Property-performance relationship of core-shell structured black TiO2 photocatalyst for environmental remediation

● Properties and performance relationship of CSBT photocatalyst were investigated. ● Properties of CSBT were controlled by simply manipulating glycerol content. ● Performance was linked to semiconducting and physicochemical properties. ● CSBT (W:G ratio 9:1) had better performance with lower energy consumption. ● Phenols were reduced by 48.30% at a cost of $2.4127 per unit volume of effluent. Understanding the relationship between the properties and performance of black titanium dioxide with core-shell structure (CSBT) for environmental remediation is crucial for improving its prospects in practical applications. In this study, CSBT was synthesized using a glycerol-assisted sol-gel approach. The effect of different water-to-glycerol ratios (W:G = 1:0, 9:1, 2:1, and 1:1) on the semiconducting and physicochemical properties of CSBT was investigated. The effectiveness of CSBT in removing phenolic compounds (PHCs) from real agro-industrial wastewater was studied. The CSBT synthesized with a W:G ratio of 9:1 has optimized properties for enhanced removal of PHCs. It has a distinct core-shell structure and an appropriate amount of Ti³⁺ cations (11.18%), which play a crucial role in enhancing the performance of CSBT. When exposed to visible light, the CSBT performed better: 48.30% of PHCs were removed after 180 min, compared to only 21.95% for TiO₂ without core-shell structure. The CSBT consumed only 45.5235 kWh/m³ of electrical energy per order of magnitude and cost $2.4127 per unit volume of treated agro-industrial wastewater. Under the conditions tested, the CSBT demonstrated exceptional stability and reusability. The CSBT showed promising results in the treatment of phenols-containing agro-industrial wastewater.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

Bacteria inactivation by sulfate radical: progress and non-negligible disinfection by-products

● Status of inactivation of pathogenic microorganisms by SO₄^•− is reviewed. ● Mechanism of SO₄^•− disinfection is outlined. ● Possible generation of DBPs during disinfection using SO₄^•− is discussed. ● Possible problems and challenges of using SO₄^•− for disinfection are presented. Sulfate radicals have been increasingly used for the pathogen inactivation due to their strong redox ability and high selectivity for electron-rich species in the last decade. The application of sulfate radicals in water disinfection has become a very promising technology. However, there is currently a lack of reviews of sulfate radicals inactivated pathogenic microorganisms. At the same time, less attention has been paid to disinfection by-products produced by the use of sulfate radicals to inactivate microorganisms. This paper begins with a brief overview of sulfate radicals’ properties. Then, the progress in water disinfection by sulfate radicals is summarized. The mechanism and inactivation kinetics of inactivating microorganisms are briefly described. After that, the disinfection by-products produced by reactions of sulfate radicals with chlorine, bromine, iodide ions and organic halogens in water are also discussed. In response to these possible challenges, this article concludes with some specific solutions and future research directions.     

Salinity exchange between seawater/brackish water and domestic wastewater through electrodialysis for potable water

● Present a general concept called “salinity exchange”. ● Salts transferred from seawater to treated wastewater until completely switch. ● Process demonstrated using a laboratory-scale electrodialysis system. ● High-quality desalinated water obtained at ~1 mL/min consuming < 1 kWh/m ³ energy. Two-thirds of the world’s population has limited access to potable water. As we continue to use up our freshwater resources, new and improved techniques for potable water production are warranted. Here, we present a general concept called “salinity exchange” that transfers salts from seawater or brackish water to treated wastewater until their salinity values approximately switch, thus producing wastewater with an increased salinity for discharge and desalinated seawater as the potable water source. We have demonstrated this process using electrodialysis. Salinity exchange has been successfully achieved between influents of different salinities under various operating conditions. Laboratory-scale salinity exchange electrodialysis (SEE) systems can produce high-quality desalinated water at ~1 mL/min with an energy consumption less than 1 kWh/m³. SEE has also been operated using real water, and the challenges of its implementation at a larger scale are evaluated.     

