Dissertations / Theses on the topic 'Infrared emission spectra'

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.

Currently there is no known method for accurately measuring the temperature of the gas phase of combustion products within a solid fuel flame. The industry standard is a suction pyrometer and thermocouple which is intrusive, both spatially and temporally averaging, and difficult to use. In this work a new method utilizing the spectral emission from water vapor is investigated through modeling and experimental measurements. This method was demonstrated along a 0.75m line of sight, averaged over 1 minute in the products of a natural gas flame but has the potential to produce a spatial resolution on the order of 5 cm and a temporal resolution of less than 1 millisecond. The method employs the collection of infrared emission from water vapor over discrete wavelength bands and then uses the ratio of those emissions to infer temperature. A 12.5 mm lens has been positioned within a water cooled probe to focus flame product gas emission into an optical fiber where the light is transmitted to a Fourier Transform Infrared Spectrometer (FTIR). The same optical setup was also used to collect light from a black body cavity at a known temperature in order to calibrate the spectral sensitivity of the optical system and FTIR detector. Experiments were conducted in the product gas of a 150 kWth methane flame comparing the optical emission results to a suction pyrometer with type K thermocouple. The optical measurement produced gas temperatures approximately 1 - 4% higher than the suction pyrometer. Broadband background emission was also seen by the optical measurement and was removed assuming grey body radiation. This background emission can be used to determine particle emission temperature and intensity. Additional work will be needed to demonstrate the method under conditions with significant particle emission. Additional work is also needed to demonstrate the work over a smaller path length and shorter time scale.

Gas giant exoplanets known as hot Jupiters orbit close to their parent stars and are heated to high temperatures. Their infrared spectra, measured by photometry during secondary eclipses, are dominated by carbon monoxide and methane, the principle reservoirs of carbon on these planets. The relative CO and CH[sub4] abundances inform us about temperature and pressure conditions and also about mixing by global winds driven by intense but asymmetric heating for these tidally locked bodies. Emission spectra collected during secondary eclipses, as the hot Jupiter passes behind its parent star, in principle allows a determination of the CO:CH[sub4] concentration ratio. Since hot Jupiters exist at temperatures of order 700 K, accurate model atmospheres require high temperature line lists for relevant molecules, for which existing data bases are apparently incomplete. Since the outer atmospheres of hot Jupiters are bombarded by intense ultraviolet radiation and energetic particles, there may even be a significant degree of ionization and non-equilibrium populations among the various molecular levels. Here we present high temperature emission spectra of CO obtained from a microwave discharge plasma, where the source of CO was carbon dioxide that dissociates under microwave heating. The spectrum was measured in the range 1800-2400 cm[super-1] at a resolution of 0.1 cm[super-1]. Vibrational transitions originating in up to the 13th vibrational level of the X [super1]greek upper case letter sigma][super+] ground electronic term were observed. From the J values for maximum intensity lines within the rotational fine structure, we obtain a temperature estimate of ~700 K, which is comparable to the atmospheric conditions of hot-Jupiters.<br>ID: 030423212; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (M.S.)--University of Central Florida, 2011.; Includes bibliographical references (p. 79-80).<br>M.S.<br>Masters<br>Physics<br>Sciences

