The nanoparticle corona phase (CP) offers a unique materials design space for constructs capable of molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs have the additional ability to transduce MR through its band gap photoluminescence (PL). DNA oligonucleotides are well-known to disperse SWCNTs through forming CPs and can be manufactured with molecular precision. Nevertheless, no generalized scheme exists for the de novo prediction of SWCNT MR based on these CPs due to their sequence-dependent three-dimensional complexity.

In aquaculture, there exist a pressing need for rapid tests for the detection of commonly harmful adulterants such as metal ions and small molecule antibiotics. Through a screening process, we found that DNA-SWCNTs offer a rich design space for MR capable of differentiating divalent metal ions. We also created best practices for the study of colloidal SWCNT analyte responses involve mitigating the effects of ionic strength, dilution kinetics, laser power and analyte response kinetics.

Next, we generated the largest DNA-SWCNT PL response library of 1408 elements against 4 adulterant targets. We leveraged machine learning (ML) techniques and used both local features (LFs) and high-level features (HLFs) of the DNA sequences were utilized as model inputs. Out-of-sample analysis of our ML model showed significant correlations between model predictions and actual sensor responses for 6 out of the 8 tested experimental conditions. 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 general DNA molecular properties. Taken as a whole, we detail the feasibility and utility of a ML-guided approach for nanoparticle CP engineering with relatively few experiments within a high-dimensional design space.