Quantifying bone extracellular matrix properties for improved clinical fracture risk prediction

Metabolic bone diseases like osteoporosis lead to increased bone fragility and consequent implications for the patient lifestyle and health expenses. In the present aging society, fragility fractures pose significant health and economic burden. Current clinical methods to assess bone health status (dual-energy X-ray absorptiometry, FRAX, quantitative computed tomography variations) depend mostly on bone mineral density (BMD) measurements. However, BMD alone only accounts for about 70% of the variance in bone strength. It is therefore of high interest and potential societal impact to investigate bone quality, i.e. measures other than BMD influencing bone strength and toughness. In the present thesis, novel laboratory methods were developed for high-throughput investigation of bone properties, with the ultimate goal to define combinations of measurements that can be used as a proxy for bone quality in a fracture risk analysis. Firstly, a novel method for quantifying mineralized collagen fibril orientation based on polarized Raman spectroscopy (qPRS) was calibrated and validated on a natural material (mineralized turkey leg tendon). This method enables the quantitative estimation of the local degree of mineralization and 3D collagen fibril orientation non-destructively at submicron resolution. It was then applied to the cortex of bovine bone samples in combination with micropillar compression, allowing to reliably determine structure-property relationships of bone at the microscale. Later, a multimodal framework for bone characterization was developed in another animal bone model (minipig jawbone). This included the development of a novel femtosecond laser ablation protocol for bone micropillar fabrication allowing high-throughput and site-matched testing without exposure to high vacuum. The key part of the research was then carried out on a set of femoral neck samples collected from patients who underwent the hip arthroplasty due to osteoarthritis or fragility fracture. The femoral neck cortex from the inferomedial region was analyzed ex vivo in a site-matched manner using a combination of micromechanical testing (nanoindentation, micropillar compression) together with micro-computed tomography and quantitative polarized Raman spectroscopy for both morphological and compositional characterization. The output bone properties were correlated with the clinical information about age, gender, and primary diagnosis (coxarthrosis or hip fracture) of the participating patients. Patient gender and diagnosis did not influence any of the investigated bone properties. Moreover, all mechanical properties as well as the tissue-level mineral density were nearly constant over all ages (45-89 y.o.). Only local tissue composition was found to change significantly with age: decline in mineral to matrix ratio and increase in collagen cross-link ratio. Site-matched microscale analysis confirmed that all investigated mechanical properties except yield strain demonstrate a positive correlation with the mineral fraction of bone. The large dataset of experimentally assessed microscale bone properties together with the available clinical information of the patients allowed the application of machine learning algorithms for fracture prediction in silico. Logistic regression classification suggests that indentation hardness, relative mineralization and micropillar yield stress are the most perspective parameters for bone fracture risk prediction. As a result of this thesis, the output database of experimental measurements is the first to integrate microscale mechanical, chemical, morphological, and clinical information about the patients. In future, it can be used to compare existing methods of bone quality assessment. Moreover, the presented data and analysis approaches may be used to improve the prediction of fracture risk in the elderly.Stoffwechselerkrankungen des Knochens wie Osteoporose führen zu einer erhöhten Knochenbrüchigkeit und haben Auswirkungen auf den Lebensstil und die Gesundheitskosten der Patienten. In der heutigen alternden Gesellschaft stellen Fragilitätsfrakturen eine erhebliche