Les sources infrarouges sont indispensables à la détection locale de gaz dans de nombreux domaines, que ce soit pour l'environnement (détection de polluants et gaz à effets de serre) ou la défense (détection de menaces biologiques et chimiques). Elles sont également nécessaires en tant que mires de calibration pour le développement de caméras multispectrales infrarouges. Pour toutes ces applications, il est nécessaire de disposer de sources performantes, capables d'émettre un rayonnement spécifique dans une direction donnée. L'objectif de cette thèse est de concevoir des sources thermiques infrarouges compactes et à coût modéré, à spectre accordable et à pertes réduites, pouvant être juxtaposées dans un même dispositif. Pour cela, ces travaux s'organisent autour de deux axes. Le premier concerne l'étude de nouveaux matériaux nanostructurés résonants, appelés métamatériaux ou métasurfaces selon les directions de la structuration, permettant de contrôler l'émissivité spectrale et spatiale afin de maîtriser la réponse spectrale en tout point. Cette étude repose à la fois sur des simulations numériques et sur des mesures expérimentales et démontre le potentiel de ces résonateurs pour la conception de sources thermiques accordables. Cependant, ces matériaux étant composés de métal, ils présentent des pertes par absorption dans l'infrarouge qui limitent leurs performances. Le deuxième axe de recherche est alors de gérer les pertes liées à l'utilisation de métaux grâce à une ingénierie des champs dans des métamatériaux, menant à des émissions spectralement très fines. Les résultats obtenus sur ce contrôle des pertes ouvrent de nombreuses perspectives pour tout le domaine des métamatériaux<br>Infrared sources are essential for local gas detection for civil applications (detection of pollutant and greenhouse gas) or military applications (detection of chemical and biological threats). They are also used as calibration targets for the development of multispectral infrared cameras. For these applications, the sources must be efficient and able to emit a specific light in a given direction. The aim of this thesis is to develop infrared thermal emitters with the following features: low cost with a compact volume, with a tunable spectral response and low losses, able to be juxtaposed on the same device. This work begins with the study of new resonant nanostructured materials, called metamaterials or metasurfaces according to the direction of the structuration, that spectrally and spatially control the emitted light up to the wavelength scale. This study relies on numerical simulations and experimental measurements and demonstrates the potential of these resonators as tunable thermal sources. However, due to the use of metals in these materials, their performance is limited by metal losses. The second study of this work is then to deal with these losses thanks to a field engineering in metamaterials, leading to very narrow spectral responses. The results on this loss control open up promising breakthroughs in the plasmonic and metamaterials field

The present work belongs to the PRANA project, the first extensive field campaign of observation of atmospheric emission spectra covering the Far InfraRed spectral region, for more than two years. The principal deployed instrument is REFIR-PAD, a Fourier transform spectrometer used by us to study Antarctic cloud properties. A dataset covering the whole 2013 has been analyzed and, firstly, a selection of good quality spectra is performed, using, as thresholds, radiance values in few chosen spectral regions. These spectra are described in a synthetic way averaging radiances in selected intervals, converting them into BTs and finally considering the differences between each pair of them. A supervised feature selection algorithm is implemented with the purpose to select the features really informative about the presence, the phase and the type of cloud. Hence, training and test sets are collected, by means of Lidar quick-looks. The supervised classification step of the overall monthly datasets is performed using a SVM. On the base of this classification and with the help of Lidar observations, 29 non-precipitating ice cloud case studies are selected. A single spectrum, or at most an average over two or three spectra, is processed by means of the retrieval algorithm RT-RET, exploiting some main IR window channels, in order to extract cloud properties. Retrieved effective radii and optical depths are analyzed, to compare them with literature studies and to evaluate possible seasonal trends. Finally, retrieval output atmospheric profiles are used as inputs for simulations, assuming two different crystal habits, with the aim to examine our ability to reproduce radiances in the FIR. Substantial mis-estimations are found for FIR micro-windows: a high variability is observed in the spectral pattern of simulation deviations from measured spectra and an effort to link these deviations to cloud parameters has been performed.

