Results 191 to 200 of about 1,083,042 (374)
Natural product chemistry of gorgonian corals of the family Plexauridae distributed in the Indo-Pacific Ocean. [PDF]
Wang LH+11 more
europepmc +1 more source
Hydrogel‐Based Capacitive Sensor Model for Ammonium Monitoring in Aquaculture
Traditional techniques for monitoring aquaculture water quality, particularly ammonium levels, harm fish. This work presents a novel capacitive sensor with an ionic hydrogel transducer to monitor ammonium concentration in real time based on the ammonium‐induced hydrogel dissociation and osmotic pressure. Monitoring aquaculture water quality, especially
Mohammad Mirzaee+3 more
wiley +1 more source
A metallic implant's higher stiffness compared to the surrounding bone leads to stress shielding problems, causing bone density reduction and bone growth interference in pediatric patients. 3D‐printed triply periodic minimal surface structures present a promising engineering solution.
Ali Ebrahimzadeh Dehaghani+4 more
wiley +1 more source
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani+2 more
wiley +1 more source
Methyltransferases from RiPP pathways: shaping the landscape of natural product chemistry. [PDF]
Schröder MP, Pfeiffer IP, Mordhorst S.
europepmc +1 more source
Impact of continuous flow chemistry in the synthesis of natural products and active pharmaceutical ingredients [PDF]
Juliana M. de Souza+6 more
openalex +1 more source
Digital Methods for the Fatigue Assessment of Engineering Steels
The use of engineering steels is often limited by their fatigue strength. In the sake of a faster product development, the fatigue behavior can be predicted by machine learning (ML). In this work, ML is applied on a heterogeneous database, covering a wide range of steel types.
Sascha Fliegener+7 more
wiley +1 more source