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Ion selective electrode (ISE) sensors have been broadly applied for real-time in situ monitoring of ion concentrations in water environments. However, ISE sensors suffer from critical problems, such as ionophore leaching, water-penetration, poor electrochemical stability, and resulting short life spans. In this study, a template-guided membrane matrix immobilization strategy was pursued as a novel ISE sensor fabrication methodology to enhance its sensing characteristics and longevity. Specifically, nano-porous anodized aluminum oxide (AAO) was used as the template for an NH 4 + -specific ISE sensor. A nano-porous nickel mesh eventually replaced the template and formed a compact, high-surface juncture with the NH 4 + ion-selective membrane matrix. The resulting template-guided nano-mesh ISE (TN-ISE) sensor displayed enhanced electrochemical stability ( i.e. , capacitance increased by 50%, reading drift reduced by 75%) when compared to a regular single-wall carbon nanotube (SW-CNT) ISE sensor used as the standard. The interface between the nano-mesh electrode and the ion selective membrane matrix was compact enough to prevent water influx at the electrode interface. This minimized ionophore leaching and increased the mechanical integrity of the TN-ISE sensor. The practical advantages of the novel sensor were validated via long-term (360 hours) tests in real wastewater, returning a small average error of 1.28% over this time. The results demonstrate the feasibility of the template-guided nano-mesh design and fabrication strategy toward ISEs for long-term continuous monitoring of water or wastewater quality. 

The rapid increase in both quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development; proper model interpretation; and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.