In my PHD work I explored the links between the physical properties of interstellar dust and other components of nearby spiral galaxies especially their stellar content. I worked on 46 disk galaxies from KINGFISH with IRAC/MIPS/PACS/SPIRE maps (3.6 - 500 microns). A bias is usually introduced in estimating disk orientations by using only a single surface brightness isophote. Thus I devised different surface brightness levels separated by constant steps in surface brightness and extracted isophotes at these levels in all FIR maps as well as in all IRAC 4.5 microns maps. To further assess the coherence of the shapes of isophotes across galactic disks, I built a quantitative indicator of the difference in shape between two ellipses with same center and same semi-major axis.I defined an acceptable level of difference between isophote shapes, by comparing disk orientations found in litterature. Using this level, I found regions inside the galactic disks where the isophotal shapes are similar. From these, I extracted one disk orientation per wavelength band. I found in the vast majority of the disk galaxy maps, be it dominated by stellar or dust emission, that a large fraction of the isophotes I extracted are coherent with the idea of an underlying disk. Comparing, for each galaxy, disk orientations extracted at all wavelengths, I found evidence in 20 galaxies out of 46, that on radial ranges as large as 1/3 of the visible disk (as measured by R25), the shapes of isophotes are morphologically similar. Thus for these 20 galaxies I devised consistent disk orientations both for the stellar and dust content. These 20 galaxies are less luminous, less emitting in the IR w.r.t. the optical, less barred, and characterized by later stage types than average. I also found that the disk orientations devised by my photometric method yield results more similar to H-alpha kinematic orientations than other photometric studies based on a single isophote level.Using the orientations I found and H-alpha dynamics disk orientations, I averaged azimuthally surface brightnesses to produce radial spectral energy distributions (SED) profiles. Once fitted with a cosmic dust emission model, they resulted in radial profiles of dust and stellar content properties. I found the dust intercepted power to be proportionnal to the product of the total dust mass and the average ISRF shining on dust. This former quantity is better correlated with the bolometric stellar luminosity than any of the dust mass or the dust heating ISRF separately. Thus the old stellar populations may be an important heating source for dust. The power intercepted by dust is also very well correlated with the total infrared power. The dust intercepts a larger quantity of power coming from stars in more actively star forming galaxies.Dust exhibit radial mass surface density profiles less well described by Sersic functions than stellar ones. When both profiles are well fitted by Sersic functions, stellar density profiles have smaller half mass radii than the isophotal optical radius (R25) separately in later type galaxies, but also in more quiescent galaxies. Sersic index and half mass radius distributions have larger widths for dust than for stellar surface density profiles.I also found that the ratio of dust over stellar surface density is an important factor to explain the variations with galactic morphological type of the ratio of dust intercepted power over the power emitted by old stellar populations. This later link could be intertwined with spiral structure strength in stage types later than 2.

Made available in DSpace on 2014-10-09T12:42:20Z (GMT). No. of bitstreams: 0<br>Made available in DSpace on 2014-10-09T13:59:41Z (GMT). No. of bitstreams: 0<br>Tese (Doutoramento)<br>IPEN/T<br>Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

碩士<br>國立臺灣大學<br>應用物理所<br>102<br>We present an ab initio method to study the sub-cycle dynamics of hydrogen, helium and neon atoms in near-infrared(NIR) laser fields subject to excitation by a single extreme ultraviolet attosecond pulse(SAP). We extended the self-interaction-free time-dependent density functional theory(TD-KLI-SIC) to describe multi-electron system and solve the time-dependent Kohn-Sham equations by time-dependent generalized pseudospectral(TDGPS) method. We calculated the photon emission spectra and population of several excited states as the function of the time delay between the NIR pulse and SAP. The phenomena can be explain by two-photon absorption.

A novel technique for measuring gas temperature and spectral particle emissivity in high-temperature gas-particle streams is presented. The main application of this optical sensor is to improve the process control of batch unit operations, such as steelmaking furnaces. The spectral emission profile of CO and CO2 and the continuous particle emission in the 3.5 to 5 μm wavelength region was recorded and analyzed in real time with a low-resolution passive sensor. The sensor consisted of light collecting optics, a dispersion element (grating spectrometer) and a 64-pixel pyroelectric array. Wavelength and radiance calibrations were performed. The temperature of the gas-particle medium (Tg+p) followed from the least-squares minimization of the difference between the measured radiance in the 4.56-4.7 μm region –which saturates due to the large CO2 concentrations and path lengths in industrial furnaces– and the corresponding blackbody radiance. Particle emissivity (εp) was calculated at 3.95 μm from an asymptotic approximation of the Radiative Transfer Equation that yields the emerging radiance from a semi-infinite particle cloud. The major source of error in the magnitude of Tg+p and εp could come from particle scattering. Through the method of embedded invariance an expression was developed to estimate the lowering effect of particle size and volume fraction on the saturation of the 4.56-4.7 μm CO2 emission region. An iterative procedure for correcting the values of the gas-particle temperature and particle emissivity was applied to the datasets from the two industrial tests. Results from the measurement campaigns with the infrared sensor prototype at two full-scale furnaces are presented. A proof-of-concept test at a coal-fired boiler for electricity production was followed by more extensive measurements at a Basic Oxygen Furnace (BOF) for steelmaking. The second test provided temperature and particle emissivity profiles for eight heats, which highlighted the simplicity of the technique in obtaining in-situ measurements for modeling studies. Through the analysis of the particle emissivity profile in the BOF and the definition of a new variable –the minimum carbon time– a novel end-point strategy to stop the injection of high-purity oxygen during low-carbon heats in BOF converters was proposed.