Co对腈纶废水厌氧处理效果的影响

利用厌氧反应装置,以腈纶废水为处理对象,通过2组试验,对比了微量金属(Co)投加前后废水中有机物降解情况、累积产气量、三维荧光光谱图以及微生物生长情况,研究了微量Co对腈纶废水厌氧处理效果的影响.结果表明:当Co的投加浓度〔以ρ(CoCl2)计〕为1.0 mg/L时,CODCr去除率达71.7%,累积产气量达到22.5 mL;当Co的投加浓度超过10 mg/L时,CODCr去除率为20%左右,累积产气量8.5 mL,未投加Co元素的空白对照组CODCr去除率为43.1%,累积产气量为10 mL.显示微量Co对腈纶废水厌氧生物降解具有一定的促进作用.      

PTA废液回收利用技术

介绍了从精对苯二甲酸（PTA）废液中回收醋酸,邻、对苯二甲酸,醋酸钴锰盐的回收工艺。醋酸得率为90％,邻、对苯二甲酸得率为70％,醋酸钴锰盐得率为95％。该技术成熟、合理,具有较好的经济效益和环境效益。     

响应面法优化赤泥负载Co催化剂制备及活性评价

以催化臭氧氧化去除水中苯扎贝特(bezafibrate,BZF)的处理效果为评价指标,采用Plackett-Burman和响应面(Response Surface Methodology,RSM)法对Co/RM催化剂的制备条件进行优化.结果表明,浸渍质量分数和焙烧温度为催化剂制备的主要影响因素,当浸渍质量分数为4.14%、煅烧温度为389℃时BZF去除率预测值最高(71.29%).采用上述最佳制备条件制备的Co/RM催化臭氧氧化去除苯扎贝特的去除率为70.74%,与模型预测值十分接近,偏差为1%(<5%),说明响应面法实验设计和数学模型具有较好可靠性.通过比较RM和Co/RM两种催化剂对BZF的催化臭氧氧化去除效果,发现Co/RM催化臭氧氧化去除BZF的效果较为显著.采用BET、XRD和UV-Vis等表征手段对两种催化剂的表面结构和组成进行分析,结果表明两种催化剂的比表面积、总孔体积存在相同的变化规律(RM相似文献   

模拟酸雨影响下红壤中钴的释放行为研究

采用室内模拟酸雨淋溶土柱的方法,研究了酸雨影响下红壤中钴的释放特征与规律.结果表明,酸雨加速了红壤中钴的淋溶损失.酸雨作用下红壤中钴的释放随淋溶量的变化具有阶段性特点,但不同土壤之间其阶段性特点存在一定的差异.在实验所施的淋溶强度下,供试的4种红壤中有3种表现为前期阶段减小和后期阶段增大的规律性,1种表现为前期阶段减小,中期阶段增大和后期阶段减小的规律性.酸雨对红壤中钴释放的促进作用,在短期内有利于提高红壤钴的可给性,但长期的酸雨淋溶可导致红壤因钴的加速淋失而缺钴.      

电感耦合等离子体质谱法测定水中钴的研究

采用电感耦合等离子体质谱仪测定水中的钴,在0μg／L～300μg／L范围内线性良好,相关系数均达到0．9999。该法对钴标准样品测定的结果在保证值范围。对三份不同浓度水样分别进行测定,其结果的RSD为0．2％～1．2％,空白水样的加标回收率在99％～107％之间。通过对水样的分析,符合标准要求,ICP－MS质谱仪具有灵敏度高,检出限低,取样量少,线性范围宽等特点,能快速测出水中的微量重金属元素含量。     

新疆某石化企业污水库周边土壤对钴的吸附行为研究

采用静态吸附法研究了新疆菜石化企业污水库周边土壤对重金属钴的吸附特征,考察了溶液pH值、钴离子平衡质量浓度、背景电解质浓度、吸附时间和环境温度等因素对钴吸附量的影响,并对吸附机理进行了初步探讨.结果表明:溶液pH值为1～4时,改变pH值对土壤吸附钴的影响显著;吸附时间在3h左右时钴吸附量趋于稳定;随溶液背景电解质浓度增大,土壤对钴的吸附量呈现先急剧后缓慢的下降趋势;土壤对钴的吸附量随温度升高而增加.拟合结果表明,Langmuir和Freundlich模型能较好地描述土壤对钴吸附的热力学过程;在3h内,土壤对钴吸附的动力学过程可以用一级反应动力学方程表示.方差分析结果表明,对土壤吸附钴量有显著性影响的因素有溶液pH值、钴离子平衡浓度、吸附时间和温度等.     

掺钴水钠锰矿对铅的吸附及对砷的氧化

为了研究过渡金属离子掺杂对锰氧化物作为环境友好材料物理化学性质的影响,在浓盐酸还原高锰酸钾制备水钠锰矿过程中添加氯化钴,在常压回流条件下一步合成了三维纳米微球状水钠锰矿,考察了其铅吸附和砷氧化能力.应用粉晶X射线衍射、化学分析、N2物理吸附、场发射扫描电镜（FE-SEM）、X射线光电子能谱（XPS）等手段表征产物晶体结...     

钴（Ⅱ）－过氧化氢－苯基荧光酮体系催化光度法测定痕量钴的研究

本文研究了钴（Ⅱ）催化过氧化氢氧化苯基荧光酮的褪色反应,测定了反应级数和表观活化能,建立了测定痕量地的新方法。２５℃时本法检出限为２９×１０－１３ｇ／ｍｌ,线性范围为０．０４～２ｎｇ／１０ｍｌ,催化反应表观活化能为５６．１５ＫＪ／ｍｏｌ。该方法己用于维生素Ｂ１２、人发和茶叶中痕量钴的测定．结果满意,相对标准偏差为２．４～３．４％。     

Chemical forms of cobalt and nickel extracted by M1, M3 and CaCl2 DTPA

Mehlich 1(M1), mehlich 3(M3) and CaCl₂ DTPA have been used to predict the available micronutrient in soil. However, the forms of micronutrient extractable by these extractants are not known. In the present study, ten soils, collected from five provinces and the capital of China, representing a wide range of chemical and physical properties, were analyzed by sequential extraction to isolate five forms of cobalt and nickel, they are exchangeable, carbonate bound, Fe Mn oxide bound, organically bound and residual forms. The chemical forms extracted by M1, M3 and CaCl₂ DTPA were also investigated. The results show strong correlation between the carbonate bound or organically bound forms of Co or Ni and the amounts of extractable by any of the above three extractants. The main forms extracted by these extractants are carbonate and organically bound forms. The ranking of these three extractants for extraction of Co and Ni are M1>M3=CaCl₂ DTPA and M1=M3>CaCl₂ DTPA, respectively.     

Predicting the risk of cobalt deprivation in grazing livestock from soil composition data

The objective was to derive predictive equations for acetic acid-extractable cobalt (A-ECo) in soils so that extensive national databases for total (T) Co in soils and stream sediments could be converted to plant available concentrations for the purpose of predicting risk of Co deficiency in grazing livestock. Data on the chemical and physical properties of 103 soils from 15 different parent materials and 54 soil series in England and Wales were used. Ranges for the mean values for parent materials were: TCo, 5.0–20.4 and A-ECo, 0.20–1.30 mg kg^–1; percentage (P) A-ECo, 3.4–13.5; soil manganese (Mn) 268–1174 mg kgDM^–1; pH, 3.7–8.0. There were significant effects of parent material on all parameters with Chalks, Old and New Red Sandstones particularly low in A-ECo. Multiple linear regression yielded the following equation for predicting A-ECo, which accounted for 56% of the variance with 12 outliers, including the lowest pH values, omitted:A-ECo(mg kg^-1) = 1.4 - 0.0619TCo (mg kg^-1) - 0.432TMn(g kg^-1) - 0.171pHPrediction of PA-ECo was less precise:PA-ECo(%) = 21.1 - 4.5TMn(g kg^-1) - 1.77pH(r ² = 44.8%; 88 d.f.REML was used on the complete, unbalanced, log-transformed data set to fit a Generalised Mixed Model with parent material as random effect and soil Mn and pH as fixed effects; the effect of parent material was no longer significant. It was concluded that A-ECo can be satisfactorily predicted for most soils in England and Wales from TCo, TMn and soil pH.