Aujourd’hui, les difficultés pour établir des liens entre caractéristiques des nanomatériaux et réponses biologiques sont principalement issues du manque de contrôle de la synthèse des nanomatériaux, ne permettant pas de faire varier leurs paramètres physico-chimiques clés une à une.Pour identifier certains mécanismes gouvernant la toxicité des nanomatériaux nous avons utilisé un nanotube inorganique modèle dont la synthèse est bien contrôlée : les Ge-imogolites. Les effets de la longueur, du nombre de parois, de la cristallinité et de la composition chimique des Ge-imogolites ont été étudiés sur une bactérie des sols: Pseudomonas brassicacearum. Il a été identifié que la présence de sites réactifs (en bordure de tubes) induit une toxicité due à une interaction forte des nanotubes avec les cellules bactériennes, ainsi que la génération d’espèces réactives de l’oxygène. Ajouter des sites réactifs via la présence de défauts structuraux augmente la dégradation des tubes ainsi que la rétention d’éléments nutritifs essentiels, ce qui augmente leur toxicité. Enfin, l’ajout de fer dans leur structure transforme les Ge-imogolites en source de fer, qui sont dégradées et deviennent promoteurs de croissance. Dans tous ces cas, les interactions entre nanomatériaux et cellules ont été identifiées comme cruciales pour comprendre et prévenir les effets des nanomatériaux. Ce travail de thèse a également permis de mettre en avant la capacité de nouveaux outils pour le suivi de l’internalisation de nanomatériaux dans les organismes. / Only a few studies of (eco)toxicology linked the physico-chemical properties of nanoparticles to the toxicity mechanisms or the stress they induce. Moreover, no clear conclusions can be drawn at present because of the variability of nanoparticles used in studies. The present study used the inorganic Ge-imogolite nanotubes as a model compound. The toxic effects of length, number of walls, structural defects, and chemical composition were assessed towards the soil bacteria Pseudomonas brassicacearum. Several mechanisms modulating the toxicity of Ge-imogolite were then identified. Indeed, reactive sites at the tube ends induce a slight toxicity via a strong cell interaction and the generation of reactive oxygen species. Creating vacant sites on the surface of Ge-imogolite (ant thus increasing the number of reactive sites), appears to cause a deficiency of nutrients in the culture media correlated with a higher degradation of the tubes, leading to a high bacterial growth decrease. Finally, structural iron incorporation into Ge-imogolite transforms them into an iron source, being degraded and becoming growth promoters. In this work, the new tools capacities for the study of nanomaterials/cells interaction have been studied.

Les images hyperspectrales sont des images complexes qui ne peuvent être partitionnées avec succès en utilisant une seule méthode de classification. Les méthodes de classification non coopératives, paramétriques ou non paramétriques peuvent être classées en trois catégories : supervisée, semi-supervisée et non supervisée. Les méthodes paramétriques supervisées nécessitent des connaissances a priori et des hypothèses sur les distributions des données à partitionner. Les méthodes semi-supervisées nécessitent des connaissances a priori limitées (nombre de classes, nombre d'itérations), alors que les méthodes de la dernière catégorie ne nécessitent aucune connaissance. Dans le cadre de cette thèse un nouveau système coopératif et non supervisé est développé pour le partitionnement des images hyperspectrales. Son originalité repose sur i) la caractérisation des pixels en fonction de la nature des régions texturées et non-texturées, ii) l'introduction de plusieurs niveaux d'évaluation et de validation des résultats intermédiaires, iii) la non nécessité d'information a priori. Le système mis en ouvre est composé de quatre modules: Le premier module, partitionne l'image en deux types de régions texturées et non texturées. Puis, les pixels sont caractérisés en fonction de leur appartenance à ces régions. Les attributs de texture pour les pixels appartenant aux régions texturées, et la moyenne locale pour les pixels appartenant aux régions non texturées. Le deuxième module fait coopérer parallèlement deux classifieurs (C-Moyen floue : FCM et l'algorithme Adaptatif Incrémental