Understanding the catalytic behavior of sulfated metal oxides has been the topic of several research studies in the past few decades. Their apparent super-acidic behavior has been correlated with the molecular structure of the surface sulfate species. Herein, we couple FTIR and Raman spectroscopies to study the molecular structural evolution of surface sulfate species on mixed metal hydroxides as well as calcined oxides. We show that on the surface of hydroxides, monodentate and possibly bidentate species are dominant, while for SnO2-rich samples, clusters of polymeric sulfate species may also be present. After calcination, sulfate species bind strongly on the surface of mixed oxides, and different configurations can be seen with a range of S=O functionalities of varying strength. Through comparison of the catalytic performance of all sulfate oxides in the tert-butylation of phenol, it was found that SnO2-rich samples show high TBA conversion, with monoalkylated phenols as the primary product.

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Skin cancer is a major health concern, with 125,000 new melanoma cases diagnosed each year. Every year, approximately 3 million non-melanoma cases are diagnosed worldwide. Initially, skin lesions were diagnosed visually using dermoscopic analysis, clinical screening, and a variety of other methods. It has been observed that inexperienced dermatologists can reduce the diagnostic accuracy of skin lesions. Early detection of skin cancer has the potential to reduce mortality. Previous research has shown that deep learning outperforms human experts in a variety of computer vision tasks. This paper proposes to develop an ensemble deep learning model to detect skin cancer from lesion images.In this analytical investigation, 10,000 photos from the HAM10000 dermoscopy image database were used, comprising lesions with and without melanoma.The photos were divided into different skin cancer classifications using a number of deep convolutional neural networks.VGG16, VGG19, and ResNet were used as the pre-trained models in an ensemble deep learning approach. The area under the receiver operating characteristic (ROC) curve for the suggested model was 0.91. A classification accuracy of 86%, sensitivity of 81%, and specificity of 88% were attained using a confidence score threshold of 0.5. Detecting skin cancer, including melanoma and non-melanoma tumors, has a high potential according to the results of deep learning. Dermatologists can identify skin cancer with the use of the indicated method.As a result, it might hasten cancer detection, which is essential for efficient therapy. The suggested model automatically extracts useful features from the input raw image for categorization.It does away with difficult lesion segmentation and feature extraction processes as a result.