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.     

Bi（25）FeO40-g-C3N4磁性催化剂的制备及其可见光催化性能

采用水热法制备了铁酸铋(Bi25FeO40),将其与类石墨相氮化碳(g-C3N4)复合得到Bi25FeO40-g-C3N4磁性光催化剂,用X-射线衍射(XRD)、扫描电子显微镜(SEM)、BET比表面积分析、UV-Vis光谱和磁滞回线对该磁性光催化剂进行了表征.考察了Bi25FeO40-g-C3N4复合催化剂对亚甲基蓝的可见光催化降解效果.结果表明,Bi25FeO40-g-C3N4复合催化剂对亚甲基蓝的可见光催化降解效率远高于Bi25FeO40.此外,Bi25FeO40-g-C3N4表现出顺磁性,可通过磁选分离并回收.     

长期不同施肥条件下红壤小麦和玉米季CO_2、N_2O排放特征

基于中国农业科学院红壤实验站红壤旱地小麦-玉米轮作长期定位试验,采用静态箱/气相色谱法,研究红壤旱地连续施肥16 a后,不同施肥条件下小麦季和玉米季土壤CO2和N2O的排放特征。结果表明,CO2和N2O排放具有明显的季节性,施肥对土壤CO2和N2O排放有明显影响,且有机肥的施用显著促进了土壤CO2和N2O排放。不施肥对照(CK)、氮磷配施(NP)、氮钾配施(NK)、氮磷钾配施(NPK)和有机无机肥配施(NPKM)处理小麦季和玉米季土壤CO2累积排放量分别为5 904、8 062、4 298、9 235、14 098和4 708、7 530、5 435、7 089、15 472 kg.hm-2,土壤N2O累积排放量分别为0.34、0.63、0.44、0.62、1.00和0.25、0.39、0.35、0.52、1.73 kg.hm-2,小麦休闲期土壤CO2和N2 O累积排放量平均占小麦生长季的63.52%和28.43%,玉米休闲季平均占玉米生长季的49.98%和32.72%,说明休闲期土壤CO2和N2O累积排放量不容忽视。除玉米季NP、NK、NPK处理外,其他各处理小麦季和玉米季土壤CO2排放通量与5 cm深处土...     

三江平原小叶章湿地生态系统对氮磷的净化效率

选取面积为1 600 m2的小叶章(Deyeuxia angustifolia)湿地进行小区域模拟试验。试验设割草区、1倍浓度区、静态对照区和2倍浓度区4个处理,研究N、P输入浓度,割草和输出径流对三江平原小叶章湿地生态系统净化N、P污染物的影响。结果表明,在小叶章生长期和成熟期,湿地生态系统对N、P保持较稳定的净化,N、P平均去除率分别为85.62%和85.94%。湿地水体中TN和NH4 -N的累积浓度显著相关。输入浓度、割草和输出径流都影响着湿地生态系统对N、P的净化和N、P在系统内的分配。输入浓度增大时,N、P去除率明显降低;割草会降低N去除率,但对P去除率影响不大;少量输出径流对N、P去除影响不大。三江平原小叶章湿地生态系统对输入的N、P具有显著净化作用,总净化率可达97.97%和99.05%。     

不同种稻方式对后茬大麦产量和土壤微生物量的影响

通过田间试验研究盖草、覆膜、裸露、水作4种种稻方式对后茬大麦产量、生物量、土壤微生物量和氮肥利用率的影响.结果表明,大麦籽粒产量以盖草后茬最高,比覆膜、裸露和水作后茬增产6.0%、16.4%和37.0%.大麦收获期地上部生物量也是盖草后茬最高,分别比覆膜、裸露和水作3个处理增加3 424、6 059、6 961 kg·hm-2.4个处理后茬大麦生长期间土壤微生物量碳、氮随着大麦生育期进程呈先升高后降低的趋势,且各个生育期均表现为盖草＞覆膜＞裸露＞水作.盖草处理氮肥农学利用率比覆膜、裸露、水作高0.9、2.2和8.0 kg·kg-1;氮肥生产率也表现出上述趋势,分别高2.8、7.0和13.3 kg·kg-1.说明旱作水稻覆盖秸秆下的稻-麦轮作是一种优化的种植模式,具有良好的生态效益和社会效益.     

湖滨带温室气体氧化亚氮(N2O)排放研究

利用不锈钢气体采集箱,在太湖梅梁湾湖区,沿水体至陆地方向对植被型和裸露型湖滨带进行温室气体N2O的原位排放观测。结果发现两种类型湖滨带N2O排放均显著高于临近的开阔水体,可以达到10~100倍,水位变幅区是湖滨带温室气体N2O排放的峰值区,是陆向和水向辐射区的5~10倍。在观测期间,N2O排放通量的范围为-159.2～1565.6μg·m-2·h-1,具有明显的时空梯度变化,时间上,9月份最高,随着气温的下降和芦苇的衰亡而逐渐减少,在1月份最低;空间上,自水向辐射区至陆向辐射区,先逐渐升高,在水位变幅区达到峰值,然后再降低。该结果初步说明了湖滨带是温室气体N2O的一个极其重要的排放源,而目前IPCC对水体N2O排放的估算可能存在很大的疏漏;同时也从一定程度上反映了湖滨带是湖泊脱氮的重要区域,其对缓解湖泊氮污染起到了举足轻重的作用。     

不同C／N下人工湿地的脱氮效果及其强化措施

以人工湿地为研究对象,分析了不同C／N下人工湿地的脱氮效果。结果表明,C／N为10时,TN、NO2-N和COD平均去除率分别为（75．36±10．55）％、（86．34±9．23）％和（67．60±4．10）％,都显著高于其他2组系统（P=0．006、0．001、0〈0．01）;随着C／N的增加,NO3-N去除率呈现出上升的趋势;NH4-N刚好相反,C／N为1时,明显要高于其他2种情况（P=0〈0．05）,平均去除率为（65．42-1-14．31）％。针对C／N为1时人工湿地脱氮效率较低（51．294-14．48）％的情况,对系统进水采取了添加一定量好氧反硝化细菌固定化小球并曝气12h的强化实验措施。结果发现,强化实验条件下TN、NOr-N和N03-N去除率均提高了10％以上,且都要明显高于非强化实验条件（P：0．003、0、0〈0．01）,同时NH4-N和COD去除率没有明显差异,研究结果表明,从微生物角度对系统进水进行前处理有助于提高人工湿地脱氮效率。     

凋落物质量和分解对中亚热带栲木荷林土壤氮矿化的影响

用控温、控水室内培养方法,研究了中亚热带栲木荷常绿阔叶林和邻近柳杉人工针叶林凋落物的分解及其对栲木荷林土壤氮矿化的影响.结果表明:栲木荷阔叶林的凋落物失重率大于针阔混合凋落物的失重率,大于柳杉针叶纯林凋落物的失重率.所有凋落物失重率都与其初始氮含量呈显著负相关,和初始碳含量呈显著正相关,而与凋落物C/N比的相关性不强.不同凋落物处理下的土壤NO3--N含量差异显著(P＜0.05,N=18),NH4 -N含量差异不显著(P＞0.05,N=18),混合凋落物处理下的土壤NO3--N和NH4 -N含量最高,分别是224.21 mg kg-1和56.77 mg kg-1,其氮转化速率也最高,硝化、氨化、氮矿化速率分别为1.74 mg kg-1 d-1、0.36 mg kg-1 d-1和1.90mg kg-1 d-1.各凋落物处理下的土壤氮含量随时间变化的规律不一致.土壤氨化速率与土壤全氮呈显著正相关(r=0.843,P＜0.05,N=21),与栲木荷林凋落物的失重率呈显著负相关(r=-0.997,P＜0.05,N=21).图3表4参29     

氮沉降下鼎湖山森林凋落物分解及与土壤动物的关系

研究了南亚热带3种森林生态系统凋落物在N沉降下的分解动态及其与土壤动物群落的关系。选取季风常绿阔叶林、针阔混交林和马尾松林建立野外模拟N沉降样地,实施四个处理组,对照(Control)、低氮(50kg·hm-2·a-1,LowN)、中氮(100kg·hm-2·a-1,MediujmN)和高氮处理(150kg·hm-2·a-1,HighN),利用凋落物网袋法,在18个月的时间内调查分析了凋落物分解过程及其中的土壤动物密度特征。研究结果表明,植被演替阶段对凋落物的分解速度存在影响,季风林凋落物降解速度显著性快于混交林和针叶林(P<0.05);18个月后,季风林各处理地凋落物残留率为0.05、0.14、0.13和0.17,混交林为0.64、0.56和0.62,针叶林为0.66、0.63和0.62。N沉降增加对凋落物分解存在一定影响。且这种影响与植被类型之间存在明显的交互作用。N沉降处理对季风林凋落物分解表现出了一定的抑制作用,而且这种差异随时间推移愈益明显,但在混交林和针叶林内,试验后期凋落物分解受到了N沉降处理的促进作用。在试验后期,尤其是12个月后,凋落物网袋土壤动物密度在不同林地和不同水平N处理下体现了差异化发展趋势。在季风林内,N处理地土壤动物密度受到了明显的抑制;在混交林和针叶林内,低N样地动物密度显示了相比对照样地的明显优势,但在较高强度的中N处理地无论在凋落物的降解速率还是在动物密度上都与对照样地没有明显差别。文章认为,N沉降处理所产生的影响可能受环境N饱和程度的调控。文章还提出,在凋落物分解进程中,土壤动物群落具有“后期进入”特征,这对于进一步准确分析森林凋落物分解进程及土壤动物的贡献有重要意义。     

Using machine learning to disentangle homonyms in large text corpora

Systematic reviews are an increasingly popular decision‐making tool that provides an unbiased summary of evidence to support conservation action. These reviews bridge the gap between researchers and managers by presenting a comprehensive overview of all studies relating to a particular topic and identify specifically where and under which conditions an effect is present. However, several technical challenges can severely hinder the feasibility and applicability of systematic reviews, for example, homonyms (terms that share spelling but differ in meaning). Homonyms add noise to search results and cannot be easily identified or removed. We developed a semiautomated approach that can aid in the classification of homonyms among narratives. We used a combination of automated content analysis and artificial neural networks to quickly and accurately sift through large corpora of academic texts and classify them to distinct topics. As an example, we explored the use of the word reintroduction in academic texts. Reintroduction is used within the conservation context to indicate the release of organisms to their former native habitat; however, a Web of Science search for this word returned thousands of publications in which the term has other meanings and contexts. Using our method, we automatically classified a sample of 3000 of these publications with over 99% accuracy, relative to a manual classification. Our approach can be used easily with other homonyms and can greatly facilitate systematic reviews or similar work in which homonyms hinder the harnessing of large text corpora. Beyond homonyms we see great promise in combining automated content analysis and machine‐learning methods to handle and screen big data for relevant information in conservation science.