Chemistry – Lifeboat News: The Blog

Apr 25
2025

A shortcut to AI computation: In-memory computing overcomes data transfer bottlenecks

As artificial intelligence (AI) continues to advance, researchers at POSTECH (Pohang University of Science and Technology) have identified a breakthrough that could make AI technologies faster and more efficient.

Professor Seyoung Kim and Dr. Hyunjeong Kwak from the Departments of Materials Science & Engineering and Semiconductor Engineering at POSTECH, in collaboration with Dr. Oki Gunawan from the IBM T.J. Watson Research Center, have become the first to uncover the hidden operating mechanisms of Electrochemical Random-Access Memory (ECRAM), a promising next-generation technology for AI. Their study is published in the journal Nature Communications.

As AI technologies advance, data processing demands have exponentially increased. Current computing systems, however, separate data storage (memory) from data processing (processors), resulting in significant time and energy consumption due to data transfers between these units. To address this issue, researchers developed the concept of in-memory computing.

Apr 25
2025

Semiconductor nanowires capture diffuse sunlight to split water and store energy as hydrogen

A U of A engineering researcher is using sunlight and semiconductor catalysts to produce hydrogen by splitting apart water molecules into their constituent elements.

“The process to form the semiconductor, called thermal condensation polymerization, uses cheap and Earth-abundant materials, and could eventually lead to a more efficient, economical path to clean energy than existing solar technologies,” says project lead Karthik Shankar of the Department of Electrical and Computer Engineering, an expert in the field of photocatalysis.

In a collaboration between the U of A and the Technical University of Munich, results of the research were published in the Journal of the American Chemical Society.

Apr 24
2025

A new way to measure uncertainty provides an important step toward confidence in AI model training

It’s obvious when a dog has been poorly trained. It doesn’t respond properly to commands. It pushes boundaries and behaves unpredictably. The same is true with a poorly trained artificial intelligence (AI) model. Only with AI, it’s not always easy to identify what went wrong with the training.

Research scientists globally are working with a variety of AI models that have been trained on experimental and theoretical data. The goal: to predict a material’s properties before taking the time and expense to create and test it. They are using AI to design better medicines and industrial chemicals in a fraction of the time it takes for experimental trial and error.

But how can they trust the answers that AI models provide? It’s not just an academic question. Millions of investment dollars can ride on whether AI model predictions are reliable.

Apr 24
2025

Seafloor disturbance in Baltic Sea turns carbon sink into surprising CO₂ source

The resuspension of seafloor sediments—triggered by human activities such as bottom trawling as well as natural processes like storms and tides—can significantly increase the release of carbon dioxide (CO₂) into the atmosphere. When these sediments are exposed to oxygen-rich seawater, large-scale oxidation of pyrite occurs.

This reaction plays a much greater role in CO₂ emissions than previously assumed, exceeding the contribution from the oxidation of organic carbon. A new study, published in Communications Earth & Environment, provides the first quantitative evidence of this effect in the western Baltic Sea.

“Fine-grained, muddy sediments are important reservoirs of organic carbon and pyrite,” says lead author Habeeb Thanveer Kalapurakkal, a Ph.D. student in the Benthic Biogeochemistry working group at GEOMAR.

Apr 24
2025

Chemical recycling turns used silicones into pure building blocks, promising infinite reuse

A study conducted by CNRS researchers describes a new method of recycling silicone waste (caulk, sealants, gels, adhesives, cosmetics, etc.). It has the potential to significantly reduce the sector’s environmental impacts.

This is the first universal recycling process that brings any type of used silicone material back to an earlier state in its life cycle where each molecule has only one silicon atom. And there is no need for the raw materials currently used to design new silicones. Moreover, since it is chemical and not mechanical recycling, the reuse of the material can be carried out infinitely.

The associated study is published in Science.

Apr 24
2025

Quantum computing prepwork made faster with graph-based data grouping algorithm

Quantum computers promise to speed calculations dramatically in some key areas such as computational chemistry and high-speed networking. But they’re so different from today’s computers that scientists need to figure out the best ways to feed them information to take full advantage. The data must be packed in new ways, customized for quantum treatment.

Apr 24
2025

Designing long-duration toxin sensors

Imagine a smoke detector that, instead of warning residents of smoke before a fire engulfs their home, is placed in mass-transit locations to alert travelers and first responders to hazardous chemicals in the air.

Apr 23
2025

‘Periodic table of machine learning’ framework unifies AI models to accelerate innovation

MIT researchers have created a periodic table that shows how more than 20 classical machine-learning algorithms are connected. The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up with new ones.

For instance, the researchers used their framework to combine elements of two different algorithms to create a new image-classification algorithm that performed 8% better than current state-of-the-art approaches.

The periodic table stems from one key idea: All these algorithms learn a specific kind of relationship between data points. While each algorithm may accomplish that in a slightly different way, the core mathematics behind each approach is the same.

Apr 23
2025

From beam to battery: Single-step laser printing supercharges high-performance lithium-sulfur batteries

A research team has developed an innovative single-step laser printing technique to accelerate the manufacturing of lithium-sulfur batteries. Integrating the commonly time-consuming active materials synthesis and cathode preparation in a nanosecond-scale laser-induced conversion process, this technique is set to revolutionize the future industrial production of printable electrochemical energy storage devices. The team was led by Prof. Mitch Li Guijun, Assistant Professor from the Division of Integrative Systems and Design at the Hong Kong University of Science and Technology (HKUST).

The findings of this study are published in the journal Nature Communications.

Lithium-sulfur batteries are expected to supersede existing lithium-ion batteries due to sulfur cathodes’ high theoretical energy density. To ensure the rapid conversion of sulfur species, these cathodes are typically composed of active materials, host materials (or catalysts), and conductive materials.