Nanoparticles Used for CO2 Capture by Adsorption: a Review

Rapid industrialization, technological advancement, and innovation have led to a significant rise in carbon emissions globally, resulting in the growing problem of climate change. With the advancement of nanotechnology, the adsorption is becoming an effective strategy to directly capture CO2 with nanomaterials. This mini-review deals with the investigation for physical adsorption, amine-modified nanomaterials for chemisorption, and moisture-swing nanomaterials for chemisorption. The purpose is to highlight the current technologies available for a simple, environmentally safe, non-toxic, low-cost CO2 capture. In detail, this study examines several CO2 capture nanomaterials with an emphasis on economical and environmentally safe low to high temperature solid adsorbents

Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease

PurposeQuantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms. MethodsTwenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles. ResultsThe combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about +/- 5 pp for FF and +/- 1.8 ms for wT2. ConclusionThis pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence