jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nan Lü | Chengxia Miao | Xiaozheng Lan
Source: Thermal Science and Engineering (article, 2024-07-01)
Field: Chemistry — Organic Chemistry

Theoretical investigation on Rh(III)-catalyzed switchable C–H alkenylation of enamide with enone and Rh(I)-catalyzed decarbonylative version of 1,2,3,4-tetrahydroquinoline with anhydride The mechanism is investigated for Rh(III)-catalyzed C-H alkenylation of enamide with enone and Rh(I)-catalyzed decarbonylative version of 1,2,3,4-tetrahydroquinoline with anhydride. The former contains β-C(sp2)−H activation of enamide, 1,2-migratory insertion of enone, β-hydride elimination or protodemetalation with additional HCl. The diastereoselectivity is kinetically controlled favoring alkenylation N-(2Z,4E)-butadiene while the regio-divergence is switchable to alkylation. The latter is composed of rate-limiting oxidative addition of anhydride to Rh(I), C8-selective C–H activation after ligand exchange producing tBuCO2H and six-membered rhodacycle, decarbonylation releasing CO as new carboxylate ligand and reductive elimination of Rh-alkenyl precursor leading to C8-alkenylated product. The whole process with huge heat release is favorable thermodynamically and all barriers capable to overcome under microwave assistance. The positive solvation effect is suggested by decreased absolute and activation energies in solution compared with in gas. These results are supported by Multiwfn analysis on FMO composition of specific TSs, and MBO value of vital bonding, breaking. Catalysis ; Enone ; Chemistry ; Ligand (biochemistry) ; Decarbonylation ; Alkylation ; Medicinal chemistry ; Hydride ; Photochemistry ; Organic chemistry ; Hydrogen ; Receptor Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Madhu Kumari Gupta
Source: International Journal of Science and Research (IJSR) (article, 2024-07-05)
Field: Chemistry — Organic Chemistry

Synthesis of Nanoparticles via Clemmensen Reduction: Methodology, Characterization, and Applications Nanoparticles synthesized via Clemmensen reduction offer unique properties and versatile applications across various fields. This review explores the synthesis methodologies, characterization techniques, and broad spectrum of applications of these nanoparticles. The Clemmensen reduction, originally devised for organic synthesis, involves converting carbonyl groups into methylene groups using amalgamated zinc in an acidic environment. This method has been adapted for nanoparticle synthesis by adjusting parameters such as temperature, pressure, solvent choice, and catalyst concentration. Variations of the Clemmensen reduction have been explored to enhance selectivity and yield, allowing for tailored nanoparticle properties. Characterization techniques such as Transmission Electron Microscopy (TEM), X -ray Diffraction (XRD), Dynamic Light Scattering (DLS), Fourier Transform Infrared Spectroscopy (FTIR), and UV -Visible spectroscopy provide insights into the morphology, crystallinity, and surface chemistry of Clemmensen -reduced nanoparticles. Factors influencing nanoparticle properties include reaction conditions and the use of stabilizing agents or surfactants, which dictate size, shape, and stability. Applications span biomedical, catalytic, and electronic fields. In biomedicine, nanoparticles are employed for drug delivery, imaging, and therapeutic purposes, leveraging their small size for targeted delivery and reduced systemic toxicity. Catalytically, these nanoparticles exhibit high efficiency in organic transformations and hydrogenation reactions. In electronic applications, their semiconductor properties and light absorption capabilities are crucial for sensors, photovoltaic devices, and LEDs. Challenges such as scalability, reproducibility, and environmental impact persist, prompting ongoing research into greener synthesis routes and improved integration into practical applications. In conclusion, Clemmensen -reduced nanoparticles represent a promising class of nanomaterials with wide -ranging applications. By refining synthesis methods and characterization techniques, these nanoparticles continue to drive technological advancements and address current societal challenges effectively. Nanoparticle ; Characterization (materials science) ; Reduction (mathematics) ; Chemistry ; Nanotechnology ; Materials science ; Mathematics Multicomponent Synthesis of Heterocycles ; Microwave-Assisted Synthesis and Applications ; Synthesis and biological activity
Source: MINAR International Journal of Applied Sciences and Technology (review, 2024-09-01)
Field: Chemistry — Organic Chemistry

A REVIEW OF NANOPARTICLES: THEIR SYNTHESIS, PROPERTIES, APPLICATION, AND ADVANTAGES Typically, nanoparticles are smaller than 100 nm in size.Because of nanotechnology's potential applications in a variety of disciplines, such as cosmetics, sunscreens, electronics, medicine, food production, construction, green energy, and cleanup, scientists all over the world have recently developed an interest in it.Producing nanoparticles with the precise size, structure, uniformity, and morphology is essential for the aforementioned applications.In nanotechnology, new processes have been created that are environmentally friendly and can be utilized to reliably produce Nanomaterials and nanoparticles.In this work, we discussed several aspects related to nanoparticles, and these aspects include: the benefits of nanoparticles related to (increasing permeability, enhancing therapeutic results, and amplifying drug effectiveness), properties of nanoparticles that depend on (size, surface, shape), and classifications of nanoparticles as they are classified to (organic, inorganic and carbon nanoparticles), the formation of nanoparticles, as there are two ways to form them, which are from the top to the bottom and the bottom to the top), methods of producing nanoparticles where the light was focused on this aspect related to the physical methods of producing nanoparticles, which includes laser ablation and plasma jet.It was found through our study that both methods are effective for producing nanoparticles with high specifications.Finally, we discussed the applications of nanoparticles in various magazines to reflect their importance in our current time. Nanoparticle ; Nanotechnology ; Materials science ; Computer science Inorganic and Organometallic Chemistry
Authors: Yuri Cho | Rubén Laplaza | Sergi Vela | Clémence Corminbœuf
Source: ChemRxiv (preprint, 2024-03-24)
Field: Chemistry — Spectroscopy

Automated Prediction of Ground State Spin for Transition Metal Complexes Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the cell2mol software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with cell2mol, we construct the TM-GSspin dataset, which contains 2,032 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 97% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes. Ground state ; State (computer science) ; Transition metal ; Spin (aerodynamics) ; Spin states ; Condensed matter physics ; Materials science ; Chemistry ; Computer science ; Physics ; Atomic physics ; Thermodynamics ; Algorithm ; Organic chemistry Molecular spectroscopy and chirality
Authors: Jun Yang | Runtong Pan | Jikai Sun | Jianzhong Wu
Source: arXiv (Cornell University) (preprint, 2024-11-06)
Field: Chemistry — Physical and Theoretical Chemistry

High-Dimensional Operator Learning for Molecular Density Functional Theory Classical density functional theory (cDFT) provides a systematic approach to predict the structure and thermodynamic properties of chemical systems through the single-molecule density profiles. Whereas the statistical-mechanical framework is theoretically rigorous, its practical applications are often constrained by challenges in formulating a reliable free-energy functional and the complexity of solving multidimensional integro-differential equations. In this work, we established an optimized operator learning method that effectively separates the high-dimensional molecular density profile into two lower-dimensional components, thereby exponentially reducing the vast input space. The convoluted operator learning network demonstrates exceptional learning capabilities, accurately mapping the relationship between the density profile of a carbon dioxide system to its one-body direct correlation function using an atomistic polarizable model. The neural operator model can be generalized to more complex systems, offering high-precision cDFT calculations at low computational cost. Density functional theory ; Operator (biology) ; Computer science ; Chemistry ; Computational chemistry ; Biochemistry ; Gene Various Chemistry Research Topics ; Machine Learning in Materials Science
Authors: Sai Pavan Kanukuntla
Source: Environment Technology Resources Proceedings of the International Scientific and Practical Conference (article, 2024-06-22)
Field: Materials Science — Biomaterials

STATE OF THE ART AND LITERATURE REVIEW RESEARCH OF HOLLOW NANOFIBERS: FOCUSING ON FABRICATION, CAD IMPLEMENTATION AND OPTIMISATION The introduction of nanotechnology has resulted in a new era of materials research, with hollow nanofibers emerging as a key innovation. These nanofibers, distinguished by their nano size diameters and hollow structures, have generated significant interest due to their potential applications in a variety of industries. However, despite their advantageous properties, manufacture and analysis of these hollow nanofibers face significant challenges, particularly in terms of mechanical stability and structural integrity when subjected to external stresses. Identifying and addressing these vulnerabilities is crucial for the advancement of hollow nanofibers in various industrial and biomedical fields. The production of hollow nanofibers, notably via the electrospinning technique, has been the topic of a great deal of research. One of the bases of this research is the utilization of computer-aided analysis (CAD) simulations, which include techniques such as Representative Volume Element (RVE) analysis, Finite Element Method (FEM), multiscale analysis, numerical simulation, and optimization strategies. These sophisticated tools offer a magnified view into the nano-structural behaviour of hollow nanofibers, enabling precise predictions about their mechanical properties and behaviours under diverse conditions. This approach is revolutionary, as it allows for the exploration of theoretical and practical aspects of material behaviours without the constraints of traditional experimental methodologies. This article is in-depth scientific review on these theoretical and practical aspects. CAD ; Nanofiber ; Fabrication ; State (computer science) ; Nanotechnology ; State of art ; Materials science ; Engineering ; Computer science ; Engineering drawing ; Biochemical engineering ; Medicine Electrospun Nanofibers in Biomedical Applications
Authors: Bibi, Amna | Zaman, Muhammad Iqbal | Niaz, Abdul | Bibi, Sania | Gul, Saba | Ahmad, Naveed | Osman, Hanan E
Source: Apollo (University of Cambridge) (article, 2024-12-01)
Field: Chemistry — Spectroscopy

