Liquid Electron Microscopy for Biomaterials Characterization
Notice bibliographique
Résumé
Advances in micro/nano-fabrication, thin electron transparent materials, holder designs, and acquisition methods have made it possible to perform meaningful experiments using liquid electron microscopy (liquid EM). Liquid EM provides researchers with micro-to-nano scale tools to explore biomaterials in liquid environments capable of capturing dynamic in situ reactions, providing characterization means in mimetic conditions to the human body. However, these emerging techniques remain in their infancy; limited work presents best practice strategies, and several challenges remain for their effective implementation, particularly for beam-sensitive, soft biological materials. This thesis seeks to address these shortcomings by exploring strategies for liquid EM of biomaterials and real-time dynamic processes using two key methods: room temperature ionic liquid (RTIL) treatment for scanning EM (SEM) and liquid cell transmission EM (TEM). With these techniques, the research explores the characterization of hard-tissue systems relevant to bone and seeks to provide new methods of exploring structurally biological culprits behind diseases like COVID-19. Research in this thesis is presented by increasing complexity, touching on three themes: (i) exploring liquid EM for the first time using RTILs for SEM of biological samples notably bone (static, micro-scale), (ii) developing new methods for high-resolution liquid biological TEM of viruses (static, nano-scale), and (iii) applying novel liquid TEM to dynamic biomineralization systems (dynamic, nano-scale). After review articles serve as introductory material in Chapter 2, in Chapter 3, healthy and pathological bone was explored in hydrated conditions with liquid SEM using a new workflow involving RTIL treatment, demonstrated to be highly efficient for biological SEM. Moving to the nanoscale, Chapter 4 presents a commercial liquid TEM option and a new liquid TEM clipped enclosure developed for imaging biological specimens, specifically virus assemblies such as Rotavirus and SARS-CoV-2. Combined with automated acquisition tools and low-dose direct electron detection, enclosures resolved high-resolution structural features in the range of ~3.5 Å – 10 Å and were correlatively used for cryo TEM. Chapter 5 applies these liquid TEM methods to study collagen mineralization, revealing in high-resolution the presence of precursor calcium phosphate mineral phases, important transitional phases to mineral platelets found in mineralized tissues. But – dynamic reactions were not captured, attributed to confinement effects, lack of heating functionality, and cumulative beam damage experienced. Chapter 6 overcomes these challenges by optimizing collagen-liquid encapsulation within a commercial liquid TEM holder mimicking physiological conditions at 37°C. Dynamic nanoscale interactions were highlighted, where evidence of the coexistence of amorphous precursor phases involving polymer-induced liquid as well as particle attachment was presented within this model. Several liquid TEM challenges remain particularly beam sensitivity and distribution for biomaterials, providing many exciting avenues in future to explore. Taken together, this thesis is advancing characterization through the development and applied use of new liquid EM strategies for studying biomaterials and dynamic reactions. Insights on these reactions and structures anticipate leading to a better understanding of diseases and treatment pathways, the key to moving Canada’s health care system forward.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».