The increased manufacturing of nanoparticles for use in cosmetics, foods, and clothing necessitates the need for an effective system to monitor and evaluate the potential environmental impact of these nanoparticles. The goal of this research was to develop a plant-based sensor network for characterizing, monitoring, and understanding the environmental impact of TiO2 nanoparticles. The network consisted of potted Arabidopsis thaliana with a surrounding water supply, which was monitored by cameras attached to a laptop computer running a machine learning algorithm. Using the proposed plant sensor network, we were able to examine the toxicity of TiO2 nanoparticles in two systems: algae and terrestrial plants. Increased terrestrial plant growth was observed upon introduction of the nanoparticles, whereas algal growth decreased significantly. The proposed system can be further automated for high-throughput screening of nanoparticle toxicity in the environment at multiple trophic levels. The proposed plant-based sensor network could be used for more accurate characterization of the environmental impact of nanomaterials.

中文翻译：

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监测二氧化钛的环境影响$_{\bf 2}$ 使用基于植物的传感器网络的纳米粒子

用于化妆品、食品和服装的纳米颗粒的生产量不断增加，因此需要一个有效的系统来监测和评估这些纳米颗粒对环境的潜在影响。这项研究的目标是开发一种基于植物的传感器网络，用于表征、监测和了解 TiO2 纳米颗粒的环境影响。该网络由盆栽拟南芥和周围供水系统组成，由连接到运行机器学习算法的笔记本电脑的摄像头监控。使用提议的植物传感器网络，我们能够检查二氧化钛纳米粒子在两个系统中的毒性：藻类和陆生植物。引入纳米颗粒后，观察到陆生植物生长增加，而藻类生长显着下降。所提出的系统可以进一步自动化，以在多个营养级别对环境中的纳米颗粒毒性进行高通量筛选。拟议的基于植物的传感器网络可用于更准确地表征纳米材料的环境影响。