Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. Science 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture.

Deciphering the Anticancer Arsenal of Piper longum: Network Pharmacology and Molecular Docking Unveil Phytochemical Targets Against Lung Cancer.

Introduction: Lung cancer, characterized by uncontrolled cellular proliferation within the lung tissues, is the predominant cause of cancer-related fatalities worldwide. The traditional medicinal herb Piper longum has emerged as a significant contender in oncological research because of its documented anticancer attributes, suggesting its potential for novel therapeutic development. Methods: This study adopted network pharmacology and omics methodology to elucidate the anti-lung cancer potential of P. longum by identifying its bioactive constituents and their corresponding molecular targets. Results: Through a comprehensive literature review and the Integrated Medicinal Plant Phytochemistry and Therapeutics database (IMPPAT), we identified 33 bioactive molecules from P. longum. Subsequent analyses employing tools such as SwissTargetPrediction, SuperPred, and DIGEP-Pred facilitated the isolation of 676 potential targets, among which 72 intersected with 666 lung cancer-associated genetic markers identified through databases including the Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), and GeneCards. Further validation through protein-protein interaction (PPI) networks, gene ontology, pathway analyses, boxplots, and overall survival metrics underscored the therapeutic potential of compounds such as 7-epi-eudesm-4(15)-ene-1ß, demethoxypiplartine, methyl 3,4,5-trimethoxycinnamate, 6-alpha-diol, and aristolodione. Notably, our findings reaffirm the relevance of lung cancer genes, such as CTNNB1, STAT3, HIF1A, HSP90AA1, and ERBB2, integral to various cellular processes and pivotal in cancer genesis and advancement. Molecular docking assessments revealed pronounced affinity between 6-alpha-diol and HIF1A, underscoring their potential as therapeutic agents for lung cancer. Conclusion: This study not only highlights the bioactive compounds of P. longum but also reinforces the molecular underpinnings of its anticancer mechanism, paving the way for future lung cancer therapeutics.

The potential of Chlorella spp. as antiviral source against African swine fever virus through a virtual screening pipeline.

African swine fever (ASF) causes high mortality in pigs and threatens global swine production. There is still a lack of therapeutics available, with two vaccines under scrutiny and no approved small-molecule drugs. Eleven (11) viral proteins were used to identify potential antivirals in in silico screening of secondary metabolites (127) from Chlorella spp. The metabolites were screened for affinity and binding selectivity. High-scoring compounds were assessed through in silico ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) predictions, compared to structurally similar drugs, and checked for off-target docking with prepared swine receptors. Molecular dynamics (MD) simulations determined binding stability while binding energy was measured in Molecular Mechanics - Generalized Born Surface Area (MMGBSA) or Poisson-Boltzmann Surface Area (MMPBSA). Only six (6) compounds passed until MD analyses, of which five (5) were stable after 100 ns of MD runs. Of these five compounds, only three had binding affinities that were comparable to or stronger than controls. Specifically, phytosterols 24,25-dihydrolanosterol and CID 4206521 that interact with the RNA capping enzyme (pNP868R), and ergosterol which bound to the Erv-like thioreductase (pB119L). The compounds identified in this study can be used as a theoretical basis for in vitro screening to develop potent antiviral drugs against ASFV.

Reactive Processing of Furan-Based Monomers via Frontal Ring-Opening Metathesis Polymerization for High Performance Materials.

Frontal ring-opening metathesis polymerization (FROMP) presents an energy-efficient approach to produce high-performance polymers, typically utilizing norbornene derivatives from Diels-Alder reactions. This study broadens the monomer repertoire for FROMP, incorporating the cycloaddition product of biosourced furan compounds and benzyne, namely 1,4-dihydro-1,4-epoxynaphthalene (HEN) derivatives. A computational screening of Diels-Alder products is conducted, selecting products with resistance to retro-Diels-Alder but also sufficient ring strain to facilitate FROMP. The experiments reveal that varying substituents both modulate the FROMP kinetics and enable the creation of thermoplastic materials characterized by different thermomechanical properties. Moreover, HEN-based crosslinkers are designed to enhance the resulting thermomechanical properties at high temperatures (>200 °C). The versatility of such materials is demonstrated through direct ink writing (DIW) to rapidly produce 3D structures without the need for printed supports. This research significantly extends the range of monomers suitable for FROMP, furthering efficient production of high-performance polymeric materials.

