Unlocking the Potential of Nanobubbles: Achieving Exceptional Gas Efficiency in Electrogeneration of Hydrogen Peroxide.

The electrogeneration of hydrogen peroxide (H2 O2 ) via the oxygen reduction reaction is a crucial process for advanced water treatment technologies. While significant effort is being devoted to developing highly reactive materials, gas provision systems used in these processes are receiving less attention. Here, using oxygen nanobubbles to improve the gas efficiency of the electrogeneration of H2 O2 is proposed. Aeration with nanobubbles is compared to aeration with macrobubbles under an identical experimental set-up, with nanobubbles showing a much higher gas-liquid volumetric mass transfer coefficient (KL a) of 2.6 × 10-2 min-1 compared to 2.7 × 10-4 min-1 for macrobubbles. Consequently, nanobubbles exhibit a much higher gas efficiency using 60% of O2 delivered to the system compared to 0.19% for macrobubbles. Further, it is observed that the electrogeneration of H2 O2 using carbon felt electrodes is enhanced using nanobubbles. Under the same dissolved oxygen levels, nanobubbles boost the reaction yield to 84%, while macrobubbles yield only 53.8%. To the authors' knowledge, this is the first study to investigate the use of nanobubbles in electrochemical reactions and demonstrate their ability to enhance gas efficiency and electrocatalytic response. These findings have important implications for developing more efficient chemical and electrochemical processes operating under gas-starving systems.

All-in-One Zinc-Doped Prussian Blue Nanozyme for Efficient Capture, Separation, and Detection of Copper Ion (Cu2+ ) in Complicated Matrixes.

Copper is a vital micronutrient for lives and an important ingredient for bactericides and fungicides. Given its indispensable biological and agricultural roles, there is an urgent need to develop simple, affordable, and reliable methods for detecting copper in complicated matrixes, particularly in underdeveloped regions where costly standardized instruments and sample dilution procedures hinder progress. The findings that zinc-doped Prussian blue nanoparticle (ZnPB NP) exhibits exceptional efficiency in capturing and isolating copper ions, and accelerates the generation of dissolved oxygen in a solution of H2 O2 with remarkable sensitivity and selectivity, the signal of which displays a positive correlation with the copper level due to the copper-enhanced catalase-like activity of ZnPB NP, are presented. Consequently, the ZnPB NP serves as an all-in-one sensor for copper ion. The credibility of the method for copper assays in human urine and farmland soil is shown by comparing it to the standard instrumentation, yielding a coefficient of correlation (R2 = 0.9890), but the cost is dramatically reduced. This ZnPB nanozyme represents a first-generation probe for copper ion in complicated matrixes, laying the groundwork for the future development of a practical copper sensor that can be applied in resource-constrained environments.

Enhanced poly-γ-glutamic acid synthesis in Corynebacterium glutamicum by reconstituting PgsBCA complex and fermentation optimization.

Previously, a novel Corynebacterium glutamicum strain for the de novo biosynthesis of tailored poly-γ-glutamic acid (γ-PGA) has been constructed by our group. The strain was based on the γ-PGA synthetase complex, PgsBCA, which is the only polyprotein complex responsible for γ-PGA synthesis in Bacillus spp. In the present study, PgsBCA was reconstituted and overexpressed in C. glutamicum to further enhance γ-PGA synthesis. First, we confirmed that all the components (PgsB, PgsC, and PgsA) of γ-PGA synthetase derived from B. licheniformis are necessary for γ-PGA synthesis, and γ-PGA was detected only when PgsB, PgsC, and PgsA were expressed in combination in C. glutamicum. Next, the expression level of each pgsB, pgsC, and pgsA was tuned in order to explore the effect of expression of each of the γ-PGA synthetase subunits on γ-PGA production. Results showed that increasing the transcription levels of pgsB or pgsC and maintaining a medium-level transcription level of pgsA led to 35.44% and 76.53% increase in γ-PGA yield (γ-PGA yield-to-biomass), respectively. Notably, the expression level of pgsC had the greatest influence (accounting for 68.24%) on γ-PGA synthesis, followed by pgsB. Next, genes encoding for PgsC from four different sources (Bacillus subtilis, Bacillus anthracis, Bacillus methylotrophicus, and Bacillus amyloliquefaciens) were tested in order to identify the influence of PgsC-encoding orthologues on γ-PGA production, but results showed that in all cases the synthesis of γ-PGA was significantly inhibited. Similarly, we also explored the influence of gene orthologues encoding for PgsB on γ-PGA production, and found that the titer increased to 17.14 ± 0.62 g/L from 8.24 ± 0.10 g/L when PgsB derived from B. methylotrophicus replaced PgsB alone in PgsBCA from B. licheniformis. The resulting strain was chosen for further optimization, and we achieved a γ-PGA titer of 38.26 g/L in a 5 L fermentor by optimizing dissolved oxygen level. Subsequently, by supplementing glucose, γ-PGA titer increased to 50.2 g/L at 48 h. To the best of our knowledge, this study achieved the highest titer for de novo production of γ-PGA from glucose, without addition of L-glutamic acid, resulting in a novel strategy for enhancing γ-PGA production.

