A low-damage plasma surface modification method of stacked graphene bilayers for configurable wettability and electrical properties.

In this work, we study surface functionalization effects of artificially stacked graphene bilayers (ASGBs) to control its wetting properties via low-damage plasma. The ASGBs were prepared on a SiO2/Si substrate by stacking two monolayer graphene, which was grown by chemical vapor deposition. As a result, the low-damage plasma functionalization of ASGBs could hold both the key characteristics of surface functionalization and electrical transport properties of graphene sheets. To characterize ASGBs, Raman and x-ray photoelectron spectroscopy (XPS) were used to determine the degree of defect formation and functionalization. Meanwhile, the degree of the wettability of the ASGBs surface was determined by optical contact angle (CA) measurements. Based on experimental results, the compositional ratio of C-OH + COOH was found to increase 67% based on the analysis of XPS spectra after low-damage plasma treatment. This treatment effect can also be found with 75.3% decrease in the CA of water droplet on graphene. In addition, we found that the ratio of 2D/(D + G') in Raman spectra shows strong correlation to the measured CA; it can be a reliable indicator of ASGBs surface wettability modification. This work showed that we obtained a higher degree functionalization of ASGBs without degrading the under-layer structure of ASGBs due to the moderate low-damage plasma treatment. The presented process technique of controllable wettability through low-damage plasma treatment can be employed for potential application in graphene-based sensors/devices.

Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness.

Solid-state nanopore (SSN)-based analytical methods have found abundant use in genomics and proteomics with fledgling contributions to virology - a clinically critical field with emphasis on both infectious and designer-drug carriers. Here we demonstrate the ability of SSN to successfully discriminate adeno-associated viruses (AAVs) based on their genetic cargo [double-stranded DNA (AAVdsDNA), single-stranded DNA (AAVssDNA) or none (AAVempty)], devoid of digestion steps, through nanopore-induced electro-deformation (characterized by relative current change; ΔI/I0). The deformation order was found to be AAVempty > AAVssDNA > AAVdsDNA. A deep learning algorithm was developed by integrating support vector machine with an existing neural network, which successfully classified AAVs from SSN resistive-pulses (characteristic of genetic cargo) with >95% accuracy - a potential tool for clinical and biomedical applications. Subsequently, the presence of AAVempty in spiked AAVdsDNA was flagged using the ΔI/I0 distribution characteristics of the two types for mixtures composed of ∼75 : 25% and ∼40 : 60% (in concentration) AAVempty : AAVdsDNA.