Model-predicted brain temperature computational imaging by multimodal noninvasive functional neuromonitoring of cerebral oxygen metabolism and hemodynamics: MRI-derived and clinical validation. [PDF]
Jiang M +13 more
europepmc +1 more source
Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76)
Luminescent MOF‐76 materials based on Eu(III) and mixed Eu(III)/Y(III) show rapid and reversible changes in emission intensity in response to O2 with very short response times. The effect is based on triplet quenching of the linker ligands that act as photosensitizers. Average emission lifetimes of a few milliseconds turn out to be mostly unaffected by
Zhenyu Zhao +5 more
wiley +1 more source
Electroactive Metal–Organic Frameworks for Electrocatalysis
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska +7 more
wiley +1 more source
A previously unreported coordination motif stabilising single Fe atoms by indigo chelation and pyridyl coordination on Au(111) has been revealed. By using planar tritopic pyridyl linkers (TPyB), extended 2D porous networks of indigo3(TPyB)2Fe6 form. These networks can be crystalline or vitreous and offer an environment where individual coordination ...
Hongxiang Xu +9 more
wiley +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
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
This review systematically highlights the latest achievements in mixed‐valence states relevant to hydrogen and oxygen evolution reactions, providing essential insights into future directions and methods for large‐scale practical implementation. This critical review is expected to provide an overview of recent advancements in diverse valence‐state metal
Jitendra N. Tiwari +4 more
wiley +1 more source
Computational Imaging Prediction of Starburst-Effect Diffraction Spikes. [PDF]
Lendermann M +3 more
europepmc +1 more source
Slight Truncation Changes in Iron Oxide Nanocubes Strongly Affect Their Magnetic Properties
Subtle variations in nanoparticle morphology can lead to significant changes in functional properties. An automated shape‐fitting method captures minor differences in corner truncation between iron oxide nanocubes of similar sizes synthesized under identical conditions, revealing pronounced disparities in their magnetic and hyperthermia behavior ...
Kingsley Poon +7 more
wiley +1 more source
Computational imaging of sperm locomotion. [PDF]
Daloglu MU, Ozcan A.
europepmc +1 more source