Linde-Buzo-Gray : AILBG) pour partitionner chaque composante. Pour rendre ces algorithmes non supervisés, le nombre de classes est estimé suivant un critère basé sur la dispersion moyenne pondérée des classes. Le troisième module évalue et gère suivant deux niveaux les conflits des résultats de classification obtenus par les algorithmes FCM et AILBG optimisés. Le premier identifie les pixels classés dans la même classe par les deux algorithmes et les reportent directement dans le résultat final d'une composante. Le second niveau utilise un algorithme génétique (GA), pour gérer les conflits entre les pixels restant. Le quatrième module est dédié aux cas des images multi-composantes. Les trois premiers modules sont appliqués tout d'abord sur chaque composante indépendamment. Les composantes adjacentes ayant des résultats de classification fortement similaires sont regroupées dans un même sous-ensemble et les résultats des composantes de chaque sous-ensemble sont fusionnés en utilisant le même GA. Le résultat de partitionnement final est obtenu après évaluation et fusion par le même GA des différents résultats de chaque sous-ensemble. Le système développé est testé avec succès sur une grande base de données d'images synthétiques (mono et multi-composantes) et également sur deux applications réelles: la classification des plantes invasives et la détection des pins. / Hyperspectral and more generally multi-component images are complex images which cannot be successfully partitioned using a single classification method. The existing non-cooperative classification methods, parametric or nonparametric can be categorized into three types: supervised, semi-supervised and unsupervised. Supervised parametric methods require a priori information and also require making hypothesis on the data distribution model. Semi-supervised methods require some a priori knowledge (e.g. number of classes and/or iterations), while unsupervised nonparametric methods do not require any a priori knowledge. In this thesis an unsupervised cooperative and adaptive partitioning system for hyperspectral images is developed, where its originality relies i) on the adaptive nature of the feature extraction ii) on the two-level evaluation and validation process to fuse the results, iii) on not requiring neither training samples nor the number of classes. This system is composed of four modules: The first module, classifies automatically the image pixels into textured and non-textured regions, and then different features of pixels are extracted according to the region types. Texture features are extracted for the pixels belonging to textured regions, and the local mean feature for pixels of non-textured regions. The second module consists of an unsupervised cooperative partitioning of each component, in which pixels of the different region types are classified in parallel via the features extracted previously using optimized versions of Fuzzy C-Means (FCM) and Adaptive Incremental Linde-Buzo-Gray algorithm (AILBG). For each algorithm the number of classes is estimated according to the weighted average dispersion of classes. The third module is the evaluation and conflict management of the intermediate classification results for the same component obtained by the two classifiers. To obtain a final reliable result, a two-level evaluation is used, the first one identifies the pixels classified into the same class by both classifiers and report them directly to the final classification result of one component. In the second level, a genetic algorithm (GA) is used to remove the conflicts between the invalidated remaining pixels. The fourth module is the evaluation and conflict management in the case of a multi-component image. The system handles all the components in parallel; where the above modules are applied on each component independently. The results of the different components are compared, and the adjacent components with highly similar results are grouped within a subset and fused using a GA also. To get the final partitioning result of the multi-component image, the intermediate results of the subsets are evaluated and fused by GA. The system is successfully tested on a large database of synthetic images (mono and multi-component) and also tested on two real applications: classification of invasive plants and pine trees detection.

Cette thèse aborde le démélange en-ligne d’images hyperspectrales acquises par un imageur pushbroom, pour la caractérisation en temps réel du matériau bois. La première partie de cette thèse propose un modèle de mélange en-ligne fondé sur la factorisation en matrices non-négatives. À partir de ce modèle, trois algorithmes pour le démélange séquentiel en-ligne, fondés respectivement sur les règles de mise à jour multiplicatives, le gradient optimal de Nesterov et l’optimisation ADMM (Alternating Direction Method of Multipliers) sont développés. Ces algorithmes sont spécialement conçus pour réaliser le démélange en temps réel, au rythme d'acquisition de l'imageur pushbroom. Afin de régulariser le problème d’estimation (généralement mal posé), deux sortes de contraintes sur les endmembers sont utilisées : une contrainte de dispersion minimale ainsi qu’une contrainte de volume minimal. Une méthode pour l’estimation automatique du paramètre de régularisation est également proposée, en reformulant le problème de démélange hyperspectral en-ligne comme un problème d’optimisation bi-objectif. Dans la seconde partie de cette thèse, nous proposons une approche permettant de gérer la variation du nombre de sources, i.e. le rang de la décomposition, au cours du traitement. Les algorithmes en-ligne préalablement développés sont ainsi modifiés, en introduisant une étape d’apprentissage d’une bibliothèque hyperspectrale, ainsi que des pénalités de parcimonie permettant de sélectionner uniquement les sources actives. Enfin, la troisième partie de ces travaux consiste en l’application de nos approches à la détection et à la classification des singularités du matériau bois. / This PhD dissertation addresses the problem of on-line unmixing of hyperspectral images acquired by a pushbroom imaging system, for real-time characterization of wood. The first part of this work proposes an on-line mixing model based on non-negative matrix factorization. Based on this model, three algorithms for on-line sequential unmixing, using multiplicative update rules, the Nesterov optimal gradient and the ADMM optimization (Alternating Direction Method of Multipliers), respectively, are developed. These algorithms are specially designed to perform the unmixing in real time, at the pushbroom imager acquisition rate. In order to regularize the estimation problem (generally ill-posed), two types of constraints on the endmembers are used: a minimum dispersion constraint and a minimum volume constraint. A method for the unsupervised estimation of the regularization parameter is also proposed, by reformulating the on-line hyperspectral unmixing problem as a bi-objective optimization. In the second part of this manuscript, we propose an approach for handling the variation in the number of sources, i.e. the rank of the decomposition, during the processing. Thus, the previously developed on-line algorithms are modified, by introducing a hyperspectral library learning stage as well as sparse constraints allowing to select only the active sources. Finally, the third part of this work consists in the application of these approaches to the detection and the classification of the singularities of wood.

<p>Cadmium (Cd) is a highly mobile and toxic heavy metal that negatively affects plants, soil biota, animals and humans, even in very low concentrations. Currently, Cd contamination of cocoa produced in Latin American countries is a significant problem, as concentrations can exceed acceptable levels set by the European Union (0.5 mg/kg), sometimes by more than 10 times allowable levels. In South America, <i>Theobroma cacao</i> is an essential component of the basic market basket. This crop contributes to the Latin-American trade balance, as these countries export cacao and chocolate-based products to major consumer countries such as the United States and Europe. Some soil amendments can alter the bioavailability and uptake of Cd into edible plant tissues, though cacao plants can accumulate Cd without displaying any visible symptoms of phytotoxicity, which makes it difficult to determine if potential remediation strategies are successful. Currently, the only effective way to quantify Cd accumulation in plant tissues is via destructive post-harvest practices that are time-consuming and expensive. New hyperspectral imaging (HSI) technologies developed for use in high-throughput plant phenotyping are powerful tools for monitoring environmental stress and predicting the nutritional status in plants. Consequently, the experiments described in this thesis were conducted to determine if HSI technologies could be adapted for monitoring plant stress caused by Cd, and estimating its concentration in vegetative plant tissues. Two leafy green crops were used in these experiments, basil (<i>Ocimum basilicum L.