Syn-Aminoauration versus Anti-Aminoauration of Alkynes in Au(I)/Au(III) Catalysis: Understanding the Origin of Selectivity.

There is no experimental evidence of whether such gold-catalyzed aminoauration reactions follow the anti- and/or syn-pathway, and hence, to understand the origin of the selectivity in Au(I)- and Au(III)-catalyzed reactions of alkynes, a thorough mechanistic study was performed using DFT methods. The NBO and ASM analyses provided significant information about the structure-stability-reactivity of the pathway-determining states (PDS). This study further reveals that the oxidation states and geometries of gold, the steric bulk, and the dihedral angles of the PDS direct the mechanistic pathways and control the turnover frequency.

Machine Learning-Enabled Predictions of Condensed Fukui Functions and Designing of Metal Pincer Complexes for Catalytic Hydrogenation of CO2.

This research showcases the machine learning (ML)-enabled homogeneous catalyst discovery to be employed in carbon dioxide hydrogenation. To achieve the desired turnover frequency (TOF), the electrophilicity of the central metal atom is a crucial factor in transition metal pincer complexes. The condensed Fukui function is a direct measure of the catalytic performance of these pincer complexes. Herein, we demonstrate that machine learning is a convenient and effiecient method to calculate condensed Fukui functions of the central metal atom. The electrophilicity values of 202 pincer complexes were calculated by using density functional theory (DFT) to train the ML model. The test data of the experimentally established pincer complexes show a direct linkage between calculated electrophilicity and experimental TOF. Further, this data was used to develop an ML protocol to screen 2,84,062 catalyst complexes to get the electrophilicity values of the Mn, Fe, Co, and Ni transition metals encompassing various permutation combinations of PNP, PNN, NNN, and PCP pincer ligands. These findings validate the efficacy of machine learning in the rapid screening of metal pincer catalysts based on condensed Fukui functions.

Insights into the CO2 Capture Capacity of Covalent Organic Frameworks.

An inexpensive computational method is designed to demonstrate the efficacy of the complex COF toward CO2 capture. The interaction energy calculations of small repeating units of COF precisely demonstrate CO2 uptake capacity at high pressure and effective dual descriptors values of these repeating units of COFs accurately establish their structure-property relationships under ambient conditions. The computational findings are in consonance with experimental results reported by Yaghi and coworkers. Further, the computationally modelled COFs confirms that the addition of -NH2 increases the effective dual descriptors values of COFs. Whereas an increase in the size of a central aromatic unit of COF6 shows better interactions. Interaction energy and effective dual descriptor calculations demonstrate the CO2 capture abilities of COFs at high pressure and low pressure respectively. The method developed by our group would be useful in high throughput designing and screening of a large number of complex COFs at different pressure.

Detection of trace volatile organic compounds in spiked breath samples: a leap towards breathomics.

Breathomics is the future of non-invasive point-of-care devices. The field of breathomics can be split into the isolation of disease-specific volatile organic compounds (VOCs) and their detection. In the present work, an array of five quartz tuning fork (QTF)-based sensors modified by polymer with nanomaterial additive has been utilized. The array has been used to detect samples of human breath spiked with ∼0.5 ppm of known VOCs namely, acetone, acetaldehyde, octane, decane, ethanol, methanol, styrene, propylbenzene, cyclohexanone, butanediol, and isopropyl alcohol which are bio-markers for certain diseases. Polystyrene was used as the base polymer and it was functionalized with 4 different fillers namely, silver nanoparticles-reduced graphene oxide composite, titanium dioxide nanoparticles, zinc ferrite nanoparticles-reduced graphene oxide composite, and cellulose acetate. Each of these fillers enhanced the selectivity of a particular sensor towards a certain VOC compared to the pristine polystyrene-modified sensor. Their interaction with the VOCs in changing the mechanical properties of polymer giving rise to change in the resonant frequency of QTF is used as sensor response for detection. The interaction of functionalized polymers with VOCs was analyzed by FTIR and UV-vis spectroscopy. The collective sensor response of five sensors is used to identify VOCs using an ensemble classifier with 92.8% accuracy of prediction. The accuracy of prediction improved to 96% when isopropyl alcohol, ethanol, and methanol were considered as one class.

Unraveling the Effect of Aromatic Groups in Mn(I)NNN Pincer Complexes on Carbon Dioxide Activation Using Density Functional Study.

Carbon dioxide utilization is necessary to reduce carbon footprint and also to synthesize value-added chemicals. The transition metal pincer complexes are attractive catalysts for the hydrogenation of carbon dioxide to formic acid. There is a need to understand the factors affecting the catalytic performance of these pincer complexes through a structure-activity relationship study using computational methods. It is a well-established fact that aromatic functionalities offer stability and selectivity to transition metal catalysts. However, their impact on the performance of the catalysts is lesser known in the case of metal pincer complexes. Hence, it is necessary to investigate the catalytic performance of Mn(I)NNN pincer complexes with variably activated aromatic functionalities. In this context, 15 catalysts are designed by placing different types of aromatic rings at the pincer carbons and two terminal nitrogen of Mn(I)NNN pincer complexes. A benzene moiety, placed at C2-C3 carbons of Mn(I)NNN pincer complex with identical aromatic groups at the terminal nitrogen, is found to be most efficient toward CO2 hydrogenation than the rest of the catalysts. On the other hand, when N,N-dimethyl aniline is placed at C2-C3 carbons of Mn(I)NNN pincer complexes, then the catalytic performance is significantly decreased. Thus, the present study unravels the impact of aromatic groups in Mn(I)NNN pincer complexes toward the catalytic hydrogenation of carbon dioxide.

Co/Co3O4 Based Nanoparticles and Their Polymer Composites for Tuned Electromagnetic Interference Shielding Application.

The magnetic properties of the metal nanoparticles (NPs) can play remarkable role in electromagnetic interference shielding (EMI Shielding) of many defence and commercial electronic devices. In the present work, coconut oil and PVA capped magnetic cobalt/cobalt oxide nanoparticles (Co/Co3O4 NPs) were synthesized by chemical reduction method and impregnated in polymer matrix to verify their EMI shielding behaviour. The coconut oil capped Co/Co3O4 NPs with presence of hcp and fcc phases were prepared in the size domain of 7-10 nm and the effect of surfactant (the oil) on size and oxidation state was studied by varying the ratios. The shielding efficiencies of Co/Co3O4 NPs PVA nanocomposites were analysed by using vector network analyser (VNA) in X- and Ku-band ranging from 8 GHz-18 GHz. The VNA results showed increased shielding efficiency with increasing concentration of NPs.