jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aya Roshdy | Randa A. Abdel Salam | Ghada M. Hadad | Fathallah Belal | Heba Elmansi
Source: Scientific Reports (article, 2024-11-14)
Field: Chemistry — Analytical Chemistry

Design-assisted HPLC-UV method for therapeutic drug monitoring of pholcodine, ephedrine, and guaifenesin in biological fluids Drug-drug interactions may amplify or diminish their intended effects, or even produce entirely new effects. Multicomponent mixture HPLC analysis offers a thorough and effective method for comprehending the makeup and behavior of complicated materials, advancing research and development across a range of scientific and industrial domains. A novel experimental design-assisted HPLC methodology for the concurrent investigation of the drug-drug interaction of pholcodine, ephedrine, and guaifenesin in biological fluids has been established. Rather than the routine methodology, the application of the factorial design-HPLC method offers a powerful and efficient tool for the analysis of these compounds. Both mixed and full factorial designs were employed to assess the impact of variable factors on chromatographic results. Utilizing an isocratic elution mode on a C<sub>18</sub> column, the chromatographic separation was carried out. 15% Methanol, 5% acetonitrile, and 80% phosphate buffer with 0.1%(v/v) triethylamine set to pH 3 make up the mobile phase flowing at rate 1.0 mL/min. The calibration curves of the drugs show excellent linearity over a concentration ranges: 0.20-13.0 µg/mL for PHO, 0.50-20.0 µg/mL for EPH and 0.70-20.0 µg/mL for GUA with LOQ values of 0.18, 0.38 and 0.50. The fast separation and quantitation in less than 6 min is an advantage. Also, the method includes a robust sample preparation protocol for the analysis of complex biological samples, ensuring high selectivity and precision. The ease, speed and cost-effectiveness of the method are ideal for supporting in vitro studies, including drug-drug interaction investigations, especially in bioanalytical labs. Chromatography ; Ephedrine ; High-performance liquid chromatography ; Factorial experiment ; Triethylamine ; Calibration curve ; Pharmaceutical formulation ; Design of experiments ; Chemistry ; Acetonitrile ; Drug ; Elution ; Computer science ; Detection limit ; Pharmacology ; Mathematics ; Medicine ; Organic chemistry Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Pharmacology and Obesity Treatment
Authors: Kapil Goswami | Peter Schmelcher | Rick Mukherjee
Source: Quantum Science and Technology (preprint, 2024-07-24)
Field: Chemistry — Physical and Theoretical Chemistry

Integer programming using a single atom Abstract Integer programming (IP), as the name suggests is an integer-variable-based approach commonly used to formulate real-world optimization problems with constraints. Currently, quantum algorithms reformulate the IP into an unconstrained form through the use of binary variables, which is an indirect and resource-consuming way of solving it. We develop an algorithm that maps and solves an IP problem in its original form to any quantum system possessing a large number of accessible internal degrees of freedom that are controlled with sufficient accuracy. This work leverages the principle of superposition to solve the optimization problem. Using a single Rydberg atom as an example, we associate the integer values to electronic states belonging to different manifolds and implement a selective superposition of different states to solve the full IP problem. The optimal solution is found within a few microseconds for prototypical IP problems with up to eight variables and four constraints. This also includes non-linear IP problems, which are usually harder to solve with classical algorithms when compared to their linear counterparts. Our algorithm for solving IP is benchmarked by a well-known classical algorithm (branch and bound) in terms of the number of steps needed for convergence to the solution. This approach carries the potential to improve the solutions obtained for larger-size problems using hybrid quantum–classical algorithms. Integer programming ; Atom (system on chip) ; Integer (computer science) ; Computer science ; Programming language ; Parallel computing ; Algorithm History and advancements in chemistry ; Machine Learning in Materials Science ; Graph theory and applications
Authors: Yvonne Leusmann | Sebastian Klink | David Vega‐Maza | Markus Richter
Source: Fuel (article, 2024-08-16)
Field: Chemistry — Spectroscopy