</i> var. Genovese) and kale (<i>Brassica oleracea L</i>. var. Lacinato), because they are fast growing, and therefore, could serve as indicator crops on cacao farms. In addition, we expected these two leafy green crops would differ in their morphological responses to Cd stress. Specifically, we predicted that stress responses would be visible in basil, but not kale, which is known to be a hyperaccumulator. The plants were subject to four levels of soil Cd (0, 5, 10 and 15 ppm), and half of the pots were amended with biochar at a rate of 3% (v/v), as this amendment has previously been demonstrated to improve plant health and reduce Cd uptake. The experiments were conducted at Purdue’s new Controlled Environment Phenotyping Center (CEPF). The plants were imaged weekly and manual measurements of plant growth and development were collected at the same times, and concentrations of Cd as well as many other elements were determined after harvest. Fourteen vegetation indices generated using HSI images collected from the side and top view of plants were evaluated for their potential to identify subtle signs of plant stress due soil Cd and the biochar amendment. In addition, three mathematical models were evaluated for their potential to estimate Cd concentrations in the plant biomass and determine if they exceed safe standards (0.28 mg/kg) set by the Food and Agriculture Organization (FAO) for leafy greens. Results of these studies confirm that like many plants, these leafy green crops can accumulate Cd levels that are well above safety thresholds for human health, but exhibit few visible symptoms of stress. The normalized difference vegetation index (NDVI) and the chlorophyll index at the red edge (CI_RE) were the best indices for detecting Cd stress in these crops, and the plant senescence and reflectance index (PSRI) and anthocyanin reflectance index (ARI) were the best at detecting subtle changes in plant physiology due to the biochar amendment. The heavy metal stress index (HMSSI), developed exclusively for detecting heavy metal stress, was only able to detect Cd stress in basil when images were taken from the top view. Results of the mathematical models indicated that principal components analysis (PCA) and partial least squares (PLS) models overfit despite efforts to transform the data, indicating that they are not capable of predicting Cd concentrations in these crops at these levels. However, the artificial neural networks (ANN) was able to predict whether leafy greens had levels of Cd that were above or below critical thresholds suggested by the FAO, indicating that HSI could be further developed to predict Cd concentrations in plant tissues. Further research conducted in the field and in the presence of other environmental stress factors are needed to confirm the utility of these tools, and determine whether they can be adapted to monitor Cd uptake in cacao plants.</p>

Background Tarkett AB Ronneby (Sweden) is a flooring solutions company, recognized for the manufacturing and recycling of homogeneous plastic flooring. Tarkett AB recycles mainly installation spill and manufacturing defects. However, Tarkett AB is considering widening its recycling capabilities to include old and torn plastic floors which may contain impurities and banned substances or plastic floors of competing brands. To accomplish this, Tarkett is considering a completely new recycling line with an automated sorting process instead of the current manual process. Thus, Tarkett proposes a dissertation to conceptualize a new automated sorting system with added capacity and increased functionality. Purpose This work aims to investigate the current sorting process and introduce conceptual solutions for a new automated sorting process capable of identifying and separating plastic floors according to the manufacturer, type, condition, and external waste by using existing technology. Method The methods and tools used in this work are mainly based on a modified product development process. Starting with data collection of the current sorting process, performing a need-finding, and extracting requirements for an automated sorting process, investigating relevant technology, evaluating technology based on scientific literature and tests. The testing was conducted in collaboration with two companies. Near-infrared scanners were tested with Holger AB, while pattern recognition systems were tested with Vision-Geek. Finally, three concepts for the automated sorting process were developed and shown through flow charts and 2D-3D illustrations. Results The results of this work showed that it was possible to use near-infrared and pattern recognition for the separation of plastic floors. Besides, three conceptual solutions for an automated sorting process were generated and showcased with schematic graphs and 2D-3D illustrations. The concepts describe how the sorting process functions and what technology is used for each step of the process. Concept 1 and Concept 2 used both pattern recognition and spectroscopy methods. While Concept 3 only used spectroscopy methods. Moreover, spectroscopy methods were used to sort plastic floors by content while pattern recognition by appearance. Conclusions Recycling of torn and old plastic flooring can be beneficial for both the environment and the recycling industry. Yet, it presents some challenges relating to reliable, fast, and