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British Scientists Use Sunlight And Converted It To Fuel

Hydrogenase is an enzyme present in algae that is capable of reducing protons into hydrogen.

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Hydrogen, Fuel
Scientists pioneer novel model to turn sunlight into fuel.Flickr

In a breakthrough move, British scientists have used natural sunlight to convert water into hydrogen and oxygen by mixing biological components and man-made technologies.

The team led by academics at the University of Cambridge, used semi-artificial photosynthesis to explore new ways to produce and store solar energy, a finding that could now be used to revolutionise the systems used for renewable energy production.

Their method also managed to absorb more solar light than natural photosynthesis.

Hydrogen Fuel
Representational Image Of Fossil Fuel plant.
Wikimedia Commons.

“Natural photosynthesis is not efficient because it has evolved merely to survive so it makes the bare minimum amount of energy needed — around 1-2 per cent of what it could potentially convert and store,” said lead author Katarzyna Soko, doctoral student at the University’s St. John’s College.

Artificial photosynthesis has been around for decades but it has not yet been successfully used to create renewable energy because it relies on the use of catalysts, which are often expensive and toxic. This means it cannot yet be used to scale up findings to an industrial level.

The new model, detailed in the journal Nature Energy, is the first to successfully use hydrogenase and photosystem II to create semi-artificial photosynthesis driven purely by solar power.

Hydrogen, Fuel
Hydrogen. Flickr

The team not only improved on the amount of energy produced and stored, they managed to reactivate a process in the algae that has been dormant for millennia.

Also Read: SpiceJet To Test A Flight Powered By BioFuel

“Hydrogenase is an enzyme present in algae that is capable of reducing protons into hydrogen. During evolution this process has been deactivated because it wasn’t necessary for survival but we successfully managed to bypass the inactivity to achieve the reaction we wanted — splitting water into hydrogen and oxygen,” Soko explained.

Soko hopes the findings will enable new innovative model systems for solar energy conversion to be developed. (IANS)

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Here’s Why Information Overload May Not be Good

The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms

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Information
In situations where people do not have background knowledge, they become more confident with the new information and make better decisions. Pixabay

Information overload may not always be a good thing. Researchers have found that in certain circumstances, having more background information may actually lead people to take worse decisions.

The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms and how healthcare professionals and financial advisors present this new information to their patients and clients.

“Being accurate is not enough for information to be useful,” said Samantha Kleinberg, Associate Professor of Computer Science at Stevens Institute of Technology in New Jersey, US.”It’s assumed that AI and Machine Learning will uncover great information, we’ll give it to people and they’ll make good decisions. However, the basic point of the paper is that there is a step missing: we need to help people build upon what they already know and understand how they will use the new information,” Kleinberg added.

For example, when doctors communicate information to patients, such as recommending blood pressure medication or explaining risk factors for diabetes, people may be thinking about the cost of medication or alternative ways to reach the same goal.

“So, if you don’t understand all these other beliefs, it’s really hard to treat them in an effective way,” said Kleinberg. For the study, the researchers asked 4,000 participants a series of questions about topics with which they would have varying degrees of familiarity.

Some participants were asked to make decisions on scenarios they could not possibly be familiar with. Other participants were asked about more familiar topics i.e. choosing how to reduce risk in a retirement portfolio or deciding between specific meals and activities to manage bodyweight.

The team compared whether people did better or worse with new information or were just using what they already knew. The researchers found that prior knowledge got in the way of choosing the best outcome. Kleinberg found the same to be true when she posed a problem about health and exercise, as it relates to diabetes.

When people without diabetes read the problem, they treated the new information at face value, believed it and used it successfully. People with diabetes, however, started second-guessing what they knew and as in the previous example, did much worse. “In situations where people do not have background knowledge, they become more confident with the new information and make better decisions,” said Kleinberg.

AI
The study, published in the journal Cognitive Research: Principles and Implications, may help reframe the idea of how we use the mountain of data extracted from Artificial Intelligence (AI) and Machine Learning (ML) algorithms and how healthcare professionals and financial advisors present this new information to their patients and clients. Pixabay

“So there’s a big difference in how we interpret the information we are given and how it affects our decision making when it relates to things we already know vs. when it’s in a new or unfamiliar setting,” she added.

Kleinberg cautioned that the point of the paper is not that information is bad. She argued only that in order to help people make better decisions, it is important to better understand what people already know and tailor information based on that mental model.

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Started in 1870, Stevens Institute of Technology is one of the oldest technological institutes in the US. (IANS)