Study of Foaming Properties and Effect of the Isomeric Distribution of Some Anionic Surfactants

Using different reaction conditions of photosulfochlorination of n-dodecane, two samples of anionic surfactants of sulfonate type are obtained. Their micellar behavior has been already reported and the relationship between their isomeric distribution and their chemical structures and micellar behaviors have been more thoroughly explored. In this investigation, we screened the foaming properties (foaming power and foam stability) by a standardized method very similar to the Ross–Miles foaming tests to identify which surfactants are suitable for applications requiring high foaming, or, alternatively, low foaming. The results obtained for the synthesized surfactants are compared to those obtained for an industrial sample of secondary alkanesulfonate (Hostapur 60) and to those of a commercial sample of sodium dodecylsulfate used as reference for anionic surfactants. The foam formation and foam stability of aqueous solutions of the two samples of dodecanesulfonate are compared as a function of their isomeric distribution. These compounds show good foaming power characterized in most cases by metastable or dry foams. The highest foaming power is obtained for the sample rich in primary isomers which also produces foam with a relatively high stability. For the sample rich in secondary isomers we observe under fixed conditions a comparable initial foam height but the foam stability turns out to be low. This property is interesting for applications requiring low foaming properties such as dishwashing liquid for machines. The best results are observed near and above the critical micellar concentrations and at 25 C for both the samples

An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant’s technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases