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The adoption of single-use plastics for fabricating lab-on-chip devices used in sensors, chemical and biomedical processes is escalating into a major environmental issue. To address the global need for developing long-term sustainable solutions, we present wood microfluidics as an alternative for electrochemical sensing. The lab-on- wood-chip (LOWC) device developed in this study demonstrated (i) versatility in electrochemical applications (electropolymerization and corrosion analysis), (ii) stability under highly acidic (pH 0.5), basic (pH 14.0) and varied temperature (4◦–60 ◦C) conditions (iii) long-term consistency in performance (>12 months), and (iv) potential for on-field nitrate sensing towards environmental monitoring – in a cost-effective, simple and sustainable manner. 

One of the most common problems studied in the context of differential privacy for graph data is counting the number of non-induced embeddings of a subgraph in a given graph. These counts have very high global sensitivity. Therefore, adding noise based on powerful alternative techniques, such as smooth sensitivity and higher-order local sensitivity have been shown to give significantly better accuracy. However, all these alternatives to global sensitivity become computationally very expensive, and to date efficient polynomial time algorithms are known only for few selected subgraphs, such as triangles, k-triangles, and k-stars. In this paper, we show that good approximations to these sensitivity metrics can be still used to get private algorithms. Using this approach, we much faster algorithms for privately counting the number of triangles in real-world social networks, which can be easily parallelized. We also give a private polynomial time algorithm for counting any constant size subgraph using less noise than the global sensitivity; we show this can be improved significantly for counting paths in special classes of graphs.