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Multiple methods of grafting graphene oxide (GO) nanosheets to natural clinoptilolite-rich zeolite particles were developed in our laboratory.

In most domains anomaly detection is typically cast as an unsupervised learning problem because of the infeasability of labelling large datasets. In this setup, the evaluation and comparison of different anomaly detection algorithms is difficult. Although some work has been published in this field, they fail to account that different algorithms can detect different kinds of anomalies. More precisely, the literature on this topic has focused on defining criteria to determine which algorithm is better, while ignoring the fact that such criteria are meaningful only if the algorithms being compared are detecting the same kind of anomalies. Therefore, in this paper we propose an equivalence criterion for anomaly detection algorithms that measures to what degree two anomaly detection algorithms detect the same kind of anomalies. First, we lay out a set of desirable properties that such an equivalence criterion should have and why; second, we propose, Gaussian Equivalence Criterion (GEC) as equivalence criterion and show mathematically that it has the desirable properties previously mentioned. Finally, we empirically validate these properties using a simulated and a real-world dataset. For the real-world dataset, we show how GEC can provide insight about the anomaly detection algorithms as well as the dataset.

In recent years, concern has grown over widespread environmental contamination of per- and polyfluoroalkyl substances (PFAS) in the environment and especially in drinking water. These manmade chemicals were first synthesized in the mid-20th century and contain thousands of individual species, some of which are still used today. A wide variety of industries use or have historically used PFAS in their products and processes. PFAS chemicals are recalcitrant by design and thus do not break down in the environment. Federal and state agencies have begun testing of drinking water and other media for PFAS in order to understand the extent of contamination and determine any necessary regulatory actions. Testing is expensive, however, and it would be beneficial to be able to prioritize wells for testing based on their likely vulnerability. This study presents a methodology for identifying locations of industrial sites likely to use PFAS, and uses Michigan’s robust well testing data alongside industry and environmental factors to identify any spatial relationships that can be used to help prioritize testing of wells.