Robustness of FTIR-Based Ultrarapid COVID-19 Diagnosis Using PLS-DA.

The World Health Organization (WHO) declared the Omicron variant (B.1.1.529) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen responsible for the Coronavirus disease 2019 (COVID-19) pandemic, as a variant of concern on 26 November 2021. By this time, 42% of the world's population had received at least one dose of the vaccine against COVID-19. As on 1 October 2022, only 68% of the world population got the first dose of the vaccine. Although the vaccination is incredibly protective against severe complications of the disease and death, the highly contagious Omicron variant, compared to the Delta variant (B.1.617.2), has led the whole world into more chaotic situations. Furthermore, the virus has a high mutation rate, and hence, the possibility of a new variant of concern in the future cannot be ruled out. To face such a challenging situation, paramount importance should be given to rapid diagnosis and isolation of the infected patient. Current diagnosis methods, including reverse transcription-polymerase chain reaction and rapid antigen tests, face significant burdens during a COVID-19 wave. However, studies reported ultrarapid, reagent-free, cost-efficient, and non-destructive diagnosis methods based on chemometrics for COVID-19 and COVID-19 severity diagnosis. These studies used a smaller sample cohort to construct the diagnosis model and failed to discuss the robustness of the model. The current study systematically evaluated the robustness of the diagnosis models trained using smaller (real and augmented spectra) and larger (augmented spectra) datasets. The Monte Carlo cross-validation and permutation test results suggest that diagnosis using models trained by larger datasets was accurate and statistically significant (Q 2 > 99% and AUROC = 100%).

Superswelling Hybrid Sponge from Water Glass for Selective Absorption of Crude Oil and Organic Solvents.

A lightweight super hydrophilic hybrid sponge is designed and demonstrated out of water glass and an organic polymer, which has a macroporous flaky nature and is superflexible with an apparent density of 0.069 g cc-1, ∼97% porosity, and 3000% water uptake. The octadecyltrimethoxy silane-modified hybrid sponge exhibits selective absorption of oil and organic solvents in open water. An absorption capacity in the range 12-23 g g-1 for the test liquids light crude oil, engine oil, paraffin oil, chloroform, kerosene, and hexane is revealed. Absorption capacity by a weight basis was directly proportional to the density and inversely proportional to the viscosity of test liquids. Trials under both stagnant and turbulent conditions verify selective uptake of oil from sea water. Complete regeneration of the absorbent was possible for ten cycles for the test liquids. The work provides design of an affordable water clean-up material alternative to commonly used polyurethane sponges.

Techno-economic analysis of microalgae-based liquid fuels production from wastewater via hydrothermal liquefaction and hydroprocessing.

In this work, a conceptual process design of wastewater-based algal biofuels production through hydrothermal liquefaction and hydroprocessing is proposed. Then a steady-state process simulation is performed to calculate the mass and energy analysis of the whole process for the production of hydrocarbons such as diesel, jet, gasoline, and H2. A discounted cash flow analysis is used to calculate a minimum selling price (MSP) of the hydrocarbon fuels. The MSP of the hydrocarbon fuels is found at US$4.3/GGE. This value is comparable with the previously reported value in the literature. In addition, the sensitivity study is carried out to study the influence of both processes and economic parameters on the minimum selling price (MSP) of the hydrocarbon fuels.

Computational Fluid Dynamics simulation of hydrothermal liquefaction of microalgae in a continuous plug-flow reactor.

Computational Fluid Dynamics (CFD) technique is used in this work to simulate the hydrothermal liquefaction of Nannochloropsis sp. microalgae in a lab-scale continuous plug-flow reactor to understand the fluid dynamics, heat transfer, and reaction kinetics in a HTL reactor under hydrothermal condition. The temperature profile in the reactor and the yield of HTL products from the present simulation are obtained and they are validated with the experimental data available in the literature. Furthermore, the parametric study is carried out to study the effect of slurry flow rate, reactor temperature, and external heat transfer coefficient on the yield of products. Though the model predictions are satisfactory in comparison with the experimental results, it still needs to be improved for better prediction of the product yields. This improved model will be considered as a baseline for design and scale-up of large-scale HTL reactor.

Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling.

The present investigation was carried out to study application of ANN as a tool for predicting sugar yields of pretreated biomass during hydrolysis process at various time intervals. Since it is known that biomass loading and particle size influences the rheology and mass transfer during hydrolysis process, these two parameters were chosen for investigating the efficiency of hydrolysis. Alkali pretreated rice straw was used as the model feedstock in this study and biomass loadings were varied from 10% to 18%. Substrate particle sizes used were <0.5mm, 0.5-1mm, >1mm and mixed size. Effectiveness of hydrolysis was strongly influenced by biomass loadings, whereas particle size did not have any significant impact on sugar yield. Higher biomass loadings resulted in higher sugar concentration in the hydrolysates. Optimum hydrolysis conditions predicted were 10 FPU/g cellulase, 5 IU/g BGL, 7500 U/g xylanase, 18% biomass loading and mixed particle size with reaction time between 12-28 h.

A fluorescent molecular probe for the identification of zinc and cadmium salts by excited state charge transfer modulation.

A fluorescent probe for the identification of a given metal salt is not known. Herein we present a new fluorescent probe 1 for the identification of different zinc and cadmium salts by exploiting the effect of the charge density of counteranions to perturb the excited state solvatochromic behavior of the probe.