Recycling urine for bioelectrochemical hydrogen production using a MoS2 nano carbon coated electrode in a microbial electrolysis cell.

In this study, a novel molybdenum disulfide (MoS2) nano-carbon (NC) coated cathode was developed for hydrogen production in a microbial electrolysis cell (MEC), while treating simulated urine with 2-6 times dilution (conductivity <20 mS cm-1). MoS2 nanoparticles were electrodeposited on the NC coated cathodes at -100, -150 and -200 µA cm-2 and their performances were evaluated in the MEC. The chronopotentiometry (CP) tests showed the improved catalytic activity of MoS2-NC cathodes with much lower cathode overpotential than non-MoS2 coated electrodes. The MoS2-NC200 cathode, electrodeposited at -200 µA cm-2, showed the maximum hydrogen production rate of 0.152 ± 0.002 m3 H2 m-2 d-1 at 0.9V of Eap, which is comparable to the previously reported Pt electrodes. It was found that high solution conductivity over 20 mS cm-1 (>600 mg L-1 NH3-N) can adversely affect the biofilm architecture and the bacterial activity at the anode of the MEC. Exoelectrogenic bacteria for this system at the anode were identified as Tissierella (Clostridia) and Bacteroidetes taxa. Maximum ammonia-nitrogen (NH3-N) and phosphorus (PO4 3--P) removal were 68.7 and 98.6%, respectively. This study showed that the newly fabricated MoS2-NC cathode can be a cost-effective alternative to the Pt cathode for renewable bioelectrochemical hydrogen production from urine.

A strategy for power generation from bilgewater using a photosynthetic microalgal fuel cell (MAFC).

Microbial fuel cells (MFCs) have recently been applied to generate electricity from oily wastewater. Although MFCs that utilize microalgae to provide a self-supporting oxygen (O2) supply at the cathode have been well discussed, those with microalgae at the anode as an active biomass for treating wastewater and producing electrons are still poorly studied and understood. Here, we demonstrated a bilgewater treatment using single- and double-chamber microalgal fuel cells (SMAFC and DMAFC) capable of generating energy with a novel microalgal strain (Chlorella sorokiniana) that was initially isolated from oily wastewater. Compared to previous MFC studies using green algae, relatively high voltage output (151.3-160.1 mV, 71.3-83.4 mV m-2 of power density) was observed in the SMAFC under O2 controlled systems (i.e., acetate addition or light/dark cycle). It was assumed that, under the O2 depletion, alternative electron acceptors such as bicarbonate may be utilized for power generation. A DMAFC showed better power density (up to 23.9%) compared to the SMAFC due to the separated cathode chamber which fully utilizes O2 as an electron acceptor. Both SMAFC and DMAFC removed 67.2-77.4% of soluble chemical oxygen demands (SCOD) from the synthetic bilgewater. This study demonstrates that the application of algae-based MFCs is a feasible strategy to treat oil-in-water emulsion while generating electricity.

Improving Electrochemical Pb2+ Detection Using a Vertically Aligned 2D MoS2 Nanofilm.

Recent advancements in MoS2 nanofilms have aided in the development for important water-related environmental applications. However, a MoS2 nanofilm-coated sensor has yet to have been applied for heavy metal detection in water-related environmental samples. In this study, a novel vertically aligned two-dimensional (2D) MoS2 (edge exposed) nanofilm was applied for in situ lead ion (Pb2+) detection. The developed sensor showed an excellent linear relationship toward Pb2+ between 0 and 20 ppb at -0.45 V vs Ag/AgCl using square wave anodic stripping voltammetry (SWASV) with the improved limit of detection (LOD) of 0.3 ppb in a tap water environment. The vertically aligned 2D MoS2 sensor exhibited improved detection sensitivity (2.8 folds greater than a previous metallic [Bi] composite electrode) with lower relative standard deviation for repetitive measurements (n = 11), indicating enhanced reproducibility for Pb2+ detection. The vertically aligned 2D MoS2 layers exhibited 2.6 times higher sensitivity than horizontally aligned 2D MoS2 (basal plane exposed). Density functional theory calculations demonstrated that adsorption energy of Pb on the MoS2 side edge was much higher (4.11 eV) than those on the basal plane (0.36 and 0.07 eV). In addition, the band gap center of vertical MoS2 was found to be higher than the Pb2+ → Pb reduction potential level and capable of reducing Pb2+. Overall, the newly developed vertically aligned 2D MoS2 sensor showed excellent performance for detecting Pb2+ in a real drinking water environment with good reliability.

