Results 161 to 170 of about 85,241 (257)
Laser‐Induced Graphene from Waste Almond Shells
Almond shells, an abundant agricultural by‐product, are repurposed to create a fully bioderived almond shell/chitosan composite (ASC) degradable in soil. ASC is converted into laser‐induced graphene (LIG) by laser scribing and proposed as a substrate for transient electronics.
Yulia Steksova +9 more
wiley +1 more source
Monte Carlo approximation of the logarithm of the determinant of large matrices with applications for linear mixed models in quantitative genetics. [PDF]
Bermann M +5 more
europepmc +1 more source
This study introduces a novel multi‐scale scaffold design using L‐fractals arranged in Archimedean tessellations for tissue regeneration. Despite similar porosity, tiles display vastly different tensile responses (1–100 MPa) and deformation modes. In vitro experiments with hMSCs show geometry‐dependent growth and activity. Over 55 000 tile combinations
Maria Kalogeropoulou +4 more
wiley +1 more source
A genetic algorithm-based framework for online sparse feature selection in data streams. [PDF]
Liu G +5 more
europepmc +1 more source
The first cryo‐EM visualization and quantification of oriented Photosystem I (PSI) on single‐layer graphene is reported. Domain‐specific covalent anchoring of PSI, with the reducing side of the biophotocatalyst toward graphene, promotes three‐fold higher anodic photocurrent generation compared to a randomly physisorbed counterpart. This approach allows
Miriam Izzo +6 more
wiley +1 more source
Data-driven head model individualization from digitized electrode positions or photogrammetry improves M/EEG source localization accuracy. [PDF]
Harmening N +2 more
europepmc +1 more source
There is a significant need for biomaterials with well‐defined stability and bioactivity to support tissue regeneration. In this study, we developed a tunable microgel platform that enables the decoupling of stiffness from porosity, thereby promoting bone regeneration.
Silvia Pravato +9 more
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

