首页> 外文期刊>RSC Advances >Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles
【24h】

Development of a nano-QSPR model to predict band gaps of spherical metal oxide nanoparticles

机译:开发纳米QSPR模型以预测球形金属氧化物纳米粒子的带隙

获取原文
           

摘要

Antibacterial activities and cytotoxicity of metal oxide nanoparticles are determined by their special band structures, which also influence their potential ecological risks. Traditional experimental determination of the band gap is time-consuming, while the accuracy of theoretical computation depends on the selected algorithm, for which higher precision algorithms, being more expensive, can give a more accurate band gap. Therefore, in this study, a quantitative structure–property relationship (QSPR) model, highlighting the influence of crystalline type and material size, was developed to predict the band gap of metal oxide nanoparticles rapidly and accurately. The structural descriptors for metal oxide nanoparticles were generated via quantum chemistry computations, among which heat of formation and beta angle of the unit cell were the most important parameters influencing band gaps. The developed model shows great robustness and predictive ability ( R ~(2) = 0.848, RMSE = 0.378 eV, RMSE _(CV) = 0.478 eV, Q _(EXT) ~(2) = 0.814, RMSE _(P) = 0.408 eV). The current study can assist in screening the ecological risks of existing metal oxide nanoparticles and may act as a reference for newly designed materials.
机译:金属氧化物纳米颗粒的抗菌活性和细胞毒性取决于其特殊的能带结构,这也会影响其潜在的生态风险。传统的带隙实验确定是耗时的,而理论计算的准确性取决于所选择的算法,为此,精度更高的算法(更昂贵)可以提供更准确的带隙。因此,在这项研究中,建立了强调晶体类型和材料尺寸影响的定量结构-性质关系(QSPR)模型,以快速,准确地预测金属氧化物纳米粒子的带隙。金属氧化物纳米粒子的结构描述是通过量子化学计算生成的,其中形成热和晶胞的β角是影响带隙的最重要参数。所开发的模型显示出强大的鲁棒性和预测能力(R〜(2)= 0.848,RMSE = 0.378 eV,RMSE _(CV)= 0.478 eV,Q _(EXT)〜(2)= 0.814,RMSE _(P)= 0.408 eV)。当前的研究可以帮助筛选现有金属氧化物纳米颗粒的生态风险,并且可以作为新设计材料的参考。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号