A Unified Complex-Fresnel Model for Physically Based Long-Wave Infrared Imaging and Simulation. [PDF]
Ter Heerdt P +3 more
europepmc +1 more source
The study presents biodegradable and recyclable mixed‐matrix membranes (MMMs), hydrogels, and cryogels using luminescent nanoscale metal‐organic frameworks (nMOFs) and biopolymers. These bio‐nMOF‐MMMs combine europium‐based nMOFs as probes for the status of the materials with the biopolymers agar and gelatine and present alternatives to conventional ...
Moritz Maxeiner +4 more
wiley +1 more source
The evolving landscape of molecular visualization. [PDF]
Torrez R, Liu H, Lee D, Iwasa JH.
europepmc +1 more source
Recent advances in the user evaluation methods and studies of non-photorealistic visualisation and rendering techniques [PDF]
Baker, S. +3 more
core +1 more source
Substrate Stress Relaxation Regulates Cell‐Mediated Assembly of Extracellular Matrix
Silicone‐based viscoelastic substrates with tunable stress relaxation reveal how matrix mechanics regulates cellular mechanosensing and cell‐mediated matrix remodelling in the stiff regime. High stress relaxation promotes assembly of fibronectin fibril‐like structures, increased nuclear localization of YAP and formation of β1 integrin‐enriched ...
Jonah L. Voigt +2 more
wiley +1 more source
Design Space and Declarative Grammar for 3D Genomic Data Visualization. [PDF]
Kouril D, Manz T, L'Yi S, Gehlenborg N.
europepmc +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley +1 more source
Microscopy Nodes: versatile 3D microscopy visualization with Blender. [PDF]
Gros A +6 more
europepmc +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Talking Head Generation Through Generative Models and Cross-Modal Synthesis Techniques. [PDF]
Nisar H, Masood S, Malik Z, Abid A.
europepmc +1 more source