Counter-gradient variation in gene expression between fish populations facilitates colonization of low-dissolved oxygen environments.

The role of phenotypic plasticity during colonization remains unclear due to the shifting importance of plasticity across timescales. In the early stages of colonization, plasticity can facilitate persistence in a novel environment; but over evolutionary time, processes such as genetic assimilation may reduce variation in plastic traits such that species with a longer evolutionary history in an environment can show lower levels of plasticity than recent invaders. Therefore, comparing species in the early stages of colonization to long-established species provides a powerful approach for uncovering the role of phenotypic plasticity during different stages of colonization. We compared gene expression between low-dissolved oxygen (DO) and high-DO populations of two cyprinid fish: Enteromius apleurogramma, a species that has undergone a recent range expansion, and E. neumayeri, a long-established native species in the same region. We sampled tissue either immediately after capture from the field or after a 2-week acclimation under high-DO conditions, allowing us to test for both evolved and plastic differences in low-DO vs high-DO populations of each species. We found that most genes showing candidate-evolved differences in gene expression did not overlap with those showing plastic differences in gene expression. However, in the genes that did overlap, there was counter-gradient variation such that plastic and evolved gene expression responses were in opposite directions in both species. Additionally, E. apleurogramma had higher levels of plasticity and evolved divergence in gene expression between field populations. We suggest that the higher level of plasticity and counter-gradient variation may have allowed rapid genetic adaptation in E. apleurogramma and facilitated colonization. This study shows how counter-gradient variation may impact the colonization of divergent oxygen environments.

Anoxia begets anoxia: A positive feedback to the deoxygenation of temperate lakes.

Declining oxygen concentrations in the deep waters of lakes worldwide pose a pressing environmental and societal challenge. Existing theory suggests that low deep-water dissolved oxygen (DO) concentrations could trigger a positive feedback through which anoxia (i.e., very low DO) during a given summer begets increasingly severe occurrences of anoxia in following summers. Specifically, anoxic conditions can promote nutrient release from sediments, thereby stimulating phytoplankton growth, and subsequent phytoplankton decomposition can fuel heterotrophic respiration, resulting in increased spatial extent and duration of anoxia. However, while the individual relationships in this feedback are well established, to our knowledge, there has not been a systematic analysis within or across lakes that simultaneously demonstrates all of the mechanisms necessary to produce a positive feedback that reinforces anoxia. Here, we compiled data from 656 widespread temperate lakes and reservoirs to analyze the proposed anoxia begets anoxia feedback. Lakes in the dataset span a broad range of surface area (1-126,909 ha), maximum depth (6-370 m), and morphometry, with a median time-series duration of 30 years at each lake. Using linear mixed models, we found support for each of the positive feedback relationships between anoxia, phosphorus concentrations, chlorophyll a concentrations, and oxygen demand across the 656-lake dataset. Likewise, we found further support for these relationships by analyzing time-series data from individual lakes. Our results indicate that the strength of these feedback relationships may vary with lake-specific characteristics: For example, we found that surface phosphorus concentrations were more positively associated with chlorophyll a in high-phosphorus lakes, and oxygen demand had a stronger influence on the extent of anoxia in deep lakes. Taken together, these results support the existence of a positive feedback that could magnify the effects of climate change and other anthropogenic pressures driving the development of anoxia in lakes around the world.

