中文摘要：

在金属离子分类基础上（按金属离子的软硬性质），以啤酒工业废酵母对10种金属离子（Pb^2＋,Ag^2＋，cr^2＋，cu^2＋，zn^2＋，cd^2＋，co^2＋，sr^2＋，Ni^2＋orCs^＋）的Langmuir理论饱和吸附容量qmax为QSAR（Quantitative Structure Activity Relationships）模型的活性参数，以金属离子的22种物理化学性质作为QSAR模型的结构参数，利用QSAR方法建立了分类条件下离子性质与酵母生物吸附容量之间的定量关系,金属的离子性质与吸附容量之间的线性回归分析结果表明，对金属离子进行适当分类可以改善拟合效果,对于含有软离子的金属离子，共价指数Xm^2r，是22种变量中预测效果最好的离子性质,离子的Xm^2r，数值越大，离子与细胞的共价结合程度越高，吸附量越大,对于不含软离子的金属离子，极化力Z^2／r、第一水解常数1logKoH1和电离势IP是qmax预测中最有价值的3个离子结构参数,（似）极化力的多种表征形式Z／r，Z／r^2，Z／AR^2，Z／AR也可以用于预测qmax,但考虑了金属离子的价层电子数、离子有效电荷和离子半径的极化力参数Z^2／r，却难以解释酵母吸附金属离子的亲和力顺序,该研究为预测金属离子的生物吸附容量、研究重金属离子一微生物的相互作用提供了新的思路和方法．

英文摘要：

Based on classification of metal ions （ hard or soft ions ）, the relationship between metal ionic characteristics and the biosorption capacity of ions was established using QSAR model. The maximum biosorption capacity （qmax） of Pb^2＋ , Ag^2＋ , Cr^2＋ , Cu^2＋ , Zn^2＋ , Cd^2＋ , Co^2＋ , Sr^2＋ , Ni^2＋ or Cs^＋ by the waste biomass of Saccharomyces cerevisiae, determined by the Langmuir isotherm model, was set as activity parameter, and twenty two physiochemical characteristics of metal ions were set as structure parameters in setting up QSAR models. Linear regression analysis showed that classification of metal ions, according to soft or hard ions principle, could improve the relationship. The covalent index Xm^2 r was correlated well to qmax for metal ions containing soft ions. The greater the covalent index value of metal ion was, the greater was potential to form covalent bonds with biological ligands, and the larger was the metal ion biosorption. For metal ions without soft ions, polarizing power Z^2/r or the first hydrolysis constant 1 log Kon 1 or ionization potential IP were the first three valuable structural parameters to predict qmax. The various expressions of （pseudo） polarizing power, Z^2/r, Z/r, Z/r^2 , Z/AR^2 or Z/AR, were also applicable for borderline ions or plus hard ions. However, only Z^＋2/r considering electron structure of valence shell electron number did not count for the affinity of metal ions by the biomass. This research provided a new way to predict the biosorption capacity of metal ions and to explore the metal-microbe interactions.