Mass Fabrication and Smart Deployment of “Calibration-free" Miniature Solid-State Potentiometric Sensors towards Digital Water Infrastructure

Abstract

Abstract Achieving digital water infrastructures requires efficient in situ collection and swift interpretation of vast amounts of data under spatial and temporal variations. Existing water sensors suffer from inconsistent quality, incur frequent (re)calibration before and after deployment, and impede reliable data interpretation across water infrastructures. In this study, a novel quality control/quality assurance (QC/QA) regime was developed for mass fabrication of miniature solid-state potentiometric ion-selective membrane (MSP-ISM) sensors through material-wise and device-wise advancements. Our goal was to minimize the discrepancy of sensor readings, assure the consistence of sensor Nernst slope (NS) and standard potential (Eo), and ultimately alleviate the needs of (re)calibration towards calibration-free (CF). Specifically, the material-wise advancement was performed by modifying the key components (e.g., ion selective membrane and solid contact) of the polymer matrix to enhance the NS consistence among multiple pieces of NH4+ MSP-ISM sensors and reduce the standard deviation (SD) from 2.38 mV/dec to 0.27 mV/dec. The devise-wise advancement was conducted by fabricate polymer membranes using electrospray to and fabricating the substratum electrode using aerosol jet printing, through which the variation of the E0 values was diminished from 7.58 mV to 1.39 mV and the uniformity and homogeneity of each layer of sensors were improved. Furthermore, the recalibration-free capability of CF-MSP-ISM sensors was examined in real wastewater over 14 days consecutively, exhibiting excellent accuracy with a discrepancy of less than 2 mg/L against the lab-based validation results. Finally, smart deployment of multiple pieces of CF-MSP-ISM sensors along the length of an anoxic/oxic (A/O) system and precision feedback control were simulated a plug-flow aerobic flexible control (PFAFC) model. The simulation results demonstrate that energy consumption saving and greenhouse gas (GHG, with N2O as the example) emission reduction can achieve 38.4% and 68.7%, respectively, compared with the one using traditional “single-point” monitoring. This high-resolution sensor profiling-based precise control strategy has a great potential to renovate the existing treatment facilities highly relying on excess energy consumption to meet the effluent requirement and explore an energy-saving and resilient operation with mitigated GHG emission.