From antibiotic to antiviral: computational screening reveals a multi-targeting antibiotic from Streptomyces spp. against Nipah virus fusion proteins.

Nipah Virus is a re-emerging zoonotic paramyxovirus that poses a significant threat to both swine industry and human health. The pursuit of potential antiviral agents with both preventive and therapeutic properties holds promise for targeting such viruses. To expedite this search, leveraging computational biology is essential. Streptomyces is renowned for its capacity to produce large and diverse metabolites with promising bioactivities. In the current study, we conducted a comprehensive structure-based virtual screening of 6524 Streptomyces spp. metabolites sourced from the StreptomeDB database to evaluate their potential inhibitory effects on three Nipah virus fusion (NiVF) protein conformations: NiVF pre-fusion 1-mer (NiVF-1mer), pre-fusion 3-mer (NiVF-3mer), and NiVF post-fusion (NiVF-PoF). Prior to virtual screening, the drug-likeness of Streptomyces spp. compounds was profiled using ADMET properties. From the 913 ADMET-filtered compounds, the subsequent targeted and confirmatory blind docking analysis revealed that S896 or virginiamycin M1, a known macrolide antibiotic, showed a maximum binding affinity with the NiVF proteins, suggesting a multi-targeting inhibitory property. In addition, the 200-ns molecular dynamics simulation and MM/PBSA analyses revealed stable and strong binding affinity between the NiVF-S896 complexes, indicating favorable interactions between S896 and the target proteins. These findings suggest the potential of virginiamycin M1, an antibiotic, as a promising multi-targeting antiviral drug. However, in vitro and in vivo experimental validations are necessary to assess their safety and efficacy.

First-Principles Computational Screening of Two-Dimensional Polar Materials for Photocatalytic Water Splitting.

The band gap constraint of the photocatalyst for overall water splitting limits the utilization of solar energy. A strategy to broaden the range of light absorption is employing a two-dimensional (2D) polar material as photocatalyst, benefiting from the deflection of the energy level due to their intrinsic internal electric field. Here, by using first-principles computational screening, we search for 2D polar semiconductors for photocatalytic water splitting from both ground- and excited-state perspectives. Applying a unique electronic structure model of polar materials, there are 13 photocatalyst candidates for the hydrogen evolution reaction (HER) and 8 candidates for the oxygen evolution reaction (OER) without barrier energies from the perspective of the ground-state free energy variation calculation. In particular, Cu2As4Cl2S3 and Cu2As4Br2S3 can catalyze HER and OER simultaneously, becoming promising photocatalysts for overall water splitting. Furthermore, by combining ground-state band structure calculations with excited-state charge distribution and transfer calculated by linear-response time-dependent density functional theory (LR-TDDFT) and time-dependent ab initio nonadiabatic molecular dynamics (NAMD), respectively, the rationality of the 2D polar material model has been manifested. The intrinsic built-in electric field promotes the separation of charge carriers while suppressing their recombination. Therefore, our computational work provides a high-throughput method to design high-performance photocatalysts for water splitting.

Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo.

With the specter of accelerating climate change, securing access to potable water has become a critical global challenge. Atmospheric water harvesting (AWH) through metal-organic frameworks (MOFs) emerges as one of the promising solutions. The standard numerical methods applied for rapid and efficient screening for optimal sorbents face significant limitations in the case of water adsorption (slow convergence and inability to overcome high energy barriers). To address these challenges, we employed grand canonical transition matrix Monte Carlo (GC-TMMC) methodology and proposed an efficient interpolation scheme that significantly reduces the number of required simulations while maintaining accuracy of the results. Through the example of water adsorption in three MOFs: MOF-303, MOF-LA2-1, and NU-1000, we show that the extrapolation of the free energy landscape allows for prediction of the adsorption properties over a continuous range of pressure and temperature. This innovative and versatile method provides rich thermodynamic information, enabling rapid, large-scale computational screening of sorbents for adsorption, applicable for a variety of sorbents and gases. As the presented methodology holds strong applicative potential, we provide alongside this paper a modified version of the RASPA2 code with a ghost swap move implementation and a Python library designed to minimize the user's input for analyzing data derived from the TMMC simulations.