Dew points for hydrogen-rich (hydrogen + propane) and (hydrogen + n-butane) mixtures determined with a microwave re-entrant cavity resonator • Dew points of hydrogen-rich binary mixtures were measured with low uncertainty. • Hydrogen can affect pressure measurement devices, thus increasing uncertainty. • Detailed comparisons with the GERG-2008 equation of state are presented. • New experimental results significantly improve the current data situation. • Data will help improve thermodynamic calculations for decarbonizing gas grids. Dew-point measurements were performed for hydrogen-rich binary mixtures with hydrocarbon mole fractions of 0.09995 C 3 H 8 , 0.1499 C 3 H 8 , 0.0513 n -C 4 H 10, and 0.09888 n -C 4 H 10 using a modified microwave re-entrant cavity apparatus. Isochoric measurements were conducted in the temperature range of (256 to 286) K with pressures up to 6.8 MPa and 2.0 MPa for mixtures containing propane and n -butane, respectively. The combined expanded uncertainties ( k = 2) in dew-point temperature and pressure were estimated to be between (0.35 and 2.0) K and (0.0011 and 0.030) MPa. The agreement with predictions of the GERG-2008 equation of state is within 5.5%. However, better agreement with predicted values was achieved using a cubic equation recently developed in-house. Moreover, experiments with short- and long-term exposure of pressure sensors to hydrogen have shown that pressure measurement, and thus the uncertainty, can be affected; further investigation on this matter is therefore inevitable to improve hydrogen thermophysical property data. Propane ; Butane ; Hydrogen ; Microwave ; Microwave cavity ; Resonator ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Organic chemistry ; Physics ; Catalysis ; Optoelectronics Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Spectroscopy and Laser Applications
Authors: Trevor Hastings | Mrinalini Mulukutla | Danial Khatamsaz | Daniel Salas | Wenle Xu | Daniel Lewis | Nicole Person | Matthew Skokan | Braden Miller | James D. Paramore | Brady G. Butler | Douglas Allaire | İbrahim Karaman | George M. Pharr | Ankit Srivastava | Raymundo Arróyave
Source: arXiv (Cornell University) (preprint, 2024-05-14)
Field: Materials Science — Materials Chemistry

An Interoperable Multi Objective Batch Bayesian Optimization Framework for High Throughput Materials Discovery In this study, we introduce a groundbreaking framework for materials discovery, we efficiently navigate a vast phase space of material compositions by leveraging Batch Bayesian statistics in order to achieve specific performance objectives. This approach addresses the challenge of identifying optimal materials from an untenably large array of possibilities in a reasonable timeframe with high confidence. Crucially, our batchwise methods align seamlessly with existing material processing infrastructure for synthesizing and characterizing materials. By applying this framework to a specific high entropy alloy system, we demonstrate its versatility and robustness in optimizing properties like strain hardening, hardness, and strain rate sensitivity. The fact that the Bayesian model is adept in refining and expanding the property Pareto front highlights its broad applicability across various materials, including steels, shape memory alloys, ceramics, and composites. This study advances the field of materials science and sets a new benchmark for material discovery methodologies. By proving the effectiveness of Bayesian optimization, we showcase its potential to redefine the landscape of materials discovery. Bayesian optimization ; Interoperability ; Throughput ; Computer science ; Bayesian probability ; Data mining ; Artificial intelligence ; World Wide Web ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Sanha Lim | Hwangho Lee | Hyun Sub Kim | Jun‐Seop Shin | Jong Min Lee | Do Heui Kim
Source: Reaction Chemistry & Engineering (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Bayesian-optimization-based design of highly active and stable Fe–Cu/SSZ-13 catalysts for the selective catalytic reduction of NO<sub><i>x</i></sub> with NH<sub>3</sub> Bayesian optimization approach facilitates an efficient optimization of multi-components catalyst for NH 3 -SCR reaction. Catalysis ; Reduction (mathematics) ; Selective catalytic reduction ; Bayesian optimization ; Chemistry ; Bayesian probability ; Materials science ; Combinatorial chemistry ; Computer science ; Mathematics ; Organic chemistry ; Machine learning ; Artificial intelligence Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Nanomaterials for catalytic reactions
Authors: Francesco Aiello | Jian Zhang | J.C. Brouwer | Mauro Salazar | Diletta Giuntini
Source: arXiv (Cornell University) (preprint, 2024-06-20)
Field: Materials Science — Ceramics and Composites

