Industries such as non-ferrous metal smelting discharge billions of gallons of highly toxic heavy metal wastewater (HMW) worldwide annually, posing a severe challenge to conventional wastewater treatment plants and harming the environment. HMW is traditionally treated via chemical precipitation using lime, caustic, or sulfide, but the effluents do not meet the increasingly stringent discharge standards. This issue has spurred an increase in research and the development of innovative treatment technologies, among which those using nanoparticles receive particular interest. Among such initiatives, treatment using nanoscale zero-valent iron (nZVI) is one of the best developed. While nZVI is already well known for its site-remediation use, this perspective highlights its application in HMW treatment with metal recovery. We demonstrate several advantages of nZVI in this wastewater application, including its multifunctionality in sequestrating a wide array of metal(loid)s (> 30 species); its capability to capture and enrich metal(loid)s at low concentrations (with a removal capacity reaching 500 mg·g−1 nZVI); and its operational convenience due to its unique hydrodynamics. All these advantages are attributable to nZVI’s diminutive nanoparticle size and/or its unique iron chemistry. We also present the first engineering practice of this application, which has treated millions of cubic meters of HMW and recovered tons of valuable metals (e.g., Cu and Au). It is concluded that nZVI is a potent reagent for treating HMW and that nZVI technology provides an eco-solution to this toxic waste.
The digital twins concept enhances modeling and simulation through the integration of real-time data and feedback. This review elucidates the foundational elements of digital twins, covering their concept, entities, domains, and key technologies. More specifically, we investigate the transformative potential of digital twins for the wastewater treatment engineering sector. Our discussion highlights the application of digital twins to wastewater treatment plants (WWTPs) and sewage networks, hardware (i.e., facilities and pipes, sensors for water quality and activated sludge, hydrodynamics, and power consumption), and software (i.e., knowledge-based and data-driven models, mechanistic models, hybrid twins, control methods, and the Internet of Things). Furthermore, two cases are provided, followed by an assessment of current challenges in and perspectives on the application of digital twins in WWTPs. This review serves as an essential primer for wastewater engineers navigating the digital paradigm shift.
Tracing the contamination origins in water sources and identifying the impacts of natural and human processes are essential for ecological safety and public health. However, current analysis approaches are not ideal, as they tend to be laborious, time-consuming, or technically difficult. Disinfection byproducts (DBPs) are a family of well-known secondary pollutants formed by the reactions of chemical disinfectants with DBP precursors during water disinfection treatment. Since DBP precursors have various origins (e.g., natural, domestic, industrial, and agricultural sources), and since the formation of DBPs from different precursors in the presence of specific disinfectants is distinctive, we argue that DBPs and DBP precursors can serve as alternative indicators to assess the contamination in water sources and identify pollution origins. After providing a retrospective of the origins of DBPs and DBP precursors, as well as the specific formation patterns of DBPs from different precursors, this article presents an overview of the impacts of various natural and anthropogenic factors on DBPs and DBP precursors in drinking water sources. In practice, the DBPs (i.e., their concentration and speciation) originally present in source water and the DBP precursors determined using DBP formation potential tests—in which water samples are dosed with a stoichiometric excess of specific disinfectants in order to maximize DBP formation under certain reaction conditions—can be considered as alternative metrics. When jointly used with other water quality parameters (e.g., dissolved organic carbon, dissolved organic nitrogen, fluorescence, and molecular weight distribution) and specific contaminants of emerging concern (e.g., certain pharmaceuticals and personal care products), DBPs and DBP precursors in drinking water sources can provide a more comprehensive picture of water pollution for better managing water resources and ensuring human health.
The potential for reducing greenhouse gas (GHG) emissions and energy consumption in wastewater treatment can be realized through intelligent control, with machine learning (ML) and multimodality emerging as a promising solution. Here, we introduce an ML technique based on multimodal strategies, focusing specifically on intelligent aeration control in wastewater treatment plants (WWTPs). The generalization of the multimodal strategy is demonstrated on eight ML models. The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control, exhibiting exceptional performance and interpretability. Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models, with a mean absolute percentage error of 4.4% and a coefficient of determination of 0.948. Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8% compared to traditional fuzzy control methods. The potential application of these strategies in critical water science domains is discussed. To foster accessibility and promote widespread adoption, the multimodal ML models are freely available on GitHub, thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.
Wastewater treatment plants (WWTPs) are important and energy-intensive municipal infrastructures. High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality. However, water-energy nexus analysis and models for WWTPs have rarely been reported to date. In this study, a cloud-model-based energy consumption analysis (CMECA) of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters. The principal component analysis (PCA) and K-means clustering were applied to classify the influent condition using water quality and volume data. The energy consumption of the WWTP is divided into five standard evaluation levels, and its cloud digital characteristics (CDCs) were extracted according to bilateral constraints and golden ratio methods. Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity. The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption, represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption. The local WWTP has high energy consumption with 0.3613 kW·h·m−3 despite low influent concentration and volumes, across four consumption levels from low (I) to relatively high (IV), showing an unsatisfactory operation and management level. The average oxygenation capacity, internal reflux ratio, and external reflux ratio during high energy efficiency days recognized by further clustering were obtained (0.2924-0.3703 kg O2·m−3, 1.9576-2.4787, and 0.6603-0.8361, respectively), which could be used as a guide for the days with low energy efficiency. Consequently, this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.
This study systematically introduces the development of the world’s first full-link and full-system ground demonstration and verification system for the OMEGA space solar power satellite (SSPS). First, the OMEGA 2.0 innovation design was proposed. Second, field-coupling theoretical models of sunlight concentration, photoelectric conversion, and transmitting antennas were established, and a systematic optimization design method was proposed. Third, a beam waveform optimization methodology considering both a high beam collection efficiency and a circular stepped beam shape was proposed. Fourth, a control strategy was developed to control the condenser pointing toward the sun while maintaining the transmitting antenna toward the rectenna. Fifth, a high-efficiency heat radiator design method based on bionics and topology optimization was proposed. Sixth, a method for improving the rectenna array’s reception, rectification, and direct current (DC) power synthesis efficiencies is presented. Seventh, high-precision measurement technology for high-accuracy beam-pointing control was developed. Eighth, a smart mechanical structure was designed and developed. Finally, the developed SSPS ground demonstration and verification system has the capacity for sun tracking, a high concentration ratio, photoelectric conversion, microwave conversion and emission, microwave reception, and rectification, and thus satisfactory results were obtained.
With the continuous miniaturization of electronic devices, microelectromechanical system (MEMS) oscillators that can be combined with integrated circuits have attracted increasing attention. This study reports a MEMS Huygens clock based on the synchronization principle, comprising two synchronized MEMS oscillators and a frequency compensation system. The MEMS Huygens clock improved short-time stability, improving the Allan deviation by a factor of 3.73 from 19.3 to 5.17 ppb at 1 s. A frequency compensation system based on the MEMS oscillator’s temperature-frequency characteristics was developed to compensate for the frequency shift of the MEMS Huygens clock by controlling the resonator current. This effectively improved the long-term stability of the oscillator, with the Allan deviation improving by 1.6343 × 105 times to 30.9 ppt at 6000 s. The power consumption for compensating both oscillators simultaneously is only 2.85 mW∙°C−1. Our comprehensive solution scheme provides a novel and precise engineering solution for achieving high-precision MEMS oscillators and extends synchronization applications in MEMS.
In an integrated electricity-gas system (IEGS), load fluctuations affect not only the voltage in the power system but also the gas pressure in the natural gas system. The static voltage stability region (SVSR) method is a tool for analyzing the overall static voltage stability in a power system. However, in an IEGS, the SVSR boundary may be overly optimistic because the gas pressure may collapse before the voltage collapses. Thus, the SVSR method cannot be directly applied to an IEGS. In this paper, the concept of the SVSR is extended to the IEGS-static stability region (SSR) while considering voltage and gas pressure. First, criteria for static gas pressure stability in a natural gas system are proposed, based on the static voltage stability criteria in a power system. Then, the IEGS-SSR is defined as a set of active power injections that satisfies multi-energy flow (MEF) equations and static voltage and gas pressure stability constraints in the active power injection space of natural gas-fired generator units (NGUs). To determine the IEGS-SSR, a continuation MEF (CMEF) method is employed to trace the boundary point in one specific NGU scheduling direction. A multidimensional hyperplane sampling method is also proposed to sample the NGU scheduling directions evenly. The obtained boundary points are further used to form the IEGS-SSR in three-dimensional (3D) space via a Delaunay triangulation hypersurface fitting method. Finally, the numerical results of typical case studies are presented to demonstrate that the proposed method can effectively form the IEGS-SSR, providing a tool for IEGS online monitoring and dispatching.
Three-dimensional (3D) printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand. It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs. Each type of 3D printing technique has its unique advantages and limitations, and the selection of a sui
The function-led design of porous hydrochar from mineral-rich biowaste for environmental applications inevitably suffers from carbon-ash recalcitrance. However, a method to alter the original carbon skeleton with ash remains elusive and hinders the availability of hydrochar. Herein, we propose a facile strategy for breaking the rigid structure of carbon-ash coupled hydrochar using phase-tunable molten carbonates. A case system was designed in which livestock manure and NaHCO3 were used to prepare the activated hydrochar, and NH3 served as the target contaminant. Due to the redox effect, we found that organic fractions significantly advanced the melting temperature of Na2CO3 below 800 °C. The Na species steadily broke the carbon-ash interaction as the thermal intensity increased and transformed inorganic constituents to facilitate ash dissolution, rebuilding the hydrochar skeleton with abundant hierarchical channels and active defect edges. The surface polarity and mesopore distribution collectively governed the five cycles NH3 adsorption attenuation process. Manure hydrochar delivered favorable potential for application with a maximum overall adsorption capacity of 100.49 mg·g−1. Integrated spectroscopic characterization and theoretical computations revealed that incorporating NH3 on the carbon surface could transfer electrons to chemisorbed oxygen, which promoted the oxidation of pyridine-N during adsorption. This work offers deep insight into the structure function correlation of hydrochar and inspires a more rational design of engineered hydrochar from high-ash biowaste.
Multiple myeloma (MM) is the second most prevalent hematological malignancy. Current MM treatment strategies are hampered by systemic toxicity and suboptimal therapeutic efficacy. This study addressed these limitations through the development of a potent MM-targeting chemotherapy strategy, which capitalized on the high binding affinity of alendronate for hydroxyapatite in the bone matrix and the homologous targeting of myeloma cell membranes, termed T-PB@M. The results from our investigations highlight the considerable bone affinity of T-PB@M, both in vitro and in vivo. Additionally, this material demonstrated a capability for drug release triggered by low pH conditions. Moreover, T-PB@M induced the generation of reactive oxygen species and triggered cell apoptosis through the poly(ADP-ribose) polymerase 1 (PARP1)-Caspase-3-B-cell lymphoma-2 (Bcl-2) pathway in MM cells. Notably, T-PB@M preferentially targeted bone-involved sites, thereby circumventing systemic toxic side effects and leading to prolonged survival of MM orthotopic mice. Therefore, this designed target-MM nanocarrier presents a promising and potentially effective platform for the precise treatment of MM.
Liver transplantation (LT) is the standard therapy for individuals afflicted with end-stage liver disease. Despite notable advancements in LT technology, the incidence of early allograft dysfunction (EAD) remains a critical concern, exacerbating the current organ shortage and detrimentally affecting the prognosis of recipients. Unfortunately, the perplexing hepatic heterogeneity has impeded characterization of the cellular traits and molecular events that contribute to EAD. Herein, we constructed a pioneering single-cell transcriptomic landscape of human transplanted livers derived from non-EAD and EAD patients, with 12 liver samples collected from 7 donors during the cold perfusion and portal reperfusion stages. Comparison of the 75 231 cells of non-EAD and EAD patients revealed an EAD-associated immune niche comprising mucosal-associated invariant T cells, granzyme B+ (GZMB +) granzyme K+ (GZMK +) natural killer cells, and S100 calcium binding protein A12+ (S100A12 +) neutrophils. Moreover, we verified this immune niche and its association with EAD occurrence in two independent cohorts. Our findings elucidate the cellular characteristics of transplanted livers and the EAD-associated pathogenic immune niche at the single-cell level, thus, offering valuable insights into EAD onset.
One-third of patients with autoimmune hepatitis (AIH) have cirrhosis at the time of diagnosis. The relevance of these variables, although unknown, is believed to be critical in AIH because of suspected interactions between the gut microbiome and genetic factors. Dysbiosis of the gut flora and elevated polymeric immunoglobulin receptor (pIgR) levels have been observed in both patients and mouse models. Moreover, there is a direct relationship between pIgR expression and transaminase levels in patients with AIH. In this study, we aimed to explore how pIgR influences the secretion of regenerating islet-derived 3 beta (Reg3b) and the flora composition in AIH using in vivo experiments involving patients with AIH and a concanavalin A-induced mouse model of AIH. Reg3b expression was reduced in pIgR gene (Pigr)-knockout mice compared to that in wild-type mice, leading to increased microbiota disruption. Conversely, exogenous pIgR supplementation increased Reg3b expression and maintained microbiota homeostasis. RNA sequencing revealed the participation of the interleukin (IL)-17 signaling pathway in the regulation of Reg3b through pIgR. Furthermore, the introduction of external pIgR could not restore the imbalance in gut microbiota in AIH, and the decrease in Reg3b expression was not apparent following the inhibition of signal transducer and activator of transcription 3 (STAT3). In this study, pIgR facilitated the upregulation of Reg3b via the STAT3 pathway, which plays a crucial role in preserving the balance of the intestinal microbiota in AIH. Through this research, we discovered new molecular targets that can be used for the diagnosis and treatment of AIH.
Technological advancements in unmanned aerial vehicles (UAVs) have revolutionized various industries, enabling the widespread adoption of UAV-based solutions. In engineering management, UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments, surpassing traditional inspection techniques. Building on this foundation, this paper delves into the optimization of UAV inspection routing and scheduling, addressing the complexity introduced by factors such as no-fly zones, monitoring-interval time windows, and multiple monitoring rounds. To tackle this challenging problem, we propose a mixed-integer linear programming (MILP) model that optimizes inspection task assignments, monitoring sequence schedules, and charging decisions. The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem (VRP), leading to a mathematically intractable model for commercial solvers in the case of large-scale instances. To overcome this limitation, we design a tailored variable neighborhood search (VNS) metaheuristic, customizing the algorithm to efficiently solve our model. Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm, demonstrating its scalability for both large-scale and real-scale instances. Sensitivity experiments and a case study based on an actual engineering project are also conducted, providing valuable insights for engineering managers to enhance inspection work efficiency.
Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks, and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower- to higher-level nodes, such as lagging birds’ howl in a flock or the opinions of lower-level individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of “bunching” effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks, which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.
This study presents a solvent-free, facile synthesis of a bio-based green antibacterial agent and aromatic monomer methacrylated vanillin (MV) using vanillin. The resulting MV not only imparted antibacterial properties to coatings layered on leather, but could also be employed as a green alternative to petroleum-based carcinogen styrene (St). Herein, MV was copolymerized with butyl acrylate (BA) to obtain waterborne bio-based P(MV-BA) miniemulsion via miniemulsion polymerization. Subsequently, MXene nanosheets with excellent photothermal conversion performance and antibacterial properties, were introduced into the P(MV-BA) miniemulsion by ultrasonic dispersion. During the gradual solidification of P(MV-BA)/MXene nanocomposite miniemulsion on the leather surface, MXene gradually migrated to the surface of leather coatings due to the cavitation effect of ultrasonication and amphiphilicity of MXene, which prompted its full exposure to light and bacteria, exerting the maximum photothermal conversion efficiency and significant antibacterial efficacy. In particular, when the dosage of MXene nanosheets was 1.4 wt%, the surface temperature of P(MV-BA)/MXene nanocomposite miniemulsion-coated leather (PML) increased by about 15 °C in an outdoor environment during winter, and the antibacterial rate against Escherichia coli and Staphylococcus aureus was nearly 100% under the simulated sunlight treatment for 30 min. Moreover, the introduction of MXene nanosheets increased the air permeability, water vapor permeability, and thermal stability of these coatings. This study provides a new insight into the preparation of novel, green, and waterborne bio-based nanocomposite coatings for leather, with desired warmth retention and antibacterial properties. It can not only realize zero-carbon heating based on sunlight in winter, reducing the use of fossil fuels and greenhouse gas emissions, but also improve ability to fight off invasion by harmful bacteria, viruses, and other microorganisms.
Realizing fast and continuous generation of reactive oxygen species (ROSs) via iron-based advanced oxidation processes (AOPs) is significant in the environmental and biological fields. However, current AOPs assisted by co-catalysts still suffer from the poor mass/electron transfer and non-durable promotion effect, giving rise to the sluggish Fe2+/Fe3+ cycle and low dynamic concentration of Fe2+ for ROS production. Herein, we present a three-dimensional (3D) macroscale co-catalyst functionalized with molybdenum disulfide (MoS2) to achieve ultra-efficient Fe2+ regeneration (equilibrium Fe2+ ratio of 82.4%) and remarkable stability (more than 20 cycles) via a circulating flow-through process. Unlike the conventional batch-type reactor, experiments and computational fluid dynamics simulations demonstrate that the optimal utilization of the 3D active area under the flow-through mode, initiated by the convection-enhanced mass/charge transfer for Fe2+ reduction and then strengthened by MoS2-induced flow rotation for sufficient reactant mixing, is crucial for oxidant activation and subsequent ROS generation. Strikingly, the flow-through co-catalytic system with superwetting capabilities can even tackle the intricate oily wastewater stabilized by different surfactants without the loss of pollutant degradation efficiency. Our findings highlight an innovative co-catalyst system design to expand the applicability of AOPs based technology, especially in large-scale complex wastewater treatment.
Photocatalysis offers a sustainable means for the oxidative removal of low concentrations of NO x (NO, NO2, N2O, N2O5, etc.) from the atmosphere. Layered double hydroxides (LDHs) are promising candidate photocatalysts owing to their unique layered and tunable chemical structures and abundant surface hydroxide (OH−) moieties, which are hydroxyl radical (.OH) precursors. However, the practical applications of LDHs are limited by their poor charge-separation ability and insufficient active sites. Herein, we developed a facile N2H4-driven etching approach to introduce dual Ni2+ and OH− vacancies (Niv and OHv, respectively) into NiFe-LDH nanosheets (hereafter referred to as NiFe-LDH-et) to facilitate improved charge-carrier separation and active Lewis acidic site (Fe3+ and Ni2+ exposed at OHv) formation. In contrast to inert pristine LDH, NiFe-LDH-et actively removed NO under visible-light illumination. Specifically, Ni76Fe24-LDH-et etched with 1.50 mmol·L−1 N2H4 solution removed 32.8% of the NO in continuously flowing air (NO feed concentration: ∼500 parts per billion (ppb)) under visible-light illumination, thereby outperforming most reported catalysts. Experimental and theoretical data revealed that the dual vacancies promoted the production of reactive oxygen species (O2.− and .OH) and the adsorption of NO on the LDH. In situ spectroscopy demonstrated that NO was preferentially adsorbed at Lewis acidic sites, particularly exposed Fe3+ sites, converted into NO+, and subsequently oxidized to NO3 − without the notable formation of the more toxic intermediate NO2, thereby alleviating risks associated with its production and emission.