Computational Design of Photosensitive Polymer Templates To Drive Molecular Nanofabrication.

Molecular electronics promises the ultimate level of miniaturization of computers and other machines as organic molecules are the smallest known physical objects with nontrivial structure and function. But despite the plethora of molecular switches, memories, and motors developed during the almost 50-years long history of molecular electronics, mass production of molecular computers is still an elusive goal. This is mostly due to the lack of scalable nanofabrication methods capable of rapidly producing complex structures (similar to silicon chips or living cells) with atomic precision and a small number of defects. Living nature solves this problem by using linear polymer templates encoding large volumes of structural information into sequence of hydrogen bonded end groups which can be efficiently replicated and which can drive assembly of other molecular components into complex supramolecular structures. In this paper, we propose a nanofabrication method based on a class of photosensitive polymers inspired by these natural principles, which can operate in concert with UV photolithography used for fabrication of current microelectronic processors. We believe that such a method will enable a smooth transition from silicon toward molecular nanoelectronics and photonics. To demonstrate its feasibility, we performed a computational screening of candidate molecules that can selectively bind and therefore allow the deterministic assembly of molecular components. In the process, we unearthed trends and design principles applicable beyond the immediate scope of our proposed nanofabrication method, e.g., to biologically relevant DNA analogues and molecular recognition within hydrogen-bonded systems.

Exploring the Potential of Metal-Organic Frameworks for Cryogenic Helium-Based Gas Gap Heat Switches via High-Throughput Computational Screening.

With the advantages of a long lifetime and high reliability, gas gap heat switches (GGHSs) are attractive in many thermal management applications, especially in space-borne cryogenic systems. The performance of a GGHS is significantly affected by the adsorption characteristics of the adsorbent in the sorption pump. Compared with the commonly used adsorbent in the GGHSs (activated carbon), metal-organic frameworks (MOFs) have larger surface areas, higher pore volumes, and exceptional tunability, which motivates this study to explore their potential for application in cryogenic GGHSs. To this end, two performance metrics, the required volume of adsorbent (vsor) and total input heat (qtot), were computed for about 6000 MOFs via molecular simulations and compared with those of activated carbon. It is found that over 2300 MOFs possess a smaller vsor than activated carbon, and the smallest vsor of MOFs is about 12.7% of that of activated carbon. vsor and qtot generally change in the same direction, which implies it is possible to reduce both parameters simultaneously by choosing a suitable MOF. Structure-performance analysis reveals that 1/vsor consistently increases first and then decreases with pore limiting diameter, largest cavity diameter, available pore volume, accessible surface area, helium void fraction, and bulk density. Descriptor ranges corresponding to high-performing MOFs were identified based on Precision-Recall analysis. Notably, Zr-containing MOFs are particularly likely to have smaller vsor values than activated carbon. It is anticipated that the promising MOFs identified by this study will motivate further experimental investigations, and the insights into structure-performance relationships can serve to guide the rational design of novel MOF candidates for GGHSs.

Computational screening of layered metal chalcogenide materials for HER electrocatalysts, and its synergy with experiments.

Layered materials have emerged as attractive candidates in our search for abundant, inexpensive and efficient hydrogen evolution reaction (HER) catalysts, due to larger specific area these offer. Among these, transition metal dichalcogenides have been studied extensively, while ternary transition metal tri-chalcogenides have emerged as promising candidates recently. Computational screening has emerged as a powerful tool to identify the promising materials out of an initial set for specific applications, and has been employed for identifying HER catalysts also. This article presents a comprehensive review of how computational screening studies based on density functional calculations have successfully identified the promising materials among the layered transition metal di- and tri-chalcogenides. Synergy of these computational studies with experiments is also reviewed. It is argued that experimental verification of the materials, predicted to be efficient catalysts but not yet tested, will enlarge the list of materials that hold promise to replace expensive platinum, and will help ushering in the much awaited hydrogen economy.