Predicting Redox Conditions in Groundwater at a National Scale Using Random Forest Classification.

Redox conditions in groundwater may markedly affect the fate and transport of nutrients, volatile organic compounds, and trace metals, with significant implications for human health. While many local assessments of redox conditions have been made, the spatial variability of redox reaction rates makes the determination of redox conditions at regional or national scales problematic. In this study, redox conditions in groundwater were predicted for the contiguous United States using random forest classification by relating measured water quality data from over 30,000 wells to natural and anthropogenic factors. The model correctly predicted the oxic/suboxic classification for 78 and 79% of the samples in the out-of-bag and hold-out data sets, respectively. Variables describing geology, hydrology, soil properties, and hydrologic position were among the most important factors affecting the likelihood of oxic conditions in groundwater. Important model variables tended to relate to aquifer recharge, groundwater travel time, or prevalence of electron donors, which are key drivers of redox conditions in groundwater. Partial dependence plots suggested that the likelihood of oxic conditions in groundwater decreased sharply as streams were approached and gradually as the depth below the water table increased. The probability of oxic groundwater increased as base flow index values increased, likely due to the prevalence of well-drained soils and geologic materials in high base flow index areas. The likelihood of oxic conditions increased as topographic wetness index (TWI) values decreased. High topographic wetness index values occur in areas with a propensity for standing water and overland flow, conditions that limit the delivery of dissolved oxygen to groundwater by recharge; higher TWI values also tend to occur in discharge areas, which may contain groundwater with long travel times. A second model was developed to predict the probability of elevated manganese (Mn) concentrations in groundwater (i.e., ≥50 µg/L). The Mn model relied on many of the same variables as the oxic/suboxic model and may be used to identify areas where Mn-reducing conditions occur and where there is an increased risk to domestic water supplies due to high Mn concentrations. Model predictions of redox conditions in groundwater produced in this study may help identify regions of the country with elevated groundwater vulnerability and stream vulnerability to groundwater-derived contaminants.

Potential Growth of Anammox Bacteria under Aerobic Conditions.

Anammox bacteria are obligate anaerobic bacteria that exist widely in nature with sufficient amounts of dissolved oxygen. However, whether anammox bacteria can grow under aerobic conditions remains unclear. In this study, we found that the production of nitrate in the anammox system under aerobic conditions was significantly higher than that under anaerobic conditions without total nitrogen loss. Anammox bacteria can grow by oxidizing nitrite and dehydrogenating hydrazine to produce electrons for carbon fixation. The hydrazine dehydrogenase in anammox bacteria was inhibited under aerobic conditions, and the nitrite oxidoreductase transcription expression of anammox bacteria increased by 2.7 times compared to that under anaerobic conditions, which was the main way for anammox bacteria perform carbon fixation. DNA-stable isotope probing with 13C bicarbonate found the existence of anammox bacteria with 13C isotopes in aerobic cultivation, further proving that anammox bacteria can grow under aerobic condition. More than half of the pathways in glycolysis, the Wood-Ljungdahl pathway, and the tricarboxylic acid cycle were upregulated in anammox bacteria in aerobic condition. Large amounts of bacterioferritins are the important antioxidative enzymes in anammox bacteria in the aerobic environment, which contributes to their stronger oxygen adaptation than other anaerobes. This study expands our understanding of the growth mechanism of anammox bacteria as well as the oxygen adaptation strategies of obligate anaerobic bacteria.

Satellite-Based Global Sea Surface Oxygen Mapping and Interpretation with Spatiotemporal Machine Learning.

