Ca and Sr co-doping induced oxygen vacancies in 3DOM La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts for boosting low-temperature oxidative coupling of methane

It is urgent to develop catalysts with application potential for oxidative coupling of methane(OCM)at relatively lower temperature.Herein,three-dimensional ordered macro porous(3 DOM)La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)(A_(2)B_(2)O_(7)-type)catalysts with disordered defective cubic fluorite phased structure were successfully prepared by a colloidal crystal template method.3DOM structure promotes the accessibility of the gaseous reactants(O2and CH4)to the active sites.The co-doping of Ca and Sr ions in La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts improved the formation of oxygen vacancies,thereby leading to increased density of surface-active oxygen species(O_(2)^(-))for the activation of CH4and the formation of C2products(C2H6and C2H4).3DOM La_(2-x)Sr_(x)Ce_(2-y)CayO_(7-δ)catalysts exhibit high catalytic activity for OCM at low temperature.3DOM La1.7Sr0.3Ce1.7Ca0.3O7-δcatalyst with the highest density of O_(2)^(-)species exhibited the highest catalytic activity for low-temperature OCM,i.e.,its CH4conversion,selectivity and yield of C2products at 650℃are 32.2%,66.1%and 21.3%,respectively.The mechanism was proposed that the increase in surface oxygen vacancies induced by the co-doping of Ca and Sr ions boosts the key step of C-H bond breaking and C-C bond coupling in catalyzing low-temperature OCM.It is meaningful for the development of the low-temperature and high-efficient catalysts for OCM reaction in practical application.

Human genes influence the interaction between Streptococcus mutans and host caries susceptibility: a genome-wide association study in children with primary dentition

Streptococcus mutans is a well-known cause of dental caries,due to its acidogenicity,aciduricity,and ability to synthesize exopolysaccharides in dental plaques.Intriguingly,not all children who carry S.mutans manifest caries,even with similar characteristics in oral hygiene,diet,and other environmental factors.This phenomenon suggests that host susceptibility potentially plays a role in the development of dental caries;however,the association between host genetics,S.mutans,and dental caries remains unclear.Therefore,this study examined the influence of host gene-by-S.mutans interaction on dental caries.Genome-wide association analyses were conducted in 709 US children (<13 years old),using the dbGap database acquired from the center for oral health research in appalachia (COHRA) and the Iowa Head Start programmes (GEIRS).A generalized estimating equation was used to examine the gene-by-S.mutans interaction effects on the outcomes (decayed and missing/filled primary teeth due to caries).Sequentially,the COHRA and GEIRS data were used to identify potential interactions and replicate the findings.Three loci at the genes interleukin 32 (IL32),galactokinase 2 (GALK2),and CUGBP,Elav-like family member 4 (CELF4) were linked to S.mutans carriage,and there was a severity of caries at a suggestive significance level among COHRA children (P < 9×10?5),and at a nominal significance level among GEIRS children (P = 0.047–0.001).The genetic risk score that combined the three loci also significantly interacted with S.mutans (P < 0.000 1).Functional analyses indicated that the identified genes are involved in the host immune response,galactose carbohydrate metabolism,and food-rewarding system,which could potentially be used to identify children at high risk for caries and to develop personalized caries prevention strategies.

A Knowledge-Enriched and Span-Based Network for Joint Entity and Relation Extraction

The joint extraction of entities and their relations from certain texts plays a significant role in most natural language processes.For entity and relation extraction in a specific domain,we propose a hybrid neural framework consisting of two parts:a span-based model and a graph-based model.The span-based model can tackle overlapping problems compared with BILOU methods,whereas the graph-based model treats relation prediction as graph classification.Our main contribution is to incorporate external lexical and syntactic knowledge of a specific domain,such as domain dictionaries and dependency structures from texts,into end-to-end neural models.We conducted extensive experiments on a Chinese military entity and relation extraction corpus.The results show that the proposed framework outperforms the baselines with better performance in terms of entity and relation prediction.The proposed method provides insight into problems with the joint extraction of entities and their relations.

Systematic degradation mechanism and pathways analysis of the immobilized bacteria:Permeability and biodegradation,kinetic and molecular simulation

In order to effectively improve the degradation rate of diesel,a systematic analysis of the degradation mechanism used by immobilized bacteria is necessary.In the present study,diesel degradation mechanisms were assessed by analyzing permeability,biodegradation,adsorption kinetics,and molecular simulation.We found that bacteria immobilized on cinnamon shells and peanut shells degraded relatively high amounts of diesel(69.94%and 64.41%,respectively).The primary degradation pathways used by immobilized bacteria included surface adsorption,internal uptake,and biodegradation.Surface adsorption was dominant in the early stage of degradation,whereas biodegradation was dominant in later stages.The diesel adsorption rate of the immobilized bacteria was in agreement with the pseudo second-order kinetic model.The immobilized bacteria and diesel interacted through hydrogen bonds.