Chemical Research Research Articles

The emergence of electron spin in interdisciplinary research in chemistry.

The emergence of electron spin in interdisciplinary research in chemistry.

INTEGRATION OF EDUCATION AND SCIENTIFIC RESEARCH IN AN EDUCATIONAL INSTITUTION

The article deals with the problem of differentiation and scientific research of scientific knowledge in chemistry at the university. The strategic foundations of science and education incorporation in modern society are presented, together with the main objectives and tasks to enhance their collaboration. The combination of science and education on the foundation of universities has been formed as a multifaceted process, involving the construction of a global data environment, a unified educational space, and the employment of various new teaching methods.One of the research disciplines of chemistry at the university was chosen as a model for studying the mechanisms of integration and differentiation. The relational integration of various fields of knowledge and scientific research in chemistry is considered in detail. The paper exposes the issues that initiate the procedure of combining pedagogical education with science; reveals the areas of work of the department that contribute to the development of scientific research and interaction of the educational process; proposes new fundamental and comprehensive professional skills required for the construction of upcoming bachelors and masters of pedagogical education ready for an innovative combination of knowledge and scientific activity centered around the exploration of the field;educational and analyzed ways to improve the effectiveness of the pedagogical university in the integration of research works are proposed.The purpose of the article is to offer an opportunity to study chemical theories, to develop knowledge and scientific and theoretical knowledge of universities.

Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy

AbstractMolecular‐level understanding of photoinduced processes is critically important for breakthroughs in transformative technologies utilizing light, ranging from photomedicine to photoresponsive materials. Theory and simulation play a crucial role in this task. Despite great advances in hardware and computational methods, the theoretical description of photoinduced phenomena in the presence of complex environments and external photoexcitation conditions still poses formidable challenges for theoreticians and there are numerous formal and computational difficulties that must be overcome. The development of predictive, accurate, and at the same time, computationally efficient theoretical approaches to describe complex problems in photochemistry and photophysics is an active field of research in contemporary theoretical and computational chemistry. In this advanced review, we discuss modern computational advances and novel approaches that have been recently developed in excited‐electronic structure methods, and multiscale modeling, with a special emphasis on coupled electron‐nuclear dynamics and spectroscopy, from fully quantum to semi‐classical methodologies—including dissipative effects, the explicit light field interaction, femtosecond time‐resolved spectroscopy, and software infrastructure.This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Combined QM/MM Methods Theoretical and Physical Chemistry > Spectroscopy Software > Molecular Modeling

Computational Analysis of Phase I Metabolism in Clinically Studied Flavoalkaloids: Molecular Docking, MMGBSA Binding Energy Calculations, and Molecular Dynamics Simulations

AbstractFlavoalkaloids, a potent class of cyclin‐dependent kinase inhibitors, have undergone extensive clinical trials across various cancer types. Among them, flavopiridol, riviciclib, and voruciclib are the most advanced candidates. These molecules are lipophilic and possess functional groups conducive to both phase I and phase II metabolism in the liver. This study employs in‐silico methods to identify the potential cytochrome P450‐mediated sites of phase I metabolism (SOM) in these compounds. According to two web‐server‐based predictions, the N‐methyl group was identified as the primary SOM in these compounds, showing the highest probability score for involvement of CYP3A4. Docking studies conducted using both Glide and the induced‐fit method further confirmed N‐demethylation as a significant metabolic pathway predominantly catalyzed by CYP3A4. The distance between the N‐methyl group of the docked ligands and the iron atom of the heme ranged from 3 to 7 Å, with flavopiridol's N‐methyl group positioned closest to the heme iron at 3.16 Å. The 100 ns molecular dynamic simulations additionally validated the stability of the interaction between flavoalkaloids and the heme Fe. Similarly, the docking‐based SOM predictions matched the experimental SOM for other approved drugs. Thus, employing docking‐based SOM predictions holds potential for enhancing lead optimization efforts in future medicinal chemistry research.

Learning Joint 2-D and 3-D Graph Diffusion Models for Complete Molecule Generation.

Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high-fidelity molecules. However, current generative models only focus on modeling 2-D bonding graphs or 3-D geometries, which are two complementary descriptors for molecules. The lack of ability to jointly model them limits the improvement of generation quality and further downstream applications. In this article, we propose a joint 2-D and 3-D graph diffusion model (JODO) that generates geometric graphs representing complete molecules with atom types, formal charges, bond information, and 3-D coordinates. To capture the correlation between 2-D molecular graphs and 3-D geometries in the diffusion process, we develop a diffusion graph transformer (DGT) to parameterize the data prediction model that recovers the original data from noisy data. The DGT uses a relational attention mechanism that enhances the interaction between node and edge representations. This mechanism operates concurrently with the propagation and update of scalar attributes and geometric vectors. Our model can also be extended for inverse molecular design targeting single or multiple quantum properties. In our comprehensive evaluation pipeline for unconditional joint generation, the experimental results show that JODO remarkably outperforms the baselines on the QM9 and GEOM-Drugs datasets. Furthermore, our model excels in few-step fast sampling, as well as in inverse molecule design and molecular graph generation. Our code is provided in https://github.com/GRAPH-0/JODO.

Molecular similarity: Theory, applications, and perspectives

Molecular similarity pervades much of our understanding and rationalization of chemistry. This has become particularly evident in the current data-intensive era of chemical research, with similarity measures serving as the backbone of many Machine Learning (ML) supervised and unsupervised procedures. Here, we present a discussion on the role of molecular similarity in drug design, chemical space exploration, chemical “art” generation, molecular representations, and many more. We also discuss more recent topics in molecular similarity, like the ability to efficiently compare large molecular libraries.

Antibiotic resistance pattern of bacterial strains emerged using frequent hand sanitizers during corona pandemic.

Hands are in fact the main route of transmission of pathogenic infections. By using proper hand sanitization, we can break the virus's transmission chain, which is especially important in the ongoing COVID pandemic outbreaks. The effectiveness of hand sanitization is solely dependent on the use of sufficient antibacterial agents, which come in a variety of levels and types, including antimicrobials commercial, water-based, or alcohol-based hand sanitizer, the latter being widely used during pandemics. Therefore, the sudden overuse of sanitizers also could lead to an increase in the tolerance limit for normal hand flora and the new development of antimicrobial resistance (AMR). In this study, we investigated the relationship between hand sanitizer-tolerant bacteria and their antibiotic resistance profile to multiple antibiotic agents. On a timely basis before and after using different hand sanitizers, bacterial strains were collected from the volunteers of CSIR-Central Salt and Marine Chemicals Research Institute (CSIR-CSMCRI). Sanitizer tolerant bacterial strains were observed also just after the application of sanitizers, which also showed the AMR phenomenon. The resultant sanitizers' resistant microbiome showed the dominant presence of Bacillus sp., Staphyloccocus sp., Paenibacillus sp., Lysinibacillus sp., Exiguobacterium sp. and Leclercia sp. All 36 nos of bacterial strains showed MDR (> 5 nos).

Development of recipescooked sausage products enriched with chlorella

The meat industry is constantly undergoing transformations and moves in accordance with global trends and basic consumer requests.Accordingly, there is a problem of increasing the nutritional and biological value of common food products, in particular cooked sausages. Development of health facilitiesof meat products enriched with plant components is the main goal that arose during the performance of this work. Improvement of the recipe of cooked sausage products due to the use of available chlorella seaweedvaluablea source of antioxidants, dietary fiber, essential amino acids, vitamins, polyunsaturated fatty acids and minerals is a promising solution. The difference between the developed minced meat recipe and the control is the use of chlorella, the replacement of lard, which is traditionally used in similar recipes, with linseed and olive oils, and the complete replacement of sodium nitrite with ascorbic acid. Comparative studies were performed using three different formulations containing 1% chlorella hydrated with water and milk whey in a ratio of 1:5 for 20 minutes at a temperature of 40º C. During the work, organoleptic (appearance, color, texture, taste and smell) and physical and chemical research methods (moisture, fat, protein, salt content). It was shown that the experimental samples of minced meat contained protein in amounts of 13.51% for sample 3; 12.77% for sample 2 and 12.36% for sample 1. The moisture content of the experimental samples exceeds the control by 2.86% for sample 3, by 1.86% for sample 2, and by 0.69% for sample 1. According to the mass fraction of fat, all samples are within the normal range and amount to less than 30%, which correspondsthe national standard for this type of product. Modification of the recipe using chlorella, linseed and olive oil differs from the control sample, which is reflected in the results of the organoleptic evaluation. According to the results of comprehensive studies, experimental formulation sample 3 can be recommended for implementation.

Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules

Due to rapid advancements in deep learning techniques, the demand for large-volume high-quality datasets grows significantly in chemical research. We developed a quantum-chemistry database that includes 443,106 small organic molecules with sizes up to 10 heavy atoms including C, N, O, and F. Ground-state geometry optimizations and frequency calculations of all compounds were performed at the B3LYP/6-31G* level with the BJD3 dispersion correction, while the excited-state single-point calculations were conducted at the ωB97X-D/6-31G* level. Totally twenty-seven molecular properties, such as geometric, thermodynamic, electronic and energetic properties, were gathered from these calculations. Meanwhile, we also established a comprehensive protocol for the construction of a high-volume quantum-chemistry dataset. Our QCDGE (Quantum Chemistry Dataset with Ground- and Excited-State Properties) dataset contains a substantial volume of data, exhibits high chemical diversity, and most importantly includes excited-state information. This dataset, along with its construction protocol, is expected to have a significant impact on the broad applications of machine learning studies across different fields of chemistry, especially in the area of excited-state research.

Controlled Reversible N-Terminal Modification of Peptides and Proteins.

A reversible modification strategy enables a switchable cage/decage process of proteins with an array of applications for protein function research. However, general N-terminal selective reversible modification strategies which present site selectivity are specifically limited. Herein, we report a general reversible modification strategy compatible with 20 canonical amino acids at the N-terminal site by the palladium-catalyzed cinnamylation of native peptides and proteins under biologically relevant conditions. This approach broadens the substrate adaptability of N-terminal modification of proteins and shows a potential impact on the more challenging protein substrates such as antibodies. In the presence of 1,3-dimethylbarbituric acid, palladium-catalyzed deconjugation released native peptides and proteins efficiently. Harnessing the reversible nature of this protocol, practical applications were demonstrated by precise function modulation of antibodies and traceless enrichment of the protein-of-interest for proteomics analysis. This novel on/off strategy working on the N-terminus will provide new opportunities in chemical biology and medicinal research.

Bibliometric analysis : Green chemistry trends and issues in chemistry education from 2019 to 2024

This study analyzes green chemistry research trends in chemistry education. This study used a quantitative bibliometric approach. The number of publications analyzed is 104 publication documents from 2019 – 2024. This research collects, processes, and filters information in Scopus journals and articles. Metadata results show that the distribution of publication frequency peaked in 2019, with 26 documents identified. The green chemistry research area is dominated by chemistry research (31.3%). The country with the most documents and the most productive in publishing green chemistry is the United States, with 30 papers identified. At the same time, Indonesia is ranked fifth as the most productive country in publishing green chemistry, with 10 documents identified. Canada ranked second with 17 papers, and Germany ranked third with 15 documents. The institutions that contributed the most came from Germany: the University of Bremen, with 11 papers 10.58%, and the University of Toronto with 8 documents (7.69%). The authors with the most citations are Chen Tse-Lun et al., with 245 citations. Meanwhile, when viewed from the number of documents published by the author, Eilks I. has 11 papers with a contribution of 4.91%. There are 5 clusters with the most popping keywords: green chemistry, human, and chemical reaction. Research and publications on this topic have been sparse in the past five years. Surveys and analyses of green chemistry literature are essential because tracking research trends in green chemistry in chemistry education is vital to directing the future.

Nanomaterials and Nanotechnology in Agricultural Pesticide Delivery: A Review.

Pesticides play a crucial role in ensuring food production and food security. Conventional pesticide formulations can not meet the current needs of social and economic development, and they also can not meet the requirements of green agriculture. Therefore, there is an urgent need to develop efficient, stable, safe, and environmentally friendly pesticide formulations to gradually replace old formulations which have high pollution and low efficacy. The rise of nanotechnology provides new possibilities for innovation in pesticide formulations. Through reasonable design and construction of an environmentally friendly pesticide delivery system (PDS) based on multifunctional nanocarriers, the drawbacks of conventional pesticides can be effectively solved, realizing a water-based, nanosized, targeted, efficient, and safe pesticide system. In the past five years, researchers in chemistry, materials science, botany, entomology, plant protection, and other fields are paying close attention to the research of nanomaterials based PDSs and nanopesticide formulations and have made certain research achievements. These explorations provide useful references for promoting the innovation of nanopesticides and developing a new generation of green and environmentally friendly pesticide formulations. This Perspective summarizes the recent advances of nanomaterials in PDSs and nanopesticide innovation, aiming to provide useful guidance for carrier selection, surface engineering, controlled release conditions, and application in agriculture.

Democratizing Microreactor Technology for Accelerated Discoveries in Chemistry and Materials Research.

