Particle size distribution as an emerging tool for the analysis of wastewater

Integrating Water Purification with Electrochemical Aptamer Sensing for Detecting SARS-CoV-2 in Wastewater

Wastewater analysis of pathogens, particularly SARS-CoV-2, is instrumental in tracking and monitoring infectious diseases in a population. This method can be used to generate early warnings regarding the onset of an infectious disease and predict the associated infection trends. Currently, wastewater analysis of SARS-CoV-2 is almost exclusively performed using polymerase chain reaction for the amplification-based detection of viral RNA at centralized laboratories. Despite the development of several biosensing technologies offering point-of-care solutions for analyzing SARS-CoV-2 in clinical samples, these remain elusive for wastewater analysis due to the low levels of the virus and the interference caused by the wastewater matrix. Herein, we integrate an aptamer-based electrochemical chip with a filtration, purification, and extraction (FPE) system for developing an alternate in-field solution for wastewater analysis. The sensing chip employs a dimeric aptamer, which is universally applicable to the wild-type, alpha, delta, and omicron variants of SARS-CoV-2. We demonstrate that the aptamer is stable in the wastewater matrix (diluted to 50%) and its binding affinity is not significantly impacted. The sensing chip demonstrates a limit of detection of 1000 copies/L (1 copy/mL), enabled by the amplification provided by the FPE system. This allows the integrated system to detect trace amounts of the virus in native wastewater and categorize the amount of contamination into trace (<10 copies/mL), medium (10-1000 copies/mL), or high (>1000 copies/mL) levels, providing a viable wastewater analysis solution for in-field use.

Effects of treatment on the characterization of organic matter in wastewater: a review on size distribution and structural fractionation

Since it is difficult to analyze the components of organic matter in complex effluent matrices individually, the use of more collective, but at the same time, specific wastewater characterization methods would be more appropriate to evaluate changes in effluent characteristics during wastewater treatment. For this purpose, size distribution and structural (resin) fractionation tools have recently been proposed to categorize wastewater. There are several case studies available in the scientific literature being devoted to the application of these fractionation methods. This paper aimed to review the most relevant studies dealing with the evaluation of changes in wastewater characteristics using size distribution and structural (resin) fractionation tools. According to these studies, sequential filtration-ultrafiltration procedures, as well as XAD resins, are frequently employed for size and structural fractionations, respectively. This review focuses on the most relevant publications including biological treatment processes, as well as chemical treatment methods such as coagulation-flocculation, electrocoagulation, the Fenton's reagent and ozonation. This study aims at providing an insight into the possible treatment mechanisms and details the understanding what structural features of wastewater components enabled or prevented efficient treatment (removal) or targeted pollutants.

Processing code-multiplexed Coulter signals via deep convolutional neural networks

Beyond their conventional use of counting and sizing particles, Coulter sensors can be used to spatially track suspended particles, with multiple sensors distributed over a microfluidic chip. Code-multiplexing of Coulter sensors allows such integration to be implemented with simple hardware but requires advanced signal processing to extract multi-dimensional information from the output waveform. In this work, we couple deep learning-based signal analysis with microfluidic code-multiplexed Coulter sensor networks. Specifically, we train convolutional neural networks to analyze Coulter waveforms not only to recognize certain sensor waveform patterns but also to resolve interferences among them. Our technology predicts the size, speed, and location of each detected particle. We show that the algorithm yields a >90% pattern recognition accuracy for distinguishing non-correlated waveform patterns at a processing speed that can potentially enable real-time microfluidic assays. Furthermore, once trained, the algorithm can readily be applied for processing electrical data from other microfluidic devices integrated with the same Coulter sensor network.