Hybrid energy harvesting systems for self-powered sustainable water purification by harnessing ambient energy

● Energy harvesters harness multiple energies for self-powered water purification. ● Hybrid energy harvesters enable continuous output under fluctuating conditions. ● Mechanical, thermal, and solar energies enable synergic harvesting. ● Perspectives of hybrid energy harvester-driven water treatment are proposed. The development of self-powered water purification technologies for decentralized applications is crucial for ensuring the provision of drinking water in resource-limited regions. The elimination of the dependence on external energy inputs and the attainment of self-powered status significantly expands the applicability of the treatment system in real-world scenarios. Hybrid energy harvesters, which convert multiple ambient energies simultaneously, show the potential to drive self-powered water purification facilities under fluctuating actual conditions. Here, we propose recent advancements in hybrid energy systems that simultaneously harvest various ambient energies (e.g., photo irradiation, flow kinetic, thermal, and vibration) to drive water purification processes. The mechanisms of various energy harvesters and point-of-use water purification treatments are first outlined. Then we summarize the hybrid energy harvesters that can drive water purification treatment. These hybrid energy harvesters are based on the mechanisms of mechanical and photovoltaic, mechanical and thermal, and thermal and photovoltaic effects. This review provides a comprehensive understanding of the potential for advancing beyond the current state-of-the-art of hybrid energy harvester-driven water treatment processes. Future endeavors should focus on improving catalyst efficiency and developing sustainable hybrid energy harvesters to drive self-powered treatments under unstable conditions (e.g., fluctuating temperatures and humidity).     

Electrocatalytic biofilm reactor for effective and energy-efficient azo dye degradation: the synergistic effect of MnOx/Ti flow-through anode and biofilm on the cathode

● MnO _x /Ti flow-through anode was coupled with the biofilm-attached cathode in ECBR. ● ECBR was able to enhance the azo dye removal and reduce the energy consumption. ● Mn^IV=O generated on the electrified MnO _x /Ti anode catalyzed the azo dye oxidation. ● Aerobic heterotrophic bacteria on the cathode degraded azo dye intermediate products. ● Biodegradation of intermediate products was stimulated under the electric field. Dyeing wastewater treatment remains a challenge. Although effective, the in-series process using electrochemical oxidation as the pre- or post-treatment of biodegradation is long. This study proposes a compact dual-chamber electrocatalytic biofilm reactor (ECBR) to complete azo dye decolorization and mineralization in a single unit via anodic oxidation on a MnO_x/Ti flow-through anode followed by cathodic biodegradation on carbon felts. Compared with the electrocatalytic reactor with a stainless-steel cathode (ECR-SS) and the biofilm reactor (BR), the ECBR increased the chemical oxygen demand (COD) removal efficiency by 24 % and 31 % (600 mg/L Acid Orange 7 as the feed, current of 6 mA), respectively. The COD removal efficiency of the ECBR was even higher than the sum of those of ECR-SS and BR. The ECBR also reduced the energy consumption (3.07 kWh/kg COD) by approximately half compared with ECR-SS. The advantages of the ECBR in azo dye removal were attributed to the synergistic effect of the MnO_x/Ti flow-through anode and cathodic biofilms. Catalyzed by Mn^IV=O generated on the MnO_x/Ti anode under a low applied current, azo dyes were oxidized and decolored. The intermediate products with improved biodegradability were further mineralized by the cathodic aerobic heterotrophic bacteria (non-electrochemically active) under the stimulation of the applied current. Taking advantage of the mutual interactions among the electricity, anode, and bacteria, this study provides a novel and compact process for the effective and energy-efficient treatment of azo dye wastewater.     

Development of machine learning multi-city model for municipal solid waste generation prediction

● A database of municipal solid waste (MSW) generation in China was established. ● An accurate MSW generation prediction model (WGMod) was constructed. ● Key factors affecting MSW generation were identified. ● MSW trends generation in Beijing and Shenzhen in the near future are projected. Integrated management of municipal solid waste (MSW) is a major environmental challenge encountered by many countries. To support waste treatment/management and national macroeconomic policy development, it is essential to develop a prediction model. With this motivation, a database of MSW generation and feature variables covering 130 cities across China is constructed. Based on the database, advanced machine learning (gradient boost regression tree) algorithm is adopted to build the waste generation prediction model, i.e., WGMod. In the model development process, the main influencing factors on MSW generation are identified by weight analysis. The selected key influencing factors are annual precipitation, population density and annual mean temperature with the weights of 13%, 11% and 10%, respectively. The WGMod shows good performance with R² = 0.939. Model prediction on MSW generation in Beijing and Shenzhen indicates that waste generation in Beijing would increase gradually in the next 3–5 years, while that in Shenzhen would grow rapidly in the next 3 years. The difference between the two is predominately driven by the different trends of population growth.     