The assessment of dissolved oxygen (DO) concentration at the sea surface is essential for comprehending the global ocean oxygen cycle and associated environmental and biochemical processes as it serves as the primary site for photosynthesis and sea-air exchange. However, limited comprehensive measurements and imprecise numerical simulations have impeded the study of global sea surface DO and its relationship with environmental challenges. This paper presents a novel spatiotemporal information embedding machine-learning framework that provides explanatory insights into the underlying driving mechanisms. By integrating extensive in situ data and high-resolution satellite data, the proposed framework successfully generated high-resolution (0.25° × 0.25°) estimates of DO concentration with exceptional accuracy (R2 = 0.95, RMSE = 11.95 µmol/kg, and test number = 2805) for near-global sea surface areas from 2010 to 2018, uncertainty estimated to be ±13.02 µmol/kg. The resulting sea surface DO data set exhibits precise spatial distribution and reveals compelling correlations with prominent marine phenomena and environmental stressors. Leveraging its interpretability, our model further revealed the key influence of marine factors on surface DO and their implications for environmental issues. The presented machine-learning framework offers an improved DO data set with higher resolution, facilitating the exploration of oceanic DO variability, deoxygenation phenomena, and their potential consequences for environments.

Closing Dichloramine Decomposition Nitrogen and Oxygen Mass Balances: Relative Importance of End-Products from the Reactive Nitrogen Species Pathway.

In drinking water chloramination, monochloramine autodecomposition occurs in the presence of excess free ammonia through dichloramine, the decay of which was implicated in N-nitrosodimethylamine (NDMA) formation by (i) dichloramine hydrolysis to nitroxyl which reacts with itself to nitrous oxide (N2O), (ii) nitroxyl reaction with dissolved oxygen (DO) to peroxynitrite or mono/dichloramine to nitrogen gas (N2), and (iii) peroxynitrite reaction with total dimethylamine (TOTDMA) to NDMA or decomposition to nitrite/nitrate. Here, the yields of nitrogen and oxygen-containing end-products were quantified at pH 9 from NHCl2 decomposition at 200, 400, or 800 µeq Cl2·L-1 with and without 10 µM-N TOTDMA under ambient DO (∼500 µM-O) and, to limit peroxynitrite formation, low DO (≤40 µM-O). Without TOTDMA, the sum of free ammonia, monochloramine, dichloramine, N2, N2O, nitrite, and nitrate indicated nitrogen recoveries ±95% confidence intervals were not significantly different under ambient (90 ± 6%) and low (93 ± 7%) DO. With TOTDMA, nitrogen recoveries were less under ambient (82 ± 5%) than low (97 ± 7%) DO. Oxygen recoveries under ambient DO were 88-97%, and the so-called unidentified product of dichloramine decomposition formed at about three-fold greater concentration under ambient compared to low DO, like NDMA, consistent with a DO limitation. Unidentified product formation stemmed from peroxynitrite decomposition products reacting with mono/dichloramine. For a 2:2:1 nitrogen/oxygen/chlorine atom ratio and its estimated molar absorptivity, unidentified product inclusion with uncertainty may close oxygen recoveries and increase nitrogen recoveries to 98% (ambient DO) and 100% (low DO).

Metabolic activity of Planktothrix rubescens and its consequences on oxygen dynamics in laboratory experiment: A stable isotope study.

Fluctuations in dissolved oxygen (DO) contents in natural waters can become intense during cyanobacteria blooms. In a reconnaissance study, we investigated DO concentrations and stable isotope dynamics during a laboratory experiment with the cyanobacterium Planktothrix rubescens in order to obtain insights into primary production under specific conditions. This observation was extended to sub-daily timescales with alternating light and dark phases. Dissolved oxygen concentrations and its isotopes (δ18ODO) ranged from 0.02 to 0.06 mmol · L-1 and from +9.6‰ to +23.4‰. The δ18ODO proved to be more sensitive than concentration measurements in response to metabolic variation and registered earlier shifts to dominance by respiration. Oxygen (O2) contents in the headspace and its isotopes (δ18OO2) ranged from 2.62 to 3.20 mmol · L-1 and from +9.8‰ to +21.9‰. Headspace samples showed less fluctuations in concentration and isotope trends because aquatic processes were hardly able to alter signals once the gas had reached the headspace. Headspace δ18OO2 values were corrected for gas-water equilibration and were determined to be higher than the mean δ18OH2O of -8.7‰. This finding suggests that counteracting respiration was important even during the highest photosynthetic activity. Additionally, headspace analyses led to the definition of a fractionation factor for respiration (αR) of this cyanobacterium with a value of 0.980. This value confirms the one commonly used for cyanobacteria. Our findings may become important for the management of water bodies where decreases in DO are caused by cyanobacteria.

