sg molecule. The analysis indicates that the infrared fundamentals are the observed bands located at 41.6 μ and 52.3 μ, which is in good agreement with the assignment of Scott, Mcullough and Kruse. Less conclusive results were found for the identification of third infrared fundamental. Intermolecular coupling in the crystal was found to have a pronounced effect on its infrared transmission spectrum. A Raman active fundamental of S_s was observed at 46.7 μ in the infrared spectra of both the crystal and liquid sulfur specimens. The identification of this band as a Raman fundamental of S_g was made from an analysis of the free S_g molecule in solution where this band did not appear. Further evidence of the strong intermolecular forces present in the crystal showed up as a shift in wavelength of the 52.3 μ band in solution to 50.6 μ in the solid state. A large number of overtone or combination bands not previously observed were found around the low frequency infrared fundamental. The extensive data collected in this experiment provided a check to the work of Scott, McCullough and Kruse. Splitting was observed in absorption bands at 41.6 μ and 52.3 μ where no splitting was seen for the 21.5 μ band. These observations seem to contradict Scott’s assignment as this assignment designated the 21.5 μ and 52.3 μ fundamentals as degenerate and the 41.6 μ infrared fundamental as non-degenerate. The bands occurring at 41.6 μ and 50.6 μ in the crystal were so intense as to seemingly rule out its application to infrared systems below 55 microns. / M. S.