gesundheitliche und wirtschaftliche Belastung dar. Die derzeitigen klinischen Methoden zur Beurteilung des Gesundheitszustands der Knochen (Dual-Röntgen-Absorptiometrie, FRAX, Quantitative Computertomographie Variationen) hängen hauptsächlich von der Messung der Knochenmineraldichte (BMD) ab. Die BMD allein macht jedoch nur etwa 70 % der Varianz in der Knochenstärke aus. Es ist daher von grossem Interesse und potenzieller gesellschaftlicher Bedeutung, die Knochenqualität zu untersuchen, d. h. andere Parameter als die BMD zu finden, welche die Knochenfestigkeit und -zähigkeit beschreiben. In der vorliegenden Arbeit wurden neuartige Labormethoden für die Hochdurchsatzuntersuchung von Knocheneigenschaften mit dem Ziel entwickelt, neue Messkombinationen zu definieren, die als Parameter für die Knochenqualität in einer Frakturrisikoanalyse verwendet werden können. Zunächst wurde eine neuartige Methode zur Quantifizierung der Orientierung mineralisierter Kollagenfibrillen auf der Grundlage der polarisierten Raman-Spektroskopie (qPRS) kalibriert und an einem natürlichen Modellmaterial (mineralisierte Sehne des Putenbeins) validiert. Dieser Ansatz ermöglicht die quantitative Abschätzung des lokalen Mineralisierungsgrades und der 3D-Kollagenfibrillenorientierung zerstörungsfrei mit einer Auflösung im Submikrometerbereich. Die Methodik wurde, in Kombination mit der Kompression von Mikrosäulen, zur Untersuchung der Rinderknochenrinde angewandt. Dadurch konnte die Struktur-Eigenschafts-Beziehungen des Knochens auf der Mikroskala zuverlässig bestimmt werden. Anschliessend wurde ein multimodaler Rahmen für die Knochencharakterisierung in einem anderen Tierknochenmodell (Minischwein-Kieferknochen) entwickelt. Dazu gehörte auch die Entwicklung eines neuartigen Femtosekunden- Laserabtragungsprotokolls für die Herstellung von Knochenmikrosäulen, das einen hohen Durchsatz und lokal angepasste Tests ohne Hochvakuum ermöglicht. Der wichtigste Teil der Forschung erfolgte an einer Reihe von Oberschenkelhalsproben, die Patienten mit Osteoarthritis und Fragilitätsfrakturen während der Implantation einer Hüfttotalendoprothese entnommen wurden. Die Schenkelhalskortikalis aus dem inferomedialen Bereich wurde ex vivo mittels einer Kombination aus mikromechanischen Tests (Nanoindentation, Mikrosäulenkompression), Mikro-Computertomographie und quantitativer polarisierter Raman-Spektroskopie zur morphologischen und kompositorischen Charakterisierung analysiert. Die ermittelten Knocheneigenschaften wurden mit den klinischen Informationen über Alter, Geschlecht und Primärdiagnose (Coxarthrose oder Hüftfraktur) der teilnehmenden Patienten korreliert. Geschlecht und Diagnose der Patienten hatten keinen Einfluss auf die untersuchten Knocheneigenschaften. Darüber hinaus waren alle mechanischen Eigenschaften sowie die Mineraldichte auf Probenebene über das Alter (45-89 Jahre) nahezu konstant. Lediglich die lokale Gewebezusammensetzung veränderte sich mit zunehmendem Alter signifikant, das Verhältnis von Mineralien zu Matrix nahm ab und das Verhältnis von Kollagenvernetzungen zu. Eine lokale Analyse auf der Mikroskala bestätigte, dass alle untersuchten mechanischen Eigenschaften mit Ausnahme der Dehngrenze eine positive Korrelation mit dem Mineralanteil des Knochens aufweisen. Der grosse Datensatz der experimentell bewerteten mikroskaligen Knocheneigenschaften ermöglichte, zusammen mit den verfügbaren klinischen Informationen der Patienten, die Anwendung von Algorithmen des maschinellen Lernens für die in silico-Frakturvorhersage. Die logistische Regressionsklassifikation legt nahe, dass die Eindruckhärte, relative Mineralisierung und das Fliessspannung der Mikrosäule die wichtigsten Parameter für die Vorhersage des Knochenbruchrisikos sind. Das Ergebnis dieser Arbeit ist die erste Datenbank mit experimentellen Messungen, die mikroskalige mechanische, chemische, morphologische und klinische Informationen über die Patienten integriert. Sie kann in Zukunft für den Vergleich bestehender Methoden zur Bewertung der Knochenqualität verwendet werden. Darüber hinaus können die vorgestellten Daten und Analyseansätze in Zukunft verwendet werden, um die Vorhersage des Frakturrisikos bei älteren Menschen zu verbessern