Effects of aeration control strategies on nitrous oxide emissions in alternating anoxic-oxic sequencing batch reactor systems.

Reducing N2O emissions is key to controlling greenhouse gases (GHG) in wastewater treatment plants (WWTPs). Although studies have examined the effects of dissolved oxygen (DO) on N2O emissions during nitrogen removal, the precise effects of aeration rate remain unclear. This study aimed to fill this research gap by investigating the influence of dynamic aeration rates on N2O emissions in an alternating anoxic-oxic sequencing batch reactor system. The emergence of DO breakthrough points indicated that the conversion of ammonia nitrogen to nitrite and the release of N2O were nearly complete. Approximately 91.73 ± 3.35% of N2O was released between the start of aeration and the DO breakthrough point. Compared to a fixed aeration rate, dynamically adjusting the aeration rates could reduce N2O production by up to 48.6%. Structural equation modeling revealed that aeration rate and total nitrogen directly or indirectly had significant effects on the N2O production. A novel regression model was developed to estimate N2O production based on energy consumption (aeration flux), water quality (total nitrogen), and GHG emissions (N2O). This study emphasizes the potential of optimizing aeration strategies in WWTPs to significantly reduce GHG and improve environmental sustainability.

Impact of varying dissolved organic carbon load on sediment phosphorus release and its periodic mechanisms.

Phosphorus (P) release from sediment poses a severe challenge for eutrophication management in the aquatic environment. The dissolved organic carbon (DOC) concentrations in riverine ecosystems have shown an increasing trend due to intensified climate change and anthropogenic activities, while their impact on sediment P cycling remains unclear. To investigate the effects of different DOC loads on sediment P release and the underlying mechanisms, we conducted a two-month experiment in 15 plexiglass tanks, with five gradient-increasing target DOC concentrations set according to reality: control (S0), 5 mg/L (S5), 10 mg/L (S10), 15 mg/L (S15), and 20 mg/L (S20). The results demonstrated that: i) DOC enrichment promoted the sediment P mobilization and release, with the underlying mechanisms exhibited periodic characteristics. ii) reduced dissolved oxygen (DO) concentration and stimulated alkaline phosphatase activity (APA) were likely the primary and sustained facilitating mechanisms. While after the termination of DOC load, elevated pH level was also considered a contributing factor when chlorophyll a (Chl a) ranged between 5.9 µg/L and 7.7 µg/L iii) ultimate concentration of total P (TP) in the overlying water depended on DOC load. After DOC addition was terminated, decreased TP concentrations were observed when DOC concentration was in the range of 5-15 mg/L, which may be attributed to the direct uptake of P by phytoplankton counteracting the minor promotion of P release induced by anoxic conditions. However, when DOC concentrations exceeded 15-20 mg/L, there were notable increments in TP concentrations. Our findings provide further insight into the response mechanisms of sediment P release to the increasing organic C load in natural ecosystems. The impact of broader C forms or C loads on sediment P cycling needs to be fully elucidated and even quantified in future studies, especially through large-scale field investigations to further clarify the coupled roles between C and P.

Microbial fuel cell in long-term operation and providing electricity for intermittent aeration to remove contaminants from sewage.

