几乎可以在地球上的任何地方找到微型污染，以及海洋生物学家日益关注的污染，其中包括海洋和水道，这些海洋和水道是脆弱的动植物的所在地。

Engineers from Cornell and North Carolina State University have proposed a creative solution: an army of swimming, self-propelled biomaterials called ‘microcleaners’ that scavenge and capture plastics so they can be decomposed by computationally-engineered microorganisms.

Their project is being funded with a $2 million grant from the National Science Foundation’s Emerging Frontiers in Research and Innovation program, and combines expertise in chemistry, biology, environmental toxicology, hydrology, artificial intelligence and computer science.

微型塑料 - 小毫米大小的塑料的微小部分 - 很容易通过废水处理设施，并且越来越多地进入海洋。

它们来自较大的塑料碎片，或者是用于皮肤清洁剂和牙膏等产品的微粒。

这些微塑料的环境含义尚未完全理解，尽管生物学家发现了证据，它们改变了鸟类，鱼类和其他野生动植物的喂养习惯。

研究小组旨在设计几类廉价，无毒的微型清洁剂，以用作微型武器，以应对这一大问题。

Cornell’s focus will be a class of oil droplets engineered to self-propel in water, collect microplastic particles and float to the surface, where they can be collected by barges or other large vessels.

The droplets will contain chemical compounds that generate gradients in tension between the oil and water, giving them the ability to self-propel.

诸如油滴的软材料的界面是尼古拉斯·雅培（Nicholas Abbott）的专业，这是史密斯化学与生物分子工程学院的提施大学教授，该项目的联合主席研究员。

Abbott said one of the many challenges underlying the collection of microplastics is that, in the world of microparticles, they are relatively large and diffuse very slowly in water.

“You can’t wait for them to come to you to be captured,” he said. “They are also present in enormous volumes of water.

Our plan is to create actively propelled microsystems based on biodegradable droplets that seek out and capture the microplastic particles.

因为他们是自行的，所以他们可以搜索大量的水。”

The key to making the microcleaners work is understanding the chemical makeup of the microplastics they aim to capture – another goal of the project.

微型清洁剂必须能够识别各种塑料化学物质，其中一些因长期暴露于阳光而改变或由覆盖表面的生物体生物膜主导。

研究团队将开发新的分析工具，以根据液晶传感器，人工智能和肽的计算设计（氨基酸的链）来表征微塑料，旨在识别和结合微塑料表面。

该肽将在北卡罗来纳州州进行设计，使用Fengqi You，Roxanne E.和Michael J. Zak能源系统工程学教授以及该项目的共同主要研究员在康奈尔州开发的机器学习方法。

You hopes to learn from the unique optical patterns produced by microplastics absorbed into novel liquid crystals designed by Abbott.

“We are taking a unique approach to develop effective deep-learning techniques for interpreting the spatial and temporal optical signals generated by microplastics adsorption at liquid crystal interfaces, thereby creating optical ‘fingerprints’ that indicate the species and concentration of microplastics,” You said.

您还将使用机器学习技术来支持研究的最后一个组成部分 - 以微小细菌形式进行工程微生物，可以降解塑料。

微生物将执行生化过程，该过程将微塑料变成更环保和潜在的有价值的材料，例如可用于生产更多微蛋白质的生物燃料或脂肪酸。

The researchers also hope to advance the science of active microcleaner design and develop microbes for sustainable processing of plastics, other aspects of the project with potential for a variety of environmental and industrial applications.

“Conventional engineering approaches to particle capture just don’t make sense on the massive scale required to impact microplastics,” Abbott said.

“Part of our strategy is to use products of deconstructed microplastics as the basis of microcleaners that can collect additional particles.

This circular approach enables a type of a scaling up of the process that is required for an engineering solution.”

北卡罗来纳州州教授卡罗尔·霍尔（Carol Hall），奥林·维勒夫（Orlin Velev）和内森·克鲁克（Nathan Crook）是该项目的联合首席调查员。

帮助指导最初的研究的是Shaoyi Jiang，Robert S. Langer ’70家人和康奈尔（Meinig）生物医学工程学院的朋友和朋友教授，以及康奈尔大学农业与生命科学学院生物与环境工程学教授托德·沃尔特（Todd Walter）。

Source: Cornell College of Engineering.