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Our sixth episode of "STEMTalk With STEMEncounters" brings exciting topics of how "Researchers 3D print lifelike heart valve models" and the 3D printing process. We go on to talk about the reason researchers from the University of Minnesota developed this groundbreaking process for multi-material 3D printing of lifelike hearts and their intended use, ultimately helping patients and doctors through their procedures. We then discuss the researchers' ultimate goals in the development of 3D printing in electronics and circuit boards in which we discuss Nanolithography and the innovation of the fantastic technique in printing circuit boards and transistors in integrated circuits. How the technique's process has led to so many advancements in society, but due to Moore's law, how long will it help us?
We then dive into researchers at the Tokyo Institute of Technology using artificial intelligence (AI) to predict the degree of water repulsion and protein adsorption by ultra-thin organic materials. How "A leap forward for biomaterials design using AI" will ultimately enable accurate water repulsion predictions and protein adsorption with hypothetical materials. We then dive into how they did their research with machine learning using an artificial neural network (ANN) to predict the properties mentioned above (degree of water repulsion and affinity to protein molecules) of self-assembled monolayers (SAMS). We talk about what this means and how biomaterials are becoming more integrated with AI.
Finally, we discuss a scorching topic in the world of science and technology: Neuralink. We discuss how Elon Musk's company has been developing implantable brain-machine interfaces (BMIs) to ultimately better humanity and their biological problems (paralysis, hearing, blindness, etc.). We discuss the recent presentation and what it means for the world. Then we dive into the Materials Science and Engineering aspect of Neuralink; how this is created with sensors and electrodes, though, there is still so much to discuss about Neuralink.
Our sixth episode of "STEMTalk With STEMEncounters" brings exciting topics of how "Researchers 3D print lifelike heart valve models" and the 3D printing process. We go on to talk about the reason researchers from the University of Minnesota developed this groundbreaking process for multi-material 3D printing of lifelike hearts and their intended use, ultimately helping patients and doctors through their procedures. We then discuss the researchers' ultimate goals in the development of 3D printing in electronics and circuit boards in which we discuss Nanolithography and the innovation of the fantastic technique in printing circuit boards and transistors in integrated circuits. How the technique's process has led to so many advancements in society, but due to Moore's law, how long will it help us?
We then dive into researchers at the Tokyo Institute of Technology using artificial intelligence (AI) to predict the degree of water repulsion and protein adsorption by ultra-thin organic materials. How "A leap forward for biomaterials design using AI" will ultimately enable accurate water repulsion predictions and protein adsorption with hypothetical materials. We then dive into how they did their research with machine learning using an artificial neural network (ANN) to predict the properties mentioned above (degree of water repulsion and affinity to protein molecules) of self-assembled monolayers (SAMS). We talk about what this means and how biomaterials are becoming more integrated with AI.
Finally, we discuss a scorching topic in the world of science and technology: Neuralink. We discuss how Elon Musk's company has been developing implantable brain-machine interfaces (BMIs) to ultimately better humanity and their biological problems (paralysis, hearing, blindness, etc.). We discuss the recent presentation and what it means for the world. Then we dive into the Materials Science and Engineering aspect of Neuralink; how this is created with sensors and electrodes, though, there is still so much to discuss about Neuralink.