No abstract

Recently, oil‐water separation has become an important topic due to its increasing commercial demand. Here, in this study, a simple technique to prepare superhydrophobic‐superoleophilic mesh for oil‐water separation is reported. The pre‐cleaned stainless steel mesh is modified by octadecyltrichlorosilane (ODS) through solution immersion method. The modified mesh shows the water contact angle (WCA) of 158 ± 2° and oil contact angle (OCA) of 0°. The prepared superhydrophobic‐superoleophilic mesh effectively separates various oils, including petrol, kerosene, diesel, vegetable oil, and coconut oil from oil–water mixtures with separation efficiency greater than 95%, and stable recyclability up to 10 cycles. In case of low viscosity oil (petrol), the modified mesh shows permeation flux of 2086.95 ± 104.34 L/(m2.h), which is higher than high viscosity oils.

In healthcare, patient information is a sparse critical asset considered as private data and is often protected by law. It is also the domain which is least explored in the field of Machine Learning. The main reason for this is to build efficient artificial intelligence (AI) based models for preliminary diagnosis of various diseases, it would require a large corpus of data which can be obtained by pooling in patient information from multiple sources. However, for these sources to agree to sharing their data across distributed systems for training algorithms and models, there has to be an assurance that there will be no disclosure of the personally identifiable information (PII) of the respective Data Owners. This paper proposes PriMed, an approach to build robust privacy preserving additions to convolutional neural networks (CNN) for training and performing inference on medical images without compromising privacy. Since privacy of the data is preserved, large amounts of data can be effectively accumulated to increase the accuracy and efficiency of AI models in the field of healthcare. This involves implementing a hybrid of privacy‐enhancing techniques like Federated Learning, Differential Privacy, and Homomorphic Encryption to provide a private and secure environment for learning through data.