Kinetics of silver ion encapsulation as nanoparticles using Vaccinium oxycoccos plant extract for the efficient sensing of Cr(iii) ions and its biological assessment Silver nanoparticle ; Antioxidant ; Nanoparticle ; Biosensor ; Kinetics ; Detection limit ; Absorption (acoustics) Molecular Sensors and Ion Detection ; Analytical Chemistry and Sensors ; Electrochemical sensors and biosensors
Authors: Guido Ori | Carlo Massobrio | Mauro Boero | Assil Bouzid | Christine Tugène | Rémi Piotrowski | Firas Shuaib | David Hamani | Pierre‐Marie Geffroy | Philippe Thomas | Olivier Masson | Gaëlle Delaizir
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Ceramics and Composites

Enhanced Structural Description of Sodium Vanadium Phosphate Glasses: A Combined Experimental and Molecular Dynamics Study Vanadium ; Molecular dynamics ; Sodium ; Phosphate ; Dynamics (music) ; Materials science ; Chemistry ; Chemical physics ; Inorganic chemistry ; Physics ; Computational chemistry ; Metallurgy ; Biochemistry Glass properties and applications ; Luminescence Properties of Advanced Materials ; Catalysis and Oxidation Reactions
Authors: Irshad Ahmad | Md Fahimul Hassan | Weqar Ahmad Siddiqui
Source: Journal of Materials Science and Chemical Engineering (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Toxicological and Life-Cycle Perspectives on Waste-Derived Nanoparticles Many nanoparticles have been created over the last few decades using a variety of techniques and used to develop environmental technology, including water treatment, the detection of persistent contaminants, and soil and water remediation. The studies of alternative inputs for nanoparticle production as well as the use of green synthesis techniques are driven by the field of materials science and engineering’s growing interest in increasing the sustainability of the processes involved in their production. In this paper, we begin by providing an overview of the fundamental principles of producing nanoparticles from different sources, such as plastic, electronic, metal, and industrial waste. We elaborate on key facts of waste identification as a workable input for the treatment and recovery of metal and carbon-based nanoparticles. We next go over several controlling factors that play a role in creating nanoparticles, pointing out probable conclusions as we go. Then, we show some instances of waste-derived nanoparticles used in a proof-of-concept experiment of technology for applications in water quality and safety. Before scaling up production and implementing waste-derived nanoparticles, there are several present problems from the toxicological and life-cycle perspectives that must be taken into account. Materials science ; Nanoparticle ; Waste management ; Environmental planning ; Nanotechnology ; Environmental science ; Engineering Nanoparticles: synthesis and applications ; Analytical chemistry methods development ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Khamis A. Atayalla
Source: Journal of Basic Sciences (article, 2024-12-25)
Field: Chemistry — Physical and Theoretical Chemistry

REVOLUTIONIZING CHEMISTRY WITH ARTIFICIAL INTELLIGENCE: ADVANCEMENTS IN RESEARCH AND APPLICATIONS يُحدث الذكاء الاصطناعي (AI) ثورة في مجال الكيمياء من خلال تسريع عمليات الاكتشاف، وتحسين الكفاءة، وتمكين التحليلات المعقدة. يستعرض هذا البحث التطبيقات الرئيسية للذكاء الاصطناعي في الكيمياء، بما في ذلك تصميم الجزيئات، والتنبؤ بالتفاعلات، واكتشاف الأدوية، وعلوم المواد. ويركز على التطورات الحديثة، والتحديات القائمة، والآفاق المستقبلية. من خلال دمج الذكاء الاصطناعي مع الكيمياء التجريبية والنظرية، يسهم الباحثون في إطلاق حقبة جديدة من الكفاءة والابتكار. Key (lock) ; Applications of artificial intelligence ; Feature (linguistics) History and advancements in chemistry ; Origins and Evolution of Life ; Chemistry and Chemical Engineering
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Phys. Chem. Chem. Phys., 2024, 26, 5725 DOI: 10.1039/D4CP90036G This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Chemistry Chemical and Physical Properties of Materials ; Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science
Authors: Caitlin A. McCandler | Antti Pihlajamäki | Sami Malola | Hannu Häkkinen | Kristin A. Persson
Source: ChemRxiv (preprint, 2024-03-07)
Field: Materials Science — Materials Chemistry

