Results 181 to 190 of about 27,975 (305)
Coagulative granular hydrogels are composed of packed thrombin‐functionalized microgels that catalyze the conversion of fibrinogen into a secondary fibrin network, filling the interstitial voids. This bio‐inspired approach stabilizes the biomaterial to match the robustness of bulk hydrogels without compromising injectability, mimicking the initial ...
Zhipeng Deng +16 more
wiley +1 more source
Parametric Investigation of Fluid Migration in Casing-Liner Cement Sheaths and Microannulus Using Computational Fluid Modeling. [PDF]
Elzenary M +7 more
europepmc +1 more source
Chamber‐specific decellularized extracellular matrices (ECMs) were developed, preserving native proteomic profiles of ventricular and atrial myocardium. These innate biochemical cues differentially modulate cardiomyocyte subtypes to drive engineered heart tissue development and function, highlighting the importance of incorporating regional ECM cues in
Dong Gyu Hwang +7 more
wiley +1 more source
Physics-informed neural network modeling of shock waves by appropriately incorporating equation of state. [PDF]
Mizuno Y, Misaka T, Furukawa Y.
europepmc +1 more source
Spontaneous helical alignment of smooth muscle cells is induced within resistance‐vessel‐sized channels patterned within a hydrogel. The extent of the cells’ orientation angle is dependent on the presence and composition of ECM proteins lining the channel wall and cell seeding density.
Victoria D. Vest +5 more
wiley +1 more source
Application of deep reinforcement learning for aerodynamic control around an angled airfoil via synthetic jet. [PDF]
Hammouda NG +7 more
europepmc +1 more source
Gonzalez Martinez and collaborators develop a strategy to formulate high performance GelMA‐based bioinks with low solids contents. The resulting bioinks enable 3D bioprinting at 37 °C of high‐fidelity structures with tunable mechanical properties that support high cell viability and function.
David A. González‐Martínez +8 more
wiley +1 more source
Physics-informed neural network with weighted loss and hard constraints for hyperbolic conservation laws. [PDF]
Ghoreishi MS, Naderan H.
europepmc +1 more source