Double-tough and ultra-strong ceramics: leveraging multiscale toughening mechanisms through Bayesian Optimization We present an optimization-driven approach to creating a double-tough ceramic material with a brick-and-mortar microstructure, where the mortar is itself transformation-toughened, engineered with the goal of simultaneously achieving high strength and fracture toughness levels. Specifically, we design a material where high-strength alumina bricks are interconnected via a ceria-stabilized zirconia mortar. As the design of such a material, driven by multiscale toughening mechanisms, requires a laborious trial-and-error approach, we propose a Bayesian optimization framework as an integral part of our methodology to streamline and accelerate the design process. We use a Gaussian process to emulate the material's mechanical response and implement a cost-aware batch Bayesian optimization to efficiently identify optimal design process parameters, accounting for the cost of experimentally varying them. This approach expedites the optimization of the material's mechanical properties. As a result, we develop a bio-inspired all-ceramic composite that exhibits an exceptional balance between bending strength (704 MPa), and fracture toughness (13.6 MPa m^0.5). Toughening ; Bayesian probability ; Ceramic ; Bayesian optimization ; Materials science ; Computer science ; Nanotechnology ; Artificial intelligence ; Composite material Advanced ceramic materials synthesis ; Injection Molding Process and Properties ; Composite Material Mechanics
Authors: Akash Singh | Mingyuan Sun | Jihua Chen | Bo Li | Yumeng Li
Source: The Journal of Physical Chemistry C (article, 2024-01-26)
Field: Materials Science — Polymers and Plastics

Templating Effect of MoSe<sub>2</sub> on Crystallization of Polyethylene: A Molecular Dynamics Simulation Study In today’s world, 2D material-based nanocomposites have become a material system that has an ever-growing technological and scientific importance. Such a hybrid material system synergizes organic molecules and inorganic 2D materials and renders a rich playground for developing functional materials toward diverse applications in electronic, photovoltaics, and nanotribology. The interfaces in the 2D materials/polymer nanocomposites are also considered as the prototypical system to study confinement-induced phase transitions, which requires a comprehensive understanding of the dynamic and static properties on the molecular length and time scales. The fundamental understanding of the interfaces can provide insights into the design of 2D material/polymer nanocomposites with desired properties through interface engineering. However, to date, our understanding about such a heterointerface is still limited due to challenges in experimental testing and theoretical modeling. In this study, polyethylene assembly on 2D MoSe2 has been investigated by conducting molecular dynamics (MD) simulations. An all-atoms model is developed to simulate the guided assembly of n-pentacosane alkane chains, a surrogate model for polyethylene, on the surface of 2D MoSe2. It has been observed that polyethylene molecules crystallize from the interface and the crystallization front moves rapidly toward the bulk. In equilibrium, the assembled polyethylene chains are orientationally registered to the 2D surface under the interplay between the conformational entropy of the polymer chains, the adhesive interface interaction, and the corrugated substrate. This work demonstrates that 2D materials like MoSe2 can be used as a template to create 2D material/polymer nanocomposites with specific bicrystallization orientations for the designed behavior in the resulting nanocomposite. Crystallization ; Molecular dynamics ; Materials science ; Polyethylene ; Dynamics (music) ; Chemical physics ; Chemical engineering ; Statistical physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Physics ; Composite material ; Engineering Polymer crystallization and properties ; Graphene research and applications ; Microplastics and Plastic Pollution
Authors: Congde Qiao | Wenke Yang | Zhongwei Li | Qinze Liu | Jinshui Yao
Source: Chinese Journal of Chromatography (article, 2024-08-01)
Field: Chemistry — Spectroscopy