Thesis (MScIng)--University of Stellenbosch, 2003. / ENGLISH ABSTRACT: The emission spectrum measured in the middle infrared (IR) band from the plume of a rocket can be used to identify rockets and track inbound missiles. It is useful to test the stealth properties of the IR fingerprint of a rocket during its design phase without needing to spend excessive amounts of money on field trials. The modelled predictions of the IR spectra from selected rocket motor design parameters therefore bear significant benefits in reducing the development costs. In a recent doctorate study it was found that a fundamental approach including quantum-mechanical and computational fluid dynamics (CFD) models was not feasible. This is first of all due to the complexity of the systems and secondly due to the inadequate calculation speeds of even the most sophisticated modern computers. A solution was subsequently investigated by use of the ‘black-box’ model of a multi-layer perceptron feed-forward neural network with a single hidden layer consisting of 146 nodes. The input layer of the neural network consists of 18 rocket motor design parameters and the output layer consists of 146 IR absorbance variables in the range from 2 to 5 μm wavelengths. The results appeared promising for future investigations. The available data consist of only 18 different types of rocket motors due to the high costs of generating the data. The 18 rocket motor types fall into two different design classes, the double base (DB) and composite (C) propellant types. The sparseness of the data is a constraint in building adequate models of such a multivariate nature. The IR irradiance spectra data set consists of numerous repeat measurements made per rocket motor type. The repeat measurements form the pure error component of the data, which adds stability to training and provides lack-of-fit ANOVA capabilities. The emphasis in this dissertation is on comparing the feed-forward neural network model to the linear and neural network partial least squares (PLS) modelling techniques. The objective is to find a possibly more intuitive and more accurate model that effectively generalises the input-output relationships of the data. PLS models are known to be robust due to the exclusion of redundant information from projections made to primary latent variables, similarly to principal components (PCA) regression. The neural network PLS techniques include feed-forward sigmoidal neural network PLS (NNPLS) and radial-basis functions PLS (RBFPLS). The NNPLS and RBFPLS algorithms make use of neural networks to find non-linear functional relationships for the inner PLS models of the NIPALS algorithm. Error-based neural network PLS (EBNNPLS) and radial-basis function network PLS (EBRBFPLS) are also briefly investigated, as these techniques make use of non-linear projections to latent variables. A modification to the orthogonal least squares (OLS) training algorithm of radial-basis functions is developed and applied. The adaptive spread OLS algorithm (ASOLS) allows for the iterative adaptation of the Gaussian spread parameters found in the radial-basis transfer functions. Over-fitting from over-parameterisation is controlled by making use of leaveone- out cross-validation and the calculation of pseudo-degrees of freedom. After cross-validation the overall model is built by training on the entire data set. This is done by making use of the optimum parameterisation obtained from cross-validation. Cross-validation also gives an indication of how well a model can predict data unseen during training. The reverse problem of modelling the rocket propellant chemical compositions and the rocket physical design parameters from the IR irradiance spectra is also investigated. This problem bears familiarity to the field of spectral multivariate calibration. The applications in this field readily make use of PLS and neural network modelling. The reverse problem is investigated with the same modelling techniques applied to the forward modelling problem. The forward modelling results (IR spectrum predictions) show that the feedforward neural network complexity can be reduced to two hidden nodes in a single hidden layer. The NNPLS model with eleven latent dimensions outperforms all the other models with a maximum average R2-value of 0.75 across all output variables for unseen data from cross-validation. The explained variance for the output data of the overall model is 94.34%. The corresponding explained variance of the input data is 99.8%. The RBFPLS models built using the ASOLS training algorithm for the training of the radialbasis function inner models outperforms those using K-means and OLS training algorithms. The lack-of-fit ANOVA tests show that there is reason to doubt the adequacy of the NNPLS model. The modelling results however show promise for future development on larger, more representative data sets. The reverse modelling results show that the feed-forward neural network model, NNPLS and RBFPLS models produce similar results superior to the linear PLS model. The RBFPLS model with ASOLS inner model training and 5 latent dimensions stands out slightly as the best model. It is found that it is feasible to separately find the optimum model complexity (number of latent dimensions) for each output variable. The average R2-value across all output variables for unseen data is 0.43. The average R2-value for the overall model is 0.68. There are output variables with R2-values of over 0.8. The forward and reverse modelling results further show that dimensional reduction in the case of PLS does produce the best models. It is found that the input-output relationships are not highly non-linear. The non-linearities are largely responsible for the compensation of both the DB- and C-class rocket motor designs predictions within the overall model predictions. For this reason it is suggested that future models can be developed by making use of a simpler, more linear model for each rocket class after a class identification step. This approach however requires additional data that must