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Register a new userMFC-based biosensor for domestic wastewater COD assessment in constructed wetlands
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In the context of natural-based wastewater treatment technologies (such as constructed wetlands - CW) the use of a low-cost, continuous-like biosensor tool for the assessment of operational conditions is of key importance for plant management optimization. The objective of the present study was to assess the potential use of constructed wetland microbial fuel cells (CW-MFC) as a domestic wastewater COD assessment tool. For the purpose of this work four lab-scale CW-MFCs were set up and fed with pre-settled domestic wastewater at different COD concentrations. Under laboratory conditions two different anodic materials were tested (graphite rods and gravel). Furthermore, a pilot-plant based experiment was also conducted to confirm the findings previously recorded for lab-scale experiments. Results showed that in spite of the low coulombic efficiencies recorded, either gravel or graphite-based anodes were suitable for the purposes of domestic wastewater COD assessment. Significant linear relationships could be established between inlet COD concentrations and CW-MFC Ecell whenever contact time was above 10 hours. Results also showed that the accuracy of the CW-MFC was greatly compromised after several weeks of operation. Pilot experiments showed that CW-MFC presents a good bio-indication response between week 3 and 7 of operation (equivalent to an accumulated organic loading between 100 and 200 g COD/m2, respectively). Main conclusion of this work is that of CW-MFC could be used as an alarm-tool for qualitative continuous influent water quality assessment rather than a precise COD assessment tool due to a loss of precision after several weeks of operation.
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The removal of organic micropollutants (OMPs) has been investigated in constructed wetlands (CWs) operated as bioelectrochemical systems (BES). The operation of CWs as BES (CW-BES), either in the form of microbial fuel cells (MFC) or microbial electrolysis cells (MEC), has only been investigated in recent years. The presented experiment used CW meso-scale systems applying a realistic horizontal flow regime and continuous feeding of real urban wastewater spiked with four OMPs (pharmaceuticals), namely carbamazepine (CBZ), diclofenac (DCF), ibuprofen (IBU) and naproxen (NPX). The study evaluated the removal efficiency of conventional CW systems (CW-control) as well as CW systems operated as closed-circuit MFCs (CW-MFCs) and MECs (CW-MECs). Although a few positive trends were identified for the CW-BES compared to the CW-control (higher average CBZ, DCF and NPX removal by 10-17% in CW-MEC and 5% in CW-MFC), these proved to be not statistically significantly different. Mesoscale experiments with real wastewater could thus not confirm earlier positive effects of CW-BES found under strictly controlled laboratory conditions with synthetic wastewaters.
CMOS-based sensor array chips provide new and attractive features as compared to todays standard tools for medical, diagnostic, and biotechnical applications. Examples for molecule- and cell-based approaches and related circuit design issues are discussed.
Viral infections are among the main reasons for serious pandemics and contagious infections; hence, they cause thousands of fatalities and economic losses annually. In the case of COVID-19, world economies have shut down for months, and physical distancing along with drastic changes in the social behavior of many humans has generated many issues for all countries. Thus, a rapid, low-cost, and sensitive viral detection method is critical to upgrade the living standards of humans while exploiting biomedicine, environmental science, bioresearch, and biosecurity. The emergence of various carbon-based nanomaterials such as carbon nanotubes, graphene, and carbon nanoparticles provided a great possibility for researchers to develop a new and wide variety of biosensors. In particular, graphene has become a promising tool for biosensor fabrication owing to its many interesting properties, such as its exceptional conductivity, ultrahigh electron mobility, and excellent thermal conductivity. This paper provides an overall perspective of graphene-based biosensors and a critical review of the recent advances in graphene-based biosensors that are used to detect different types of viruses, such as Ebola, Zika, and influenza.
Microbial fuel cells implemented in constructed wetlands (CW-MFCs), albeit a relatively new technology still under study, have shown to improve treatment efficiency of urban wastewater. So far the vast majority of CW-MFC systems investigated were designed as lab-scale systems working under rather unrealistic hydraulic conditions using synthetic wastewater. The main objective of this work was to quantify CW-MFCs performance operated under different conditions in a more realistic setup using meso-scale systems with horizontal flow fed with real urban wastewater. Operational conditions tested were organic loading rate (4.9+-1.6, 6.7+-1.4 and 13.6+-3.2 g COD/m2.day) and hydraulic regime (continuous vs intermittent feeding) as well as different electrical connections: CW control (conventional CW without electrodes), open-circuit CW-MFC (external circuit between anode and cathode not connected) and closed-circuit CW-MFC (external circuit connected). Eight horizontal subsurface flow CWs were operated for about four months. Each wetland consisted of a PVC reservoir of 0.193 m2 filled with 4/8 mm granitic riverine gravel. All wetlands had intermediate sampling points for gravel and interstitial liquid sampling. The CW-MFCs were designed as three MFCs incorporated one after the other along the flow path of the CWs. Results showed no significant differences between tested organic loading rates, hydraulic regimes or electrical connections, however, on average, systems operated in closed-circuit CW-MFC mode under continuous flow outperformed the other experimental conditions. Closed-circuit CW-MFC compared to conventional CW control systems showed around 5% and 22% higher COD and ammonium removal, respectively. Correspondingly, overall bacteria activity, as measured by the fluorescein diacetate technique, was higher (4% to 34%) in closed-circuit systems when compared to CW control systems.
Cox proportional hazard model (CPH) is commonly used in clinical research for survival analysis. In quantitative medical imaging (radiomics) studies, CPH plays an important role in feature reduction and modeling. However, the underlying linear assumption of CPH model limits the prognostic performance. In addition, the multicollinearity of radiomic features and multiple testing problem further impedes the CPH models performance. In this work, using transfer learning, a convolutional neural network (CNN) based survival model was built and tested on preoperative CT images of resectable Pancreatic Ductal Adenocarcinoma (PDAC) patients. The proposed CNN-based survival model outperformed the traditional CPH-based radiomics approach in terms of concordance index by 22%, providing a better fit for patients survival patterns. The proposed CNN-based survival model outperforms CPH-based radiomics pipeline in PDAC prognosis. This approach offers a better fit for survival patterns based on CT images and overcomes the limitations of conventional survival models.
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