Biodiesel production from green seaweed Ulva fasciata catalyzed by novel waste catalysts from Pakistan Steel Industry

This research article is based on the biodiesel synthesis from the marine green macroalga Ulva fasciata, collected from the coast of Karachi, Pakistan using new and the most potential waste catalysts from Pakistan Steel Industry.The oil was extracted with n-hexane then it was analyzed by GC, TLC and by the examination of fuel properties.The metal analysis of catalysts was carried out by chemical tests and flame atomic absorption spectroscopy(FAAS). The thermal treatment of catalysts at 1500–1700 °C during various processes in steel manufacturing industry converted the metals to metal oxides. The presence of CaO, MgO and ZnO in these catalysts made them highly reactive for biodiesel synthesis. The basicity of waste industrial catalysts was calculated to know their basic strength. The transesterification of U. fasciata oil was performed by fast stirring using 9:1 molar ratio of methanol/oil in the presence of seven different waste industrial catalysts for 6 h at 80–100 °C. The solid catalysts were easily separated from product for re-use. In addition, the rate of reaction in the presence of these catalysts was found to be quite feasible. The waste brown dust from the steel converter gave the highest yield(88%) of biodiesel. The production of biodiesel was confirmed by TLC examination and fuel properties in comparison with the ASTM standards.

Biodiesel synthesis via metal oxides and metal chlorides catalysis from marine alga Melanothamnus afaqhusainii

This research article demonstrates the most comprehensive comparative catalytic study of different metal oxides and metal chlorides towards the methanolysis of triglycerides of marine red macroalga Melanothamnus afaqhusainii.CaO was found to be the most reactive metal oxide that yielded 80% biodiesel while ZnCl_2 was the most reactive metal chloride that produced 60% biodiesel by mechanical stirring for 6 h at 100–110 °C.The overall reactivity order of the catalysts was found to be CaO>MgO>PbO_2>ZnCl_2>TiCl_4>PbO>HgCl_2>ZnO>AlCl_3>SnCl_2>TiO_2whereas,CaCl_2,MgCl_2,Al_2O_3,HgO,PbCl_2,MnO_2,MnCl_2,Fe_2O_3 and FeCl_3 were found to be non-reactive for transesterification of triglycerides.In addition,a detailed study of the screening of mobile phases and spraying reagents was conducted which showed that petroleum ether :chloroform :toluene(7:2:1)is the best mobile phase,whereas iodine crystals/silica gel is the best visualizing agent for the thin layer chromatography(TLC)examination of biodiesel.Biodiesel production was confirmed by comparative TLC examination.It was further supported by the determination of fuel properties of biodiesel,which were found to be similar to the standard limits of American Society for Testing and Materials(ASTM).

Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

The Coronavirus Disease 2019(COVID-19)pandemic poses the worldwide challenges surpassing the boundaries of country,religion,race,and economy.The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction(RT-PCR)testing.Nevertheless,this testing method is accurate enough for the diagnosis of COVID-19.However,it is time-consuming,expensive,expert-dependent,and violates social distancing.In this paper,this research proposed an effective multimodality-based and feature fusion-based(MMFF)COVID-19 detection technique through deep neural networks.In multi-modality,we have utilized the cough samples,breathe samples and sound samples of healthy as well as COVID-19 patients from publicly available COSWARA dataset.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.Several useful features were extracted from the aforementioned modalities that were then fed as an input to long short-term memory recurrent neural network algorithms for the classification purpose.Extensive set of experimental analyses were performed to evaluate the performance of our proposed approach.The experimental results showed that our proposed approach outperformed compared to four baseline approaches published recently.We believe that our proposed technique will assists potential users to diagnose the COVID-19 without the intervention of any expert in minimum amount of time.