Results 91 to 100 of about 931,717 (291)

Selective Laser Sintering 3D Printing of Drug‐Loaded Intravitreal Implants

Advanced Functional Materials, EarlyView.
Selective laser sintering enables the fabrication of biodegradable and biocompatible intravitreal implants with tunable microstructures for sustained drug delivery. By modulating laser scanning speed, the polymer matrix architecture is engineered to control the release kinetics of dexamethasone and riboflavin over several months. This approach offers a
Iria Seoane‐Viaño, Carlos Bendicho‐Lavilla, Haya Alfassam, Bárbara Blanco‐Fernández, Abdul W. Basit, Francisco J. Otero‐Espinar   +5 more
wiley   +1 more source

Recycling of Thermoplastics with Machine Learning: A Review

Advanced Functional Materials, EarlyView.
This review shows how machine learning is revolutionizing mechanical, chemical, and biological pathways, overcoming traditional challenges and optimizing sorting, efficiency, and quality. It provides a detailed analysis of effective feature engineering strategies and establishes a forward‐looking research agenda for a truly circular thermoplastic ...
Rodrigo Q. Albuquerque, Florian Stephan, Annalena Pongratz, Christian Brütting, Katharina Krause, Holger Ruckdäschel   +5 more
wiley   +1 more source

Nanoplasmonics Reveal Ionic‐Strength‐Driven Hydration of Nanoparticles

Advanced Functional Materials, EarlyView.
Localized surface plasmon resonance (LSPR) of gold nanoparticles (AuNPs) is modulated by ionic‐strength‐dependent hydration shell compression. A predictive model connects shell thickness to non‐radiative damping and spectral shifts over seven orders of magnitude.
Yeeun Song, Abhijit Ganguly, Zoe Bradley, Doojin Lee, Nikhil Bhalla   +4 more
wiley   +1 more source

Late- and Postglacial Climatic Change in the Northern Midwest, USA: Quantitative Estimates Derived from Fossil Pollen Spectra by Multivariate Statistical Analysis [PDF]

, 1972
Thompson Webb, Reid A. Bryson
openalex   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Differential Phase Contrast Imaging to Predict MSC Immune Function

Advanced Healthcare Materials, EarlyView.
This work employs quantitative differential phase contrast (qDPC) imaging technique to non‐invasively extract morphological features from live Mesenchymal Stromal Cells (MSCs) for single cell prediction on cell immunomodulatory capacity. This work establishes a foundation for scalable, non‐destructive monitoring of MSCs immunomodulatory capacity ...
Kejie Rui, Priyanka Priyadarshani, Adrian Ross Liversage, Carlos Munoz, Steven Stice, Luke J. Mortensen   +5 more
wiley   +1 more source

Seeing inside the Body Using Wearable Sensing and Imaging Technologies

Advanced Healthcare Materials, EarlyView.
This review explores wearable technologies for noninvasive internal health monitoring. It categorizes approaches into indirect sensing (e.g., bioelectrical and biochemical signals) and direct imaging (e.g., wearable ultrasound and EIT), highlighting multimodal integration and system‐level innovation toward personalized, continuous healthcare.
Sumin Kim, Seunghun Han, Sehwan Park, Jahyun Koo   +3 more
wiley   +1 more source

Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications

Advanced Materials, EarlyView.
This review highlights the significance of molecular simulations in expanding the understanding of metal–organic frameworks (MOFs) and improving their gas adsorption applications. The historical development and implementation of molecular simulations in the MOF field are given, high‐throughput computational screening studies used to unlock the ...
Hilal Daglar, Hasan Can Gulbalkan, Gokhan Onder Aksu, Seda Keskin   +3 more
wiley   +1 more source

Multivariate analysis of exploratory behavior in gerbils [PDF]

, 1978
Jeffrey Rosenfeld, Lane A. Lasko, Edward C. Simmel   +2 more
openalex   +1 more source

Synthetic Aspects and Characterization Needs in MOF Chemistry – from Discovery to Applications

Advanced Materials, EarlyView.
Overcoming the challenges of phase discovery, synthesis optimization and scale‐up, characterization, and computational studies is essential to accelerate the large‐scale application of MOFs. Life‐cycle analyses and techno‐economic analyses need to be performed to realistically assess their potential for industrial relevance.
Bastian Achenbach, Aysu Yurdusen, Norbert Stock, Guillaume Maurin, Christian Serre   +4 more
wiley   +1 more source