Microbial fuel cells (MFCs) show promise in sewage treatment because they can directly convert organic matter (OM) into electricity. This study aimed to demonstrate MFCs stability over 750 days of operation and efficient removal of OM and nitrogenous compounds from sewage. To enhance contaminant removal, oxygen was provided into the anode chamber via a mini air pump. This pump was powered by the MFCs' output voltage, which was boosted using a DC-DC converter. The experimental system consisted of 12 sets of cylindrical MFCs within a 246L-scale reactor. The boosted voltage reached 4.7 V. This voltage was first collected in capacitors every 5 min and then dispensed intermittently to the air pump for the MFCs reactor in 4 s. This corresponds to receiving average DO concentration reaching 0.34 ± 0.44 mg/L at 10 cm above the air-stone. Consequently, the degradation rate constants (k) for chemical oxygen demand (COD) and biological oxygen demand (BOD) in the presence of oxygen were 0.048 and 0.069, respectively, which surpassed those without oxygen by 0.039 and 0.044, respectively. Aeration also marginally improved the removal of ammonia because of its potential to create a favorable environment for the growth of anammox and ammonia-oxidizing bacteria such as Candidatus brocadia and Nitrospira. The findings of this study offer in-depth insight into the benefits of boosted voltage in MFCs, highlighting its potential to enhance contaminant degradation. This serves as a foundation for future research focused on improving MFCs performance, particularly for the removal of contaminants from wastewater.

Unveiling the temporal variability of gas transfer coefficients of streams based on high-frequency dissolved oxygen measurements.

Greenhouse gas (GHG) emissions from streams and rivers are important sources of global GHG emissions. As a crucial parameter for estimating GHG emissions, the gas transfer coefficient (expressed as K600 at water temperature of 20 °C) has uncertainties. This study proposed a new approach for estimating K600 based on high-frequency dissolved oxygen (DO) data and an ecosystem metabolism model. This approach combines the numerical solution method with the Markov Chain Monte Carlo analysis. This study was conducted in the Chaohu Lake watershed in Southeastern China, using high-frequency data collected from six streams from 2021 to 2023. This study found: (1) The numerical solution of K600 demonstrated distinct dynamic variability for all streams, ranging from 0 to 111.39 cm h-1 (2) Streams with higher discharge (>10 m3 s-1) exhibited significant seasonal differences in K600 values. The monthly average discharge and water temperature were the two factors that determined the variation in K600 values. (3) K600 was a major source of uncertainty in CO2 emission fluxes, with a relative contribution of 53.72%. An integrated K600 model of riverine gas exchange was developed at the watershed scale and validated using the observed DO change. Our study stressed that K600 dynamics can better represent areal change to reduce uncertainty in estimating GHG emissions.

Enhanced predictive modeling of dissolved oxygen concentrations in riverine systems using novel hybrid temporal pattern attention deep neural networks.

Monitoring water quality and river ecosystems is vital for maintaining public health and environmental sustainability. Over the past decade, data-driven methods have been extensively used for river water quality modeling, including dissolved oxygen (DO) concentrations. Despite advancements, challenges persist regarding accuracy, scalability, and adaptability of data-driven models to diverse environmental conditions. Previous studies primarily employed singular models or basic combinations of machine learning techniques, lacking advanced integration of adaptive mechanisms to process complex and evolving datasets. The current study introduces innovative hybrid models that integrate temporal pattern attention (TPA) mechanisms with advanced neural networks, including feed-forward neural networks (FFNNs) and long short-term memory networks (LSTMs). This approach leverages the synergistic strengths of individual models, significantly enhancing the accuracy of DO predictions. The models were rigorously tested against water quality data obtained from two distinct riverine environments, the Illinois River (ILL) and Des Plaines River (DP). Daily measured water quality data, including DO, chlorophyll-a, nitrate plus nitrite, water temperature, specific conductance, and pH, from 2017 to 2024 provided a robust foundation for comprehensive analysis of DO dynamics in these rivers. We conducted 10 scenarios with different model inputs, wherein the hybrid TPACWRNN-LSTM-10 model particularly excelled, achieving coefficient of determination values of 0.993 and 0.965, and root mean squared errors of 0.241 mg/L and 0.450 mg/L for DO predictions at the ILL and DP stations, respectively. The model's reliability was further confirmed by Willmott's index values of 0.998 and 0.992 and Nash-Sutcliffe efficiency values of 0.990 and 0.961 at the ILL and DP stations, respectively. Additionally, Shapley additive explanations (SHAP) values were utilized to interpret each predictor's contribution, revealing key drivers of DO predictions. We believe the novel hybrid modeling approach presented in this study could benefit utilities and water resource management systems for predicting water quality in complex systems.