Microelectrode Investigation on the Corrosion Initiation at Lead-Brass Galvanic Interfaces in Chlorinated Drinking Water.

In this study, the effects of pH, dissolved inorganic carbon (DIC), and flow on changes in surface chemistry (pH, dissolved oxygen, and free chlorine) of lead-brass joints at initial stages of corrosion were investigated using microelectrodes. Surface measurements showed that the water chemistry at the metal surfaces was highly heterogeneous. At pH 7 and during water stagnation, local pH difference between anodic (leaded-solder) and cathodic (brass) regions differed by as much as 7.5 pH units. High DIC water under the water flowing condition showed minimal pH changes on the surface, whereas in low DIC water, a pH range of 7.6-5.4 (ΔpH 2.2) was observed over the surface. Free chlorine consumption near the lead-brass surface was greater under stagnation, regardless of bulk pH. It was also found that flow can move the low pH plume that originated at the anode. Overall, this study provides direct evidence for highly localized galvanic corrosion in a chlorinated drinking water environment.

Silica-quaternary ammonium "Fixed-Quat" nanofilm coated fiberglass mesh for water disinfection and harmful algal blooms control.

Intensification of pollution loading worldwide has promoted an escalation of different types of disease-causing microorganisms, such as harmful algal blooms (HABs), instigating detrimental impacts on the quality of receiving surface waters. Formation of unwanted disinfection by-products (DBPs) resulting from conventional disinfection technologies reveals the need for the development of new sustainable alternatives. Quaternary Ammonium Compounds (QACs) are cationic surfactants widely known for their effective biocidal properties at the ppm level. In this study, a novel silica-based antimicrobial nanofilm was developed using a composite of silica-modified QAC (Fixed-Quat) and applied to a fiberglass mesh as an active surface via sol-gel technique. The synthesized Fixed-Quat nanocoating was found to be effective against E. coli with an inactivation rate of 1.3 × 10-3 log reduction/cm min. The Fixed-Quat coated fiberglass mesh also demonstrated successful control of Microcystis aeruginosa with more than 99% inactivation after 10 hr of exposure. The developed antimicrobial mesh was also evaluated with wild-type microalgal species collected in a water body experiencing HABs, obtaining a 97% removal efficiency. Overall, the silica-functionalized Fixed-Quat nanocoating showed promising antimicrobial properties for water disinfection and HABs control, while decreasing concerns related to DBPs formation and the possible release of toxic nanomaterials into the environment.

Three-Dimensional Free Chlorine and Monochloramine Biofilm Penetration: Correlating Penetration with Biofilm Activity and Viability.

Disinfectant biofilm penetration and its effect on biofilm aerobic activity and viability are still unclear. In this study, free chlorine and monochloramine were applied until full biofilm penetration occurred, and their effects on biofilm aerobic activity and viability were investigated in three dimensions throughout the entire biofilm depth, extending previous work where viability analysis was limited to the upper biofilm (50 µm depth), free chlorine penetration did not reach completion, and only one-dimensional (depth) profiles were obtained. The free chlorine and monochloramine biofilm concentration profiles were correlated spatially and temporally with aerobic microbial activity and cell-membrane integrity based viability using a combination of (1) microelectrode measurements for disinfectant penetration and (2) LIVE/DEAD BacLight staining, cryo-cross-sectioning, and confocal micrographs analysis for viability measurements throughout the entire biofilm depth. Compared to monochloramine, free chlorine penetration (1) was slower, (2) led to a greater decrease in biofilm thickness from sloughing, and (3) corresponded directly with a viability decrease. In addition, biofilm heterogeneity led to minor differences in either disinfectant's biofilm penetration, and prior biofilm exposure to monochloramine provided little impact to subsequent free chlorine biofilm penetration.

