Lifeboat Foundation

Scientists in China and Sweden have determined that a pinch of capsaicin, the chemical compound that gives chili peppers their spicy sting, may be a secret ingredient for more stable and efficient perovskite solar cells. The research, published January 13 in the journal Joule, determined that sprinkling capsaicin into the precursor of methylammonium lead triiodide (MAPbI₃) perovskite during the manufacturing process led to a greater abundance of electrons (instead of empty placeholders) to conduct current at the semiconductor’s surface. The addition resulted in polycrystalline MAPbI3 solar cells with the most efficient charge transport to date.

“In the future, green and sustainable forest-based biomaterial additive technology will be a clear trend in non-toxic lead-free perovskite materials,” says Qinye Bao, a senior author of the study from East China Normal University. “We hope this will eventually yield a fully green perovskite solar cell for a clean energy source.”

While metal halide perovskite semiconductors represent a promising component for state-of-the-art solar cell technologies, they are plagued by nonradiative recombination, an undesirable electron-level process that reduces efficiency and exacerbates heat losses. Bao and colleagues sought out a natural, forest-based, inexpensive additive to overcome this limitation and enhance solar cell performance.

Sodium-ion batteries are a potential replacement for lithium batteries, but the anodes—positively charged electrodes—that work well for lithium-ion batteries don’t provide the same level of performance for sodium-ion batteries.

Amorphous carbon, which lacks a crystalline structure, is known to be a useful anode, because it has defects and voids that can be used to store sodium ions. Nitrogen/phosphorus-doped carbon also offers appealing electrical properties.

In Applied Physics Reviews, researchers in China from Zhejiang University, Ningbo University, and Dongguan University of Technology describe how they applied basic physical concepts of atomic scale to build high-performance anodes for sodium-ion batteries.

Two researchers at Duke University have recently devised a useful approach to examine how essential certain variables are for increasing the reliability/accuracy of predictive models. Their paper, published in Nature Machine Intelligence, could ultimately aid the development of more reliable and better performing machine-learning algorithms for a variety of applications.

“Most people pick a predictive machine-learning technique and examine which variables are important or relevant to its predictions afterwards,” Jiayun Dong, one of the researchers who carried out the study, told TechXplore. “What if there were two models that had similar performance but used wildly different variables? If that was the case, an analyst could make a mistake and think that one variable is important, when in fact, there is a different, equally good model for which a totally different set of variables is important.”

Dong and his colleague Cynthia Rudin introduced a method that researchers can use to examine the importance of variables for a variety of almost-optimal predictive models. This approach, which they refer to as “variable importance clouds,” could be used to gain a better understanding of machine-learning models before selecting the most promising to complete a given task.

How true Eric Klien.

Only 5% of internet users currently connect via satellite in the US. But that number could jump significantly when SpaceX’s service finally gets off the ground, according to a new survey.

Even for the brain of a worm, the best theory on offer would, she says, take several billion years to calculate. That can’t be the right answer.

Stuck at home with time on their hands, millions of amateurs around the world are gathering information on everything from birds to plants to Covid-19 at the request of institutional researchers. And while quarantine is mostly a nightmare for us, it’s been a great accelerant for science.

From backyard astronomy to birding, amateurs have been busy collecting data — and making real discoveries.

Continue reading “Citizen science is booming during the pandemic” »

The process of systems integration (SI) functionally links together infrastructure, computing systems, and applications. SI can allow for economies of scale, streamlined manufacturing, and better efficiency and innovation through combined research and development.

New to the systems integration toolbox are the emergence of transformative technologies and, especially, the growing capability to integrate functions due to exponential advances in computing, data analytics, and material science. These new capabilities are already having a significant impact on creating our future destinies.

The systems integration process has served us well and will continue to do so. But it needs augmenting. We are on the cusp of scientific discovery that often combines the physical with the digital—the Techno-Fusion or merging of technologies. Like Techno-Fusion in music, Techno-Fusion in technologies is really a trend that experiments and transcends traditional ways of integration. Among many, there are five grouping areas that I consider good examples to highlight the changing paradigm. They are: Smart Cities and the Internet of Things (IoT); Artificial Intelligence (AI), Machine Learning (ML), Quantum and Super Computing, and Robotics; Augmented Reality (AR) and Virtual Reality Technologies (VR); Health, Medicine, and Life Sciences Technologies; and Advanced Imaging Science.

The new clinical trial data shows a single shot of the vaccine “gives sustainable antibodies,” Dr. Paul Stoffels, J&J chief scientific officer, told CNBC.

I never thought about it, but these 3D magic eyes also work on computers.