Results 151 to 160 of about 152,714 (315)
An Algorithm for Calculating Terms of the Stirling’s Formula Remainder
Fábio Antonio Dorini +1 more
openalex +2 more sources
Shellac, a centuries‐old natural resin, is reimagined as a green material for flexible electronics. When combined with silver nanowires, shellac films deliver transparency, conductivity, and stability against humidity. These results position shellac as a sustainable alternative to synthetic polymers for transparent conductors in next‐generation ...
Rahaf Nafez Hussein +4 more
wiley +1 more source
Correction: Discrimination of missing data types in metabolomics data based on particle swarm optimization algorithm and XGBoost model. [PDF]
Yuan Y +5 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
Revisiting the calculation of PAL from physical activity data. [PDF]
Magkos F.
europepmc +1 more source
3D‐Printed Sulfur‐Derived Polymers With Controlled Architectures for Lithium‐Sulfur Batteries
Rheology‐guided formulation design for direct ink writing enables the fabrication of 3D sulfur copolymer cathodes with controlled architectures for lithium‐sulfur batteries. The printed electrodes exhibit multiscale porosity and high sulfur utilization, delivering enhanced electrochemical performance compared to conventional cast electrodes.
Bin Ling +7 more
wiley +1 more source
A Tutorial on Sample Size Calculation for Inter-rater and Intra-rater Agreement Studies. [PDF]
Madadizadeh F, Bahariniya S.
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

