In this work, an octahedral-shaped iron nanocluster (NC) electrocatalyst has been modeled to examine the pathways of electrochemical nitrogen reduction reaction (NRR) and analyze the catalytic activity over the (110) surface. The Heyrovsky-type associative and dissociative NRR mechanisms on the (110) facet and edge of the NC are systematically elucidated by calculating reaction free energies for all the possible elementary reaction steps in NRR. Our results show that the most of the NRR intermediates (*N 2 , *N 2 H, *N 2 H 2 , *N, *NH, *NH 2 , and *NH 3 ) bind weakly on different sites of the NC in comparison to that on the periodic Fe(110) surface. Importantly, the reaction free energy change for the potential determining step (PDS) in the distal associative mechanism with the formation of *NNH on the NC facet is lower than the edge of NC and periodic Fe(110) surface. Our study also indicates that the PDS (*NH 2 formation) associated with the periodic Fe(110) surface is no longer the same as the reaction is catalyzed by the NC. The calculated value of working potential is observed lower for Fe 85 NC in comparison to that of the periodic Fe(110) surface. Furthermore, the current density plot indicates that the NC shows less hydrogen evolution reaction (HER) activity compared to other considered Fe based systems. Apart from the working potential study, the positive shift of dissolution potential has also been considered for dissolution behavior of Fe from the NC with respect to surface, confirming its stability in an electrochemical environment. The Fe 85 NC electrocatalyst possess quite a low overpotential of 0.29 V for NRR with reduced HER activity, which is further lower compared to that of the well-established Re(111) and enhanced stability toward Fe dissolution in comparison to that of the periodic Fe(110) surface. Therefore, such an NC system may perform as an efficient catalyst for an electrochemical NRR.

The revolutionary development of machine learning and data science and exploration of its application in material science are huge achievements of the scientific community in the past decade. In this work, we have reported an efficient approach of machine learning-aided high-throughput screening for finding selective earth-abundant high-entropy alloy-based catalysts for CO 2 to methanol formation using a machine learning algorithm and microstructure model. For this, we have chosen earth-abundant Cu, Co, Ni, Zn, and Mg metals to form various alloy-based compositions (bimetallic, trimetallic, tetrametallic, and high-entropy alloys) for selective CO 2 reduction reaction toward CH 3 OH. Since there are several possible surface microstructures for different alloys, we have used machine learning along with DFT calculations for high-throughput screening of the catalysts. In this study, the stability of various 8-atom fcc periodic (111) surface unit cells has been calculated using the atomic-size difference factor (δ) as well as the ratio taken from Gibbs free energy of mixing (Ω). Thinking about the simplicity and accuracy, microstructure models by considering the neighboring atoms of the adsorption sites and others as Cu atoms have been considered for different adsorption sites (on-top, bridge, and hollow-hcp). Moreover, the adsorption energies of the *H, *O, *CO, *HCO, *H 2 CO, and *H 3 CO intermediates have been predicted using the best fitted algorithm of the training set. The predicted adsorption energies have been screened based on the pure Cu adsorption energy. Furthermore, the screened catalysts have been correlated among different adsorption site microstructures. At the end, we were able to find seven active catalysts, among which two catalysts are CuCoNiZn-based tetrametallic, three catalysts are CuNiZnbased trimetallic, and two catalysts are CuCoZn-based trimetallic alloys. Hence, this work demonstrates not an ultimate but an efficient approach for finding new product-selective catalysts, and we expect that it can be convenient for other similar types of reactions in forthcoming days.

Luminescent metal–organic frameworks (LMOFs) are promising functional materials for sustainable applications, where an analyte-induced multiresponsive system with good recyclability is beneficial for detecting numerous lethal pollutants. We designed and built the dual-functionalized, three-dimensional Zn­(II)–framework [Zn3(bpg)1.5(azdc)3]·(DMF)5.9·(H2O)1.05 (CSMCRI-1) using an −OH group-integrated bpg linker and a −NN– moiety containing H2 azdc ligand, which functions as a unique tetrasensoric fluorescent probe. The activated CSMCRI-1 (1′) represents the hitherto unreported pillar-layer framework for extremely selective fluorescence quenching by nitrofurazone antibiotics as well as explosive nitro-aromatic 2,4,6-trinitrophenol, where ultrasensitive detection is achieved for both the electron-lacking analytes. Impressively, 1′ represents the first ever MOF for significant fluorescence “turn-on” detection of toxic and electron-rich 4-aminophenol in the concurrent presence of isomeric analogues. Density functional theory calculations highlight the specific importance of pillar functionalization in the “turn-on” or “turn-off” responses of 1′ by electronically divergent toxic organics and provide further proof of supramolecular interactions between the framework and analytes. The fluorescence intensity of 1′ dramatically quenches by a trace amount of Fe3+ ions over other competing metal ions, alongside visible colorimetric change of the framework in solid and solution phase upon Fe3+ encapsulation. The sensing ability of 1′ remains unaltered for multiple cycles toward all lethal pollutants. The sensing mechanism is attributed to both dynamic and static quenching as well as resonance energy transfer, which strongly comply with the predictions of theoretical simulations. Considering the long-term and real-time monitoring, AND as well as OR molecular logic gates are constructed based on the discriminative fluorescence response for each analyte that provides a platform to fabricate smart LMOFs with multimode logic operations.

Catalytic conversion of CO 2 to carbon neutral fuels can be ecofriendly and allow for economic replacement of fossil fuels. Here, we have investigated high-throughput screening of high entropy alloy (Cu, Co, Ni, Zn, and Sn) based catalysts through machine learning (ML) for CO 2 hydrogenation to methanol. Stability and catalytic activity studies of these catalysts have been performed for all possible combinations, where different elemental, compositional, and surface microstructural features were used as input parameters. Adsorption energy values of CO 2 reduction intermediates on the CuCoNiZnMg-and CuCoNiZnSn-based catalysts have been used to train the ML models. Successful prediction of adsorption energies of the adsorbates using CuCoNiZnMg-based training data is achieved except for two intermediates. Hence, we show that activity and selectivity of these catalysts can be successfully predicted for CO 2 hydrogenation to methanol and have screened a series of high entropy-based catalysts (from 36750 considered catalysts) which could be promising for methanol synthesis.

Mixed-linker CSMCRI-2 demonstrates the turn-on and nanomolar detection of antibiotics and biophosphates with antibiotic-triggered reversible fluorescence switching and keypad lock function.