Energy level alignments between organic and inorganic layers in 2D layered perovskites: conjugation vs. substituent.

2D layered hybrid perovskites have attracted huge attention due to their interesting optoelectronic properties and chemical flexibility. Depending upon their electronic structures and properties, these materials can be utilised in various optoelectronic devices like photovoltaics, LEDs and so on. In this context, study of the excited energy levels of the organic spacers can help us to align the excited energy levels of the organic unit with the excitonic level of the inorganic unit according to the requirement of a particular optoelectronic device. We have explored the role of 3-phenyl-2-propenammonium on the electronic structure of a perovskite containing this cation as a spacer. Our results clearly demonstrate the active participation of conjugated ammonium spacers in the electronic structure of a perovskite. Also, we have considered a variety of amines to identify the best alignment with common inorganic units and studied the role of substituents and conjugation on the energy level alignment. Placing the triplet excited level of an organic spacer below the lowest excitonic level of the inorganic unit can induce energy transfer from the inorganic to organic unit, finally resulting in phosphorescence emission. We have shown that the triplet energy level of 3-anthracene-2-propeneamine/3-pyrene-2-propeneamine can be tuned in such a way that there can be an excitonic energy transfer from the Pb2I7/PbI4 inorganic unit-based perovskites. Therefore, perovskite material with such combinations of organic spacer cations will be very useful for light emission applications.

Rational Designing of Bimetallic/Trimetallic Hydrogen Evolution Reaction Catalysts Using Supervised Machine Learning.

Cost-efficient electrocatalysts to replace precious platinum group metals- (PGMs-) based catalysts for the hydrogen evolution reaction (HER) carry significant potential for sustainable energy solutions. Machine learning (ML) methods have provided new avenues for intelligent screening and predicting efficient heterogeneous catalysts in recent years. We coalesce density functional theory (DFT) and supervised ML methods to discover earth-abundant active heterogeneous NiCoCu-based HER catalysts. An intuitive generalized microstructure model was designed to study the adsorbate's surface coverage and generate input features for the ML process. The study utilizes optimized eXtreme Gradient Boost Regression (XGBR) models to screen NiCoCu alloy-based catalysts for HER. We show that the most active HER catalysts can be screened from an extensive set of catalysts with this approach. Therefore, our approach can provide an efficient way to discover novel heterogeneous catalysts for various electrochemical reactions.

Machine Learning Assisted Exploration of High Entropy Alloy-Based Catalysts for Selective CO2 Reduction to Methanol.

Catalytic conversion of CO2 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 CO2 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 CO2 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 CO2 hydrogenation to methanol and have screened a series of high entropy-based catalysts (from 36750 considered catalysts) which could be promising for methanol synthesis.

Mechanistic exploration of CO2 conversion to dimethoxymethane (DMM) using transition metal (Co, Ru) catalysts: an energy span model.

The conversion of CO2 to DMM is an important transformation for various reasons. Co and Ru-based triphos catalysts have been investigated using density functional theory (DFT) calculations to understand the mechanistic pathways of the CO2 to DMM conversion and the role of noble/non-noble metal-based catalysts. The reaction has been investigated sequentially through methylformate (MF) and methoxymethane (MM) intermediates as they are found to be important intermediates. For the hydrogenation of CO2 and MF, the hydrogen sources such as H2 and methanol have been investigated. The calculated reaction free energy barriers for all the possible pathways suggest that both hydrogen sources are important for the Co-triphos catalyst. However, in the case of the Ru-triphos catalyst, molecular H2 is calculated to be the only hydrogen source. Various esterification and acetalization possibilities have also been explored to find the most favorable pathway for the conversion of CO2 to DMM. We find that the hydride transfer to the CO2 is the rate determining step (RDS) for the overall reaction. Our mechanistic investigation reveals that the metal center is the active part for the catalysis rather than the Brønsted acid and the redox triphos ligand plays an important role through the push-pull mechanism. The implemented microkinetic study shows that the reaction is also quite dependent on the concentration of the gaseous reactants and the rate constant increases exponentially above 363 K.

Computational Screening of First-Row Transition-Metal Based Alloy Catalysts-Ligand Induced N2 Reduction Reaction Selectivity.

Large-scale ammonia production through sustainable strategies from naturally abundant N2 under ambient conditions represents a major challenge from a future perspective. Ammonia is one of the promising carbon-free alternative energy carriers. The high energy required for N≡N bond dissociation during the Haber-Bosch process demands extreme reaction conditions. This problem could be circumvented by tuning Fe catalyst composition with the help of an induced ligand effect on the surface. In this work, we utilized density functional theory calculations on the Fe(110) surface alloyed with first-row transition-metal (TM) series (Fe-TM) to understand the catalytic activity that facilitates the electrochemical nitrogen reduction reaction (NRR). We also calculated the selectivity against the competitive hydrogen evolution reaction (HER) under electrochemical conditions. The calculated results are compared with those from earlier reports on the periodic Fe(110) and Fe(111) surfaces, and also on the (110) surface of the Fe85 nanocluster. Surface alloying with late TMs (Co, Ni, Cu) shows an improved NRR activity, whereas the low exchange current density observed for Fe-Co indicates less HER activity among them. Considering various governing factors, Fe-based alloys with Co (Fe-Co) showed enhanced overall performance compared to the periodic surface as well as other pure iron-based structures previously reported. Therefore, the iron-alloy based structured catalysts may also provide more opportunities in the future for enhancing NRR performance via electrochemical reduction pathways.