Gold-Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential Mono-layer protected metal clusters comprise a rich class of molecular systems, and are promising candidate materials for a variety of applications. While a growing number of protected nanoclusters have been synthe- sized and characterized in crystalline forms, their dynamical behavior in solution, including pre-nucleation cluster formation, is not well understood due to limitations both in characterization and first-principles mod- eling techniques. Recent advancements in machine-learned interatomic potentials are rapidly enabling the study of complex interactions such as dynamical behavior and reactivity at the nanoscale. Here, we develop an Au-S-C-H Atomic Cluster Expansion (ACE) interatomic potential for efficient and accurate molecular dynamics simulations of thiolate-protected gold nanoclusters (Aun (SCH3)m ). Trained on more than 30,000 density functional theory calculations of gold nanoclusters, the interatomic potential exhibits ab initio level accuracy in energies and forces, and replicates nanocluster dynamics including thermal vibration and chiral inversion. Long dynamics simulations (up to 0.1 μs time scale) reveal a novel mechanism explaining the ther- mal instability of neutral Au25(SR)18 clusters. Specifically, we observe multiple stages of isomerization of the Au25(SR)18 cluster, including a novel chiral isomer. Additionally we simulate coalescence of two Au25(SR)18 clusters and observe series of new clusters where the formation mechanisms are critically mediated by ligand exchange in the form of [Au–S]n rings. Interatomic potential ; Nanotechnology ; Molecular dynamics ; Dynamics (music) ; Computer science ; Chemistry ; Materials science ; Computational chemistry ; Physics Nanocluster Synthesis and Applications ; Machine Learning in Materials Science
Authors: Akhil S. Nair | Lucas Foppa | Matthias Scheffler
Source: arXiv (Cornell University) (preprint, 2024-12-08)
Field: Materials Science — Materials Chemistry

Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and sparsifying operator (SISSO) symbolic-regression approach. SISSO identifies the few, key parameters correlated with a given materials property via analytical expressions, out of many offered primary features. Crucially, we train ensembles of SISSO models in order to quantify mean predictions and their uncertainty, enabling the use of SISSO in AL. By combining bootstrap sampling to obtain training datasets with Monte-Carlo feature dropout, the high prediction errors observed by a single SISSO model are improved. Besides, the feature dropout procedure alleviates the overconfidence issues observed in the widely used bagging approach. We demonstrate the SISSO-guided AL workflow by identifying acid-stable oxides for water splitting using high-quality DFT-HSE06 calculations. From a pool of 1470 materials, 12 acid-stable materials are identified in only 30 AL iterations. The materials property maps provided by SISSO along with the uncertainty estimates reduce the risk of missing promising portions of the materials space that were overlooked in the initial, possibly biased dataset. Workflow ; Electrocatalyst ; Computer science ; Regression ; Symbolic regression ; Artificial intelligence ; Chemistry ; Statistics ; Mathematics ; Database Machine Learning in Materials Science
Authors: Suchona Akter | Yong Li | Min‐Bum Kim | Md Omar Faruque | Zhonghua Peng | Praveen K. Thallapally | Mohammad R. Momeni
Source: arXiv (Cornell University) (preprint, 2024-07-27)
Field: Chemistry — Inorganic Chemistry

Fine-tuning Microporosity of Crystalline Vanadomolybdate Frameworks for Selective Adsorptive Separation of Kr from Xe Selective adsorptive capture and separation of chemically inert Kr and Xe noble gases with very low ppmv concentrations in air and industrial off-gases constitute an important technological challenge. Here, using a synergistic combination of experiment and theory, the microporous crystalline vanadomolybdates (MoVOx) as highly selective Kr sorbents are studied in detail. By varying the Mo/V ratios, we show for the first time that their one-dimensional pores can be fine-tuned for the size-selective adsorption of Kr over the larger Xe with selectivities reaching &gt;100. Using extensive electronic structure calculations and grand canonical Monte-Carlo simulations, the competition between Kr uptake with CO2 and N2 was also investigated. As most materials reported so far are selective toward the larger, more polarizable Xe than Kr, this work constitutes an important step toward robust Kr-selective sorbent materials. This work highlights the potential use of porous crystalline transition metal oxides as energy-efficient and selective noble gas capture sorbents for industrial applications. Separation (statistics) ; Chemical engineering ; Materials science ; Nanotechnology ; Computer science ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Chemical Synthesis and Characterization