Microreactor technologies have emerged as versatile platforms with the potential to revolutionize chemistry and materials research, offering sustainable solutions to global challenges in environmental and health domains. This survey paper provides an in-depth review of recent advancements in microreactor technologies, focusing on their role in facilitating accelerated discoveries in chemistry and materials. Specifically, we examine the convergence of microfluidics with machine intelligence and automation, enabling the exploitation of the cyber-physical environment as a highly integrated experimentation platform for rapid scientific discovery and process development. We investigate the applicability and limitations of microreactor-enabled discovery accelerators in various chemistry and materials contexts. Despite their tremendous potential, the integration of machine intelligence and automation into microreactor-based experiments presents challenges in establishing fully integrated, automated, and intelligent systems. These challenges can hinder the broader adoption of microreactor technologies within the research community. To address this, we review emerging technologies that can help lower barriers and facilitate the implementation of microreactor-enabled discovery accelerators. Lastly, we provide our perspective on future research directions for democratizing microreactor technologies, with the aim of accelerating scientific discoveries and promoting widespread adoption of these transformative platforms.

Outreach in coordinated individual events: the GCS format of CNR Italy

Abstract Global Conversation on Sustainability (GCS) 2023 was enthusiastically welcomed by researchers of the National Research Council of Italy (CNR). Highly committed to international organizations such as IUPAC and ISC, the promoters participated to the 2023 edition of the Global Conversation on Sustainability hosting a local in person event held in one of the major CNR institute for chemistry research. The format was a round-table discussion involving colleagues of different age, gender, experience, and area of expertise, who participated to an informal dialogue with the aim of reflecting and sharing aspects of the respective research activities contributing to sustainable development goals. From many chemistry research areas, it emerged a wide range of subjects, which stimulated an interesting and purpose-oriented discussion. Therefore, GCS 2023 at CNR prompted a reflection on how improving chemists’ impact on sustainability including laboratories’ practises and on considering communication becoming part of scientific process. Transformation requires continuous investment in research and innovation and evolving scientific activities, but also requires synergies, collaborations and policy making on cross-cutting issues that can make this successful. Global initiatives as GCS, in the framework of international scientific organizations, represent a key global vehicle for enhancing the big area of science communication. Overcoming time and spatial limitations, these initiatives enable to link global and locally tailored in person events.

Jupyter widgets and extensions for education and research in computational physics and chemistry

Interactive notebooks are a precious tool for creating graphical user interfaces and teaching materials. Python and Jupyter are becoming increasingly popular in this context, with Jupyter widgets at the core of the interactive functionalities. However, while packages and libraries which offer a broad range of general-purpose widgets exist, there is limited development of specialized widgets for computational physics, chemistry and materials science. This deficiency implies significant time investments for the development of effective Jupyter notebooks for research and education in these domains. Here, we present custom Jupyter widgets that we have developed to target the needs of these communities. These widgets constitute high-quality interactive graphical components and can be employed, for example, to visualize and manipulate data, or to explore different visual representations of concepts, clarifying the relationships existing between them. In addition, we discuss with one example how similar functionality can be exposed in the form of JupyterLab extensions, modifying the JupyterLab interface for an enhanced user experience when working with applications within the targeted scientific domains.

Proline-based Organocatalyst for the Synthesis of Arylidene Benzofuranone Intermediates Enabling the Construction of Aurone-derived Azadienes

Organocatalysis has been recognized as a part of chemical research for a long time, and it gained significant attention in catalysis in recent decades. Amine catalyst is a substantial type of organocatalysis, and it is successively employed for the activation of carbonyl compounds. This manuscript delves into the exploration of a proline-based organocatalyst for the synthesis of arylidene benzofuranone intermediates, a critical step that facilitates the subsequent construction of aurone-derived azadienes. In this work, we successfully reported the synthesis of arylidene benzofuranone intermediates through Aldol condensation of benzofuranone with different aldehydes enabled by proline-derived organic catalysts. To achieve this strategy, six examples of amine organocatalysts (A1-A6) were evaluated to showcase the optimal catalyst for this transformation. Moreover, the arylidene benzofuranone intermediates were further employed for the synthesis of interesting aurone-derived azadiene substrates through its reaction with TsNH2. Notably, the using of organocatalyst A6 resulted in the delivery of the product with the best yield (94% isolated yield). Under the optimized conditions, different aromatic and heterocyclic containing aldehydes were effectively tolerated to generate the corresponding arylidene benzofuranone intermediates, which further converted to the azadiene products in high to excellent yield. The claimed structures were confirmed by the spectral analysis.

Cross-Modal Prediction of Spectral and Structural Descriptors via a Pretrained Model Enhanced with Chemical Insights.

