Academic literature on the topic 'Infrared emission spectra'

Several hydrides of Group 2 and 12 elements were generated in the gas phase using an emission source that combines an electrical discharge with a high temperature furnace, and their high-resolution infrared emission spectra were recorded with a Fourier transform spectrometer. Two classes of molecules were studied: <em>a)</em> diatomic metal hydrides BeH, MgH, CaH, SrH, ZnH and CdH; <em>b)</em> linear triatomic metal hydrides BeH₂, MgH₂, ZnH₂ and HgH₂. <br /><br /> Infrared emission spectra of BeH, MgH, CaH, SrH, ZnH and CdH free radicals contained several vibration-rotation bands in their ²SIGMA⁺ ground electronic state. The new data were combined with all the previous ground state data from diode laser infrared spectra and pure rotation spectra available in the literature. Spectroscopic constants, i. e. , vibrational band origins, rotational, centrifugal distortion, and spin-rotation interaction constants, were determined for each observed vibrational level by least-squares fitting of all the data. In addition, the data from all isotopologues were fitted simultaneously using the empirical Dunham-type energy level expression for ²SIGMA⁺ states, and correction parameters due to the breakdown of the Born-Oppenheimer approximation were determined. The equilibrium internuclear distances (<em>r</em>_e) of ⁹BeH, ²⁴MgH, ⁴⁰CaH, ⁸⁸SrH, ⁶⁴ZnH and ¹¹⁴CdH were determined to be 1. 342424(2), 1. 729721(1), 2. 002360(1), 2. 146057(1), 1. 593478(2) and 1. 760098(3) angstroms, respectively, and the corresponding <em>r</em>^e distances for ⁹BeD, ²⁴MgD, ⁴⁰CaD, ⁸⁸SrD, ⁶⁴ZnD and ¹¹⁴CdD are 1. 341731(2), 1. 729157(1), 2. 001462(1), 2. 145073(1), 1. 593001(2) and 1. 759695(2) angstroms, respectively. <br /><br /> Gaseous BeH², MgH², ZnH² and HgH² molecules were discovered and unambiguously identified by their high-resolution infrared emission spectra. The &nu;₃ antisymmetric stretching fundamental band and several hot bands in the &nu;₃ region were rotationally analyzed, and spectroscopic constants were obtained for almost all naturally-occurring isotopologues. The rotational constants of the 000 ground states were used to determine the <em>r</em>₀ internuclear distances. For BeH₂, ZnH₂, ZnD₂, HgH₂ and HgD₂ molecules, the rotational constants of the 000, 100, 01¹0 and 001 levels were used to determine the equilibrium rotational constants (<em>B</em>_e) and the associated equilibrium internuclear distances <em>r</em>_e. The <em>r</em>_e distances of ZnH₂ and ZnD₂ differed by about 0. 01%, and those of HgH₂ and HgD₂ differed by about 0. 005%. These discrepancies were larger than the statistical uncertainties by one order of magnitude, and were attributed to the breakdown of the Born-Oppenheimer approximation.

We use spectroscopy to study infrared optoelectronic inter and intraband semiconductor carrier dynamics. The overall aim of this thesis was to study both III-V and Pb chalcogenide material systems in order to show their future potential use in infrared emitters. The effects of bandstructure engineering have been studied in the output characteristics of mid-IR III-V laser diodes to show which processes (defects, radiative, Auger and phonon) dominate and whether non-radiative processes can be suppressed. A new three-beam pump probe experiment was used to investigate interband recombination directly in passive materials. Experiments on PbSe and theory for non-parabolic near-mirror bands and non-degenerate statistics were in good agreement. Comparisons with HgCdTe showed a reduction in the Auger coefficient of 1-2 orders of magnitude in the PbSe. Using Landau confinement to model spatial confinement in quantum dots (QDs) "phonon bottlenecking" was studied. The results obtained from pump probe and cyclotron resonance saturation measurements showed a clear suppression in the cooling of carriers when Landau level separation was not resonant with LO phonon energy. When a bulk laser diode was placed in a magnetic field to produce a quasi quantum wire device the resulting enhanced differential gain and reduced Auger recombination lowered Ith by 30%. This result showed many peaks in the light output which occurred when the LO phonon energy was a multiple of the Landau level separation. This showed for the first time evidence of the phonon bottleneck in a working laser device. A new technique called time resolved optically detected cyclotron resonance, was used as a precursor to finding the earner dynamics within a spatially confined quantum dot. By moving to the case of a spatial QD using an optically detected intraband resonance it was possible to measure the energy separation interband levels and conduction and valence sublevels within the dot simultaneously. Furthermore this technique has been shown that the inhomogeneous broadening of the photoluminescence spectrum is not purely affected by just size and composition. We suggest that other processes such as state occupancy, In roughing, and exciton binding energies may account for the extra energy.

Thesis (MScIng)--University of Stellenbosch, 2003.<br>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.<br>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.

We report on high dispersion spectroscopy of the Ha absorption line of the cool DA white dwarf G 29 -38. This is the star for which a recently detected infrared excess has been suggested to be due to a possible brown dwarf companion by Zuckerman and Becklin (1986, 1987). Three echelle spectra obtained at the Multiple Mirror Telescope and at the Kitt Peak Mayall 4m telescope in 1987 December show no evidence for radial velocity variations larger than -'1.1 ± 8.7 km s -1 and are used to derive a weighted heliocentric radial velocity Vr = 33.7 ± 4.3 km s -1 for the white dwarf. No emission component from the hypothesized secondary star is detected. These negative results do not constitute strong evidence against the companion hypothesis, since the expected orbital velocity of the white dwarf component could be quite small, and the companion's line emission could be too faint to be detected. However, the observation of a sharp absorption line core restricts the possible rotation of the white dwarf to < 40 km s -1 and ensures that any surface magnetic field has a strength < 105 gauss. These results make it unlikely that the DA white dwarf has previously been in a cataclysmic variable accretion phase.

The procedures and results of experimental and/or theoretical studies of four transient molecules, GeO, WO, BeH, and MgH are reported in the thesis. Two of them, GeO and WO, are diatomic molecules composed of relatively heavy atoms, and the other two are diatomic molecules with hydrogen as one of their component atoms. The GeO species was generated using a high temperature furnace. The rovibrational spectrum of five isotopomers were detected in emission using a Bruker IFS 120 HR Fourier transform spectrometer. Combined-isotopomer Dunham-type molecular constants have been derived for GeO using the DSParFit computer program. Analysis shows that the Born-Oppenheimer approximation is valid, as expected, for a molecule containing heavy atoms. The WO molecule was generated using a microwave discharge cell, and the spectra of electronic transitions of various systems were detected in emission using both the Bruker IFS 120 HR Fourier transform spectrometer at Waterloo and the McMath Pierce One-Meter Fourier transform spectrometer at the National Solar Observatory in Arizona. The ground electronic state has been confirmed to be X³&#931;^- based on the analysis of seven 0-0 bands. BeH and MgH are typical molecules with hydrogen as one of their component atoms, and the effects of Born-Oppenheimer breakdown were expected. Both of these molecules have rotational perturbations in their excited electronic states. A 'new' method of data processing was used, i. e. , treating the electronic data as if they were from fluorescence series. Thus the harmful influence of the perturbed upper electronic states on the ground electronic state molecular constants is eliminated. By using the DSParFit computer program, accurate sets of combined-isotopomer Dunham-type molecular constants have been derived for the ground electronic states of the two molecules, and Born-Oppenheimer breakdown correction terms have been obtained.