Nanoparticle corona phase (CP) design offers a unique approach toward molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs can additionally transduce MR through its band-gap photoluminescence (PL). While DNA oligonucleotides have been used as SWCNT CPs, no generalized scheme exists for MR prediction de novo due to their sequence-dependent three-dimensional complexity. This work generated the largest DNA-SWCNT PL response library of 1408 elements and leveraged machine learning (ML) techniques to understand MR and DNA sequence dependence through local (LFs) and high-level features (HLFs). Out-of-sample analysis of our ML model showed significant correlations between model predictions and actual sensor responses for 6 out of 8 experimental conditions. Different HLF combinations were found to be uniquely correlated with different analytes. Furthermore, models utilizing both LFs and HLFs show improvement over that with HLFs alone, demonstrating that DNA-SWCNT CP engineering is more complex than simply specifying molecular properties.

纳米粒子电晕相 (CP) 设计为传感应用的分子识别 (MR) 提供了一种独特的方法。单壁碳纳米管 (SWCNT) CP 还可以通过其带隙光致发光 (PL) 来转换 MR。虽然 DNA 寡核苷酸已被用作 SWCNT CP，但由于其序列相关的三维复杂性，不存在用于从头预测 MR 的通用方案。这项工作生成了最大的 1408 个元素的 DNA-SWCNT PL 响应库，并利用机器学习 (ML) 技术通过局部 (LF) 和高级特征 (HLF) 了解 MR 和 DNA 序列依赖性。我们的 ML 模型的样本外分析显示，对于 8 个实验条件中的 6 个，模型预测与实际传感器响应之间存在显着相关性。发现不同的 HLF 组合与不同的分析物具有独特的相关性。此外，使用 LF 和 HLF 的模型比单独使用 HLF 的模型显示出改进，这表明 DNA-SWCNT CP 工程比简单地指定分子特性更复杂。