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO2 Hydrogenation to Methanol.

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 CO2 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 CO2 reduction reaction toward CH3OH. 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, *H2CO, and *H3CO 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 CuNiZn-based 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.

Identifying the preferential pathways of CO2 capture and hydrogenation to methanol over an Mn(I)-PNP catalyst: a computational study.

CO2 hydrogenation to CH3OH is a crucial conversion for several purposes. Density functional theory (DFT) studies have been performed to explore the mechanistic pathways of newly reported CO2 capture and hydrogenation to methanol. The present study describes the multistep transformation of CO2 to methanol. In this case we have introduced 2-amino-1-propanol to capture CO2 and hydrogenation of the CO2 captured product (oxazolidinone) in the presence of an active Mn(i)-PNP based catalyst. All the plausible pathways for oxazolidinone hydrogenation to methanol have been explored in detail. Here, hydride and proton transfer steps are very important for oxazolidinone hydrogenation, whereas heterolytic H2 cleavage is the most important step for the regeneration of the catalyst. Our detailed study shows that C-N bond hydrogenation followed by C-O and C[double bond, length as m-dash]O bond hydrogenations or C-O bond hydrogenation followed by C-N and C[double bond, length as m-dash]O bond hydrogenations are the most favourable pathways for oxazolidinone hydrogenation to methanol with a total reaction free energy barrier of 36.9 kcal mol-1 for both the pathways in the presence of a Mn(i)-PNP catalyst.

Chemical Degradation of Mercury Alkyls Mediated by Copper Selenide Nanosheets.

Methylation and demethylation of mercury compounds are two important competing processes that control the net production of highly toxic mercury alkyls, methylmercury (MeHg+ ) and dimethylmercury (Me2 Hg), in environment. Although the microbial and the photochemical methylation and demethylation processes are well studied in recent years but the chemical methylation and demethylation processes have not been studied well. Herein, we report for the first time that the CuSe nanosheet has remarkable ability to activate the highly inert Hg-C bonds of various MeHg+ and Me2 Hg compounds at room temperature (21 °C). It facilitates the conversion of MeHg+ into Me2 Hg in the absence of any proton donors. Whereas, in the presence of any proton source, it has unique ability to degrade MeHg+ into CH4 and inorganic mercury (Hg2+ ). Detailed studies revealed that the relatively fast Hg-C bond cleavage was observed in case of MeHgSPh or MeHgI in comparison to MeHgCl, indicating that the Hg-C bond in MeHgCl is relatively inert in nature. On the other hand, the Hg-C bond in Me2 Hg is considered to be exceedingly inert and, thus, difficult to cleave at room temperature. However, CuSe nanosheets showed unique ability to degrade Me2 Hg into CH4 and Hg2+ , via the formation of MeHg+ , under acidic conditions at room temperature. DFT calculations revealed that the Hg-C bond activation occurs through adsorption on the surface of (100)-faceted CuSe nanosheets.

A computational study on ligand assisted vs. ligand participation mechanisms for CO2 hydrogenation: importance of bifunctional ligand based catalysts.

CO2 hydrogenation products are not only useful chemical sources but also promising hydrogen storage materials. A DFT study has been carried out on the CO2 hydrogenation reaction catalyzed by a series of bifunctional aminomethyl based Mn(i) complexes. We find that the N-H functionality in the aminomethyl ligand shows a metal-ligand cooperation (MLC) mechanism for the CO2 hydrogenation reaction. Here, the N-H functionality assists the MLC mechanism by stabilizing the formate anion via N-HO hydrogen bonding interactions. This is opposite to the MLC mechanism proposed by Noyori for ketone hydrogenation, where the N-H functionality actively participates in the reaction mechanism via cleavage/formation of N-H/M-H bonds. Furthermore, the stabilized formate anion initiates heterolytic H2 cleavage, which requires a very low barrier compared to external base/ligand participation heterolytic H2 cleavage. Therefore, the bifunctional aminomethyl based Mn(i) complexes are promising for the CO2 hydrogenation reaction and our study may be very helpful for experimentalists for the development of efficient bifunctional ligand-based catalysts for the CO2 hydrogenation reaction.

Guest-Induced Ultrasensitive Detection of Multiple Toxic Organics and Fe3+ Ions in a Strategically Designed and Regenerative Smart Fluorescent Metal-Organic Framework.

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.