Changes and performance thanks to the predictive model of machine learning. This discovery makes possible the elimination of 12% of global waste
A Enzyme It appears that the modified e can decompose Swallow the plastic within 48 hours. The predictive machine learning model, depending on the type of material to be discarded, is sufficient to recommend appropriate modifications to achieve previously unprecedented results. This finding, which is often yet to be tested, was achieved by researchersUniversity of TexasBut the first results of the study have already been published and published in a special journal Natural. 12% of the waste produced worldwide is plastic And the more complex aspect is about removing it (The Video), Being an indestructible type of material, does not deteriorate even after many years of its development, even in the face of environmental adverse effects such as sudden changes in temperature or contact with external agents. Therefore, plastic is the most difficult type of waste to dispose of, with obvious effects on the ecosystem, both on land and at sea.
How it works
In particular, the enzyme was renamed Faster PETase In a very short time it becomes very easy to break very large pieces of plastic, cut them into very small pieces, recycle and reuse them to make new products. A solution to get rid of it is being explored Polyethylene terephthalate (PET), A polymer commonly found in most packaging, such as general containers, fruit and vegetable packaging, and bottles. At two-day intervals, even at low temperatures, the effects obtained were significant. This discovery opens the door to a potential solution to environmental pollution, which can be further purified by the process. Mechanical learning. The forecasting model gradually identifies more effective changes to break down plastics at the time of registration, compared to a type of waste that has been hazardous to the environment for centuries. Hence the machine learning model, which periodically enhances the activity of the enzyme. The tests have worked so far More than fifty different plastic components, Including polyester fibers, fabrics and water bottles. Moreover, this enzyme seems to be characterized by a strong stability, so it is suitable for large-scale adoption of even the simplest but most basic solution of natural environments.
He thinks about describing the benefits that the modified enzyme provides Hall AlberAuthor McCotta Department Of Chemical Engineering Austin. Not only is it a breakthrough in plastic disposal, but every company, in any field, has the opportunity to play a leading role in recycling their products. Only with these advances can the real circle economy of plastics be imagined. The recent findings should not be underestimated as a result of intermediate studies that use both knowledge and skills related to artificial intelligence in artificial biology. He opined that combining different sectors would once again provide unexpected results Andrew Ellington, Professor of Artificial Biology leading the team that developed the machine learning model. Currently, researchers have filed Patent application Technology, but they believe the soon-to-be-modified enzyme could be used in other applications as well, and analyze perhaps unprecedented targets.
May 15, 2022 (Change May 15, 2022 | 16:03)
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