Application of Porous Materials for CO2 Reutilization: A Review

CO2 reutilization processes contribute to the mitigation of CO2 as a potent greenhouse gas (GHG) through reusing and converting it into economically valuable chemical products including methanol, dimethyl ether, and methane. Solar thermochemical conversion and photochemical and electrochemical CO2 reduction processes are emerging technologies in which solar energy is utilized to provide the energy required for the endothermic dissociation of CO2. Owing to the surface-dependent nature of these technologies, their performance is significantly reliant on the solid reactant/catalyst accessible surface area. Solid porous structures either entirely made from the catalyst or used as a support for coating the catalyst/solid reactants can increase the number of active reaction sites and, thus, the kinetics of CO2 reutilization reactions. This paper reviews the principles and application of porous materials for CO2 reutilization pathways in solar thermochemical, photochemical, and electrochemical reduction technologies. Then, the state of the development of each technology is critically reviewed and evaluated with the focus on the use of porous materials. Finally, the research needs and challenges are presented to further advance the implementation of porous materials in the CO2 reutilization processes and the commercialization of the aforementioned technologies.

Mutations in RBD of SARS-CoV-2 Omicron variant result stronger binding to human ACE2 protein

The spreading of SARS-CoV-2 virus resulted the COVID-19 pandemic, which has caused more than 5 millions of death globally. Several major variants of SARS-CoV-2 have emerged and placed challenges in controlling the infections. The recently emerged Omicron variant raised serious concerns about reducing efficacy of antibodies or vaccines, due to its vast mutations. We modelled the complex structure of human ACE2 protein and the receptor binding domain of Omicron variant, then conducted atomistic molecular dynamics simulations to study the binding interactions. The analysis shows that the Omicron variant RBD binds more strongly to the human ACE2 protein than the original strain. The mutation at the ACE2-RBD interface enhanced the tight binding by increasing hydrogen bonding interaction and enlarging buried solvent accessible surface area.

A Binary Matrix Method to Enumerate, Hierarchically Order and Structurally Classify Peptide Aggregation

Protein aggregation is a common and complex phenomenon in biological processes, yet a robust analysis of this aggregation process remains elusive. The commonly used methods such as centre-of-mass to centre-of-mass (COM-COM) distance, the radius of gyration (Rg), hydrogen bonding (HB) and solvent accessible surface area (SASA) do not quantify the aggregation accurately. Herein, a new and robust method that uses an aggregation matrix (AM) approach to investigate peptide aggregation in a MD simulation trajectory is presented. A nxn two-dimensional aggregation matrix (AM) is created by using the inter-peptide CA-CA cut-off distances which are binarily encoded (0 or 1). These aggregation matrices are analysed to enumerate, hierarchically order and structurally classify the aggregates. Moreover, the comparison between the present AM method and the conventional Rg, COM-COM and HB methods shows that the conventional methods grossly underestimate the aggregation propensity. Additionally, the conventional methods do not address the hierarchy and structural ordering of the aggregates, which the present AM method does. Finally, the present AM method utilises only nxn two-dimensional matrices to analyse aggregates consisting of several peptide units. To the best of our knowledge, this is a maiden approach to enumerate, hierarchically order and structurally classify peptide aggregation.

Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation

Insulin has been commonly adopted as a peptide drug to treat diabetes given its ability to facilitate the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin’s proteolytic stability and reduce its dimerization propensity, but the understanding of such phenomena at the molecular level is still evasive. To address this challenge, we propose and test several structural determinants that could potentially in uence insulin’s proteolytic stability and dimerization propensity. We used these as the metrics to assess the properties of interest from 10 s aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, as was in part the solvent accessible surface area of proteolytic sites, while other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation in uences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms.

Identification of Benzothiazole‒Rhodanine Derivatives as α-Amylase and α-Glucosidase Inhibitors: Design, Synthesis, In-silico and In-Vitro Analysis

In search for possible antidiabetic agents, a new series of Benzothiazole-Rhodanine derivatives (A1-10) have been synthesized and characterized by spectral data(IR, 1H-NMR, C13-NMR,and HR-MS).All the designed compounds were subjected to In-silico studies using Schrodinger softwareand evaluated for In-vitro antidiabetic activityby α-amylase and α-glucosidase inhibitory assays.Among the tested compoundsA5, A6 and A9 showed good activity when compared to the standard Acarbose. Also, Molecular dynamic (MD) simulations were conducted to evaluate the stability of the ligand-protein complex by the calculation of the root mean of square deviation (RMSD), root mean square fluctuation (RMSF), and solvent accessible surface area (SASA).

Fabrication of Co3O4/NiCo2O4 Nanocomposite for Detection of H2O2 and Dopamine

Herein, the Co3O4/NiCo2O4 nanocomposite has been prepared as a novel electrochemical sensor to accurately detect hydrogen peroxide (H2O2) and glucose. ZIF-67 is a metal-organic framework (MOF) with Co as the center metal ion. Co3O4 can be obtained by calcination of ZIF-67 at 700 °C, which can retain the structure of ZIF-67. The hollow Co3O4 nanocrystal was synthesized based on a calcination process of ZIF-67. This open structure can promote the whole Co3O4/NiCo2O4 nanocomposite larger accessible surface area and reactive sites. Co3O4 has good electrocatalytic performance, which has been applied in many fields. Moreover, H2O2 and dopamine sensing tests indicate that the as-prepared non-enzymatic electrochemical biosensor has good detection properties. The testing results indicate the as-prepared biosensor has a wide detection range, low detection limit, high selectivity, and long-term stability. These testing results suggest the potential application in food security, biomedicine, environmental detection, and pharmaceutical analysis.

Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation

Insulin has been commonly adopted as a peptide drug to treat diabetes given its ability to facilitate the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin’s proteolytic stability and reduce its dimerization propensity, but the understanding of such phenomena at the molecular level is still evasive. To address this challenge, we propose and test several structural determinants that could potentially in uence insulin’s proteolytic stability and dimerization propensity. We used these as the metrics to assess the properties of interest from 10 s aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, as was in part the solvent accessible surface area of proteolytic sites, while other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation in uences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms.

Detailed investigations of the influence of catalyst packing porosity on the performance of THAI-CAPRI process for in situ catalytic upgrading of heavy oil and bitumen

Heavy oils and bitumen are indispensable resources for a turbulent-free transition to a decarbonized global energy and economic system. This is because according to the analysis of the International Energy Agency’s 2020 estimates, the world requires up to 770 billion barrels of oil from now to year 2040. However, BP’s 2020 statistical review of world energy has shown that the global total reserves of the cheap-to-produce conventional oil are roughly only 520.2 billion barrels. This implies that the huge reserves of the practically unexploited difficult-and-costly-to-upgrade-and-produce heavy oils and bitumen must be immediately developed using advanced upgrading and extraction technologies which have greener credentials. Furthermore, in accordance with climate change mitigation strategies and to efficiently develop the heavy oils and bitumen resources, producers would like to maximize their upgrading within the reservoirs by using energy-efficient and environmentally friendly technologies such as the yet-to-be-fully-understood THAI-CAPRI process. The THAI-CAPRI process uses in situ combustion and in situ catalytic reactions to produce high-quality oil from heavy oils and bitumen reservoirs. However, prolonging catalyst life and effectiveness and maximizing catalytic reactions are a major challenge in the THAI-CAPRI process. Therefore, in this work, the first ever-detailed investigations of the effects of alumina-supported cobalt oxide–molybdenum oxide (CoMo/γ-Al2O3) catalyst packing porosity on the performance of the THAI-CAPRI process are performed through numerical simulations using CMG STARS. The key findings in this study include: the larger the catalyst packing porosity, the higher the accessible surface area for the mobilized oil to reach the inner coke-uncoated catalysts and thus the higher the API gravity and quality of the produced oil, which clearly indicated that sulphur and nitrogen heteroatoms were catalytically removed and replaced with hydrogen. Over the 290 min of combustion period, slightly more oil (i.e. an additional 0.43% oil originally in place (OOIP)) is recovered in the model which has the higher catalyst packing porosity. In other words, there is a cumulative oil production of 2330 cm3 when the catalyst packing porosity is 56% versus a cumulative oil production of 2300 cm3 in the model whose catalyst packing porosity is 45%. The larger the catalyst packing porosity, the lower the mass and thus cost of the catalyst required per m3 of annular space around the horizontal producer well. The peak temperature and the very small amount of produced oxygen are only marginally affected by the catalyst packing porosity, thereby implying that the extents of the combustion and thermal cracking reactions are respectively the same in both models. Thus, the higher upgrading achieved in the model whose catalyst packing porosity is 56% is purely due to the fact that the extent of the catalytic reactions in the model is larger than those in the model whose catalyst packing porosity is 45%.

Screening of Potent Phytochemical Inhibitors Against SARS-CoV-2 Main Protease: An Integrative Computational Approach

Coronavirus disease 2019 (COVID-19) is a potentially lethal and devastating disease that has quickly become a public health threat worldwide. Due to its high transmission rate, many countries were forced to implement lockdown protocols, wreaking havoc on the global economy and the medical crisis. The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative virus for COVID-19, represent an effective target for the development of a new drug/vaccine because it is well-conserved and plays a vital role in viral replication. Mpro inhibition can stop the replication, transcription as well as recombination of SARS-CoV-2 after the infection and thus can halt the formation of virus particles, making Mpro a viable therapeutic target. Here, we constructed a phytochemical dataset based on a rigorous literature review and explored the probability that various phytochemicals will bind with the main protease using a molecular docking approach. The top three hit compounds, medicagol, faradiol, and flavanthrin, had binding scores of −8.3, −8.6, and −8.8 kcal/mol, respectively, in the docking analysis. These three compounds bind to the active groove, consisting of His41, Cys45, Met165, Met49, Gln189, Thr24, and Thr190, resulting in main protease inhibition. Moreover, the multiple descriptors from the molecular dynamics simulation, including the root-mean-square deviation, root-mean-square fluctuation, solvent-accessible surface area, radius of gyration, and hydrogen bond analysis, confirmed the stable nature of the docked complexes. In addition, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis confirmed a lack of toxicity or carcinogenicity for the screened compounds. Our computational analysis may contribute toward the design of an effective drug against the main protease of SARS-CoV-2.

Identification of Benzothiazole‒Rhodanine Derivatives as α-Amylase and α-Glucosidase Inhibitors: Design, Synthesis, In-silico and In-Vitro Analysis

In search for possible antidiabetic agents, a new series of Benzothiazole-Rhodanine derivatives (A1-10) have been synthesized and characterized by spectral data (IR, 1 H-NMR, C 13 -NMR, and HR-MS). All the designed compounds were subjected to In-silico studies using Schrodinger software and evaluated for In-vitro antidiabetic activity by α-amylase and α-glucosidase inhibitory assays. Among the tested compounds A5, A6 and A9 showed good activity when compared to the standard Acarbose. Also, Molecular dynamic (MD) simulations were conducted to evaluate the stability of the ligand-protein complex by the calculation of the root mean of square deviation (RMSD), root mean square fluctuation (RMSF), and solvent accessible surface area (SASA).