Proposal of pharmacophore model for HIV reverse transcriptase inhibitors: Combined mutational effect analysis, molecular dynamics, molecular docking and pharmacophore modeling study.

OBJECTIVES: Antiretroviral therapy (ART) efficacy is jeopardized by the emergence of drug resistance mutations in HIV, compromising treatment effectiveness. This study aims to propose novel analogs of Effavirenz (EFV) as potential direct inhibitors of HIV reverse transcriptase, employing computer-aided drug design methodologies. METHODS: Three key approaches were applied: a mutational profile study, molecular dynamics simulations, and pharmacophore development. The impact of mutations on the stability, flexibility, function, and affinity of target proteins, especially those associated with NRTI, was assessed. Molecular dynamics analysis identified G190E as a mutation significantly altering protein properties, potentially leading to therapeutic failure. Comparative analysis revealed that among six first-line antiretroviral drugs, EFV exhibited notably low affinity with viral reverse transcriptase, further reduced by the G190E mutation. Subsequently, a search for EFV-similar inhibitors yielded 12 promising molecules based on their affinity, forming the basis for generating a pharmacophore model. RESULTS: Mutational analysis pinpointed G190E as a crucial mutation impacting protein properties, potentially undermining therapeutic efficacy. EFV demonstrated diminished affinity with viral reverse transcriptase, exacerbated by the G190E mutation. The search for EFV analogs identified 12 high-affinity molecules, culminating in a pharmacophore model elucidating key structural features crucial for potent inhibition. CONCLUSION: This study underscores the significance of EFV analogs as potential inhibitors of HIV reverse transcriptase. The findings highlight the impact of mutations on drug efficacy, particularly the detrimental effect of G190E. The generated pharmacophore model serves as a pivotal reference for future drug development efforts targeting HIV, providing essential structural insights for the design of potent inhibitors based on EFV analogs identified in vitro.

High throughput selection of organic cathode materials.

Efficient and affordable batteries require the design of novel organic electrode materials to overcome the drawbacks of the traditionally used inorganic materials, and the computational screening of potential candidates is a very efficient way to identify prospective solutions and minimize experimental testing. Here we present a DFT high-throughput computational screening where 86 million molecules contained in the PUBCHEM database have been analyzed and classified according to their estimated electrochemical features. The 5445 top-performing candidates were identified, and among them, 2306 are expected to have a one-electron reduction potential higher than 4 V versus (Li/Li+ ). Analogously, one-electron energy densities higher than 800 Whkg-1 have been predicted for 626 molecules. Explicit calculations performed for certain materials show that at least 69 candidates with a two-electron energy density higher than 1300 Whkg-1 . Successful molecules were sorted into several families, some of them already commonly used electrode materials, and others still experimentally untested. Most of them are small systems containing conjugated CO, NN, or NC functional groups. Our selected molecules form a valuable starting point for experimentalists exploring new materials for organic electrodes.

Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design.

The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generating process.

In Silico Identification of Inhibitory Compounds for SARS-Cov-2 Papain-Like Protease.

Virtual screening with high-performance computers is a powerful and cost-effective technique in drug discovery. A chemical database is searched to find candidate compounds firmly bound to a target protein, judging from the binding poses and/or binding scores. The severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infectious disease has spread worldwide for the last three years, causing severe slumps in economic and social activities. SARS-Cov-2 has two viral proteases: 3-chymotrypsin-like (3CL) and papain-like (PL) protease. While approved drugs have already been released for the 3CL protease, no approved agent is available for PL protease. In this work, we carried out in silico screening for the PL protease inhibitors, combining docking simulation and molecular mechanics calculation. Docking simulations were applied to 8,820 molecules in a chemical database of approved and investigational compounds. Based on the binding poses generated by the docking simulations, molecular mechanics calculations were performed to optimize the binding structures and to obtain the binding scores. Based on the binding scores, 57 compounds were selected for in vitro assay of the inhibitory activity. Five inhibitory compounds were identified from the in vitro measurement. The predicted binding structures of the identified five compounds were examined, and the significant interaction between the individual compound and the protease catalytic site was clarified. This work demonstrates that computational virtual screening by combining docking simulation with molecular mechanics calculation is effective for searching candidate compounds in drug discovery.

