High-Throughput Computational Design of 2D Ternary Chalcogenides for Sustainable Energy.

Two-dimensional materials are considered to be promising for next-generation electronic and energy devices. However, the limited availability of 2D materials hinders their applications. Herein, we employed high-throughput computation to discover new 2D materials by cleaving the bulk and to investigate their electronic, thermoelectric, and optoelectronic properties. Using our database containing 810 structures of chalcogenides ABX3 (A or B = Al, Ga, In, Si, Ge, Sn, P, As, Sb, and Bi; X = S, Se, and Te), we identified 204 new 2D compounds promising for experimental preparation according to the exfoliation energy. Notably, 96 of them are more easily exfoliated than graphene, 52 compounds show higher Seebeck coefficients than Bi2Te3 at 300 K, and 20 compounds have power factors beyond 2 × 10-3 Wm-1 K-2 at 900 K. Also, 6 new compounds exhibit high theoretical photovoltaic efficiency exceeding 30%. Our findings expand the 2D materials family and provide new 2D compounds for sustainable thermoelectric and optoelectronic energy applications.

Accelerating the Discovery of Transition Metal Borides by Machine Learning on Small Data Sets.

Accurate and efficient prediction of the stability and structure-stability relationship is important to discover materials; however, it requires tremendous efforts via traditional trial-and-error schemes. Here, we presented a small-data set machine learning (ML) method to accelerate the discovery of promising ternary transition metal boride (MAB) candidates. Based on data sets obtained by ab initio calculations, we developed three robust neural networks to predict the decomposition energy (ΔHd) and assess the thermodynamic stability of 212-typed MABs (M2AB2). The quantitative relation between ΔHd and stability was unraveled by several composition-and-structure descriptors. Three hexagonal M2AB2, i.e., Nb2PB2, Nb2AsB2, and Zr2SB2, were discovered to be stable with negative ΔHd, and 75 metastable MABs were identified with ΔHd less than 70 meV/atom. Finally, the dynamical stability and mechanical properties of MABs were investigated by ab initio calculations, whose results further verified the reliability of our ML models. This work provided a ML approach on small data sets to accelerate the discovery of compounds and expanded the MAB phase family to VA and VIA groups.

A Study of Risk Relevance Reasoning Based on a Context Ontology of Railway Accidents.

With the application of risk management and accident response in the railway domain, risk detection and prevention have become key research topics. Many dangers and associated risk sources must be considered in collaborative scenarios of heavy-haul railways. In these scenarios, (1) various risk sources are involved in different data sources, and context affects their occurrence, (2) the relationships between contexts and risk sources in the accident cause mechanism need to be explicitly defined, and (3) risk knowledge reasoning needs to integrate knowledge from multiple data sources to achieve comprehensive results. To express the association rules among core concepts, this article constructs two ontologies: The accident-risk ontology and the context ontology. Concept analysis is based on railway domain knowledge and accident analysis reports. To sustainably integrate knowledge, an integrated evolutionary model called scenario-risk-accident chain ontology (SRAC) is constructed by introducing new data sources. The SRAC is integrated through expert rules between the two ontologies, and its evolution process involves new knowledge through a new risk source database. After three versions of the upgrade process, potential risk sources can be mined and evaluated in specific contexts. To evaluate the risk source level, a long short-term memory (LSTM) neural network model is used to capture context and risk text features. A model comparison for different neural network structures is performed to find the optimal evaluation results. Finally, new concepts, such as risk source level, and new instances are updated in the context-aware risk knowledge reasoning framework.

Combining microwave irradiation with sodium citrate addition improves the pre-treatment on anaerobic digestion of excess sewage sludge.

This study investigated the synergistic effect of sodium citrate (SC; Na3C3H5O(COO)3) and microwave (MW) treatment on the efficiency of the anaerobic digestion of excess sewage sludge. In terms of the methane yield, an increase of the digestion's efficiency was observed. Taking into account the cost for the MW energy supplied to the system, the optimum treatment conditions were a MW energy input of 20 MJ/kg TS and a SC concentration of 0.11 g/g TS, obtaining a methane yield of 218.88 ml/g VS, i.e., an increase of 147.7% compared to the control. MW treatment was found to break the sludge structure, thereby improving the release of extracellular polymeric substances (EPS) and volatile fatty acids (VFAs). The treatment of sodium citrate further strengthened the breakage of loosely bound extracellular polymeric substances (LB-EPS) and tightly bound extracellular polymeric substances (TB-EPS). The increased VFA content stressed the improved digestion by this pretreatment. Furthermore, the preliminary economic analysis showed that at this point in the research, only operational but no financial gains were achieved.

High-Sensitive Electrochemiluminescence C-Peptide Biosensor via the Double Quenching of Dopamine to the Novel Ru(II)-Organic Complex with Dual Intramolecular Self-Catalysis.

Here, a novel Ru(II)-organic complex (Ru-PEI-ABEI) with high electrochemiluminescence (ECL) efficiency was proposed to construct a sensitive quenching-typed ECL biosensor for C-peptide (C-P) measurement based on the double quenching effect of dopamine (DA). The high ECL efficiency of Ru-PEI-ABEI was originated from the dual intramolecular self-catalysis including intramolecular coreaction between polyethylenimine (PEI) and Ru(bpy)2(mcbpy)2+, and intramolecular ECL resonance energy transfer (ECL-RET) from N-(aminobutyl)-N-(ethylisoluminol) (ABEI) to Ru(bpy)2(mcbpy)2+, which would generate a strong initial ECL signal. Through sandwiched immunoreaction and 3D DNA walking machine, a certain amount of target C-P was converted to a large amount of intermediate DNA that could further trigger hybridization chain reaction (HCR) to introduce into massive DA which not only could quench the ECL of Ru(bpy)2(mcbpy)2+, but also quench the ECL of ABEI. Thus, the double quenching effect of DA would effectively quench the ECL of Ru-PEI-ABEI, leading to an obviously decreased final ECL signal. Thus, a sensitive quenching-typed ECL biosensor was constructed for C-P detection with a linear range from 50 fg mL-1 to 16 ng mL-1 and an estimated detection limit of 16.7 fg mL-1. The dual intramolecular self-catalyzed strategy and the double quenching strategy based on one quencher to the same luminous reagent proposed in this work would provide new thought in both ECL signal enhancement and quenching efficiency improvement.

Evaluation of A Novel Split-Feeding Anaerobic/Oxic Baffled Reactor (A/OBR) For Foodwaste Anaerobic Digestate: Performance, Modeling and Bacterial Community.

To enhance the treatment efficiency from an anaerobic digester, a novel six-compartment anaerobic/oxic baffled reactor (A/OBR) was employed. Two kinds of split-feeding A/OBRs R2 and R3, with influent fed in the 1st, 3rd and 5th compartment of the reactor simultaneously at the respective ratios of 6:3:1 and 6:2:2, were compared with the regular-feeding reactor R1 when all influent was fed in the 1st compartment (control). Three aspects, the COD removal, the hydraulic characteristics and the bacterial community, were systematically investigated, compared and evaluated. The results indicated that R2 and R3 had similar tolerance to loading shock, but the R2 had the highest COD removal of 91.6% with a final effluent of 345 mg/L. The mixing patterns in both split-feeding reactors were intermediate between plug-flow and completely-mixed, with dead spaces between 8.17% and 8.35% compared with a 31.9% dead space in R1. Polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) analysis revealed that the split-feeding strategy provided a higher bacterial diversity and more stable bacterial community than that in the regular-feeding strategy. Further analysis indicated that Firmicutes, Bacteroidetes, and Proteobacteria were the dominant bacteria, among which Firmicutes and Bacteroidetes might be responsible for organic matter degradation and Proteobacteria for nitrification and denitrification.

Asymmetric Synthesis of α-Amino Ketones by Brønsted Acid Catalysis.

The highly efficient, regioselective, and enantioselective transfer hydrogenation of α-keto ketimines and reductive amination of diketones by Brønsted acid catalysis is described. A series of chiral α-amino ketones is prepared in high yields (up to >99%), excellent regioselectivities (up to >99:1), and enantioselectivities (up to 98% ee). This method has broad substrate scope.

The anaerobic digestion of biologically and physicochemically pretreated oily wastewater.

To enhance the degradation of oily wastewater and its biogas production, a biological-physicochemical pretreatment was introduced prior to the anaerobic digestion system. The digestion thereafter proceeded more efficiently due to the inoculation by oil degrading bacteria (Bacillus). A 2-stage pre-mixing is more effective than directly mixing. The effects on the methane production were also investigated by pre-treatment with ultrasonic (US) treatment, combined with citric acid (CA) addition. US pre-treatment was found to improve the initial methane production, and CA pre-treatment could maintain this improvement during the whole digestion stage. Pre-mixing Bacillus at 9 wt.% inoculation, combined with US for 10 min and a CA concentration of 500 mg/L provided the optimum conditions. The most effective enhancement of methane yield was 1100.46 ml/g VS, exceeding that of the control by 280%. The change of coenobium shape and fatty acid content further proved that such pretreatment of oily wastewater can facilitate digestion.

The effect of a buffer function on the semi-continuous anaerobic digestion.

The characteristics of the long term (90 d) anaerobic semi-continuous digestion of food wastes/animal slurry wastewater with different C/N ratio were investigated. The research demonstrates that, in the semi-continuous digestion process, different C/N ratios of substrate led to a significant effect to the characteristic of anaerobic digestion (AD). It is undoubtedly that the semi-continuous AD is efficient within a wide range of C/N ratios, and the lower C/N substrate promoted the activity of the methanogen in a long time, which maintained a stable high CH4 concentration and total organic carbon (TOC) utilization. During the whole AD process, an active buffer system was formed from NH4(+) and VFAs (by-products of TOC): this buffer system provided high concentrations of VFAs, thus increasing both the CH4 yield and TOC utilization; and also maintained a high tolerance to NH4(+) and VFAs in the system, which defusing the impact of NH4(+).

The anaerobic co-digestion of food waste and cattle manure.

This study assessed the anaerobic co-digestion of food waste and cattle manure, in order to identify the key parameters that determine the biogas and methane yield. Results of both batch and semi-continuous tests indicated that the total methane production is enhanced in co-digestion, with an optimum food waste (FM) to cattle manure (CM) ratio of 2. At this ratio, the total methane production in batch tests was enhanced by 41.1%, and the corresponding methane yield was 388 mL/g-VS. In the semi-continuous mode, the total methane production in co-digestion, at the organic loading rate (OLR) of 10 g-VSFW/L/d, increased by 55.2%, corresponding to the methane yield of 317 mL/g-VS. Addition of cattle manure enhanced the buffer capacity (created by NH4+ and VFAs), allowing high organic load without pH control. The C/N ratio and the higher biodegradation of lipids might be the main reasons for the biogas production improvement.