be acquired. / AFRIKAANSE OPSOMMING: Die emissiespektra van die uitlaatpluime van vuurpyle in die middel-infrarooi (IR) band kan gebruik word om die vuurpyle te herken en om inkomende vuurpyle op te spoor. Dit is nuttig om die uitstralingseienskappe van ‘n vuurpyl se IR afdruk te toets, sonder om groot bedrae geld op veldtoetse te spandeer. Die gemodelleerde IR spektrale voorspellings vir ‘n bepaalde stel vuurpylmotor ontwerpsparameters kan dus grootliks bydra om motorontwikkelingskostes te bemoei. In ‘n onlangse doktorale studie is gevind dat ‘n fundamentele benadering van kwantum-meganiese en vloeidinamika-modelle nie lewensvatbaar is nie. Dit is hoofsaaklik as gevolg van die onvoldoende vermoë van selfs die mees gesofistikeerde moderne rekenaars. ‘n Moontlike oplossing tot die probleem is ondersoek deur gebruik te maak van ‘n multilaag perseptron voorwaartse neurale netwerk met 146 nodes in ‘n enkele versteekte laag. Die laag van invoer veranderlikes bestaan uit agtien vuurpylmotor ontwerpsparameters en die uitvoerlaag bestaan uit 146 IR-absorbansie veranderlikes in die reeks golflengtes vanaf 2 tot 5 μm. Dit het voorgekom dat die resultate belowend lyk vir toekomstige ondersoeke. Weens die hoë kostes om die data te genereer bestaan die beskikbare data uit slegs agtien verskillende tipes vuurpylmotors. Die agtien vuurpyl tipes val verder binne twee ontwerpsklasse, naamlik die dubbelbasis (DB) en saamgestelde (C) dryfmiddeltipes. Die yl data bemoeilik die bou van doeltreffende multiveranderlike modelle. Die datastel van IR uitstralingspektra bestaan uit herhaalde metings per vuurpyltipe. Die herhaalde metings vorm die suiwer fout komponent van die data. Dit verskaf stabilitieit tot die opleiding op die data en verder die vermoë om ‘n analise van variansie (ANOVA) op die data uit te voer. In hierdie tesis lê die klem op die vergelyking tussen die voorwaartse neurale netwerk en die lineêre en neurale netwerk parsiële kleinste kwadrate (PLS) modelleringstegnieke. Die doel is om ‘n moontlik meer insiggewende en akkurate model te vind wat effektief die in- en uitvoer verhoudings kan veralgemeen. Dit is bekend dat PLS modelle meer robuus kan wees weens die weglating van oortollige inligting deur projeksies op hoof latente veranderlikes. Dit is analoog aan hoofkomponente (PCA) regressie. Die neurale netwerk PLS-tegnieke sluit in voorwaartse sigmoïdale neurale netwerk PLS (NNPLS) en radiale-basis funksies PLS (RBFPLS). Die NNPLS en RBFPLS algoritmes maak gebruik van die neurale netwerke om nie-lineêre funksionele verbande te kry vir die binne PLS-modelle van die nie-lineêre iteratiewe parsiële kleinste kwadrate (NIPALS) algoritme. Die fout-gebaseerde neurale netwerk PLS (EBNNPLS) en radiale-basis funksies PLS (EBRBFPLS) is ook weens hulle nie-lineêre projeksies na latente veranderlikes kortiliks ondersoek. ‘n Aanpassing tot die ortogonale kleinste kwadrate (OLS) opleidingsalgoritme vir radiale-basis funksies is ontwikkel en toegepas. Die aangepaste algoritme (ASOLS) behels die iteratiewe aanpassing van die verspreidingsparameters binne die Gauss-funksies van die radiale-basis transformasie funksies. Die oormatige parameterisering van ‘n model word beheer deur kruisvalidering met enkele weglatings en die berekening van pseudo-vryheidsgrade. Na kruisvalidering word die algehele model gebou deur opleiding op die volledige datastel. Dit word gedoen deur van die optimale parameterisering gebruik te maak wat deur kruisvalidering bepaal is. Kruisvalidering gee ook ‘n goeie aanduiding van hoe goed ‘n model ongesiende data kan voorspel. Die modellering van die vuurpyle se chemiese en fisiese ontwerpsparameters (omgekeerde probleem) is ook ondersoek. Hierdie probleem is verwant aan die veld van spektrale multiveranderlike kalibrasie. Die toepassings in die veld maak gebruik van PLS en neurale netwerk modelle. Die omgekeerde probleem word dus ondersoek met dieselfde modelleringstegnieke wat gebruik is vir die voorwaartse probleem. Die voorwaartse modelleringsresultate (IR voorspellings) toon dat die kompleksiteit van die voorwaartse neurale netwerk tot twee versteekte nodes in ‘n enkele versteekte laag gereduseer kan word. Die NNPLS model met elf latente dimensies vaar die beste van alle modelle, met ‘n maksimum R2-waarde van 0.75 oor alle uitvoer veranderlikes vir die ongesiende data (kruisvalidering). Die verklaarde variansie vir die uitvoer data vanaf die algehele model is 94.34%. Die verklaarde variansie van die ooreenstemmende invoer data is 99.8%. Die RBFPLS modelle wat gebou is deur van die ASOLS algoritme gebruik te maak om die PLS binne modelle op te lei, vaar beter in vergelyking met die K-gemiddeldes en OLS opleidingsalgoritmes. Die toetse wat ‘n ‘tekort-aan-passing’ ANOVA behels, toon dat daar rede is om die geskiktheid van die NNPLS model te wantrou. Die modelleringsresultate lyk egter belowend vir die toekomstige ontwikkeling van modelle op groter, meer verteenwoordigde datastelle. Die omgekeerde modellering toon dat die voorwaartse neurale netwerk, NNPLS en RBFPLS modelle soortgelyke resultate produseer wat die lineêre PLS model s’n oortref. Die RBFPLS model met ASOLS opleiding van die PLS binne modelle word beskou as die beste model. Dit is lewensvatbaar om die optimale modelkompleksiteite van elke uitvoerveranderlike individueel te bepaal. Die gemiddelde R2-waarde oor alle uitvoerveranderlikes vir ongesiende data is 0.43. Die gemiddelde R2-waarde vir die algehele model is 0.68. Daar is van die uitvoer veranderlikes wat R2-waardes van 0.8 oortref. Die voor- en terugwaartse modelleringsresultate toon verder dat dimensionele reduksie in die geval van PLS die beste modelle lewer. Daar is ook gevind dat die nie-lineêriteite grootliks vergoed vir die voorspellings van beide DB- en Ctipe vuurpylmotors binne die algehele model. Om die rede word voorgestel dat toekomstige modelle ontwikkel kan word deur gebruik te maak van eenvoudiger, meer lineêre modelle vir elke vuurpylklas nadat ‘n klasidentifikasiestap uitgevoer is. Die benadering benodig egter addisionele praktiese data wat verkry moet word.

