A New Ammonium Smart Sensor with Interference Rejection

Abstract

In many water samples, it is important to determine the ammonium concentration in order to obtain an overall picture of the environmental impact of pollutants and human actions, as well as to detect the stage of eutrophization. Ion selective electrodes (ISEs) have been commonly utilized for this purpose, although the presence of interfering ions (potassium and sodium in the case of NH4+-ISE) represents a handicap in terms of the measurement quality. Furthermore, random malfunctions may give rise to incorrect measurements. Bearing all of that in mind, a smart ammonium sensor with enhanced features has been developed and tested in water samples, as demonstrated and commented on in detail following the presentation of the complete set of experimental measurements that have been successfully carried out. This has been achieved through the implementation of an expert system that supervises a set of ISEs in order to (a) avoid random failures and (b) reject interferences. Our approach may also be suitable for in-line monitoring of the water quality through the implementation of wireless sensor networks.

Smart sensors in environmental/water quality monitoring using IoT and cloud services

“…ISEs have been commonly utilized for this purpose, but potentially interfering ions (Na + and K + ), as well as random failures, may affect the measurement quality. A smart NH4 + sensor with improved characteristics was designed to overcome these drawbacks [35], and its performance was verified in water samples. Its success is due mainly to the implementation of a knowledge-based system that is in charge of supervising a group of ISEs with a double goal: (a) avoid random malfunctions and (b) interference rejection.…”

IoT Technologies in Chemical Analysis Systems: Application to Potassium Monitoring in Water

“…The corresponding regression analysis according to the procedure described in [ 3 ] produced the results presented in Table 3 and Table 4 . This procedure consisted of the application of a linear regression of the measurements obtained in the previous experiments to calculate the coefficients of the following polynomial (1): [K + ] estimated = m 1 × [K + ] measured + m 2 × [NH 4 + ] measured + m 3 × [Na + ] measured + b …”

“…This information, which already has high added value (it is in the cloud, free from interference, conveniently stored, and available to applications), was reprocessed again to avoid errors due to sensor malfunction. For this purpose, the TMR module follows the algorithm proposed in [ 2 , 3 ], where the application of the TMR technique to environmental monitoring is described in detail. As a final result, the TMR offers us a single measurement that is immune to the malfunctioning of “real” sensors, since it either provides error-free measurement or indicates the existence of errors, which prevents erroneous decisions from being made (based on erroneous information); in this way, it is also possible to determine which sensor or sensors are giving erroneous results, with a view to their repair.…”

“…In some of our previous work [ 2 , 3 ], we focused on the research and development of techniques that allow for the implementation—by means of expert systems—of intelligent sensors that provide an increase in reliability and the rejection of interference from the point of view of efficiency, low cost, and reduced energy consumption.…”

Fabrication of an All-Solid-State Ammonium Paper Electrode Using a Graphite-Polyvinyl Butyral Transducer Layer

“…The potentiometric selectivity coefficient (K pot i,j ) indicates the ability of the electrode to respond only to the target ion i in the presence of other ions j [42]. One of the major challenges in the development of ammonium ISEs is to overcome problems associated with interfering ions in real matrices.…”