Influence of the Reaction Temperature on the Nature of the Active and Deactivating Species During Methanol-to-Olefins Conversion over H‑SAPO-34

The selectivity toward lower olefins during the methanol-to-olefins conversion over H-SAPO-34 at reaction temperatures between 573 and 773 K has been studied with a combination of operando UV–vis diffuse reflectance spectroscopy and online gas chromatography. It was found that the selectivity toward propylene increases in the temperature range of 573–623 K, while it decreases in the temperature range of 623–773 K. The high degree of incorporation of olefins, mainly propylene, into the hydrocarbon pool affects the product selectivity at lower reaction temperatures. The nature and dynamics of the active and deactivating hydrocarbon species with increasing reaction temperature were revealed by a non-negative matrix factorization of the time-resolved operando UV–vis diffuse reflectance spectra. The active hydrocarbon pool species consist of mainly highly methylated benzene carbocations at temperatures between 573 and 598 K, of both highly methylated benzene carbocations and methylated naphthalene carbocations at 623 K, and of only methylated naphthalene carbocations at temperatures between 673 and 773 K. The operando spectroscopy results suggest that the nature of the active species also influences the olefin selectivity. In fact, monoenylic and highly methylated benzene carbocations are more selective to the formation of propylene, whereas the formation of the group of low methylated benzene carbocations and methylated naphthalene carbocations at higher reaction temperatures (i.e., 673 and 773 K) favors the formation of ethylene. At reaction temperatures between 573 and 623 K, catalyst deactivation is caused by the gradual filling of the micropores with methylated naphthalene carbocations, while between 623 and 773 K the formation of neutral poly aromatics and phenanthrene/anthracene carbocations are mainly responsible for catalyst deactivation, their respective contribution increasing with increasing reaction temperature. Methanol pulse experiments at different temperatures demonstrate the dynamics between methylated benzene and methylated naphthalene carbocations. It was found that methylated naphthalene carbocations species are deactivating and block the micropores at low reaction temperatures, while acting as the active species at higher reaction temperatures, although they give rise to the formation of extended hydrocarbon deposits

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of “big data”, a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC’s diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process