Selective ADA2 inhibition for enhancing anti-tumor immune response in glioma: Insights from computational screening of flavonoid compounds.

Brain tumors, particularly gliomas, remain difficult to treat due to their complex and dynamic microenvironment and high mortality rate. The presence of tumor-associated macrophages (TAMs) is considered one of the primary factors contributing to a poor prognosis in Glioma. Previous reports have linked elevated levels of Adenosine deaminase 2 (ADA2) with immunosuppression, tumor progression, and angiogenesis via MAPK, PDGFß signaling pathway in the glioma microenvironment. In contrast, Adenosine deaminase 1 (ADA1), another type of adenosine deaminase, plays a pivotal role in purine metabolism, which is essential for lymphocyte survival. Hence, selectively targeting ADA2 while preserving ADA1 activity could offer a viable approach for regulating macrophage polarization and enhancing the anti-tumor immune response. In pursuit of this objective, our study employed a computational approach, unveiling the remarkable attributes of Daidzin, characterized by its exceptional specificity, and binding affinity towards ADA2 while displaying minimal affinity towards ADA1. Furthermore, Define Secondary Structure of Proteins (DSSP) analysis revealed that Daidzin elicits conspicuous conformational alterations within the dimerization domain of the ADA2 receptor, which could have a crucial impact on its activity. However, the ADA1 structure remained unaltered. Our study offers the potential use of Daidzin as a specific therapeutic agent for modulating the tumor microenvironment and revolutionizing glioma management.

Revolutionizing antiretroviral therapy for human immunodeficiency virus/AIDS: A computational approach using molecular docking, virtual screening, and 3D pharmacophore building to address therapeutic failure and propose highly effective candidates.

OBJECTIVES: In the context of human immunodeficiency virus (HIV) treatment, the emergence of therapeutic failures with existing antiretroviral drugs presents a significant challenge. This study aims to employ advanced molecular modeling techniques to identify potential alternatives to current antiretroviral agents. METHODS: The study focuses on three essential classes of antiretroviral drugs: nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs). Computational analyses were performed on a database of 3,343,652 chemical molecules to evaluate their binding affinities, pharmacokinetic properties, and interactions with viral reverse transcriptase and protease enzymes. Molecular docking, virtual screening, and 3D pharmacophore modeling were utilized to identify promising candidates. RESULTS: Molecular docking revealed compounds with high binding energies and strong interactions at the active sites of target enzymes. Virtual screening narrowed down potential candidates with favorable pharmacological profiles. 3D pharmacophore modeling identified crucial structural features for effective binding. Overall, two molecules for class 1, 7 molecules for class 2, and 2 molecules for class 3 were selected. These compounds exhibited robust binding affinities, interactions with target enzymes, and improved pharmacokinetic properties, showing promise for more effective HIV treatments in cases of therapeutic failures. CONCLUSION: The combination of molecular docking, virtual screening, and 3D pharmacophore modeling yielded lead compounds that hold potential for addressing HIV therapeutic failures. Further experimental investigations are essential to validate the efficacy and safety of these compounds, with the ultimate goal of advancing toward clinical applications in HIV management.

In Silico Screening as a Tool to Prepare Drug-Drug Cocrystals of Ibrutinib-Ketoconazole: a Strategy to Enhance Their Solubility Profiles and Oral Bioavailability.

Ibrutinib (IBR) is a biopharmaceutical classification system (BCS) class II drug and an irreversible Bruton's tyrosine kinase (BTK) inhibitor. IBR has an extremely low oral bioavailability due to the activity of the CYP3A4 enzyme. The current intention of the research was to enhance solubility followed by oral bioavailability of IBR using the hot melt extrusion (HME) technique by formulating drug-drug cocrystals (DDCs). Ketoconazole (KET) is an active CYP3A4 inhibitor and was selected based on computational studies and solubility parameter prediction. Differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR), powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA), proton nuclear magnetic resonance (1H NMR), and scanning electron microscopy (SEM) evaluations were employed for estimating the formation of IBR-KET DDCs. The IBR-KET DDC system was discovered to have a hydrogen bond (H-bond) and π-π-stacking interactions, in accordance with the computational results. Further, IBR-KET DDCs showed enhanced solubility, stability, powder dissolution, in vitro release, and flow properties. Furthermore, IBR-KET-DDCs were associated with enhanced cytotoxic activity in K562-CCL-243 cancer cell lines when compared with IBR and KET alone. In vivo pharmacokinetic studies have shown an enhanced oral bioavailability of up to 4.30 folds of IBR and 2.31 folds of KET through IBR-KET-DDCs compared to that of the IBR and KET suspension alone. Thus, the prepared IBR-KET-DDCs using the HME technique stand as a favorable drug delivery system that augments the solubility and oral bioavailability of IBR along with KET.

Comprehensive Study Addressing the Challenge of Efficient Electrocatalytic Biomass Upgrading of 5-(Hydroxymethyl)Furfural (HMF) with a CH3 NH2 Ionic Liquid on Metal-Embedded Mo2 B2 MBene Nanosheets.

Amine-containing derivatives are important intermediates in drug manufacturing; sustainable synthesis of amine compounds from green carbon-based biomass derivatives has attracted increasing attention, especially the reductive amination of biomass molecules via electrochemical upgrading. To achieve efficient reductive amination of 5-(hydroxymethyl)furfural (HMF) via electrocatalytic biomass upgrading, this work proposes a new HMF biomass upgrading strategy based on metal supported on Mo2 B2 MBene nanosheets using a density functional theory comprehensive study. HMF and methylamine (CH3 CH2 ) can be reduced to 5-(hydroxymethyl) aldiminefurfural (HMMAMF) via electrocatalytic biomass upgrading, which is identified as a promising technology to produce pharmaceutical intermediates. Based on the proposed reaction mechanisms of HMF reductive amination, this work performs a systematic study of HMF amination to HMMAMF using an atomic model simulation method. This study aims to design a high-efficiency catalyst based on Mo2 B2 @TM nanosheets via the reductive amination of 5-HMF and provide insights into the intrinsic relation between thermochemical and material electronic properties and the role of dopant metals. This work establishes the Gibbs free energy profiles of each reaction HMF Biomass Upgrading on Mo2 B2 systems and obtained the limiting potentials of the rate-determining step, which included the kinetic stability of dopants, HMF adsorbability, and the catalytic activity and selectivity of the hydrogen evolution reaction or surface oxidation. Furthermore, charge transfer, d-band center (εd ), and material property (φ) descriptors are applied to establish a linear correlation to determine promising candidate catalysts for reductive amination of HMF. The candidates Mo2 B2 @Cr, Mo2 B2 @Zr, Mo2 B2 @Nb, Mo2 B2 @Ru, Mo2 B2 @Rh, and Mo2 B2 @Os are suitable high-efficiency catalysts for HMF amination. This work may contribute to the experimental application of biomass upgrading catalysts for biomass energy and guide the future development of biomass conversion strategies and utilization.

Role of transition metal d-orbitals in single-atom catalysts for nitric oxide electroreduction to ammonia.

Recently, surging interests exist in direct electrochemical ammonia (NH3) synthesis from nitric oxide (NO) due to the dual benefit of NH3 synthesis and NO removal. However, designing highly efficient catalysts is still challenging. Based on density functional theory, the best ten candidates of transition-metal atoms (TMs) embedded in phosphorus carbide (PC) monolayer is screened out as highly active catalysts for direct NO-to-NH3 electroreduction. The employment of machine learning-aided theoretical calculations helps to identify the critical role of TM-d orbitals in regulating NO activation. A V-shape tuning rule of TM-d orbitals for the Gibbs free energy change of NO or limiting potentials is further revealed as the design principle of TM embedded PC (TM-PC) for NO-to-NH3 electroreduction. Moreover, after employing effective screening strategies including surface stability, selectivity, the kinetic barrier of potential-determining step, and thermal stability comprehensively studied for the ten TM-PC candidates, only Pt embedded PC monolayer has been identified as the most promising direct NO-to-NH3 electroreduction with high feasibility and catalytic performance. This work not only offers a promising catalyst but also sheds light on the active origin and design principle of PC-based single-atom catalysts for NO-to-NH3 conversion.