Porphyrin Metal-organic Framework Sensors for Chemical and Biological Sensing

Porphyrins and porphyrin derivatives have been intensively explored for a number of applications such as sensing, catalysis, adsorption, and photocatalysis due to their outstanding photophysical properties. Their usage in sensing applications, however, is limited by intrinsic defects such as physiological instability and self-quenching. To reduce self-quenching susceptibility, researchers have developed porphyrin metal-organic frameworks (MOFs). Metal-organic frameworks (MOFs), a unique type of hybrid porous coordination polymers comprised of metal ions linked by organic linkers, are gaining popularity. Porphyrin molecules can be integrated into MOFs or employed as organic linkers in the production of MOFs. Porphyrin-based MOFs are a separate branch of the huge MOF family that combines the distinguishing qualities of porphyrins (e.g., fluorescent nature) and MOFs (e.g., high surface area, high porosity) to enable sensing applications with higher sensitivity, specificity, and extended target range. The key synthesis techniques for porphyrin-based MOFs, such as porphyrin@MOFs, porphyrinic MOFs, and composite porphyrinic MOFs, are outlined in this review article. This review article focuses on current advances and breakthroughs in the field of porphyrin-based MOFs for detecting a variety of targets (for example, metal ions, anions, explosives, biomolecules, pH, and toxins). Finally, the issues and potential future uses of this class of emerging materials for sensing applications are reviewed.

Heterocyclic-Based Analogues against Sarcine-Ricin Loop RNA from Escherichia coli: In Silico Molecular Docking Study and Machine Learning Classifiers

BACKGROUND

Heterocyclic-based drugs have strong bioactivities, are active pharmacophores, and are used to design several antibacterial drugs. Due to the diverse biodynamic properties of well-known heterocyclic cores, such as quinoline, indole, and its derivatives, they have a special place in the chemistry of nitrogen-containing heterocyclic molecules.

OBJECTIVES

The objective of this study is to analyze the interaction of several heterocyclic molecules using molecular docking and machine learning approaches to find out the possible antibacterial drugs.

METHODS

The molecular docking analysis of heterocyclic-based analogues against the sarcin-Ricin Loop RNA from E. coli with a C2667-2'-OCF3 modification (PDB ID: 6ZYB) is discussed.

RESULTS

Many heterocyclic-based derivatives show several residual interaction, affinity, and hydrogen bonding with sarcin-Ricin Loop RNA from E. coli with a C2667-2'-OCF3 alteration which are identified by the investigation of in silico molecular docking analysis of such heterocyclic derivatives.

CONCLUSION

The dataset from the molecular docking study was used for additional optimum analysis, and the molecular descriptors were classified using a variety of machine learning classifiers, including the GB Classifier, CB Classifier, RF Classifier, SV Classifier, KNN Classifier, and Voting Classifier. The research presented here showed that heterocyclic derivatives may operate as potent antibacterial agents when combined with other compounds to produce highly efficient antibacterial agents.

Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF4/CH4, CH4/H2, CH4/N2, and N2/H2 mixtures

The ease of functionalization of metal-organic frameworks (MOFs) can unlock unprecedented opportunities for gas adsorption and separation applications as the functional groups can impart favorable/unfavorable regions/interactions for the desired/undesired adsorbates. In this study, the effects of the presence of multiple functional groups in MOFs on their CF₄/CH₄, CH₄/H₂, CH₄/N₂, and N₂/H₂ separation performances were computationally investigated combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The most promising adsorbents showing the best combinations of selectivity, working capacity, and regenerability were identified for each gas separation. 15, 13, and 16 out of the top 20 MOFs identified for the CH₄/H₂, CH₄/N₂, and N₂/H₂ adsorption-based separation, respectively, were found to have -OCH₃ groups as one of the functional groups. The biggest improvements in CF₄/CH₄, CH₄/H₂, CH₄/N₂, and N₂/H₂ selectivities were found to be induced by the presence of -OCH₃-OCH₃ groups in MOFs. For CH₄/H₂ separation, MOFs with two and three functionalized linkers were the best adsorbent candidates while for N₂/H₂ separation, all the top 20 materials involve two functional groups. Membrane performances of the MOFs were also studied for CH₄/H₂ and CH₄/N₂ separation and the results showed that MOFs having -F-NH₂ and -F-OCH₃ functional groups present the highest separation performances considering both the membrane selectivity and permeability.

Tuning the target composition of amine-grafted CPO-27-Mg for capture of CO2 under post-combustion and air filtering conditions: a combined experimental and computational study

A computational and experimental study is performed to determine the optimal composition that enhances the adsorption performance at low pressure.