nondestructive identification for sorting and separation purposes. New and proven technology such as near-infrared hyperspectral imaging and pattern recognition can be used. However, high-quality pattern and spectrum libraries of multiple plastic floors have to be created for optimal and reliable reference models. Furthermore, pattern recognition and near-infrared methods need to be tested further at an industrial scale. / Bakgrund Tarkett AB Ronneby (Sverige) är ett golvlösning företag, erkänt för tillverkning och återvinning av homogent plastgolv. Tarkett AB återvinner huvudsakligen installations spill och tillverkningsfel. Tarkett AB överväger dock att utvidga sina återvinnings förmågor till att omfatta gamla och sönderrivna plastgolv som kan innehålla föroreningar och förbjudna ämnen eller plastgolv från konkurrerande varumärken. För att åstadkomma detta överväger Tarkett en helt ny återvinnings linje med en automatiserad sorteringsprocess istället för den aktuella manuella processen. Således föreslår Tarkett ett examensarbete för att konceptualisera ett nytt automatiserat sorteringssystem med ökad kapacitet och ökad funktionalitet. Syfte Detta arbete syftar till att undersöka den nuvarande sorterings processen och introducera konceptuella lösningar för en ny automatiserad sorteringsprocess som kan identifiera och separera plastgolv efter tillverkare, typ, skick och externt avfall med befintlig teknik. Metod De metoder och verktyg som används i detta arbete är huvudsakligen baserade på en modifierad produktutvecklingsprocess. Vilket börja med datainsamling av den aktuella sorterings processen, hitta behov och extrahera krav för en automatiserad sorteringsprocess, undersöka relevant teknik, utvärdera tekniken baserad på vetenskaplig litteratur och tester. Testningen genomfördes i samarbete med två företag. Nära-infraröda skannrar testades med Holger AB, medan mönsterigenkänning system testades med Vision-Geek. Slutligen utvecklades tre koncept för den automatiserade sorterings processen och visades genom flödesscheman och 2D-3D-illustrationer. Resultat Resultaten av detta arbete visade att det var möjligt att använda nära-infraröd och mönsterigenkänning för separering av plastgolv. Dessutom genererades tre konceptuella lösningar för en automatiserad sorteringsprocess och visades med schematiska grafer och 2D-3D-illustrationer. Begreppen beskriver hur sorterings processen fungerar och vilken teknik som används för varje steg i processen. Koncept 1 och Koncept 2 använde både mönsterigenkänning och spektroskopi metoder. Medan Koncept 3 bara använde spektroskopi metoder. Spektroskopi metoderna användes för att sortera plastgolv efter innehåll medan mönsterigenkänning efter utseende. Slutsats Återvinning av sönderrivna plastgolv kan vara fördelaktigt för både miljön och återvinningsindustrin. Dock finns det några utmaningar med anknytning till pålitlig, snabb och icke-förstörande identifiering för sorterings- och separation ändamål. Ny och beprövad teknik som nästan infraröd hyperspektral avbildning och mönsterigenkänning kan användas. Emellertid måste mönster- och spektrum bibliotek av hög kvalitet av flera plastgolv skapas för optimala och pålitliga referens-modeller. Dessutom måste mönsterigenkänning och nära-infraröda metoder testas vidare i industriell skala.

Coil coatings are durable  organic coatings used to protect metal sheets from corrosion and improve their aesthetic properties. Because of their extensive use, coil coatings have long been of interest for industrial and academic researchers. This interest has recently been furthered by a societal push towards the replacement of fossil-based raw materials with alternatives that are biobased and renewable. The aim of this licentiate thesis is to demonstrate how analyses on the macro-, micro-, and nanoscale can be used to better understand the degradation process of polyester-based coil coatings. The included manuscripts showcase methods for evaluating and comparing different coil coating formulations and for verifying accelerated weathering techniques. Multiple techniques, focusing on infrared (IR) spectroscopy and atomic force microscopy (AFM), were used to analyze coating systems before and after different types of weathering. IR data acquired from techniques without spatial resolution, such as attenuated total reflection (ATR) and photoacoustic spectroscopy (PAS) have been expanded upon with spatially resolved focal plane array (FPA) and s-SNOM  (scattering-type scanning near-field optical microscopy) measurements. Spatially resolved chemical data of coating cross sections were acquired and used to assess how the degradation at the surface and in the bulk was related. Additionally, differences between the degradation behavior of a standard fossil-based coating and a similar coating with biobased components as well as differences between the degradation caused by artificial and natural weathering was discussed. Nanoscale mechanical measurements of simplified coating surfaces showed that weathering increased nanomechanical stiffness and led to homogenization of mechanical