Proposing and utilizing machine learning descriptors for chemical property prediction and material screening have become a cutting-edge field in artificial intelligence-enabled chemical research. However, a single descriptor typically captures only partial features of a chemical object, resulting in an information deficiency and limiting generalizability. Obtaining a comprehensive set of descriptors is essential but challenging, especially when accessing some microlevel structural and electronic features due to technological limitations. Herein, we exploit multimodal chemical descriptors to construct an encoder-decoder machine learning framework that enables the cross-modal prediction of spectral and structural descriptors. By pretraining the model to endow it with chemical insights, the multimodal data fusion is implemented in a descriptor-encoded hidden layer. The model's capabilities are validated in the system of CO/NO adsorption on Au/Ag surfaces, demonstrating successful reciprocal prediction of infrared spectra, Raman spectra, and internal coordinates. This work provides a proof-of-concept for the feasibility of cross-modal predictions between different chemical features and will significantly reduce the machine learning model's dependence on complete physicochemical parameters and improve its multitarget prediction capabilities.

Enhanced control of RNA modification and CRISPR-Cas activity through redox-triggered disulfide cleavage

Chemical RNA modification has emerged as a flexible approach for post-synthetic modifications in chemical biology research. Guide RNA (gRNA) plays a crucial role in the clustered regularly interspaced short palindromic repeats and associated protein system (CRISPR-Cas). Several toolkits have been developed to regulate gene expression and editing through modifications of gRNA. However, conditional regulation strategies to control gene editing in cells as required are still lacking. In this context, we introduce a strategy employing a cyclic disulfide-substituted acylating agent to randomly acylate the 2′-OH group on the gRNA strand. The CRISPR-Cas systems demonstrate off–on transformation activity driven by redox-triggered disulfide cleavage and undergo intramolecular cyclization, which releases the functionalized gRNA. Dithiothreitol (DTT) exhibits superior reductive capabilities in cleaving disulfides compared to glutathione (GSH), requiring fewer reductants. This acylation method with cyclic disulfides enables conditional control of CRISPR-Cas9, CRISPR-Cas13a, RNA hybridization, and aptamer folding. Our strategy facilitates precise in vivo control of gene editing, making it particularly valuable for targeted applications.

Perspective on Theoretical and Experimental Advances in Atmospheric Photochemistry.

Research that explores the chemistry of Earth's atmosphere is central to the current understanding of global challenges such as climate change, stratospheric ozone depletion, and poor air quality in urban areas. This research is a synergistic combination of three established domains: earth observation, for example, using satellites, and in situ field measurements; computer modeling of the atmosphere and its chemistry; and laboratory measurements of the properties and reactivity of gas-phase molecules and aerosol particles. The complexity of the interconnected chemical and photochemical reactions which determine the composition of the atmosphere challenges the capacity of laboratory studies to provide the spectroscopic, photochemical, and kinetic data required for computer models. Here, we consider whether predictions from computational chemistry using modern electronic structure theory and nonadiabatic dynamics simulations are becoming sufficiently accurate to supplement quantitative laboratory data for wavelength-dependent absorption cross-sections, photochemical quantum yields, and reaction rate coefficients. Drawing on presentations and discussions from the CECAM workshop on Theoretical and Experimental Advances in Atmospheric Photochemistry held in March 2024, we describe key concepts in the theory of photochemistry, survey the state-of-the-art in computational photochemistry methods, and compare their capabilities with modern experimental laboratory techniques. From such considerations, we offer a perspective on the scope of computational (photo)chemistry methods based on rigorous electronic structure theory to become a fourth core domain of research in atmospheric chemistry.

Quantifying the diurnal variation in atmospheric NO2 from Geostationary Environment Monitoring Spectrometer (GEMS) observations

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation in several important trace gas and aerosol pollutants including nitrogen dioxide (NO2), which is the focus of this work. NO2 is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of NO2 tropospheric column diurnal variation which can be greater than 50 % of the column amount, especially in polluted environments. The NO2 distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low-Earth-orbit satellite observation. We use GEMS data in combination with TROPOspheric Monitoring Instrument (TROPOMI) satellite and Pandora ground-based remote sensing measurements and Multi-Scale Infrastructure for Chemistry and Aerosols (Version 0, MUSICAv0) 3D chemical transport model analysis to examine the NO2 diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be key to including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar NO2 diurnal cycles in the new Earth Venture Instrument-1 Tropospheric Emissions: Monitoring Pollution (TEMPO) data over North America, and later over Europe with Sentinel-4.