| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Müller, Martin
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (38/38 displayed)
- 2024Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures
- 2024Efficient Phase Segmentation of Light-Optical Microscopy Images of Highly Complex Microstructures Using a Correlative Approach in Combination with Deep Learning Techniques
- 2023Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning
- 2023Influence of the Sequence Motive Repeating Number on Protein Folding in Spider Silk Protein Filmscitations
- 2023Klassifizierung komplexer Gefüge mit maschinellem Lernen am Beispiel bainitischer Stähle
- 2022Addressing materials’ microstructure diversity using transfer learningcitations
- 2022Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopycitations
- 2022Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy
- 2022Addressing materials' microstructure diversity using transfer learningcitations
- 2021High Hydrogen Mobility in an Amide–Borohydride Compound Studied by Quasielastic Neutron Scatteringcitations
- 2021A Complementary and Revised View on the N-Acylation of Chitosan with Hexanoyl Chloride
- 2021Effectiveness and Utility of Virtual Reality Simulation as an Educational Tool for Safe Performance of COVID-19 Diagnostics: Prospective, Randomized Pilot Trialcitations
- 2021Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques
- 2021A deep learning approach for complex microstructure inferencecitations
- 2021A dangerously underrated entity? Non-specific complaints at emergency department presentation are associated with utilisation of less diagnostic resourcescitations
- 2021Deformation Behavior of Cross-Linked Supercrystalline Nanocompositescitations
- 2020Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques
- 2020Towards Quantitative Interpretation of Fourier-Transform Photocurrent Spectroscopy on Thin-Film Solar Cellscitations
- 2020Catechol Containing Polyelectrolyte Complex Nanoparticles as Local Drug Delivery System for Bortezomib at Bone Substitute Materialscitations
- 2019Hierarchical supercrystalline nanocomposites through the self-assembly of organically-modified ceramic nanoparticles
- 2019Hierarchical supercrystalline nanocomposites through the self-assembly of organically-modified ceramic nanoparticlescitations
- 2019Iron oxide-based nanostructured ceramics with tailored magnetic and mechanical properties: Development of mechanically robust, bulk superparamagnetic materials
- 2019Iron oxide-based nanostructured ceramics with tailored magnetic and mechanical properties: development of mechanically robust, bulk superparamagnetic materialscitations
- 2019Modulating the Mechanical Properties of Supercrystalline Nanocomposite Materials via Solvent–Ligand Interactionscitations
- 2018Bioinspired thermoresponsive nanoscaled coatings: Tailor-made polymer brushes with bioconjugated arginine-glycine-aspartic acid-peptidescitations
- 2018Phase Transformations in the Brazing Joint during Transient Liquid Phase Bonding of a γ-TiAl Alloy Studied with In Situ High-Energy X-Ray Diffractioncitations
- 2017Local flow stresses in interpenetrating-phase composites based on nanoporous gold — In situ diffractioncitations
- 2016Local flow stresses in interpenetrating-phase composites based on nanoporous gold — in situ diffraction
- 2016In-situ Observation of Cross-Sectional Microstructural Changes and Stress Distributions in Fracturing TiN Thin Film during Nanoindentationcitations
- 2016Phase Transformation and Residual Stress in a Laser Beam Spot-Welded TiAl-Based Alloycitations
- 2016High-temperature stable Zirconia particles doped with Yttrium, Lanthanum, and Gadoliniumcitations
- 2016In Situ Synchrotron Radiation Diffraction of The Solidificationof Mg-Dy(-Zr) Alloys
- 2015Synthesis and thermal stability of zirconia and yttria-stabilized zirconia microspheres
- 2014Nanocomposite coatings with stimuli-responsive catalytic activitycitations
- 2010Studying the influence of chemical structure on the surface properties of polymer filmscitations
- 2009Charging and structure of zwitterionic supported bilayer lipid membranes studied by streaming current measurements, fluorescence microscopy, and attenuated total reflection Fourier transform infrared spectroscopycitations
- 2001Origin and effect of fiber attack for the processing of C/SiC
- 2001Improving damage tolerance of C/SiC
Places of action
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article
Modulating the Mechanical Properties of Supercrystalline Nanocomposite Materials via Solvent–Ligand Interactions
Abstract
Supercrystalline nanocomposite materials with micromechanicalproperties approaching those of nacre or similarstructural biomaterials can be produced by self-assembly oforganically modified nanoparticles and further strengthened bycross-linking. The strengthening of these nanocomposites iscontrolled via thermal treatment, which promotes the formation ofcovalent bonds between interdigitated ligands on the nanoparticlesurface. In this work, it is shown how the extent of the mechanicalproperties enhancement can be controlled by the solvent used duringthe self-assembly step. We find that the resulting mechanicalproperties correlate with the Hansen solubility parameters of thesolvents and ligands used for the supercrystal assembly: the hardnessand elastic modulus decrease as the Hansen solubility parameter of the solvent approaches the Hansen solubility parameter ofthe ligands that stabilize the nanoparticles. Moreover, it is shown that self-assembled supercrystals that are subsequentlyuniaxially pressed can deform up to 6 %. The extent of this deformation is also closely related to the solvent used during the selfassemblystep. These results indicate that the conformation and arrangement of the organic ligands on the nanoparticle surfacenot only control the self-assembly itself but also influence the mechanical properties of the resulting supercrystalline material.The Hansen solubility parameters may therefore serve as a tool to predict what solvents and ligands should be used to obtainsupercrystalline materials with good mechanical properties.