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AI-engineered enzyme eats entire plastic containers


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.

How Artificial Intelligence is Used in Chemical Industry


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

AI helps scientists design novel plastic-eating enzyme


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 could be a thing of the past thanks to new PET-eating enzyme

Daily Mail - Science & tech

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.

Spectroscopy and Chemometrics/Machine-Learning News Weekly #17, 2022


LINK "Feasibility of Near-Infrared Spectroscopy for Rapid Detection of Available Nitrogen in Vermiculite Substrates in Desert Facility Agriculture" LINK "Establishment of a Nondestructive Analysis Method for Lignan Content in Sesame using Near Infrared Reflectance Spectroscopy" LINK "Near Infrared Spectroscopy: A useful technique for inline monitoring of the enzyme catalyzed biosynthesis of third-generation biodiesel from waste cooking oil" LINK "A Study on Nitrogen Concentration Detection Model of Rubber Leaf Based on Spatial-Spectral Information with NIR Hyperspectral Data" LINK "Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement" LINK "Estimating Forest Soil Properties for Humus Assessment--Is Vis-NIR the Way to Go?" LINK "Association and solubility of chlorophenols in CCl4: MIR/NIR spectroscopic and DFT study" LINK "Prediction of rhodinol content in Java citronella oil using NIR spectroscopy in the initial stage ...

Spectroscopy and Chemometrics/Machine-Learning News Weekly #16, 2022


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Optimization of a Thermal Cracking Reactor Using Genetic Algorithm and Water Cycle Algorithm


With the global production of 150 million tons in 2016, ethylene is one of the most significant building blocks in today's chemical industry. Most ethylene is now produced in cracking furnaces by thermal cracking of fossil feedstocks with steam. This process consumes around 8% of the main energy used in the petrochemical industry, making it the single most energy-intensive process in the chemical industry. This paper studies a tubular thermal cracking reactor fed by propane and the molecular mechanism of the reaction within the reactor. After developing the reaction model, the existing issues, such as the reaction, flow, momentum, and energy, were resolved by applying heat to the outer tube wall.

Machine Learning Will be one of the Best Ways to Identify Habitable Exoplanets - Universe Today


The field of extrasolar planet studies is undergoing a seismic shift. To date, 4,940 exoplanets have been confirmed in 3,711 planetary systems, with another 8,709 candidates awaiting confirmation. With so many planets available for study and improvements in telescope sensitivity and data analysis, the focus is transitioning from discovery to characterization. Instead of simply looking for more planets, astrobiologists will examine "potentially-habitable" worlds for potential "biosignatures." This refers to the chemical signatures associated with life and biological processes, one of the most important of which is water. As the only known solvent that life (as we know it) cannot exist, water is considered the divining rod for finding life.



With the country's unique position within the Ring of Fire, such natural hazards have become part and parcel of everyday life in Japan. Accordingly, the nation is considered a model for disaster preparedness: each resident is advised to carry fireproof evacuation bags with first aid, sanitation products as well as food and water. Meanwhile, buildings constructed after 1981 are required to have earthquake-resistant structures, meaning thicker beams, pillars and walls as well as shock-absorbers to reduce shaking in taller buildings. And yet, the 2011 Great East Japan Earthquake came as a huge shock--literally. On March 11, 2011, the Tohoku region along Japan's eastern coast was rocked by a magnitude 9.0 earthquake for six minutes; the strongest in the country's records so far.

Computational modeling guides development of new materials


Metal-organic frameworks, a class of materials with porous molecular structures, have a variety of possible applications, such as capturing harmful gases and catalyzing chemical reactions. Made of metal atoms linked by organic molecules, they can be configured in hundreds of thousands of different ways. To help researchers sift through all of the possible metal-organic framework (MOF) structures and help identify the ones that would be most practical for a particular application, a team of MIT computational chemists has developed a model that can analyze the features of a MOF structure and predict if it will be stable enough to be useful. The researchers hope that these computational predictions will help cut the development time of new MOFs. "This will allow researchers to test the promise of specific materials before they go through the trouble of synthesizing them," says Heather Kulik, an associate professor of chemical engineering at MIT.