Fourth-generation biomedical materials
Materials Today, 2016 Ning, Chengyun, Zhou, Lei, Tan, Guoxin
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Advanced Functional Materials, EarlyView.A programmable interpenetrating double‐network architecture, created via 3D‐TIPS printing and resin infusion, synergistically combines thermoplastic and thermosetting elastomers to balance structural rigidity and surface softness—crucial for paediatric laryngeal stents.
Elizabeth F. Maughan +14 more
wiley +1 more source
The Physiological Inorganic Polymers Biosilica and Polyphosphate as Key Drivers for Biomedical Materials in Regenerative Nanomedicine. [PDF]
Int J NanomedicineMüller WEG +4 more
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Application of FTIR Method for the Assessment of Immobilization of Active Substances in the Matrix of Biomedical Materials. [PDF]
Materials (Basel), 2019 Kowalczuk D, Pitucha M.
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Inactivating SARS‐CoV‐2 Virus with MOF‐Composites as Smart Face Masks
Advanced Functional Materials, EarlyView.In situ preparation and functionalization of MOF@Cotton fabrics as smart face masks for the immobilization of proteins and inactivation viruses, such as SARS‐CoV‐2. Abstract The significant impact of the SARS‐CoV‐2 (COVID‐19) pandemic outbreak on people's lives has highlighted the urgent need for effective personal protective equipment.
Romy Ettlinger +9 more
wiley +1 more source
Advanced Functional Materials, EarlyView.A multifunctional nanodiamond platform enables pH‐triggered Diazoxide (DZX) delivery and quantum sensing of subcellular radical dynamics in triple‐negative breast cancer cells. Diamond relaxometry revealed reduced lysosomal radicals during DZX‐induced mitochondrial radical elevation, providing insights into redox modulation and organelle‐ resolved ...
Kaiqui Wu +8 more
wiley +1 more source
Special Issue: Novel Advances and Approaches in Biomedical Materials Based on Calcium Phosphates. [PDF]
Materials (Basel), 2019 Mucalo MR.
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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 +7 more
wiley +1 more source
Light‐Responsive Enzyme‐Loaded Nanoparticles for Tunable Adhesion and Mechanical Wound Contraction
Advanced Functional Materials, EarlyView.This study presents a photoactivatable enzyme‐loaded mesoporous nanoparticle system (MPDA_PaTy) that enables light‐triggered tunable tissue adhesion and facilitates mechanical wound contraction. Controlled enzymatic crosslinking at tissue or hydrogel interfaces allows on‐demand adhesion.
Junghyeon Ko +10 more
wiley +1 more source
Graphene and its derivatives as biomedical materials: future prospects and challenges. [PDF]
Interface Focus, 2018 Banerjee AN.
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