properties on the local level. In addition, measurements with nanoscale FTIR correlated with macroscale FTIR. Even relatively minor changes in band intensities could be tracked on a local scale. Although the simplified samples were chemically homogeneous, nanoscale FTIR shows great promise for the assessment of local degradation of full systems. / Bandlackering är en process för att applicera stabila organiska beläggningar på metallytor för att skydda från korrosion och förbättra deras utseende. På grund av beläggningarnas omfattande användning så har utvärdering och analys av dem varit av intresse för både akademi och industri i flera årtionden. Detta långvariga intresse har ytterligare främjats av en ökade miljömedvetenhet och ett tryck att ersätta miljöfarliga och fossila råmaterial mot biobaserade och förnyelsebara alternativ. Målet med denna licentiatavhandling är att visa hur analysmetoder på makro-, mikro-, och nanonivå kan användas för att bättre förstå nedbrytning av bandlackerade beläggningar. Denna förståelse kan användas både för att utvärdera prestandan hos både nya redan befintliga system, men också för att kunna verifiera accelererade testmetoder vars mål är att minska tiden som krävs för utvärdering. Flera tekniker, med fokus på infraröd (IR) spektroskopi och atomkrafts-mikroskopi  (AFM) använts för att analysera beläggningar före och efter att de blivit utsatta för olika typer av aggressiva miljöer. Spektroskopiska data utan spatial upplösning som attenuerad totalreflektions FTIR (ATR) och fotoakustisk spektroskopi (PAS) har kompletterats med spatialt upplösta fokalplans array (FPA) och s-SNOM mätningar. Kemisk information med spatial upplösning har använts för att utvärdera hur nedbrytningen nära ytan relaterade till nedbrytningen längre ner i beläggningen. Likheter och skillnader i nedbrytningen som skedde i en standardbeläggning och ett system med biobaserade additiv jämfördes efter både väderbestendighets-testning som skedde utomhus och i labb. Skillnader mellan dessa exponeringsmetoder diskuterades också. Nanomekanisk analys med hjälp av atomkraftsmikroskopi användes för att bestämma lokala förändringar av mekaniska egenskaper i förenklade klarlacker. Mätningarna visade att exponeringar i aggressiva miljöer leder till en lokal homogenisering av mekaniska egenskaper och ökad styvhet. Utöver detta så utvärderades likheter och skillnader mellan FTIR spektra som tagits på makro- och nanonivå. Dessa mätningar gav lovande resultat för fortsatta ytanalyser. / <p>QC 2023-05-15</p>

For manufacturers in the wood industry, an important way to make the production more effective is to automate the process of detecting defects and different attributes on boards. One important attribute on most boards is heartwood and sapwood. This thesis project was conducted at the company MiCROTEC and aims to investigate methods to classify heartwood and sapwood on boards. The dataset used in this project consisted of oak boards. In order to increase the amount of information retrieved from the boards, hyperspectral imaging was used instead of conventional RGB cameras. Based on this data, deep learning models in the form of U-Net and U-within-U-Net architecture as well as different spectral dimensionality reduction methods were developed to segment boards in heartwood and sapwood. The performance of these deep learning models was compared to PLS-DA and SVM. PLS-DA has already been used at MiCROTEC and has been used in this work for comparison as a baseline model.   The result of the thesis work showed that a deep learning approach could increase the F1-Score from 0.730 for the baseline classifier PLS-DA to an F1-Score of 0.918, and that the different spectral reduction methods only had a small impact on the result. The increase in F1-score was mainly due to an increase in precision, since the PLS-DA had a similar recall as the deep learning models.

No description available.

No description available.

Spontaneous Raman spectroscopy (SR) is a versatile method for analysis and visualization of ferroelectric crystal structures, including domain walls. Nevertheless, the necessary acquisition time makes SR impractical for in situ analysis and large scale imaging. In this work, we introduce broadband coherent anti-Stokes Raman spectroscopy (B-CARS) as a high-speed alternative to conventional Raman techniques and demonstrate its benefits for ferroelectric domain wall analysis. Using the example of poled lithium niobate, we compare the spectral output of both techniques in terms of domain wall signatures and imaging capabilities. We extract the Raman-like resonant part of the coherent anti-Stokes signal via a Kramers–Kronigbased phase retrieval algorithm and compare the raw and phase-retrieved signals to SR characteristics. Finally, we propose a mechanism for the observed domain wall signal strength that resembles a Cerenkov-like behavior, in close analogy to domain wall signatures obtained by secondharmonic generation imaging.We, thus, lay here the foundations for future investigations on other poled ferroelectric crystals using B-CARS.