A plastic-degrading enzyme enhanced by amino acid changes designed by a machine-learning algorithm can depolymerise polyethylene terephthalate (PET) at least twice as fast and at lower temperatures than the next best engineered enzyme. Six years ago scientists sifting through debris of a plastic bottle recycling plant discovered a bacterium that can degrade PET. The organism has two enzymes that hydrolyse the polymer first into mono-(2-hydroxyethyl) terephthalate and then into ethylene glycol and terephthalic acid to use as an energy source. One enzyme in particular, PETase, has become the target of protein engineering efforts to make it stable at higher temperatures and boost its catalytic activity. A team around Hal Alper from the University of Texas at Austin in the US has created a PETase that can degrade 51 different PET products, including whole plastic containers and bottles.

The article contains an overview of AI and machine learning applied in Chemistry along with libraries like RDKit. Image Credits Introduction Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. One of the chief goals of chem

In brief A synthetic enzyme designed using machine-learning software can break down waste plastics in 24 hours, according to research published in Nature. Scientists at the University of Texas Austin studied the natural structure of PETase, an enzyme known to degrade polymer chains in polyethylene. Next, they trained a model to generate mutations of the enzyme that work fast at low temperatures, let the software loose, and picked from the output a variant they named FAST-PETase to synthesize. FAST stands for functional, active, stable, and tolerant. FAST-PETase, we're told, can break down plastic in as little as 24 hours at temperatures between 30 and 50 degrees Celsius.

Plastic waste dumped in landfill could be cleared sooner than expected, after engineers developed an enzyme that can break it down in just a few hours. Millions of tons of plastic is left abandoned every year, pilling up in landfills and pollution the land and waterways - typically taking centuries to degrade. A team from the University of Texas in Austin created a new enzyme variant that can supercharge recycling on a large scale, reducing the impact of plastic pollution. The work focusing on PET (polyethylene terephthalate), which is a polymer found in most consumer plastic including bottles, packaging and some textiles. The enzyme was able to complete a'circular process' of breaking down the plastic into smaller parts and chemically putting it back together in as little as 24 hours. They've called it FAST-PETase (functional, active, stable, and tolerant PETase), developed from a natural PETase that allows bacteria to degrade and modify plastic.

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Facebook's parent company, Meta, has used AI to develop a new way of creating concrete which it claims produces 40 per cent less carbon emissions than standard mixtures, and is already using it in its latest data centre. But experts say that concrete mixtures with similar emissions are already in use across Europe, and that constructing new buildings is incompatible with reducing carbon pollution. Meta is investing heavily in AI research, including building the world's most powerful AI-specific supercomputer. Its main aims are to develop better speech-recognition tools, automatically translate between different languages and help build a 3D virtual metaverse, but the company is also using AI to work on projects such as concrete production. The company says that this construction material is a major contributor to its carbon footprint as it builds data centres around the world for its online services.

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The proportion of mining industry operations and technologies companies hiring for artificial intelligence related positions rose in March 2022 compared with the equivalent month last year, with 38.8% of the companies included in our analysis recruiting for at least one such position. This latest figure was higher than the 29.9% of companies who were hiring for artificial intelligence related jobs a year ago but a decrease compared to the figure of 41.4% in February 2022. When it came to the rate of all job openings that were linked to artificial intelligence, related job postings rose in March 2022, with 2% of newly posted job advertisements being linked to the topic. This latest figure was the highest monthly figure recorded in the past year and is an increase compared to the 1.9% of newly advertised jobs that were linked to artificial intelligence in the equivalent month a year ago. Artificial intelligence is one of the topics that GlobalData, from whom our data for this article is taken, have identified as being a key disruptive force facing companies in the coming years.

Industrial demand for silver soared to a record high of 508m ounces last year, according to The Silver Institute. The post Industrial silver demand reached record highs...

Here, Claudia Jarrett, US country manager at automation parts supplier EU Automation, explains how big players can learn from local businesses, using the IoT to their advantage. Harvard Business Review reports that multinational companies are finding it difficult to optimize their products, services and culture to local markets. The article says that big players are finding international growth costly and cumbersome, especially in countries where they don't have staff who are familiar with local cultures and customers or reliable local supply chain partners. Local companies understand the culture, language and compliance issues, of course, which raises the question: is there a better and more cost-effective way for large manufacturers to integrate local businesses and workers into their networks? Let's look at some steps big manufacturers can take, and why the IoT will prove essential. Three quarters of German manufacturers surveyed in Pricewaterhouse Cooper (PwC)'s Digital Factories 2020 report named regionalization as their main driver for investing in digital factories.