No description available.

No description available.

No description available.

A study of how the matrix environment impacts the structure and IR spectra of N-acetylglycine conformers. The conformational composition of this compound is determined according to an analysis of the FTIR spectra of N-acetylglycine isolated in low temperature argon matrices. Bands of three N-acetylglycine conformers are identified based on the spectra: one major and two minor. The structure of all observed conformers is stabilized by different intramolecular hydrogen bonds. The Gibbs free energies of the conformers were calculated (CCSD(T)/CBS method), and these energy values were used to calculate conformer population at a temperature of 360 K, of which 85.3% belonged to the main conformer, and 9.6% and 5.1% to the minor conformers. We also determined the size and shape of the cavities that form when the N-acetylglycine conformers are embedded in the argon crystal during matrix deposition. It is established that the most energetically favorable cavity for the planar main conformer is the cavity that forms when 7 argon atoms are replaced. At the same time, bulky minor conformers were embedded into cavities that correspond to 8 removed argon atoms. We calculated the complexation energy between argon clusters and conformers, and the deformation energy of the argon crystal and the N-acetylglycine conformers. The matrix-induced shifts to the conformer oscillation frequency are calculated. Published by AIP Publishing.

This dissertation describes computational and experimental studies of synthetic complexes that model the active site of the iron-iron hydrogenase [FeFe]H2ase enzyme. Simple dinuclear iron dithiolate complexes act as functional models of the ironiron hydrogenase enzyme by catalyzing isotopic exchange in D2/H2O mixtures. Density Functional Theory (DFT) calculations and new experiments have been performed that suggest reasonable mechanistic explanations for this reactivity. Evidence for the existence of an acetone derivative of the di-iron complex, as suggested by theory, is presented. Bis-phosphine substituted dinuclear iron dithiolate complexes react with the electrophilic species, H+ and Et+ (Et+ = CH3CH2 +) with differing regioselectivity; H+ reacts to form a 3c-2eâ Fe-H-Fe bond, while Et+ reacts to form a new C-S bond. The instability of a bridging ethyl complex is attributed to the inability of the ethyl group, in contrast to a hydride, to form a stable 3c-2eâ bond with the two iron centers. Gas-phase density functional theory calculations are used to predict the solutionphase infrared spectra for a series of CO and CN-containing dinuclear iron complexes dithiolate. It is shown that simple linear scaling of the computed C-O and C-N stretching frequencies yields accurate predictions of the experimentally determined ν(CO) and ν(CN) values. An N-heterocyclic carbene containing [FeFe]H2ase model complex, whose X-ray structure displays an apical carbene, is shown to undergo an unexpected simultaneous two-electron reduction. DFT shows, in addition to a one-electron Fe-Fe reduction, that the aryl-substituted N-heterocyclic carbene can accept a second electron more readily than the Fe-Fe manifold. The juxtaposition of these two one-electron reductions resembles the [FeFe]H2ase active site with an FeFe di-iron unit joined to the electroactive 4Fe4S cluster. Simple synthetic di-iron dithiolate complexes synthesized to date fail to reproduce the precise orientation of the diatomic ligands about the iron centers that is observed in the molecular structure of the reduced form of the enzyme active site. Herein, DFT computations are used for the rational design of synthetic complexes as accurate structural models of the reduced form of the enzyme active site.

Thesis (Ph. D.)--Institute of Paper Chemistry, 1976. / Includes bibliographical references (p. 138-139).