Cellular micropatterning has become an important tool to precisely design cell-to-substrate attachment for cell biology studies, tissue engineering, cell-based biosensors, biological assays, and drug screens. This paper describes a new technique for micropatterning of cells that is based on the use of oxygen plasma as a patterning tool. The technique consists of (1) homogeneously grafting a glass substrate with a protein-repellent interpenetrating polymeric network (IPN) of poly(acrylamide) and poly(ethyleneglycol) [P(AAm-co-EG)] prepared with commercially available reagents and (2) selectively removing this coating using oxygen plasma. We use elastomeric stencils (i.e. self-sealing membranes with through-holes) and microchannels as removable masks for the selective oxygen plasma etch of the IPN areas that are not protected by the mask. The stencil or microchannels are peeled off to reveal cell-adhesive regions separated by the nonadhesive coating. Our method offers a convenient way of patterning a robust protein-repelling material, allows for independently controlling the chemistry of the regions reserved for cell attachment, and can be used to create coculture systems.

High‐fidelity micrometer‐scale patterns of alternating hydrophobic and hydrophilic materials were created on polystyrene using pulsed plasma polymerization. The hydrophobic material was formed using C3F8. Grids with 80–100 µm holes were used to generate patterns with acrylic acid, 2‐hydroxyethyl methacrylate, N‐vinyl‐2‐pyrrolidinone, N‐vinylformamide, allylamine, and hexylamine. The materials were characterized with angle‐resolved XPS, spectroscopic ellipsometry, and static CA measurements. Excellent pattern fidelity with all monomers was confirmed with SEM, SAM, XPS imaging, and static ToF‐SIMS imaging.

Time-of-flight SIMS (ToF-SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF-SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF-SIMS image data. Three established denoising algorithms -down-binning, boxcar and wavelet filtering -were applied to ToF-SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down-binning the images to increase pixel size and signal was the most effective technique to improve PCA performance.