Effect of Cu-ZSM-5 catalysts with different CuO particle size on selective catalytic oxidation of N,N-Dimethylformamide

● A series of Cu-ZSM-5 catalysts were tested for DMF selective catalytic oxidation. ● Cu-6 nm samples showed the best catalytic activity and N₂ selectivity. ● Redox properties and chemisorbed oxygen impact on DMF catalytic oxidation. ● Isolated Cu²⁺ species and weak acidity have effects on the generation of N₂. N, N-Dimethylformamide (DMF), a nitrogen-containing volatile organic compound (NVOC) with high emissions from the spray industry, has attracted increasing attention. In this study, Cu-ZSM-5 catalysts with different CuO particle sizes of 3, 6, 9 and 12 nm were synthesized and tested for DMF selective catalytic oxidation. The crystal structure and physicochemical properties of the catalyst were studied by various characterization methods. The catalytic activity increases with increasing CuO particle size, and complete conversion can be achieved at 300–350 °C. The Cu-12 nm catalyst has the highest catalytic activity and can achieve complete conversion at 300 °C. The Cu-6 nm sample has the highest N₂ selectivity at lower temperatures, reaching 95% at 300 °C. The activity of the catalysts is determined by the surface CuO cluster species, the bulk CuO species and the chemisorbed surface oxygen species. The high N₂ selectivity of the catalyst is attributed to the ratio of isolated Cu²⁺ and bulk CuO species, and weak acidity is beneficial to the formation of N₂. The results in this work will provide a new design of NVOC catalytic oxidation catalysts.     

Simple fabrication of carboxymethyl cellulose and κ-carrageenan composite aerogel with efficient performance in removal of fluoroquinolone antibiotics from water

● A composite aerogel was simply obtained to remove various fluoroquinolones (FQs). ● The structural and textural properties of this composite aerogel are improved. ● Its adsorption capacity was improved at a low content of coexisting Cu²⁺ or Fe³⁺ ion. ● Two substructural analogs of FQs are compared to explore the adsorption mechanisms. ● This aerogel after saturated adsorption can be reused directly for Cu²⁺ adsorption. 3D composite aerogels (CMC-CG) composed of carboxymethyl cellulose and κ-carrageenan were designed and fabricated using the one-pot synthesis technique. The optimized CMC-CG showed a good mechanical property and a high swelling ratio due to its superior textural properties with a proper chemically cross-linked interpenetrating network structure. CMC-CG was utilized for the removal of various fluoroquinolones (FQs) from water and exhibited high adsorption performance because of effective electrostatic attraction and hydrogen bonding interactions. Ciprofloxacin (CIP), a popular FQ, was used as the representative. The optimized CMC-CG had a theoretically maximal CIP uptake of approximately 1.271 mmol/g at the pH of 5.0. The adsorption capacity of CMC-CG was improved in the presence of some cations, Cu²⁺ and Fe³⁺ ions, at a low concentration through the bridging effect but was reduced at a high concentration. The investigation of adsorption mechanisms, based on the adsorption kinetics, isotherms and thermodynamic study, Fourier transform infrared spectrometry and X-ray photoelectron spectroscopy analyses before and after adsorption, and changes in the adsorption performance of CMC-CG toward two molecular probes, further indicated that electrostatic attraction was the dominant interaction rather than hydrogen bonding in this adsorption. CMC-CG after saturated adsorption of CIP could be easily regenerated using a dilute NaCl aqueous solution and reused efficiently. Moreover, the disused aerogel could still be reused as a new adsorbent for effective adsorption of Cu²⁺ ion. Overall, this study suggested the promising applications of this composite aerogel as an eco-friendly, cost-effective, and recyclable adsorbent for the efficient removal of FQs from water.     

