Performance of Nitrogen Reduction Reaction on Metal Bound g-C6N6: Combined Approach of Machine Learning and DFT

Navigating the evolution of two-dimensional carbon nitride research: integrating machine learning into conventional approaches

Carbon nitride research has reached a promising point in today's research endeavours with diverse applications including photocatalysis, energy storage, and sensing due to their unique electronic and structural properties. Recent advances in machine learning (ML) have opened new avenues for exploring and optimizing the potential of these materials. This study presents a comprehensive review of the integration of ML techniques in carbon nitride research with an introduction to CN classifications and recent advancements. We discuss the methodologies employed, such as supervised learning, unsupervised learning, and reinforcement learning, in predicting material properties, optimizing synthesis conditions, and enhancing performance metrics. Key findings indicate that ML algorithms can significantly reduce experimental trial-and-error, accelerate discovery processes, and provide deeper insights into the structure-property relationships of carbon nitride. The synergistic effect of combining ML with traditional experimental approaches is highlighted, showcasing studies where ML driven models have successfully predicted novel carbon nitride compositions with enhanced functional properties. Future directions in this field are also proposed, emphasizing the need for high-quality datasets, advanced ML models, and interdisciplinary collaborations to fully realize the potential of carbon nitride materials in next-generation technologies.

Exploring the multiple therapeutic mechanisms and challenges of mesenchymal stem cell-derived exosomes in Alzheimer's disease

Alzheimer's disease (AD) is a severe neurodegenerative disorder, and the current treatment options are limited. Mesenchymal stem cell-derived exosomes (MSC-Exos) have garnered significant attention due to their unique biological properties, showcasing tremendous potential as an acellular alternative therapy for AD. MSC-Exos exhibit excellent biocompatibility and low immunogenicity, enabling them to effectively cross the blood-brain barrier (BBB) and deliver therapeutic molecules directly to target cells. They are highly efficacious in delivering nucleic acid-based drugs. Moreover, the production process of MSC-Exos benefits from a high proliferation capacity and multilineage differentiation potential, allowing for production while maintaining a stable composition. Despite the significant theoretical advantages of MSC-Exos, their clinical use still faces multiple challenges, including cross-contamination during isolation and purification processes, the complexity of their components, and the presence of potential adverse paracrine factors. Future research needs to focus on optimizing separation and purification techniques, enhancing delivery methods to improve therapeutic efficacy, and performing detailed analyses of the components of MSC-Exos. In summary, MSC-Exos hold promise as an effective option for the treatment of AD and other neurodegenerative diseases, driving their clinical research and use in related fields.

Machine Learning-Enhanced Screening of Single-Atom Alloy Clusters for Nitrogen Reduction Reaction

The electrochemical nitrogen reduction reaction (eNRR) under ambient conditions is a promising method to generate ammonia (NH3), a crucial precursor for fertilizers and chemicals, without carbon emissions. Single-atom alloy catalysts (SAACs) have reinvigorated catalytic processes due to their high activity, selectivity, and efficient use of active atoms. Here, we employed density functional theory (DFT) calculations integrated with machine learning (ML) to investigate dodecahedral nanocluster-based SAACs for analyzing structure-activity relationships in eNRR. Over 300 nanocluster-based SAACs were screened with all the transition metals as dopants to develop an ML model predicting stability and catalytic performance. Facet sites were identified as optimal doping positions, particularly with late group transition metals showing superior stability and activity. Utilizing DFT+ML, we identified 8 highly suitable SAACs for eNRR. Interestingly, the number of valence d-electrons in dopants proved crucial in screening for eNRR activity. These catalysts exhibited low activity in hydrogen evolution reaction, further enhancing their suitability for eNRR. This successful ML-driven approach accelerates catalyst design and discovery, holding significant practical implications.

Advancing electrocatalytic reactions through mapping key intermediates to active sites via descriptors

Descriptors play a crucial role in electrocatalysis as they can provide valuable insights into the electrochemical performance of energy conversion and storage processes. They allow for the understanding of different catalytic activities and enable the prediction of better catalysts without relying on the time-consuming trial-and-error approaches. Hence, this comprehensive review focuses on highlighting the significant advancements in commonly used descriptors for critical electrocatalytic reactions. First, the fundamental reaction processes and key intermediates involved in several electrocatalytic reactions are summarized. Subsequently, three types of descriptors are classified and introduced based on different reactions and catalysts. These include d-band center descriptors, readily accessible intrinsic property descriptors, and spin-related descriptors, all of which contribute to a profound understanding of catalytic behavior. Furthermore, multi-type descriptors that collectively determine the catalytic performance are also summarized. Finally, we discuss the future of descriptors, envisioning their potential to integrate multiple factors, broaden application scopes, and synergize with artificial intelligence for more efficient catalyst design and discovery.

Single B-vacancy enriched α1-borophene sheet: an efficient metal-free electrocatalyst for CO2 reduction

By employing first principles calculations, we have studied the electronic structures of pristine (α1) and different defective (α1-t1, α1-t2) borophene sheets to understand the efficacy of such systems as metal-free electrocatalysts for the CO2 reduction reaction. Among the three studied systems, only α1-t1, the defective borophene sheet created by removal of a 5-coordinated boron atom, can chemisorb and activate a CO2 molecule for its subsequent reduction processes, leading to different C1 chemicals, followed by selective conversion into C2 products by multiple proton coupled electron transfer steps. The computed onset potentials for the C1 chemicals such as CH3OH and CH4 are low enough. On the other hand, in the case of the C2 reduction process, the C-C coupling barrier is only 0.80 eV in the solvent phase which produces CH3CHO and CH3CH2OH with very low onset potential values of -0.21 and -0.24 V, respectively, suppressing the competing hydrogen evolution reaction.

First-principles Calculations Reveal Frictional Advantage for C2 N/C6 N6 van der Waals Heterostructures

Friction at the atomic scale is determined for three different carbon nitride structures namely C2 N/C2 N, C6 N6 /C6 N6 and C6 N6 /C2 N employing ab-initio density functional theory (DFT). The sliding path along the lowest energy corrugations determines the static frictional forces. Both the homo-layer structures (C2 N/C2 N and C6 N6 /C6 N6 ) have higher corrugation energy and correspondingly higher static lateral forces with respect to the hetero-layer structure (C2 N/C6 N6 ). The corrugation energy for the C2 N/C6 N6 heterostructure (δ c o r r ${{\delta }_{corr}}$ =0.29 meV/atom) is one-order lower than C2 N/C2 N (δ c o r r ${{\delta }_{corr}}$ =2.08 meV/atom) and C6 N6 /C6 N6 (δ c o r r ${{\delta }_{corr}}$ =4.37 meV/atom). Such a significantly lower corrugation energy for the heterostructure arises due to the reduced fluctuation in the interfacial charge density along the sliding pathway. Moreover, the change in the interlayer distance along the sliding pathway is only 0.2 Å for the heterostructure while its 0.3 Å and 0.4 Å for C2 N and C6 N6 homo-layers respectively. The friction coefficients (FL /FN , FL =static lateral force; FN =normal force) decrease with increasing load for all the systems with the lowest value (0.04) for C2 N/C6 N6 at 2 GPa. The van der Waals heterostructures are, therefore, predicted to be highly efficient lubricant materials for reducing friction at the atomic scale.

Recent Advances in Metal-Organic Framework-Based Nanomaterials for Electrocatalytic Nitrogen Reduction

The production of ammonia under moderate conditions is of environmental and sustainable importance. The electrochemical nitrogen reduction reaction (E-NRR) method has been intensively investigated in the recent decades. Nowadays, the further development of E-NRR is largely hindered by the lack of competent electrocatalysts. Metal-organic frameworks (MOFs) are considered as the next-generation catalysts for E-NRR, featuring their tailorable structures, abundant active sites and favorable porosity. To present a comprehensive review on both the fundamental and advanced development in MOFs catalyst-based E-NRR field, this paper first introduces the basic principles of E-NRR, including the reaction mechanism, major apparatus components, performance criteria, and ammonia detection protocols. Next, the synthesis and characterization methods for MOFs and their derivatives are discussed. In addition, a reaction mechanism study via density functional theory calculations is also presented. After that, the recent advancement of MOF-based catalysts in the E-NRR field as well as the modification approaches on MOFs for E-NRR optimization is elaborated. Finally, the current challenges and outlook of MOF catalyst-based E-NRR field are emphasized.

Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives

It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.

Single-Atom Low-Valent Alkaline-Earth-Metal Catalysts for Electrochemical Nitrogen Reduction with an Acceptance-Backdonation Mechanism

Single-atom catalysts (SACs) have drawn great attention in developing highly active and low-cost catalysts for electrocatalytic nitrogen reduction reaction (NRR) in ammonia synthesis, but the atomic metal centers are mainly limited to transition metals. Here, four stable alkaline-earth-metal (AEM)-based SACs are proposed by anchoring AEM on nitrogen-doped graphene nanoribbons, based on first-principles calculations. All SACs exhibit excellent NRR performance with competitive limiting potentials compared to stepped Ru (0001), and Ca-based SAC achieves optimal activity with a potential of -0.716 V. It is revealed that the low oxidation state of AEM is crucial for the activation of N₂ through an acceptance-backdonation mechanism. The antibonding 2π* orbital of N₂ can accept residual s electrons of low-valent AEM and backdonate electrons to the empty d orbitals of AEM, resulting in activation of N₂ molecules. In particular, the activation degree of N₂ and NRR activity is linearly associated with the charge states of AEMs. Our work reveals the underlying mechanism of AEMs for N₂ activation and reduction and presents the potential of AEM SACs as efficient electrochemical NRR catalysts.