Catalytic performance of copper and cerium oxides-based γ-Al_2O_3 on combustion of toluene

以γ-Al2O3为载体,以复合氧化物CuxCe1-xO2为活性组分,其中,x=0.1,0.2,0.4,0.6,0.8,通过浸渍法制备了一系列CuxCe1-xO2/γ-Al2O3催化剂.在固定床反应器中评价了催化剂对甲苯的催化活性,通过XRD、SEM对催化剂进行表征,并运用ICP-MS分析并计算Cu、Ce的摩尔比以及活性组分的负载量.结果表明,在CuxCe1-xO2/γ-Al2O3催化剂中Cu、Ce摩尔比的实际值与理论值相近,活性组分的负载量在19%以上,而且对甲苯都有较好的低温催化活性,其中当x=0.2时,即Cu0.2Ce0.8O2/γ-Al2O3催化剂对甲苯的催化活性最高,其中T10=160℃,T90=265℃;当甲苯的进口浓度在700～3000mg·m-3时,进口浓度对Cu0.2Ce0.8O2/γ-Al2O3催化剂的催化活性影响较小,且经过连续80h的稳定性操作后转化率仍然保持在90%以上.     

零价铁对污泥高温厌氧消化过程中四环素抗性基因及第一类整合子的消减影响

为研究污泥高温厌氧消化过程中零价铁(Fe0)对抗生素抗性基因的消减影响,采用荧光定量PCR方法定量检测了7种四环素抗性基因(TC-ARGs,包括tetA、tetC、tetG、tetM、tetO、tetW和tetX)及第一类整合子int I1基因在厌氧消化过程中的丰度变化,同时检测分析了挥发性脂肪酸(VFAs)的质量浓度变化,并探讨了目标基因与VFAs之间的相关关系.结果表明,适量Fe0(如0.10 g·g~(-1),以Fe0/VSS计)不仅可以强化污泥厌氧消化进程,显著提高总VFAs和乙酸的产生量,而且能够增强TCARGs和int I1基因的消减效果.尽管如此,过量Fe0(如1.17 g·g~(-1),以Fe0/VSS计)对污泥高温厌氧消化过程中TC-ARGs和int I1基因消减的增强效果并不显著,推测可能是由于基因水平转移的发生所导致的.通过相关性分析可知,TC-ARGs(除tetO基因外)和int I1基因与乙酸之间存在显著负相关性,表明乙酸可能对TC-ARGs和int I1基因的消减具有促进作用.     

中国东北区域本底大气中酸性气体的研究

龙凤山是我国区域本底站之一,NO_X和SO₂浓度远低于我国空气质量Ⅰ级标准。从1994年8月15日到1995年7月31日首次在龙凤山（127°36′E,44°44′N,330.5m）测得空气中NO_X和SO₂。观测到很强的季节变化,其夏季值最低,冬季值最高。NO_S和SO₂浓度和气象要素密切相关,例如风速、温度和相对湿度。基于排放源以及风频的不均一,不同扇区来的空气质量能有很大变化。在SSE－SW扇区出现高浓度SO₂、较高浓度NO_X和高〔SO₂〕/〔NO_X〕,表明该扇区有城市污染物的输送。SW－NNE扇区有高NO_X、低SO₂及低的〔SO₂/〔NO_X〕,表明该方向城市烟羽难以直接影响测点,在这个扇区高NOX来源于当地农民做饭、取暖时秸杆焚烧。低NO_X、SO₂观测在NNE－SSE扇区,这意味着这是最清洁扇区。     

厌氧水解酸化-好氧氧化A1/A2/O工艺剩余污泥减量

在碱减量印染废水A₁/A₂/O生物处理系统,通过污泥回流、把剩余污泥回流到A₁段,利用A段污泥的水解、酸化和O段微生物的好氧氧化作用可有效实现对回流剩余污泥的减量,同时O段好氧污泥的表观产率系数下降,系统产生的剩余污泥量减少.厌氧水解酸化-好氧氧化A₁/A₂/O工艺实现污泥减量由3段共同完成:A₁段实现回流剩余污泥的“液化”和惰性化;A₂段对系统污泥减量起到了强化作用;而O段微生物的合成作用在逐渐减弱.6个月连续运行的动态试验表明,在A₁段的容积负荷(COD_Cr)为2.54 kg·(m³·d)^-1、水力停留时间为7.56h条件下,A₁段利用水解酸化作用对回流剩余污泥的减量达到67.87%,系统O段好氧污泥的表观产率系数也下降到试验初始时的45.5%.在A₁/A₂/O污泥减量系统中,各段污泥性状发生了变化,A₁段污泥[MLVSS/MLSS]值从试验开始时的0.718 2下降到0.592 2,A₂段污泥[MLVSS/MLSS]值从0.667 3下降到0.526 7,剩余污泥的减量是活性污泥逐渐惰性化过程,污泥的粒度分析也证明了这一点.     

离子液体1-丁基-3-甲基咪唑氯盐对斜生栅藻的毒性效应

按照毒性试验标准方法研究了不同浓度离子液体1-丁基-3-甲基咪唑氯盐([BMIM]Cl)对斜生栅藻的影响,测定了半数抑制浓度(IC50)、叶绿素含量、超氧化物歧化酶(SOD)活性、过氧化氢酶(CAT)活性以及藻细胞通透性的变化,观察了80mg/L[BMIM]Cl处理96h对藻细胞的影响.结果表明:[BMIM]Cl对斜生栅藻生长具有抑制作用,随着浓度的增大抑制率增加,叶绿素含量下降, 48h IC50、72h IC50和96h IC50分别为103.77、76.44和68.49mg/L.藻细胞对[BMIM]Cl有一定的氧化应激反应,CAT和SOD活性随[BMIM]Cl浓度升高而降低.[BMIM]Cl对藻细胞的细胞膜有一定程度的破坏作用,藻细胞通透性随浓度升高而增大,同时藻细胞的超微结构受到了[BMIM]Cl的影响,造成质壁分离,叶绿体片层断裂,线粒体嵴数量减少.     

锂离子电池正极材料的回收及再制备

通过湿化学法从废旧锂离子电池中回收得到Li+、Co2+的硝酸溶液,以此为钴源,与Ni(NO3)2.6H2O、Mn(NO3)2按化学计量比混合,用草酸盐共沉淀法制备草酸盐前驱体,然后按Li过量5%加入Li2CO3混合,在空气中于800～950°C煅烧15 h得LiCo1/3Mn1/3Ni1/3O2,研究了煅烧温度对材料结构、形貌以及电化学性能的影响。结果表明,900℃保温15 h制备的LiCo1/3Mn1/3Ni1/3O2正极材料,为理想的(α-NaFeO2层状结构,颗粒为大小均匀的球形,在2.5～4.3 V的电压范围内,以0.1 C的恒定电流进行充放电,其首次充放电比容量分别为194.3和150.9 mAh/g,10次循环之后的容量保持率为89.9%,放电比容量为135.0 mAh/g。     

1/f噪声源与其在半导体器件可靠性评估中的应用

1／f噪声,由于其能够反映器件的质量与可靠性参数,其研究受到重视。本文首先较为系统地介绍了1／f噪声源两种较为成熟的理论：迁移率涨落模型和载流子涨落模型,最后介绍了几个1／f噪声与半导体器件参数漂移相关的实例。     

高灵敏XRF测定废水中痕量砷

本文提出了在硫酸介质中,以锌粒还原产生氢化物,溴化铜溶液吸收,微孔滤膜过滤制成薄样,Ｘ射线荧光测定水中痕量砷的分析方法。     

Scaling range sizes to threats for robust predictions of risks to biodiversity

Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red‐list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale‐sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1–1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer‐scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid‐measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape‐scale threats to species and ecosystems.     

Efficacy of extracting indices from large‐scale acoustic recordings to monitor biodiversity

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic‐index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random‐forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R² ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random‐forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R² ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.