Relationship between groundwater cadmium and vicinity resident urine cadmium levels in the non-ferrous metal smelting area,China

● This study systematically examined the relationship between groundwater Cd and UCL. ● The study covered 211 UCL and sociological characteristic from nine groundwater samples. ● We found a significant positive correlation between groundwater Cd and UCL. ● Smoking status and education level also significantly affected UCL. Cadmium (Cd) has received widespread attention owing to its persistent toxicity and non-degradability. Cd in the human body is mainly absorbed from the external environment and is usually assessed using urinary Cd. Hunan Province is the heartland of the Chinese non-ferrous mining area, where several serious Cd pollution events have occurred, including high levels of Cd in the urine of residents. However, the environmental factors influencing high urinary Cd levels (UCLs) in nearby residents remain unclear. Therefore, 211 nearby residents’ UCLs and the corresponding sociological characteristics from nine groundwater samples in this area were analyzed using statistical analysis models. Groundwater Cd concentration ranged from 0.02 to 1.15 μg/L, aligning with class III of the national standard; the range of UCL of nearby residents was 0.37–36.60 μg/L, exceeding the national guideline of 0–2.5 μg/L. Groundwater Cd levels were positively correlated with the UCL (P < 0.001, correlation coefficient 95 % CI = 9.68, R² = 0.06). In addition, sociological characteristics, such as smoking status and education level, also affect UCL. All results indicate that local governments should strengthen the prevention and abatement of groundwater Cd pollution. This study is the first to systematically evaluate the relationship between groundwater Cd and UCL using internal and external environmental exposure data. These findings provide essential bases for relevant departments to reduce Cd exposure in regions where the heavy metal industry is globally prevalent.     

A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

● A novel deep learning framework for short-term water demand forecasting. ● Model prediction accuracy outperforms other traditional deep learning models. ● Wavelet multi-resolution analysis automatically extracts key water demand features. ● An analysis is performed to explain the improved mechanism of the proposed method. Short-term water demand forecasting provides guidance on real-time water allocation in the water supply network, which help water utilities reduce energy cost and avoid potential accidents. Although a variety of methods have been proposed to improve forecast accuracy, it is still difficult for statistical models to learn the periodic patterns due to the chaotic nature of the water demand data with high temporal resolution. To overcome this issue from the perspective of improving data predictability, we proposed a hybrid Wavelet-CNN-LSTM model, that combines time-frequency decomposition characteristics of Wavelet Multi-Resolution Analysis (MRA) and implement it into an advanced deep learning model, CNN-LSTM. Four models - ANN, Conv1D, LSTM, GRUN - are used to compare with Wavelet-CNN-LSTM, and the results show that Wavelet-CNN-LSTM outperforms the other models both in single-step and multi-steps prediction. Besides, further mechanistic analysis revealed that MRA produce significant effect on improving model accuracy.     

Evaluation of 1,4-naphthoquinone derivatives as antibacterial agents: activity and mechanistic studies

● All 1,4-naphthoquinone hybrids exhibited significant antimicrobial activity. ● Presence of a hydroxyl group on aromatic B-ring of juglone was crucial for activity. ● Juglone can cause DNA damage by producing ROS and downregulation of RecA. ● Juglone has the potential to become a disinfectant. The diverse and large-scale application of disinfectants posed potential health risks and caused ecological damage during the 2019-nCoV pandemic, thereby increasing the demands for the development of disinfectants based on natural products, with low health risks and low aquatic toxicity. In the present study, a few natural naphthoquinones and their derivatives bearing the 1,4-naphthoquinone skeleton were synthesized, and their antibacterial activity against selected bacterial strains was evaluated. In vitro antibacterial activities of the compounds were investigated against Escherichia coli and Staphylococcus aureus. Under the minimum inhibitory concentration (MIC) of ≤ 0.125 μmol/L for juglone (1a), 5,8-dimethoxy-1,4-naphthoquinone (1f), and 7-methyl-5-acetoxy-1,4-naphthoquinone (3c), a strong antibacterial activity against S. aureus was observed. All 1,4-naphthoquinone derivatives exhibited a strong antibacterial activity, with MIC values ranging between 15.625 and 500 μmol/L and EC₅₀ values ranging between 10.56 and 248.42 μmol/L. Most of the synthesized compounds exhibited strong antibacterial activities against S. aureus. Among these compounds, juglone (1a) showed the strongest antibacterial activity. The results from mechanistic investigations indicated that juglone, a natural naphthoquinone, caused cell death by inducing reactive oxygen species production in bacterial cells, leading to DNA damage. In addition, juglone could reduce the self-repair ability of bacterial DNA by inhibiting RecA expression. In addition to having a potent antibacterial activity, juglone exhibited low cytotoxicity in cell-based investigations. In conclusion, juglone is a strong antibacterial agent with low toxicity, indicating that its application as a bactericidal agent may be associated with low health risks and aquatic toxicity.     

Removal of ammonium and nitrate through Anammox and FeS-driven autotrophic denitrification

● Simultaneous NH₄⁺/NO₃^– removal was achieved in the FeS denitrification system ● Anammox coupled FeS denitrification was responsible for NH₄⁺/NO₃^– removal ● Sulfammox, Feammox and Anammox occurred for NH₄⁺ removal ● Thiobacillus, Nitrospira , and Ca. Kuenenia were key functional microorganisms An autotrophic denitrifying bioreactor with iron sulfide (FeS) as the electron donor was operated to remove ammonium (NH₄⁺) and nitrate (NO₃⁻) synergistically from wastewater for more than 298 d. The concentration of FeS greatly affected the removal of NH₄⁺/NO₃⁻. Additionally, a low hydraulic retention time worsened the removal efficiency of NH₄⁺/NO₃⁻. When the hydraulic retention time was 12 h, the optimal removal was achieved with NH₄⁺ and NO₃⁻ removal percentages both above 88%, and the corresponding nitrogen removal loading rates of NH₄⁺ and NO₃⁻ were 49.1 and 44.0 mg/(L·d), respectively. The removal of NH₄⁺ mainly occurred in the bottom section of the bioreactor through sulfate/ferric reducing anaerobic ammonium oxidation (Sulfammox/Feammox), nitrification, and anaerobic ammonium oxidation (Anammox) by functional microbes such as Nitrospira, Nitrosomonas, and Candidatus Kuenenia. Meanwhile, NO₃⁻ was mainly removed in the middle and upper sections of the bioreactor through autotrophic denitrification by Ferritrophicum, Thiobacillus, Rhodanobacter, and Pseudomonas, which possessed complete denitrification-related genes with high relative abundances.     

Low-temperature caproate production,microbial diversity,and metabolic pathway in xylose anaerobic fermentation

● Converting xylose to caproate under a low temperature of 20 °C by MCF was verified. ● Final concentration of caproate from xylose in a batch reactor reached 1.6 g/L. ● Changing the substrate to ethanol did not notably increase the caproate production. ● Four genera, including Bifidobacterium , were revealed as caproate producers. ● The FAB pathway and incomplete RBO pathway were revealed via metagenomic analysis. Mixed culture fermentation (MCF) is challenged by the unqualified activity of enriched bacteria and unwanted methane dissolution under low temperatures. In this work, caproate production from xylose was investigated by MCF at a low temperature (20 °C). The results showed that a 9 d long hydraulic retention time (HRT) in a continuously stirred tank reactor was necessary for caproate production (~0.3 g/L, equal to 0.6 g COD/L) from xylose (10 g/L). The caproate concentration in the batch mode was further increased to 1.6 g/L. However, changing the substrate to ethanol did not promote caproate production, resulting in ~1.0 g/L after 45 d of operation. Four genera, Bifidobacterium, Caproiciproducens, Actinomyces, and Clostridium_sensu_stricto_12, were identified as the enriched caproate-producing bacteria. The enzymes in the fatty acid biosynthesis (FAB) pathway for caproate production were identified via metagenomic analysis. The enzymes for the conversion of (C_n+2)-2,3-Dehydroxyacyl-CoA to (C_n+2)-Acyl-CoA (i.e., EC 1.3.1.8 and EC 1.3.1.38) in the reverse β-oxidation (RBO) pathway were not identified. These results could extend the understanding of low-temperature caproate production.     

