Lifeboat Foundation

Additive manufacturing techniques used to produce metal alloys have gained popularity due to their ability to be fabricated in complex shapes for use in various engineering applications. Yet the majority of studies conducted have centered around developing single-phase materials.

Dr. Kelvin Xie’s team in the Department of Materials Science and Engineering at Texas A&M University employed advanced characterization techniques to reveal the microstructure of the 3D-printed dual-phase multi-principal elements, also known as high entropy alloys (HEAs), that display ultra-strong and ductile properties. This work is a collaboration with Dr. Wen Chen from the University of Massachusetts at Amherst and Dr. Ting Zhu from the Georgia Institute of Technology.

This study was recently published in Nature.

A world-first study led by Monash University engineers has demonstrated how cutting-edge 3D-printing techniques can be used to produce an ultra strong commercial titanium alloy—a significant leap forward for the aerospace, space, defense, energy and biomedical industries.

Australian researchers, led by Professor Aijun Huang and Dr. Yuman Zhu from Monash University, used a 3D-printing method to manipulate a novel microstructure. In doing so, they achieved unprecedented mechanical performance.

This research, published in Nature Materials, was undertaken on commercially available alloys and can be applied immediately.

Laser powder bed fusion, a 3D-printing technique, offers potential in the manufacturing industry, particularly when fabricating nickel-titanium shape memory alloys with complex geometries. Although this manufacturing technique is attractive for applications in the biomedical and aerospace fields, it has rarely showcased the superelasticity required for specific applications using nickel-titanium shape memory alloys. Defects generated and changes imposed onto the material during the 3D-printing process prevented the superelasticity from appearing in 3D-printed nickel-titanium.

Researchers from Texas A&M University recently showcased superior tensile superelasticity by fabricating a shape memory alloy through laser powder bed fusion, nearly doubling the maximum superelasticity reported in literature for 3D printing.

This study was recently published in vol. 229 of the Acta Materialia journal.

Researchers from the Department of Materials Science and Engineering at Texas A&M University have used an Artificial Intelligence Materials Selection framework (AIMS) to discover a new shape memory alloy. The shape memory alloy showed the highest efficiency during operation achieved thus far for nickel-titanium-based materials. In addition, their data-driven framework offers proof of concept for future materials development.

This study was recently published in the Acta Materialia journal.

Shape memory alloys are utilized in various fields where compact, lightweight and solid-state actuations are needed, replacing hydraulic or pneumatic actuators because they can deform when cold and then return to their original shape when heated. This unique property is critical for applications, such as airplane wings, jet engines and automotive components, that must withstand repeated, recoverable large-shape changes.

The energy industry is looking to carbon capture and sequestration to mitigate climate change. That means finding lots of pore space.

They will need thousands of these underground sites to store tens of millions of tons of CO₂ to help mitigate climate change.

Scientists are ringing alarm bells about a significant new threat to U.S. water quality: as winters warm due to climate change, they are unleashing large amounts of nutrient pollution into lakes, rivers, and streams.

The first-of-its-kind national study finds that previously frozen winter nutrient pollution—unlocked by rising winter temperatures and rainfall—is putting water quality at risk in 40% of the contiguous U.S., including over 40 states.

Nutrient runoff into rivers and lakes—from phosphorus and nitrogen in fertilizers, manure, animal feed, and more—has affected water quality for decades. However, most research on nutrient runoff in snowy climates has focused on the growing season. Historically, cold temperatures and a continuous snowpack froze nutrients like nitrogen and phosphorous in place until the watershed thawed in the spring, when plants could help absorb excess nutrients.

Deep generative models are a popular data generation strategy used to generate high-quality samples in pictures, text, and audio and improve semi-supervised learning, domain generalization, and imitation learning. Current deep generative models, however, have shortcomings such as unstable training objectives (GANs) and low sample quality (VAEs, normalizing flows). Although recent developments in diffusion and scored-based models attain equivalent sample quality to GANs without adversarial training, the stochastic sampling procedure in these models is sluggish. New strategies for securing the training of CNN-based or ViT-based GAN models are presented.

They suggest backward ODEsamplers (normalizing flow) accelerate the sampling process. However, these approaches have yet to outperform their SDE equivalents. We introduce a novel “Poisson flow” generative model (PFGM) that takes advantage of a surprising physics fact that extends to N dimensions. They interpret N-dimensional data items x (say, pictures) as positive electric charges in the z = 0 plane of an N+1-dimensional environment filled with a viscous liquid like honey. As shown in the figure below, motion in a viscous fluid converts any planar charge distribution into a uniform angular distribution.

A positive charge with z 0 will be repelled by the other charges and will proceed in the opposite direction, ultimately reaching an imaginary globe of radius r. They demonstrate that, in the r limit, if the initial charge distribution is released slightly above z = 0, this rule of motion will provide a uniform distribution for their hemisphere crossings. They reverse the forward process by generating a uniform distribution of negative charges on the hemisphere, then tracking their path back to the z = 0 planes, where they will be dispersed as the data distribution.

Professor Vincent Pasque and his colleagues at KU Leuven have used stem cells to create a new kind of human cell in the lab. The new cells closely mirror their natural counterparts in early human embryos. As a result, scientists are better able to understand what occurs just after an embryo implants in the womb. The was recently published in the journal Cell Stem Cell.

A human embryo implants in the womb around seven days after fertilization if everything goes correctly. Due to technological and ethical constraints, the embryo becomes unavailable for study at that point. That is why scientists have already created stem cell models for various kinds of embryonic and extraembryonic cells in order to investigate human development in a dish.

Tesla announced today that it is moving away from using ultrasonic sensors in its suite of Autopilot sensors in favor of its camera-only “Tesla Vision” system.

Last year, Tesla announced it would transition to its “Tesla Vision” Autopilot without radar and start producing vehicles without a front-facing radar.

Originally, the suite of Autopilot sensors – which Tesla claimed would include everything needed to achieve full self-driving capability eventually – included eight cameras, a front-facing radar, and several ultrasonic sensors all around its vehicles.