In Situ Monitoring of Pb2+ Leaching from the Galvanic Joint Surface in a Prepared Chlorinated Drinking Water.

A novel method using a micro-ion-selective electrode (micro-ISE) technique was developed for in situ lead monitoring at the water-metal interface of a brass-leaded solder galvanic joint in a prepared chlorinated drinking water environment. The developed lead micro-ISE (100 µm tip diameter) showed excellent performance toward soluble lead (Pb2+) with sensitivity of 22.2 ± 0.5 mV decade-1 and limit of detection (LOD) of 1.22 × 10-6 M (0.25 mg L-1). The response time was less than 10 s with a working pH range of 2.0-7.0. Using the lead micro-ISE, lead concentration microprofiles were measured from the bulk to the metal surface (within 50 µm) over time. Combined with two-dimensional (2D) pH mapping, this work clearly demonstrated that Pb2+ ions build-up across the lead anode surface was substantial, nonuniform, and dependent on local surface pH. A large pH gradient (ΔpH = 6.0) developed across the brass and leaded-tin solder joint coupon. Local pH decreases were observed above the leaded solder to a pH as low as 4.0, indicating it was anodic relative to the brass. The low pH above the leaded solder supported elevated lead levels where even small local pH differences of 0.6 units (ΔpH = 0.6) resulted in about four times higher surface lead concentrations (42.9 vs 11.6 mg L-1) and 5 times higher fluxes (18.5 × 10-6 vs 3.5 × 10-6 mg cm-2 s-1). Continuous surface lead leaching monitoring was also conducted for 16 h.

In Situ Characterization of Oil-in-Water Emulsions Stabilized by Surfactant and Salt Using Microsensors.

Chemically stabilized emulsions are difficult to break because of micelle stability. Many physical and chemical processes have been used for emulsion breaking/separation; however, most operational parameters are based on empirical data and bulk analysis. A multiscale understanding of emulsions is required before these processes can advance further. This study utilized needle-type microsensors and confocal laser scanning microscopy (CLSM) for characterizing simulated bilge water emulsions with different types of surfactants (Triton X-100 and sodium dodecyl sulfate [SDS]) under various NaCl concentrations at microscale. Using microsensors, a diffusion process was clearly visualized across the oil/water interface which appears to be related to emulsion formation kinetics and mass transfer. While emulsion stability decreased with NaCl concentrations, SDS (anionic surfactant) is more likely to form emulsion as salinity increases, requiring more salinity to coalesce SDS emulsions than Triton X-100 (nonionic surfactant) emulsions. Triton X-100 emulsions showed the potential to exhibit particle stabilized emulsions with NaCl concentration below 10-2.5 M. The research demonstrated that the use of nonionic surfactant allows better oil-in-water separation than anionic surfactant. Significant pH changes of emulsions from unknown additives have implications when operating pH-sensitive emulsion breaking/separation processes (e.g., electrocoagulation).

Algal Growth Potential of Microcystis aeruginosa from Reclaimed Water.

Algal growth potential (AGP) of the cyanobacterium Microcystis aeruginosa (M. aeruginosa, NIES-298) using reclaimed water from various wastewater reclamation pilot plants was investigated to evaluate the feasibility of the reclaimed water usage for recreational purposes. After completing the coagulation and ultrafiltration processes, the concentrations of most contaminants in the reclaimed water were lower than the reuse guidelines for recreational water. However, M. aeruginosa successfully adapted to low levels of soluble reactive phosphorus (PO(3-)(4)) concentrations. The AGP values of M. aeruginosa decreased with the progression of treatment processes, and with the increases in the dilution volume. Also, both the AGP and chlorophyll-a values can be estimated a priori without conducting the AGP tests. Therefore, aquatic ecosystems in locations prone to environmental conditions favorable for the growth of M. aeruginosa require more rigorous nutrient management plans (e.g., reverse osmosis and dilution with clean water resources) to reduce the nutrient availability.

Selective reactivity of monochloramine with extracellular matrix components affects the disinfection of biofilm and detached clusters.

The efficiency of monochloramine disinfection was dependent on the quantity and composition of extracellular polymeric substances (EPS) in biofilms, as monochloramine has a selective reactivity with proteins over polysaccharides. Biofilms with protein-based (Pseudomonas putida) and polysaccharide based EPS (Pseudomonas aeruginosa), as well as biofilms with varied amount of polysaccharide EPS (wild-type and mutant P. aeruginosa), were compared. The different reactivity of EPS components with monochloramine influenced disinfectant penetration, biofilm inactivation, as well as the viability of detached clusters. Monochloramine transport profiling measured by a chloramine-sensitive microelectrode revealed a broader diffusion boundary layer between bulk and biofilm surface in the P. putida biofilm compared to those of P. aeruginosa biofilms. The reaction with proteins in P. putida EPS multiplied both the time and the monochloramine mass required to achieve a full biofilm penetration. Cell viability in biofilms was also spatially influenced by monochloramine diffusion and reaction within biofilms, showing a lower survival in the surface section and a higher persistence in the middle section of the P. putida biofilm compared to the P. aeruginosa biofilms. While polysaccharide EPS promoted biofilm cell viability by obstructing monochloramine reactive sites on bacterial cells, protein EPS hindered monochloramine penetration by reacting with monochloramine and reduced its concentration within biofilms. Furthermore, the persistence of bacterial cells detached from biofilm (over 70% for P. putida and ∼40% for polysaccharide producing P. aeruginosa) suggested that currently recommended monochloramine residual levels may underestimate the risk of water quality deterioration caused by biofilm detachment.

Recent advances in algal bloom detection and prediction technology using machine learning.

Harmful algal blooms (HAB) including red tides and cyanobacteria are a significant environmental issue that can have harmful effects on aquatic ecosystems and human health. Traditional methods of detecting and managing algal blooms have been limited by their reliance on manual observation and analysis, which can be time-consuming and costly. Recent advances in machine learning (ML) technology have shown promise in improving the accuracy and efficiency of algal bloom detection and prediction. This paper provides an overview of the latest developments in using ML for algal bloom detection and prediction using various water quality parameters and environmental factors. First, we introduced ML for algal bloom prediction using regression and classification models. Then we explored image-based ML for algae detection by utilizing satellite images, surveillance cameras, and microscopic images. This study also highlights several real-world examples of successful implementation of ML for algal bloom detection and prediction. These examples show how ML can enhance the accuracy and efficiency of detecting and predicting algal blooms, contributing to the protection of aquatic ecosystems and human health. The study also outlines recent efforts to enhance the field applicability of ML models and suggests future research directions. A recent interest in explainable artificial intelligence (XAI) was discussed in an effort to understand the most influencing environmental factors on algal blooms. XAI facilitates interpretations of ML model results, thereby enhancing the models' usability for decision-making in field management and improving their overall applicability in real-world settings. We also emphasize the significance of obtaining high-quality, field-representative data to enhance the efficiency of ML applications. The effectiveness of ML models in detecting and predicting algal blooms can be improved through management strategies for data quality, such as pre-treating missing data and integrating diverse datasets into a unified database. Overall, this paper presents a comprehensive review of the latest advancements in managing algal blooms using ML technology and proposes future research directions to enhance the utilization of ML techniques.

Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1 hour data to cover time frequencies ranging from 1 hour to 1 month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

Microelectrode investigation of iron and copper surfaces aged in presence of monochloramine.

Ductile iron and copper coupons were aged 137-189 days and 2 days, respectively, with 2 mg Cl2 L-1 monochloramine under four water chemistries (pH 7 or 9 and 0 or 3 mg L-1 orthophosphate). Subsequently, microelectrode profiles of monochloramine concentration, oxygen concentration, and pH were measured from the bulk water to near the coupon reactive surface, allowing estimation of flux and apparent surface reaction rate constants for monochloramine and oxygen. Both metals showed similar trends with orthophosphate where orthophosphate decreased metal reactivity with monochloramine (pH 9) and oxygen (pH 7). Comparing iron and copper coupons, apparent surface reaction rate constants for monochloramine and oxygen were one and two orders of magnitude greater, respectively, for iron coupons under all conditions. Overall, this research provides the first insights into monochloramine concentration, oxygen concentration, and pH by direct measurement near ductile iron and copper reactive surfaces aged in the presence of monochloramine.

Applying microelectrodes to investigate aged ductile iron and copper coupon reactivity during free chlorine application.

In drinking water distribution systems, including premise plumbing, dissolved oxygen (DO) and free chlorine (FC) are common oxidants and ductile iron (DI) and copper (Cu) are commonly used pipe materials. Microelectrodes as a tool have been applied in previous corrosion research and were used in this study to collect quantifiable data and understand DO and FC reactivity and pH changes at the water-metal interface. Using microelectrodes, pH, DO, and FC profiles from the bulk water to near and at the surface of aged DI (154-190 d) and Cu (2 d and 86-156 d) coupons were investigated during periods of flow and stagnation (30 min). Using the measured microelectrode profiles, oxidant fluxes and apparent surface reaction rate constants were calculated to elucidate differences between DO and FC reactivity with the coupons. Microelectrodes were successfully applied to measure pH, DO, and FC profiles from the bulk water to near aged DI and Cu coupon surfaces; Cu coupons aged quickly and exhibited less reactivity at 2 d with DO and FC than aged DI coupons did after 154-190 d; and for the aged DI coupon experiments, orthophosphate presence stabilized pH profiles where without orthophosphate pH fluctuations of greater than 2 pH units occurred from the bulk water to the DI coupon surface.

Microelectrode evaluation of in situ oxidant reactivity and pH variability at new ductile iron and copper coupon surfaces.

Thirty-two short term (∼7.5 h) abiotic experiments were conducted with new ductile iron and copper coupons exposed to various water qualities, including pH (7 or 9), dissolved inorganic carbon (DIC, 10 or 50 mg C L-1) and phosphate (0 or 3 mg P L-1) concentrations and 4 mg Cl2 L-1 free chlorine or monochloramine. To quantify oxidant reactivity with the new metal coupons, microelectrodes were used to obtain oxidant (free chlorine or monochloramine and dissolved oxygen (DO)) concentration and pH microprofiles from the bulk water to near the metal coupon surface. From the microprofiles, apparent surface reaction rate constants (k) were determined for each oxidant. An ANOVA analysis evaluated if the five variables (Material, Oxidant, Phosphate, DIC, and pH) significantly affected estimates of k, finding that the Material and Oxidant variables and their interaction were statistically significant (p<0.05), but the effect of variables of Phosphate, DIC, and pH on k values were not significant in this study. In general, both ductile iron and copper coupons showed significant surface reactivity towards free chlorine and monochloramine. For ductile iron, DO consumption was greater than for copper, which showed minimal DO reactivity, and DO was less reactive towards the copper surface than either free chlorine or monochloramine. Furthermore, pH microprofiles provided insight into the complexity that might exist near corroding metal surfaces where the bulk water pH may be substantially different from that measured near metal surfaces which is significant as pH is a controlling variable in terms of scale formation and metal solubility. This study represents an important first step towards using microelectrodes to (1) understand and provide direct measurement of oxidant microprofiles from the bulk water to the metal surface; (2) determine pipe wall reactivity using the directly measured concentrations profiles versus estimated pipe wall reactivity from bulk water measurements, and (3) understand how variables measured by bulk water samples (e.g., pH) may be drastically different from what is occurring at and near the metal surface. Together, these insights will assist in understanding disinfectant residual maintenance, corrosion, and metal release.

Photodegradation of MC-LR using a novel Au-decorated Ni metal-organic framework (Au/Ni-MOF).

