Real-time in situ auto-correction of K+ interference for continuous and long-term NH4+ monitoring in wastewater using solid-state ion selective membrane (S-ISM) sensor assembly

MXene-Coated Ion-Selective Electrode Sensors for Highly Stable and Selective Lithium Dynamics Monitoring

Lithium holds immense significance in propelling sustainable energy and environmental systems forward. However, existing sensors used for lithium monitoring encounter issues concerning their selectivity and long-term durability. Addressing these challenges is crucial to ensure accurate and reliable lithium measurements during the lithium recovery processes. In response to these concerns, this study proposes a novel approach involving the use of an MXene composite membrane with incorporated poly(sodium 4-styrenesulfonate) (PSS) as an antibiofouling layer on the Li+ ion selective electrode (ISE) sensors. The resulting MXene-PSS Li+ ISE sensor demonstrates exceptional electrochemical performance, showcasing a superior slope (59.42 mV/dec), lower detection limit (10-7.2 M), quicker response time (∼10 s), higher selectivity to Na+ (-2.37) and K+ (-2.54), and reduced impedance (106.9 kΩ) when compared to conventional Li+ ISE sensors. These improvements are attributed to the unique electronic conductivity and layered structure of the MXene-PSS nanosheet coating layer. In addition, the study exhibits the long-term accuracy and durability of the MXene-PSS Li+ ISE sensor by subjecting it to real wastewater testing for 14 days, resulting in sensor reading errors of less than 10% when compared to laboratory validation results. This research highlights the great potential of MXene nanosheet coatings in advancing sensor technology, particularly in challenging applications, such as detecting emerging contaminants and developing implantable biosensors. The findings offer promising prospects for future advancements in sensor technology, particularly in the context of sustainable energy and environmental monitoring.

IR-based device to acquire real-time online heart ECG signals of fish (Cyprinus carpio) to evaluate the water quality

Water quality monitoring is a challenging task due to continuous pollution. The rapid development of engineering technologies has paved the way for the development of efficient and convenient computer-based online continuous water-quality assessment techniques. Techniques based on biological-responses are gaining attention, worldwide. Different biosensors have been developed in recent years to monitor real-time biological responses to evaluate water-quality. The survival and function of various organs of the organism depends on the cardiac system. Alterations in the cardiac system could signify the occurrence/initiation of stress in the organism. We developed a real-time online cardiac function assessment system-OCFAS to acquire fish ECG-signals. We obtained P-wave, R-wave, T-wave, PR-intervals, QT-intervals and QRS-complex continuously, which did not affect the normal activities of carp. We exposed Cyprinus carpio to different concentrations (National Environmental Quality Standards) of ammonia for 48 h. Our OCFAS has precisely acquired the required ECG-signals. A real-time dataset reveals sensitivity to ammonia in carp ECG-indexes. Compared with the control group the P-wave, R-wave and T-wave were weaker in ammonia-treated groups. In contrast, the PR-intervals, QT-intervals and QRS-complex were prolonged in the ammonia-treatment groups. The self-organizing map signifies that the PR-intervals, the QRS-complex and the QT-intervals are consistent with environmental stress. Linear regression analysis also quantitatively signifies that the PR interval has the highest R2 value and the lowest SSE-value, followed by the QRS complex and the QT interval. A concentration-related effect was observed in the ammonia treated groups. The integrated biomarker response (IBRv2) index was used to determine the overall stress of ammonia on carp heart ECG-indexes. IBRv2 also supports the real-time response of carp to ammonia stress. Ammonia levels in the aquaculture and water environment require special attention to avoid its adverse effects on the health of aquatic biota. Our study emphasizes the importance of online real-time fish ECG for water-quality assessment.

Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine

In wastewater monitoring, detecting extremely high pollutant concentrations is necessary to properly calibrate the treatment process. However, existing hardware sensors have a limited linear range which may fail to measure extremely high levels of pollutants; and likewise, the conventional "soft" model sensors are not suitable for the highly-skewed data distributions either. This study developed a new soft sensor by using eXtreme Gradient Boosting (XGBoost) machine learning to 'measure' the wastewater organics (in terms of 5-day biochemical oxygen demand (BOD5)). The soft sensor was tested on influent and effluent BOD5 of two different wastewater treatment plants to validate the results. The model results showed that XGBoost can detect these extreme values better than conventional soft sensors. This new soft sensor can function using a sparse input matrix via XGBoost's sparsity awareness algorithm - which can address the limitation of the conventional soft sensor with the fallibility of supporting hardware sensors even.

Electrospraying Zwitterionic Copolymers as an Effective Biofouling Control for Accurate and Continuous Monitoring of Wastewater Dynamics in a Real-Time and Long-Term Manner

Long-term continuous monitoring (LTCM) of water quality can provide high-fidelity datasets essential for executing swift control and enhancing system efficiency. One roadblock for LTCM using solid-state ion-selective electrode (S-ISE) sensors is biofouling on the sensor surface, which perturbs analyte mass transfer and deteriorates the sensor reading accuracy. This study advanced the anti-biofouling property of S-ISE sensors through precisely coating a self-assembled channel-type zwitterionic copolymer poly(trifluoroethyl methacrylate-random-sulfobetaine methacrylate) (PTFEMA-r-SBMA) on the sensor surface using electrospray. The PTFEMA-r-SBMA membrane exhibits exceptional permeability and selectivity to primary ions in water solutions. NH₄⁺ S-ISE sensors with this anti-fouling zwitterionic layer were examined in real wastewater for 55 days consecutively, exhibiting sensitivity close to the theoretical value (59.18 mV/dec) and long-term stability (error <4 mg/L). Furthermore, a denoising data processing algorithm (DDPA) was developed to further improve the sensor accuracy, reducing the S-ISE sensor error to only 1.2 mg/L after 50 days of real wastewater analysis. Based on the dynamic energy cost function and carbon footprint models, LTCM is expected to save 44.9% NH₄⁺ discharge, 12.8% energy consumption, and 26.7% greenhouse emission under normal operational conditions. This study unveils an innovative LTCM methodology by integrating advanced materials (anti-fouling layer coating) with sensor data processing (DDPA).

Making waves: Time for chemical surface water quality monitoring to catch up with its technical potential

A comprehensive real-time evaluation of the chemical status of surface water bodies is still utopian, but in our opinion, it is time to use the momentum delivered by recent advanced technical, infrastructural, and societal developments to get significantly closer. Procedures like inline and online analysis (in situ or in a bypass) with close to real-time analysis and data provision are already available in several industrial sectors. In contrast, atline and offline analysis involving manual sampling and time-decoupled analysis in the laboratory is still common practice in aqueous environmental monitoring. Automated tools for data analysis, verification, and evaluation are changing significantly, becoming more powerful with increasing degrees of automation and the introduction of self-learning systems. In addition, the amount of available data will most likely in near future be increased by societal awareness for water quality and by citizen science. In this analysis, we highlight the significant potential of surface water monitoring techniques, showcase "lighthouse" projects from different sectors, and pin-point gaps we must overcome to strike a path to the future of chemical monitoring of inland surface waters.