Alkane-translocated cells of Rhodococcus strains utilize dissolved oxygen in the alkane phase of an aqueous-alkane two-phase culture.

To clarify the growth mechanisms of Rhodococcus in the alkane phase, we measured oxygen utilization in the alkane phase. The results showed that dissolved oxygen decreased significantly when viable cells were present in the alkane phase. The findings suggested that Rhodococcus strains can grow in alkanes and utilize the resident dissolved oxygen.

Completely noninvasive multi-analyte monitoring system for cell culture processes.

Although online monitoring of dissolved O2, pH, and dissolved CO2 is critical in bioprocesses, nearly all existing technologies require some level of direct contact with the cell culture environment, posing risks of contamination. This study addresses the need for an accurate, and completely noninvasive technique for simultaneous measurement of these analytes. A "non-contact" technique for simultaneous monitoring of dissolved O2, pH, and dissolved CO2 was developed. Instead of direct contact with the culture media, the measurements were made through permeable membranes via either a sampling port in the culture vessel wall or a flow cell. The efficacy of the "non-contact" technique was validated in Escherichia coli (E.coli), Chinese hamster ovary (CHO) culture processes, and dynamic environments created by sparging gases in cell culture medium. The measurements obtained through the developed techniques were comparable to those obtained through control methods. The noninvasive monitoring system can offer accurate, and contamination-minimized monitoring of critical process parameters including dissolved O2, pH, and dissolved CO2. These advancements will enhance the control and optimization of cell culture processes, promising improved cell culture performance.

Benthic primary production decreases internal phosphorus loading from lake sediments under light supplement.

In aquatic ecosystems, light penetrating the sediment surface in shallow lakes may regulate the internal phosphorus (P) release through benthic primary production, which subsequently affects oxidation, pH levels, and alkaline phosphatase activity in the upper sediment. To study the effects of light exposure on the P dynamics at the sediment-water interface under eutrophic conditions, a two-month mesocosm experiment was conducted in twelve cement tanks (1000 L each). The tanks were equipped with Light-Emitting Diode (LED) lights, and surface sediments collected from eutrophic Lake Nanhu (China) were exposed to four different light intensities (0, 50, 100, 200 µmol m-2 s-1). The results revealed that: 1) Both the total phosphorus concentration and the phosphorus release flux from the sediment were lower in the light treatments (mean value, 0.59-0.71 mg L-1 and 0.00-0.01 mg m-2 d-1, respectively) than in the control treatment (0.77 mg L-1 and 0.01 mg m-2 d-1, respectively), indicating that light supplement could decrease the internal P release. 2) Benthic primary production promoted by light directly absorbed soluble reactive phosphorus and decreased the internal P release. The resulting improved production could also increase dissolved oxygen concentrations at the sediment-water interface, thus indirectly inhibiting internal P release. 3) The relative contributions of direct absorption and indirect inhibition on the internal P release ranged between 23% to 69% and 31% to 77% depending on the light intensity.

Assessment and optimization of wastewater treatment plant in terms of effluent quality, energy footprint, and greenhouse gas emissions: An integrated modeling approach.