Microcystins (MCs) are toxins produced by cyanobacteria commonly found in harmful algal blooms (HAB) occurring in many surface waters. Conventional methods for removing MC-LR such as membrane filtration and activated carbon are only phase change removal methods and are often expensive in operation and maintenance. It is urgent to develop a rapid, easy-to-use, and cost-effective method for the degradation of MC-LR. In this study, a novel Au-decorated Ni-metal-organic framework (Au/Ni-MOF) was newly developed on a hydrophilic carbon fiber paper (2 cm × 2 cm) using an air spraying method. The Au/Ni-MOF was then applied for the photodegradation of MC-LR in water under UV-Vis. The addition of Au onto the surface of the Ni-MOF resulted in a nearly fivefold enhancement in the reaction rate coefficient (k), reaching a value of 0.0599 min-1 for the photodegradation of MC-LR (initial concentration of 20 ppb). It was found that 94.2% of MC-LR removal was attributed to photodegradation, with the remaining 5.8% from adsorption. The rate coefficient of 20 ppb of MC-LR in the surface water sample (pH 6.0) was 0.06 min-1 likely due to the presence of other contaminates including scavenger agents within the sample which inhibits the degradation reaction of the MC-LR. Overall, this study demonstrated the potential for the novel Au/Ni-MOF to effectively reduce the concentration of the MC-LR toxin in the contaminated water.

Interpretation of ensemble learning to predict water quality using explainable artificial intelligence.

Algal bloom is a significant issue when managing water quality in freshwater; specifically, predicting the concentration of algae is essential to maintaining the safety of the drinking water supply system. The chlorophyll-a (Chl-a) concentration is a commonly used indicator to obtain an estimation of algal concentration. In this study, an XGBoost ensemble machine learning (ML) model was developed from eighteen input variables to predict Chl-a concentration. The composition and pretreatment of input variables to the model are important factors for improving model performance. Explainable artificial intelligence (XAI) is an emerging area of ML modeling that provides a reasonable interpretation of model performance. The effect of input variable selection on model performance was estimated, where the priority of input variable selection was determined using three indices: Shapley value (SHAP), feature importance (FI), and variance inflation factor (VIF). SHAP analysis is an XAI algorithm designed to compute the relative importance of input variables with consistency, providing an interpretable analysis for model prediction. The XGB models simulated with independent variables selected using three indices were evaluated with root mean square error (RMSE), RMSE-observation standard deviation ratio, and Nash-Sutcliffe efficiency. This study shows that the model exhibited the most stable performance when the priority of input variables was determined by SHAP. This implies that on-site monitoring can be designed to collect the selected input variables from the SHAP analysis to reduce the cost of overall water quality analysis. The independent variables were further analyzed using SHAP summary plot, force plot, target plot, and partial dependency plot to provide understandable interpretation on the performance of the XGB model. While XAI is still in the early stages of development, this study successfully demonstrated a good example of XAI application to improve the interpretation of machine learning model performance in predicting water quality.

Cost-effective screen-printed carbon electrode biosensors for rapid detection of microcystin-LR in surface waters for early warning of harmful algal blooms.

Microcystins (MCs) are toxins produced by cyanobacteria commonly found in harmful algal blooms (HABs). Due to their toxicity to humans and other organisms, the World Health Organization (WHO) sets a guideline of 1 µg L-1 for microcystin-leucine-arginine (MC-LR) in drinking water. However, current analytical techniques for the detection of MC-LR such as liquid chromatography-mass spectrometry (LC-MS) and ELISA are costly, bulky, time-consuming, and mostly conducted in a laboratory, requiring highly trained personnel. An analytical method that can be used in the field for rapid determination is essential. In this study, an anti-MC-LR/MC-LR/cysteamine-coated screen-printed carbon electrode (SPCE) biosensor was newly developed to detect MC-LR, bioelectrochemically, in water. The functionalization of the electrode surface was confirmed with surface characterization methods. The sensor performance was evaluated by electrochemical impedance spectroscopy (EIS), obtaining a linear working range of MC-LR concentrations between 0.1 and 100 µg L-1 with a limit of detection (LOD) of 0.69 ng L-1. Natural water samples experiencing HABs were collected and analyzed using the developed biosensor, demonstrating the excellent performance of the biosensor with a relative standard deviation (RSD) of 0.65%. The interference tests showed minimal error and RSD values against other common MCs and possible coexisting ions found in water. The biosensor showed acceptable functionality with a shelf life of up to 12 weeks. Overall, the anti-MC-LR/MC-LR/cysteamine/SPCE biosensors can be an innovative solution with characteristics that allow for in situ, low-cost, and easy-to-use capabilities which are essential for developing an overarching and integrated "smart" environmental management system.