Bioinspired Materials Design: A Text Mining Approach to Determining Design Principles of Biological Materials

Biological materials are often more efficient and tend to have a wider range and combination of properties than present-day engineered materials. Despite the limited set of components, biological materials are able to achieve great diversity in their material properties by the arrangements of the material components, which form unique structures. The structure-property relationships are known as structural design principles. With the utilization of these design principles, materials designers can develop bioinspired engineered materials with similarly improved effectiveness. While considerable research has been conducted on biological materials, identifying beneficial structural design principles can be time-intensive. To aid materials designers, the research in this dissertation focuses on the development of a text mining algorithm that can quickly identify potential structural design principles of biological materials with respect to a chosen material property or combination of properties. The development of the text mining tool involves four separate stages. The first stage centers on the creation of a basic information retrieval algorithm to extract passages describing property-specific structural design principles from a corpus of materials journal articles. Although the Stage 1 tool identifies over 90% of the principles (recall), only 32% of the returned passages are relevant (precision). The second stage investigates text classification techniques to refine the program in order to improve precision. The classic techniques of machine learning classifiers, statistical features, and part-of-speech analyses, are evaluated for effectiveness in sorting passages into relevant and irrelevant classes. In the third stage, manual identification of patterns in the returned passages is employed to create a rule-based method. The resulting Stage 3 algorithm’s precision values increase to 45%. In the final stage of algorithm development, the manual rule-based classification method is revisited to identify stricter rules to further emphasize precision. The Stage 4 algorithm successfully improves overall precision to 65% and reduces the number of returned passages by 74%, which allows a materials designer to more quickly identify useful principles. Finally, the research concludes with a validation that the text mining tool effectively identifies structural design principles and that the principles can be used in the development of bioinspired materials

Nanomechanics of plasticity in ultra-strength metals and shape memory alloys

We study the plasticity mechanisms of diffusionless martensite phase transformation in Nickel-Titanium, one of the most widely used shape memory alloys. The research here involves four thrusts focusing on different length and time scales: (I) Molecular statics and dynamics simulations are applied to study the nanotwin structures and temperature-driven B2 → B19′ phase transitions. (II) Molecular dynamics simulations are performed to explore the stress-driven martensitic phase transformation governing the pseudoelasticity and shape memory effects in NiTi nanopillars. (III) Monte Carlo simulations are conducted to characterize the temperature- driven B2 → B19 phase transition and the patterning of martensitic nanotwins in NiTi thin films. (IV) Phase field simulations are performed to predict the formation and evolution of complex martensitic microstructures, including the detailed analysis of twin compatibility under complex loading conditions. We also study the nucleation-controlled plasticity mechanisms in different metals of Cu, Al and Ni. Our work focuses on understanding how dislocations nucleate in single crystals. Interatomic potential finite element method is applied to determine when, where and how dislocations nucleate during nanoindentation in metals such as Cu, Al and Ni.PhDCommittee Member: Arash Yavari; Committee Member: David McDowell; Committee Member: Ken Gall; Committee Member: Olivier Pierron; Committee Member: Ting Zh

Advancing the mechanical performance of glasses: Perspectives and challenges

Glasses are materials that lack a crystalline microstructure and long‐range atomic order. Instead, they feature heterogeneity and disorder on superstructural scales, which have profound consequences for their elastic response, material strength, fracture toughness, and the characteristics of dynamic fracture. These structure–property relations present a rich field of study in fundamental glass physics and are also becoming increasingly important in the design of modern materials with improved mechanical performance. A first step in this direction involves glass‐like materials that retain optical transparency and the haptics of classical glass products, while overcoming the limitations of brittleness. Among these, novel types of oxide glasses, hybrid glasses, phase‐separated glasses, and bioinspired glass–polymer composites hold significant promise. Such materials are designed from the bottom‐up, building on structure–property relations, modeling of stresses and strains at relevant length scales, and machine learning predictions. Their fabrication requires a more scientifically driven approach to materials design and processing, building on the physics of structural disorder and its consequences for structural rearrangements, defect initiation, and dynamic fracture in response to mechanical load. In this article, a perspective is provided on this highly interdisciplinary field of research in terms of its most recent challenges and opportunities.The mechanical performance of glassy materials presents a major challenge in modern glass science and technology. With a focus on visually transparent, inorganic and hybrid glasses, a perspective on the most recent developments in the field is provided herein, emphasizing the importance of translating fundamental insight from glass physics into future applications

Advanced Approaches Applied to Materials Development and Design Predictions

This thematic issue on advanced simulation tools applied to materials development and design predictions gathers selected extended papers related to power generation systems, presented at the XIX International Colloquium on Mechanical Fatigue of Metals (ICMFM XIX), organized at University of Porto, Portugal, in 2018. In this issue, the limits of the current generation of materials are explored, which are continuously being reached according to the frontier of hostile environments, whether in the aerospace, nuclear, or petrochemistry industry, or in the design of gas turbines where efficiency of energy production and transformation demands increased temperatures and pressures. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on failure mechanism modeling and simulation of materials are covered. As the Guest Editors, we would like to thank all the authors who submitted papers to this Special Issue. All the papers published were peer-reviewed by experts in the field whose comments helped to improve the quality of the edition. We also would like to thank the Editorial Board of Materials for their assistance in managing this Special Issue

Roadmap on Li-ion battery manufacturing research

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture