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The recent observed manipulatable redox potential of trivalent americium ion in the aqueous phase by modifying an electrode offers an alternative to accomplish the separation. In order to understand extensively the speciation of Am, which is the prerequisite to understanding the mechanism of the oxidation of Am, we conducted a density functional study to identify the potential species of Am in its tri-, tetra-, and pentavalent states in aqueous phase. Based on the speciation analysis, the calculations implicate a stepwise mechanism for the oxidation of hydrated Am(III), which predominantly exists in its hydrated monatomic cationic form (Am

Dissolved oxygen (DO) plays an important role in the performance of biological wastewater treatment systems. This study investigated the effect of the DO concentration on nutrient removal performance and microbial community structure in side-stream activated sludge hydrolysis (SSH) and conventional anaerobic/anoxic/aerobic (A2O) processes. The results showed that the change in DO had little effect on the removal performance of chemical oxygen demand (COD), and the removal efficiencies were about 90% for both reactors. Compared with the high DO level (4.1–6.9 mg/L), the A2O and SSH reactors had better nitrogen removal performance at low (0.5–2.2 mg/L) and moderate (2.2–3.9 mg/L) DO levels, with ammonia (NH4+-N) removal efficiencies of 88–89% and 89–91%, respectively, and total nitrogen (TN) removal efficiencies of 74–76% and 75–81%, respectively. Directly reducing the DO concentration from high to low reduced the phosphate removal efficiencies of the A2O and SSH reactors from 80.2% and 86.2% to 63.1% and 70.6%, respectively, while re-elevating the DO concentration to moderate levels significantly improved the phosphate removal efficiencies to 94.6% and 96.0%, respectively. Compared to the A2O reactor, the SSH reactor had more stable and better nutrient removal performance under different DO conditions, partly due to the additional carbon sources produced through the sludge fermentation in the side-stream reactor. The decrease in the DO concentration resulted in a decrease in the relative abundance of Acinetobacter but an increase in the relative abundance of Competibacter, potentially leading to the deterioration in phosphorus removal.

In the process of microemulsion flooding, microemulsion phase state may be affected by the adsorption of core and the distribution of oil and water, and the upper phase, the middle phase or the lower phase microemulsion will appear. Accurate description of microemulsion phase state and quantitative discrimination of equilibrium phase composition are of great theoretical value and research significance for the design of microemulsion flooding system and improving the recovery efficiency of microemulsion. Therefore, in this paper, microemulsion phase model is deeply studied. Aiming at the difficulty of solving the existing Hand model and the unknown parameters of the improved HLD-NAC model, we introduce the binodal curve range parameter D and the asymmetric migration degree parameter B, and establish a new phase behavior description method of the binodal curve, the two-phase plait point lines and the III-phase node line. Then the phase discrimination programming of microemulsion was carried out, and the change laws of phase number, phase type and mass fraction of equilibrium phases of any microemulsion system under different salinity were revealed. The results show that the simulation results of microemulsions accord with the salinity scanning law, and can accurately identify the complex microemulsion phase states.

Variable Stiffness Actuator (VSA) is the core mechanism to achieve physical human–robot interaction, which is an inevitable development trend in robotic. The existing variable stiffness actuators are basically single degree-of-freedom (DOF) rotating joints, which are achieving multi-DOF motion by cascades and resulting in complex robot body structures. In this paper, an integrated 2-DOF actuator with variable stiffness is proposed, which could be used for bionic wrist joints or shoulder joints. The 2-DOF motion is coupling in one universal joint, which is different from the way of single DOF actuators cascade. Based on the 2-DOF orthogonal motion generated by the spherical wrist parallel mechanism, the stiffness could be adjusted by varying the effective length of the springs, which is uniformly distributed in the variable stiffness unit. The variable stiffness principle, the model design, and theoretical analysis of the VSA are discussed in this work. The independence of adjusting the equilibrium position and stiffness of the actuator is validated by experiments. The results show that the measured actuator characteristics are sufficiently matched the theoretical values. In the future, VSA could be used in biped robot or robotic arm, ensuring the safety of human–robot interaction.

Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space, which can reduce the temporal and spatial complexity of downstream network data mining such as node classification and graph clustering. This paper addresses the problem that neighborhood information-based network representation learning algorithm ignores the global topological information of the network. We propose the Network Representation Learning Algorithm Based on Community Folding (CF-NRL) considering the influence of community structure on the global topology of the network. Each community of the target network is regarded as a folding unit, the same network representation learning algorithm is used to learn the vector representation of the nodes on the folding network and the target network, then the vector representations are spliced correspondingly to obtain the final vector representation of the node. Experimental results show the excellent performance of the proposed algorithm. &nbsp

Side-stream Enhanced biological phosphorus removal (S2EBPR) has been incorporated with B-stage process to enable simultaneous phosphorus and nitrogen removal. However, the dominating phosphorus accumulating organisms (PAOs) in this novel configuration has not been evaluated. The dominance of Acinetobacter was confirmed by 16S sequencing. In addition, single cell Raman spectrum (SCRS) analysis in couple with in situ fluorescence in situ hybridization (FISH) was applied to obtain the feature spectrum and verify the phosphorus release/uptake activity of Acinetobacter spp. The phenotypic profiling further suggested the dominance of Acinetobacter-like organisms among all poly-phosphorus containing organisms and only certain phenotypic Acinetobacter (oligotype 1) contribution to P-removal in a unique HRAS-P(D)N-S2EBPR system. The findings suggest that Acinetobacter may outcompete other heterotrophic organisms in EBPR systems due to their sensitivity to operational conditions. However, stable P-removal was only observed during a specific section of the operation period, coinciding with an increase in the VFA/P ratio. Further research is needed to identify the phenotypes of Acinetobacter responsible for P-removal in EBPR systems. The study contributes to a better understanding of the microbial ecology and engineering aspects of EBPR systems and wastewater treatment in general.

A novel integrated pilot-scale A-stage high rate activated sludge, B-stage short-cut biological nitrogen removal and side-stream enhanced biological phosphorus removal (A/B-shortcut N- S2EBPR) process for treating municipal wastewater was demonstrated with the aim to achieve simultaneous and carbon- and energy-efficient N and P removal. In this studied period, an average of 7.62 ± 2.17 mg-N/L nitrite accumulation was achieved through atypical partial nitrification without canonical known NOB out-selection. Network analysis confirms the central hub of microbial community as Nitrospira, which was one to two orders of magnitude higher than canonical aerobic oxidizing bacteria (AOB) in a B-stage nitrification tank. The contribution of comammox Nitrospira as AOB was evidenced by the increased amoB/nxr ratio and higher ammonia oxidation activity. Furthermore, oligotyping analysis of Nitrospira revealed two dominant sub-clusters (microdiveristy) within the Nitrospira. The relative abundance of oligotype II, which is phylogenetically close to Nitrospira_midas_s_31566, exhibited a positive correlation with nitrite accumulation in the same operational period, suggesting its role as comammox Nitrospira. Additionally, the phylogenetic investigation suggested that heterotrophic organisms from the family Comamonadacea and the order Rhodocyclaceae embedding ammonia monooxygenase and hydroxylamine oxidase may function as heterotrophic nitrifiers. This is the first study that elucidated the impact of integrating the S2EBPR on nitrifying populations with implications on short-cut N removal. The unique conditions in the side-stream reactor, such as low ORP, favorable VFA concentrations and composition, seemed to exert different selective forces on nitrifying populations from those in conventional biological nutrient removal processes. The results provide new insights for integrating EBPR with short-cut N removal process for mainstream wastewater treatment.

Achieving mainstream short-cut nitrogen removal via nitrite has been a carbon and energy-efficient goal which wastewater engineers are dedicated to explore. This study applied a novel pilot-scale A-B-S2EBPR system process integrated with sidestream enhanced biological phosphorus removal) to achieve the nitrite accumulation and downstream anammox for treating municipal wastewater. Nitrite accumulated to 5.5 ± 0.3 mg N/L in the intermittently aerated tanks of B-stage with the nitrite accumulation ratio (NAR) of 79.1 ± 6.5%. The final effluent concentration and removal efficiency of total inorganic nitrogen (TIN) were 4.6 ± 1.8 mg N/L and 84.9 ± 5.6%, respectively. Batch nitrification/denitrification activity tests and functional gene abundance of ammonium oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) suggested that the nitrite accumulation was mostly caused by partial denitrification without NOB- selection. The unique features of S2EBPR (longer anaerobic HRT/SRT, lower ORPs, high and more complex VFAs etc.) seemed to impact the nitrogen microbial communities: the conventional AOB kept at a very low level of 0.13 ± 0.13% during the operation period, and the dominant candidate internal carbon-accumulating heterotrophic genera ofAcinetobacter(17.8 ± 15.5)% andComamonadaceae(6.7 ± 3.4%) were highly enriched. Furthermore, the single-cell Raman spectroscopy-based intracellular polymer analysis revealed the dominate microorganisms that could utilize polyhydroxyalkanoates (PHA) as the potential internal carbon source to drive partial denitrification. This study provides insights and a new direction for implementing the mainstream PdNA short-cut nitrogen removal via incorporating S2EBPR into sustainable A-B process.

Here we report on the facile formation of asymmetric heterojunctions between laterally size different 2D flakes, which leads to a prominent gradient in charge distribution at the nanocontact interface and triggers ionic diode-like transport behaviour with a rectification ratio of 110.

Climbing robots have broad application prospects in aerospace equipment inspection, forest farm monitoring, and pipeline maintenance. Different types of climbing robots in existing research have different advantages. However, the self-adaptability and stability have not been achieved at the same time. In order to realize the self-adaptability of holding and climbing stability, this work proposes a new type of climbing robot under the premise of minimizing the driving source. The robot realizes stable multifinger holding and wheeled movement through two motors. At the same time, the robot has two different working modes, namely pole climbing and ground crawling. The holding adaptability and climbing stability are realized by underactuated holding mechanism and model reference adaptive controller (MRAC). On the basis of model design and parameter analysis, a prototype of the climbing robot is built. Experiments prove that the proposed climbing robot has the ability to stably climb poles of different shapes. The holding and climbing stability, self-adaptability, and climbing and crawling speed of the proposed climbing robot are verified by experiments.

Metal-free boron-based catalytic systems are growing to be promising choices in the oxidative dehydrogenation (ODH) of light alkanes to olefins. However, the ambiguity in the understanding of the m...

Microbial fuel cells (MFCs) have shown great advantages in electricity production, heavy metal removal, and energy recovery. However, the impact and mechanism of conflicting effects of numerous electron acceptors on heavy metal removal remain unknown. The effects of different initial heavy metal concentrations, cathodic dissolved oxygen, and electrode materials on the electricity generation and heavy metal removal efficiencies of Cu(II) and Cr(VI) were investigated in this study. When the initial concentration of Cr(VI) increased from 10 mg/L to 150 mg/L, the maximum voltage, coulomb efficiency, and maximum power density declined from 99 to 44 mV, 28.63% to 18.97%, and 14.29 to 0.62 mW/m2, and the removal efficiencies of Cu(II) and Cr(VI) decreased dramatically from 98.34% and 99.92% to 67.09% and 37.06%, respectively. Under anaerobic cathodic conditions, the removal efficiency and removal rate of Cu(II) and Cr(VI) were lower than those under aerobic conditions. When the cathode electrode was titanium sheet and graphite plate, the coulomb efficiency and maximum power density increased to 38.18%, 50.71%, 33.95 mW/m2, and 62.23 mW/m2. The removal efficiency and removal rates of Cu(II) and Cr(VI) were significantly increased to 98.09%, 86.13%, and 0.47, 0.50 mg/(L h) with a graphite plate, respectively. The pH of the cathode varied considerably greater as the MFC current increased. Cu(II) and Cr(VI) were removed and reduced to elemental Cu, Cu2O, and its oxides as well as Cr(OH)3 and Cr2O3 precipitates on the cathode electrode by cathodic bioelectrochemical reduction.

The photocatalytic conversion of soluble U(VI) into insoluble U(IV) is a robust strategy to harvest aqueous uranium, but remains challenging owing to the intermittent availability of solar influx and reoxidation of U(IV) without illumination. Herein, a dual platform based on K

The octadentate hydroxypyridonate ligand 3,4,3-LI(1,2-HOPO) (

Bioremediation techniques utilizing sulfate-reducing bacteria (SRB) for acid mine drainage (AMD) treatment have attracted growing attention in recent years, yet substrate bioavailability for SRB is a key factor influencing treatment effectiveness and long-term stability. This study investigated the effects of external organic substrates, including four complex organic wastes (i.e., sugarcane bagasse, straw compost, shrimp shell (SS), and crab shell (CS)) and a small-molecule organic acid (i.e., propionate), on AMD removal performance and associated microbial communities during the 30-day operation of sulfate-reducing microcosms. The results showed that the pH values increased in all five microcosms, while CS exhibited the highest neutralization ability and a maximum alkalinity generation of 1507 mg/L (as CaCO

Rapid progress in various advanced analytical methods such as single-cell technologies enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares dataset-wise distributional differences with no requirements on human intervention or prior knowledge-based pre-classification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of datasets with both linear and non-linear relationships. The model was then validated in a case study with eight SCRS phenotyping datasets each sampled from a different enhanced biological phosphorus removal (EBPR) activated sludge community located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. For example, an approximated sampling size of 50 or 100 spectra for full-scale EBPR-related ecosystems at 5% or 2% OPU cluster resolution. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages exhibit the capability of generalizing to other single-cell technologies or environmental applications, provided that the input datasets contain only continuous features.TOC

Drug discovery is vitally important for protecting human against disease. Target-based screening is one of the most popular methods to develop new drugs in the past several decades. This method efficiently screens candidate drugs inhibiting target protein in vitro, but it often fails due to inadequate activity of the selected drugs in vivo. Accurate computational methods are needed to bridge this gap. Here, we propose a novel graph multi task deep learning model to identify compounds carrying both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, the proposed MATIC model shows advantages comparing with traditional method in screening effective compounds in vivo. Next, we explored the model interpretability and found that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attentions. Based on these findings, we utilized a monte carlo based reinforcement learning generative model to generate novel multi-property compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery.

Objectives The aim of this study was to evaluate the role of antiviral drugs in reducing the risk of developing severe illness in patients with moderate COVID-19 pneumonia. Methods This retrospective cohort study included 403 adult patients with moderate COVID-19 pneumonia who were admitted to Shenzhen Third People’s Hospital, China. The antiviral drugs arbidol, interferon alpha-1b, lopinavir–ritonavir and ribavirin were distributed to the patients for treatment. The primary endpoint of this study was the time to develop severe illness. Results Of the 462 patients admitted, 403 had moderate COVID-19 symptoms at hospital admission and were included in this study. 90 of the 403 (22.3%) patients progressed to severe illness. The use of arbidol was associated with a lower severity rate 3.5% compared to control group 30.5%, p-value p = 0.033). The use of interferon alpha-1b was associated with a lower severity rate 15.5% compared to control group 29.3%, with p-value p = 0.0005). The use of lopinavir–itonavir and ribavirin did not show significant differences in adjusted regression models. Early use of arbidol within 7 days of symptom onset was significantly associated with a reduced recovery time of − 5.2 days (IQR − 3.0 to − 7.5, p = 4e−06) compared with the control group. Conclusion Treatment with arbidol and interferon alpha-1b contributes to reducing the severity of illness in patients with moderate COVID-19 pneumonia. Early use of arbidol may reduce patients’ recovery time.

Rivers and streams contribute significant quantities of methane (CH4) to the atmosphere. However, there is a lack of CH4 flux and ebullitive (bubble) emission data from urban rivers, which might lead to large underestimations of global aquatic CH4 emissions. Here, we conducted high-frequency surveys using the boundary layer model (BLM) supplemented with floating chambers (FCs) and bubble traps to investigate the seasonal and diurnal variability in CH4 emissions in a eutrophic urban river and to evaluate whether the contribution of bubbles is important. We found that ebullition contributed nearly 99% of CH4 emissions and varied on hourly to seasonal time scales, ranging from 0.83 to 230 mmol m-2 d-1, although diffusive emissions and CH4 concentrations in bubbles did not exhibit temporal variability. Ebullitive CH4 emissions presented high temperature sensitivity (r = 0.6 and p < 0.01) in this urban river, and eutrophication might have triggered this high temperature sensitivity. The ebullitive CH4 flux is more likely to be underestimated at low temperatures because capturing the bubble flux is more difficult, given the low frequency of ebullition events. This study suggests that future ebullition measurements on longer time scales are needed to accurately quantify the CH4 budgets of eutrophic urban rivers.

The assessment of protein–ligand interactions is critical at early stage of drug discovery. Computational approaches for efficiently predicting such interactions facilitate drug development. Recently, methods based on deep learning, including structure- and sequence-based models, have achieved impressive performance on several different datasets. However, their application still suffers from a generalizability issue because of insufficient data, especially for structure based models, as well as a heterogeneity problem because of different label measurements and varying proteins across datasets. Here, we present an interpretable multi-task model to evaluate protein–ligand interaction (Multi-PLI). The model can run classification (binding or not) and regression (binding affinity) tasks concurrently by unifying different datasets. The model outperforms traditional docking and machine learning on both binary classification and regression tasks and achieves competitive results compared with some structure-based deep learning methods, even with the same training set size. Furthermore, combined with the proposed occlusion algorithm, the model can predict the important amino acids of proteins that are crucial for binding, thus providing a biological interpretation. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-021-00510-6.

Octogen (HMX) is widely used as a high explosive and constituent in plastic explosives, nuclear devices, and rocket fuel. The direct discharge of wastewater generated during HMX production threatens the environment. In this study, we used the electrochemical oxidation (EO) method with a PbO2-based anode to treat HMX wastewater and investigated its degradation performance, mechanism, and toxicity evolution under different conditions. The results showed that HMX treated by EO could achieve a removal efficiency of 81.2% within 180 min at a current density of 70 mA/cm2, Na2SO4 concentration of 0.25 mol/L, interelectrode distance of 1.0 cm, and pH of 5.0. The degradation followed pseudo-first-order kinetics (R2 > 0.93). The degradation pathways of HMX in the EO system have been proposed, including cathode reduction and indirect oxidation by •OH radicals. The molecular toxicity level (expressed as the transcriptional effect level index) of HMX wastewater first increased to 1.81 and then decreased to a non-toxic level during the degradation process. Protein and oxidative stress were the dominant stress categories, possibly because of the intermediates that evolved during HMX degradation. This study provides new insights into the electrochemical degradation mechanisms and molecular-level toxicity evolution during HMX degradation. It also serves as initial evidence for the potential of the EO-enabled method as an alternative for explosive wastewater treatment with high removal performance, low cost, and low environmental impact.

Oxidative dehydrogenation (ODH) of alkane over boron nitride (BN) catalyst exhibits high olefin selectivity as well as a small ecological carbon footprint. Here we report an unusual phenomenon that the in-situ formed olefins under reactions are in turn actively accelerating parent alkane conversion over BN by interacting with hydroperoxyl and alkoxyl radicals and generating reactive species which promote oxidation of alkane and olefin formation, through feeding a mixture of alkane and olefin and DFT calculations. The isotope tracer studies reveal the cleavage of C-C bond in propylene when co-existing with propane, directly evidencing the deep-oxidation of olefins occur in the ODH reaction over BN. Furthermore, enhancing the activation of ethane by the in-situ formed olefins from propane is successfully realized at lower temperature by co-feeding alkane mixture strategy. This work unveils the realistic ODH reaction pathway over BN and provides an insight into efficiently producing olefins.

Electrochemical impedance spectroscopy (EIS) is an efficient and non-destructive test for analyzing the bioelectrochemical processes of microbial fuel cells (MFCs). The key factors limiting the output performance of an MFC can be identified by quantifying the contribution of its various internal parts to the total impedance. However, little attention has been paid to the measurement conditions and diagrammatic processes of the EIS for MFC. This review, starting with the analysis of admittance of bioelectrode, introduces conditions for the EIS measurement and summarizes the representative equivalent circuit plots for MFC. Despite the impedance from electron transfer and diffusion process, the effect of unnoticeable capacitance obtained from the Nyquist plot on MFCs performance is evaluated. Furthermore, given that distribution of relaxation times (DRT) is an emerging method for deconvoluting EIS data in the field of fuel cell, the application of DRT-analysis to MFC is reviewed here to get insight into bioelectrode reactions and monitor the biofilm formation. Generally, EIS measurement is expected to optimize the construction and compositions of MFCs to overcome the low power generation.

By means of molecular dynamics simulations, this work addresses the conformational flexibility and migration of trivalent neodymium (Nd