Wastewater treatment plants (WWTPs) can benefit from utilizing digital technologies to reduce greenhouse gas (GHG) emissions and to comply with effluent quality standards. In this study, the GHG emissions and electricity consumption of a WWTP were evaluated via computer simulation by varying the dissolved oxygen (DO), mixed liquor recirculation (MLR), and return activated sludge (RAS) parameters. Three different measures, namely, effluent water quality, GHG emissions, and energy consumption, were combined as water-energy-carbon coupling index (WECCI) to compare the effects of the parameters on WWTPs, and the optimal operating condition was determined. The initial conditions of the A2O process were set to 4.0 mg/L of DO, 100 % MLR, and 90.7 % RAS. Eighty scenarios with various DO, MLR, and RAS were simulated under steady-state condition to optimize the biological treatment process. The optimal operating conditions were found to be 1.5 mg/L of DO, 190 % MLR, and 90.9 % RAS, which had the highest WECCI of 2.40 when compared to the WECCI of the initial condition (1.07). This optimal condition simultaneously reduced GHG emissions by 1348 kg CO2-eq/d and energy consumption by 11.64 MWh/d. This implies that controlling DO, MLR, and RAS through sensors, valves, and pumps offers a promising approach to operating WWTPs with reduced electricity consumption and GHG emissions while attaining effluent quality standards. Additionally, the nitrous oxide stripping rate exhibited linear relationships with the effluent total ammonia and nitrite concentrations in the aerobic reactor, suggesting that monitoring dissolved nitrogen compounds in the effluent and reactor could be a viable strategy to control MLR and DO in the biological reactor. The digital-based assessment and optimization tools developed in this study are expected to hold promise for application in broader environmental management efforts.

Research on the Influence of Core Sensing Components on the Performance of Galvanic Dissolved Oxygen Sensors.

The galvanic dissolved oxygen sensor finds widespread applications in multiple critical fields due to its high precision and excellent stability. As its core sensing components, the oxygen-permeable membrane, electrode, and electrolyte significantly impact the sensor's performance. To systematically investigate the comprehensive effects of these core sensing components on the performance of galvanic dissolved oxygen sensors, this study selected six types of oxygen-permeable membranes made from two materials (Perfluoroalkoxy Polymer (PFA) and Fluorinated Ethylene Propylene Copolymer (FEP)) with three thicknesses (0.015 mm, 0.03 mm, and 0.05 mm). Additionally, five concentrations of KCl electrolyte were configured, and four different proportions of lead-tin alloy electrodes were chosen. Single-factor and crossover experiments were conducted using the OxyGuard dissolved oxygen sensor as the experimental platform. The experimental results indicate that under the same membrane thickness conditions, PFA membranes provide a higher output voltage compared to FEP membranes. Moreover, the oxygen permeability of FEP membranes is more significantly affected by temperature. Furthermore, the oxygen permeability of the membrane is inversely proportional to its thickness; the thinner the membrane, the better the oxygen permeability, resulting in a corresponding increase in sensor output voltage. When the membrane thickness is reduced from 0.05 mm to 0.015 mm, the sensor output voltage for PFA and FEP membranes increases by 86% and 74.91%, respectively. However, this study also observed that excessively thin membranes might compromise measurement accuracy. In a saturated, dissolved oxygen environment, the sensor output voltage corresponding to the six oxygen-permeable membranes used in the experiment exhibits a highly linear inverse relationship with temperature (correlation coefficient ≥ 98%). Meanwhile, the lead-tin ratio of the electrode and electrolyte concentration have a relatively minor impact on the sensor output voltage, demonstrating good stability at different temperatures (coefficient of variation ≤ 0.78%). In terms of response time, it is directly proportional to the thickness of the oxygen-permeable membrane, especially for PFA membranes. When the thickness increases from 0.015 mm to 0.05 mm, the response time extends by up to 2033.33%. In contrast, the electrode material and electrolyte concentration have a less significant effect on response time. To further validate the practical value of the experimental results, the best-performing combination of core sensing components from the experiments was selected to construct a new dissolved oxygen sensor. A performance comparison test was conducted between this new sensor and the OxyGuard dissolved oxygen sensor. The results showed that both sensors had the same response time (49 s). However, in an anaerobic environment, the OxyGuard sensor demonstrated slightly higher accuracy by 2.44%. This study not only provides a deep analysis of the combined effects of oxygen-permeable membranes, electrodes, and electrolytes on the performance of galvanic dissolved oxygen sensors but also offers scientific evidence and practical guidance for optimizing sensor design.