Adsorption of herring sperm DNA onto pine sawdust biochar: Thermodynamics and site energy distribution

● Adsorption of environmental deoxyribonucleic acid on biochar was studied. ● π−π interaction and electrostatic repulsion worked in the adsorption. ● Thermodynamics indicated the adsorption was spontaneous and endothermic. Environmental deoxyribonucleic acid (eDNA), which includes antibiotic resistance genes, is ubiquitous in the environment. The interactions between eDNA and biochar, a promising material widely used in soil amendment and water treatment, greatly affect the environmental behavior of eDNA. Hitherto few experimental evidences are available yet, especially on the information of thermodynamics and energy distribution to explains the interactions between biochar and eDNA. This study investigated the adsorption of herring sperm DNA (hsDNA) on pine sawdust biochar, with a specific emphasis on the adsorption thermodynamics and site energy distribution. The adsorption of hsDNA on biochar was enhanced by an increase in the pyrolysis and adsorption temperatures. The higher surface area, stronger π−π interaction, and weaker electrostatic repulsion between hsDNA and biochars prepared at high pyrolysis temperatures facilitated the adsorption of hsDNA. The thermodynamics indicated that the adsorption of hsDNA on biochar was spontaneous and endothermic. Therefore, higher temperature was beneficial for the adsorption of hsDNA on biochar; this was well explained by the increase in E^* and F(E^*) with the adsorption temperature. These results are useful for evaluating the migration and transformation of eDNA in the presence of biochar.     

Effect of loading rate on shear strength parameters of mechanically and biologically treated waste

● Mechanical behavior of MBT waste affected by loading rate was investigated. ● Shear strength ratio of MBT waste increases with an increase in loading rate. ● Cohesion is inversely related to loading rate. ● Internal friction angles are positively related to loading rate. ● MBT waste from China shows smaller range of φ. Mechanical biological treatment (MBT) technology has attracted increasing attention because it can reduce the volume of waste produced. To deal with the current trend of increasing waste, MBT practices are being adopted to address waste generated in developing urban societies. In this study, a total of 20 specimens of consolidated undrained triaxial tests were conducted on waste obtained from the Hangzhou Tianziling landfill, China, to evaluate the effect of loading rate on the shear strength parameters of MBT waste. The MBT waste samples exhibited an evident strain-hardening behavior, and no peak was observed even when the axial strain exceeded 25%. Further, the shear strength increased with an increase in the loading rate; the effect of loading rate on shear strength under a low confining pressure was greater than that under a high confining pressure. Furthermore, the shear strength parameters of MBT waste were related to the loading rate. The relationship between the cohesion, internal friction angle, and logarithm of the loading rate could be fitted to a linear relationship, which was established in this study. Finally, the ranges of shear strength parameters cohesion c and effective cohesion c ´ were determined as 1.0–8.2 kPa and 2.1–14.9 kPa, respectively; the ranges of the internal friction angle φ and effective internal friction angle φ ´ were determined as 16.2°–29° and 19.8°–43.9°, respectively. These results could be used as a valuable reference for conducting stability analyses of MBT landfills.     

Catalytic urea hydrolysis by composite metal oxide catalyst towards efficient urea-based SCR process: performance evaluation and mechanism investigation

