The toxic truth about carbon nanotubes in water purification: a perspective view

Without nanosafety guidelines, the long-term sustainability of carbon nanotubes (CNTs) for water purifications is questionable. Current risk measurements of CNTs are overshadowed by uncertainties. New risks associated with CNTs are evolving through different waste water purification routes, and there are knowledge gaps in the risk assessment of CNTs based on their physical properties. Although scientific efforts to design risk estimates are evolving, there remains a paucity of knowledge on the unknown health risks of CNTs. The absence of universal CNT safety guidelines is a specific hindrance. In this paper, we close these gaps and suggested several new risk analysis roots and framework extrapolations from CNT-based water purification technologies. We propose a CNT safety clock that will help assess risk appraisal and management. We suggest that this could form the basis of an acceptable CNT safety guideline. We pay particular emphasis on measuring risks based on CNT physico-chemical properties such as diameter, length, aspect ratio, type, charge, hydrophobicity, functionalities and so on which determine CNT behaviour in waste water treatment plants and subsequent release into the environment

Additional file 1: Figure S1. of Can We Optimize Arc Discharge and Laser Ablation for Well-Controlled Carbon Nanotube Synthesis?

Schematic representation of the birth of CNT through various routes. (DOCX 2313 kb

A tractable method for measuring nanomaterial risk using Bayesian Networks

While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator