Spherical DNA for Probing Wettability of Microplastics.

Wettability of microplastics may change due to chemical or physical transformations at their surface. In this work, we studied the adsorption of spherical nucleic acids (SNAs) with a gold nanoparticle core and linear DNA of the same sequence to probe the wettability of microplastics. Soaking microplastics in water at room temperature for 3 months resulted in the enhancement of SNA adsorption capacity and affinity, whereas linear DNA adsorption was the same on the fresh and soaked microplastics. Drying of the soaked microplastics followed by rehydration decreased the adsorption of the SNA, suggesting that the effect of soaking was reversible and related to physical changes instead of chemical changes of the microplastics. Raman spectroscopy data also revealed no chemical transformations of the soaked microplastics. Heating of microplastics over a short period induced a similar effect to long-term soaking. We propose that soaking or heating removes air entrapped in the nanosized pores at the water-plastic interface, increasing the contact surface area of the SNA to afford stronger adsorption. However, such wetted porosity would not change the adsorption of linear DNA because of its much smaller size.

Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman Microscopy.

Raman spectroscopy is commonly used in microplastics identification, but equipment variations yield inconsistent data structures that disrupt the development of communal analytical tools. We report a strategy to overcome the issue using a database of high-resolution, full-window Raman spectra. This approach enables customizable analytical tools to be easily created─a feature we demonstrate by creating machine-learning classification models using open-source random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. These models yield >95% classification accuracy when trained on spectroscopic data with spectroscopic data downgraded to 1, 2, 4, or 8 cm-1 spacings in Raman shift. The accuracy can be maintained even in non-ideal conditions, such as with spectroscopic sampling rates of 1 kHz and when microplastic particles are outside the focal plane of the laser. This approach enables the creation of classification models that are robust and adaptable to varied spectrometer setups and experimental needs.