● Bimetallic oxide composite catalyst was designed for the urea-based SCR process. ● Surface chemical state and typical microstructure of catalyst was determined. ● Reaction route was improved based on intermediates and active site identification. ● TiO₂@Al₂O₃ presents an obvious promotion for urea hydrolysis. As a promising option to provide gaseous NH₃ for SCR system, catalytic urea hydrolysis has aroused great attention, and improving surface area and activity of catalysis are the crucial issues to be solved for efficient urea hydrolysis. Therefore, a composite metal oxide (TiO₂@Al₂O₃) catalyst was prepared by a simple hydrothermal method, with mesoporous alumina (γ-Al₂O₃) as substrate. The results verify the mesoporous structure and submicron cluster of TiO₂@Al₂O₃, with exposed crystal faces of (101) and (400) for TiO₂ and γ-Al₂O₃, respectively. The electronegativity difference of Ti⁴⁺ and Al³⁺ changes the charge distribution scheme around the interface, which provides abundant acid/base sites to boost the urea hydrolysis. Consequently, for an optimal proportioning with nano TiO₂ content at 10 wt.%, the hydrolysis efficiency can reach up to 35.2 % at 100 °C in 2 h, increasing by ~7.1 % than that of the blank experiment. ¹³C NMR spectrum measurements provide the impossible intermediate species during urea hydrolysis. Theoretical calculations are performed to clarify the efficient H₂O decomposition at the interface of TiO₂@Al₂O₃. The result offers a favorable technology for energy-efficiency urea hydrolysis.     

Penicillin fermentation residue biochar as a high-performance electrode for membrane capacitive deionization

● We have provided an activated method to remove the toxicity of antibiotic residue. ● PFRB can greatly improve the salt adsorption capacity of MCDI. ● The hierarchical porous and abundant O/N-doped played the key role for the high-capacity desalination. ● A new field of reuse of penicillin fermentation residue has been developed. Membrane capacitive deionization (MCDI) is an efficient desalination technology for brine. Penicillin fermentation residue biochar (PFRB) possesses a hierarchical porous and O/N-doped structure which could serve as a high-capacity desalination electrode in the MCDI system. Under optimal conditions (electrode weight, voltage, and concentration) and a carbonization temperature of 700 °C, the maximum salt adsorption capacity of the electrode can reach 26.4 mg/g, which is higher than that of most carbon electrodes. Furthermore, the electrochemical properties of the PFRB electrode were characterized through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) with a maximum specific capacitance of 212.18 F/g. Finally, biotoxicity tests have showed that PFRB was non-biotoxin against luminescent bacteria and the MCDI system with the PFRB electrode remained stable even after 27 adsorption–desorption cycles. This study provides a novel way to recycle penicillin residue and an electrode that can achieve excellent desalination.     

Advancing ion-exchange membranes to ion-selective membranes: principles,status, and opportunities

● IEM ion/ion selectivities of charge, valence, & specific ion are critically assessed. ● Ion/molecule selectivities of ion/solvent and ion/uncharged solute are reviewed. ● Approaches to advance the selectivities through sorption and migration are analyzed. ● The permeability-selectivity tradeoff appears to be pervasive. ● Ion/molecule selectivities are comparatively underdeveloped and poorly understood. Ion-exchange membranes (IEMs) are utilized in numerous established, emergent, and emerging applications for water, energy, and the environment. This article reviews the five different types of IEM selectivity, namely charge, valence, specific ion, ion/solvent, and ion/uncharged solute selectivities. Technological pathways to advance the selectivities through the sorption and migration mechanisms of transport in IEM are critically analyzed. Because of the underlying principles governing transport, efforts to enhance selectivity by tuning the membrane structural and chemical properties are almost always accompanied by a concomitant decline in permeability of the desired ion. Suppressing the undesired crossover of solvent and neutral species is crucial to realize the practical implementation of several technologies, including bioelectrochemical systems, hypersaline electrodialysis desalination, fuel cells, and redox flow batteries, but the ion/solvent and ion/uncharged solute selectivities are relatively understudied, compared to the ion/ion selectivities. Deepening fundamental understanding of the transport phenomena, specifically the factors underpinning structure-property-performance relationships, will be vital to guide the informed development of more selective IEMs. Innovations in material and membrane design offer opportunities to utilize ion discrimination mechanisms that are radically different from conventional IEMs and potentially depart from the putative permeability-selectivity tradeoff. Advancements in IEM selectivity can contribute to meeting the aqueous separation needs of water, energy, and environmental challenges.     

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

● Hybrid deep-learning model is proposed for water quality prediction. ● Tree-structured Parzen Estimator is employed to optimize the neural network. ● Developed model performs well in accuracy and uncertainty. ● Usage of the proposed model can reduce carbon emission and energy consumption. Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m³/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of COD_eff and biogas yield, R² values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.