Developing low-cost electrochemical biosensor for disinfection by-product (DPB) detection in drinking water

饮用水安全面临的最大挑战之一是公众对致癌消毒副产品(DBP)污染的日益关注，因为化学消毒(例如氯化)被广泛采用，并被称为能够防止水传播疾病(或致病)爆发的单一过程。对于自来水公司来说，分析这些DBPs的过程通常需要三天时间，这需要将水样运送到实验室，而且费用昂贵(例如每个样本200 GB)和劳动密集型。因此，从采样到分析结果的快速周转时间是一个无法满足的需求，在怀疑供水受到污染的情况下。本博士项目的关键目标是设计、制造和验证基于低成本碳传感器的电化学生物传感器平台，用于快速、灵敏和可靠地检测水中致癌二苯并呋喃。这将把英国自来水公司现有的DBP污染现场分析时间从三天减少到三分钟，从而可以实时控制和优化水处理过程(包括饮用水和向环境排放的废水)。由此产生的知识、证据和创新将通过减少水质故障的风险、它们的环境足迹以及未来对水质管理基础设施的过度投资的可能性来影响英国当前的城市水基础设施和管理。这个博士项目非常适合ESPRC的工程主题和优先研究领域：水工程，该领域包含与水质评估和控制相关的技术的设计和优化的目标。在第一阶段，博士生将有机会利用废咖啡渣(SCG)开发新型低成本传感器电极。在英国，大约90%的咖啡渣被浪费，每年产生数十万吨SCG--这是最丰富的食物垃圾类型之一，基本上仍未被工业利用。SCG含有大量的碳，可以首先通过改进的碳化工艺将其转变为多孔和导电的碳粉，然后通过大容量丝网印刷技术批量生产成传感器电极。这种从近乎零成本和有毒的SCG废物中制造传感器电极的新回收方法将有助于英国实现食品循环经济的长期目标。在第二阶段，博士生将使用人工神经网络方法开发DBPS的模式识别算法。因此，配备了人工智能(AI)衍生算法的低成本SCG传感器阵列将能够同时识别每个DBP的电化学响应特征模式，从而导致对饮用水中一系列关键污染物进行现场多路评估的创新技术。

One of the greatest challenges in drink water safety is the increasing public concern with the contamination of carcinogenic disinfection by products (DBP), since chemical disinfection (e.g. chlorination) is widely adopted and referred to as the single process able to prevent water-borne disease (or pathogenic) outbreaks. For water utility companies, typical process to analyse these DBPs takes three days, which requires the transportation of water samples to a lab, and is also expensive (e.g. £200 per sample) and labour intensive. Therefore, quick turnaround time from sampling to analytical result is a unmet imperative where contamination of water supplies is suspected.The key objectives of this PhD project is to design, fabricate and validate electrochemical biosensor platform based on low-cost carbon transducers for the rapid, sensitive and reliable detection of carcinogenic DBPs in water. This will reduce the existing analysis time of UK water companies for DBP contamination from three days to three minutes in situ, so that water treatment processes (both drinking water and wastewater discharge to the environment) can be controlled and optimized in real time. The resulting knowledge, evidence and innovation will impact current urban water infrastructures and management in the UK by reducing the risk of water quality failure, their environmental footprint and the likelihood of excessive future investment on the infrastructures of water quality management.This PhD project fits perfectly in the ESPRC 'Engineering' theme and priority research area: Water Engineering, which encompasses the aims of design and optimisation of technologies relating to assessment and control of water quality. In first stage, PhD student will be given the opportunity to develop novel low-cost sensor electrode from spent coffee grounds (SCG). Approximately 90% of coffee grounds in the UK are wasted, creating hundreds of thousands of tonnes of SCG per year-one of the most abundant type of food waste that remains largely unexploited by industry. SCG contains a high volume of carbon, which can be first turned into porous and conductive carbon powder through a modified carbonisation process and then mass produced into sensor electrodes via high-volume screen-printing technique. This novel recycling approach of sensor electrode fabrication from near zero-cost and toxic SCG waste will contribute to the UK long-term goal of food circular economy. In second stage, PhD student will develop pattern recognition algorithms for DBPs using artificial neural network method. As a result, low cost SCG sensor arrays -equipped with artificial intelligence (AI) derived algorithms- will then be able to identify characteristic patterns of electrochemical response for each DBP simultaneously, thus leading to innovative technology for in situ multiplex assessment of a range of key contaminants in drinking water.