Nanoparticle-embedded hydrogel synthesized electrodes for electrochemical oxidation of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS).

In this study, sliver (Ag) and gold (Au) nanoparticles (NPs) were embedded on poly (acrylic acid) (PAA)/poly (allylamine) hydrochloride (PAH) hydrogel fibers for improved electrochemical oxidation (EO) of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) removal. The NPs-loaded PAA/PAHs shows the better charge transport compared to the ceramic nanofiber membranes (CNM) electrodes. At 10 mA cm-2 of current density, the Ag-PAA/PAH electrodes showed a faster removal of PFAS compared to the Ag-CNM electrode probably due to large surface area-volume ratio and high porosity from the hydrogel. Among NPs-loaded PAA/PAH electrodes, the Ag/Au-PAA/PAH electrodes showed the highest removal of PFOA (72%) and PFOS (91%) in 2 h with the maximum removal rate of PFOA (0.0046 min-1) and PFOS (0.0093 min-1). The rapid PFOS removal is possibly due to the high activity of electron transfer with a higher redox potential of SO4•- than •OH. The highly stable F- generation was obtained from each electrode during reproducibility (n = 3). The net energy consumption from Ag/Au-PAA/PAH electrode was 164.9 kWh m-3 for 72% PFOA removal and 90 kWh m-3 for 91% PFOS removal, respectively. The developed Au-PAA/PAH electrodes were applied to lake water samples and showed acceptable PFOS removal (65%) with relative standard deviations (RSD) of 10.2% (n = 3) at 10 mA cm-2 of current density. Overall, the NP-embedded hydrogel nanofibers were proven to be a promising sustainable catalyst for the electrochemical PFAS oxidation in water.

Predicting bilgewater emulsion stability by oil separation using image processing and machine learning.

Bilgewater is a shipboard multi-component oily wastewater, combining numerous wastewater sources. A better understanding of bilgewater emulsions is required for proper wastewater management to meet discharge regulations. In this study, we developed 360 emulsion samples based on commonly used Navy cleaner data and previous bilgewater composition studies. Oil value (OV) was obtained from image analysis of oil/creaming layer and validated by oil separation (OS) which was experimentally determined using a gravimetric method. OV (%) showed good agreement with OS (%), indicating that a simple image-based parameter can be used for emulsion stability prediction model development. An ANOVA analysis was conducted of the five variables (Cleaner, Salinity, Suspended Solids [SS], pH, and Temperature) that significantly impacted estimates of OV, finding that the Cleaner, Salinity, and SS variables were statistically significant (p < 0.05), while pH and Temperature were not. In general, most cleaners showed improved oil separation with salt additions. Novel machine learning (ML)-based predictive models of both classification and regression for bilgewater emulsion stability were then developed using OV. For classification, the random forest (RF) classifiers achieved the most accurate prediction with F1-score of 0.8224, while in regression-based models the decision tree (DT) regressor showed the highest prediction of emulsion stability with the average mean absolute error (MAE) of 0.1611. Turbidity also showed a good emulsion prediction with RF regressor (MAE of 0.0559) and RF classifier (F1-score of 0.9338). One predictor variable removal test showed that Salinity, SS, and Temperature are the most impactful variables in the developed models. This is the first study to use image processing and machine learning for the prediction of oil separation for the application of bilgewater assessment within the marine sector.