Experimental teaching reform of polymer molar mass determination using gel permeation chromatography coupled with light scattering Gel permeation chromatography coupled with light scattering (GPC-LS) is among the most common methods for determining the molar masses of polymers. GPC-LS is widely used in polymer science research and has been adopted for many industrial applications owing to its high sensitivity, accuracy, and precision. The determination of polymer molar masses using GPC-LS is an important experimental component of the "Polymer Physics Experiments" course. However, the present GPC-LS experimental teaching content tends to be overly simplistic and lacking in depth. Herein, the original experimental content is expanded and multiple sets of experiments are redesigned: (1) Using commercial polystyrene as an experimental sample, the molar mass, molar mass distribution, radius of gyration, and other molecular structure parameters are determined using GPC-LS; (2) Using two polyacrylonitriles with similar molecular structure parameters, subtle differences in the molar mass distributions of the samples are explored using differential mass distribution curves; (3) By comparing the chromatograms of a series of polyethylene glycols with different molar masses, the effect of molar mass on chromatographic peaks is investigated; and (4) For three different polymers (polyacrylonitrile, poly(methyl methacrylate), and poly(<i>β</i>-cyclodextrin)), the polymer chain conformations are analyzed using conformation plots (i.e., radius of gyration vs. molar mass). In addition, the experimental teaching method is modified to convert passive learning into active learning, thereby improving the students' self-directed learning ability. This experimental teaching reform will help students obtain a more comprehensive understanding of GPC-LS principles and applications, stimulate their enthusiasm for learning, and improve the teaching quality of the experimental course. Molar mass ; Gel permeation chromatography ; Chemistry ; Molar mass distribution ; Polymer ; Radius of gyration ; Chromatography ; Molecular mass ; Analytical Chemistry (journal) ; Polystyrene ; Methyl methacrylate ; Polymer chemistry ; Organic chemistry ; Copolymer Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Various Chemistry Research Topics
Authors: Kun Yu | Ryo Tachibana | Corentin Rumo | Nico V. Igareta | Kailin Zhang | Thomas R. Ward
Source: ChemCatChem (article, 2024-04-17)
Field: Chemistry — Organic Chemistry

Artificial Metalloenzyme‐Catalyzed Enantioselective Carboamination of Alkenes Abstract Relying on ubiquitous alkenes, carboamination reactions enable the difunctionalization of the double bond by the concurrent formation of a C−N and a C−C single bond. In the past years, several groups have reported on elegant strategies for the carboamination of alkenes relying on homogeneous catalysts or enzymes. Herein, we report on an artificial metalloenzyme for the enantioselective carboamination of dihydrofuran. Genetic optimization, combined with a Bayesian optimization of catalytic performance, afforded the disubstituted tetrahydrofuran product in up to 22 TON and 85 % ee. X‐ray analysis of the evolved artificial carboaminase shed light on critical amino acid residues that affect catalytic performance. Enantioselective synthesis ; Chemistry ; Catalysis ; Homogeneous ; Organic chemistry ; Biocatalysis ; Tetrahydrofuran ; Combinatorial chemistry ; Stereochemistry ; Reaction mechanism Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Peptidase Inhibition and Analysis
Authors: Dong Hwan Kim | Eugene Oh | Sehyun Kim | Jaegeun Lee
Source: Carbon letters (article, 2024-07-29)
Field: Materials Science — Materials Chemistry

Data analytics approach to evaluating the impact of acetylene and ethylene precursors on the crystallinity of carbon nanotubes synthesized in a fluidized bed reactor Crystallinity ; Materials science ; Acetylene ; Carbon nanotube ; Ethylene ; Chemical engineering ; Carbon fibers ; Nanotechnology ; Catalysis ; Composite material ; Organic chemistry ; Composite number ; Chemistry Carbon Nanotubes in Composites ; Graphene research and applications ; Zeolite Catalysis and Synthesis
Authors: Yi-Fan Hou | Quanhao Zhang | Pavlo O. Dral
Source: ChemRxiv (preprint, 2024-05-27)
Field: Chemistry — Organic Chemistry

Surprising new dynamics phenomena in Diels–Alder reaction of C60 uncovered with AI Our recently developed physics-informed active learning allowed us to perform extensive AI-accelerated quasi-classical molecular dynamics investigation of the time-resolved mechanism of two Diels–Alder reactions. This investigation revealed that despite the high similarity between static transition state geometries in reactions with ethene and fullerene C60 as dienophiles, the dynamics around the transitions are remarkably different. In a substantial fraction (10%) of reactive trajectories, the larger C60 non-covalently attracts the 2,3-dimethyl-1,3-butadiene long before the barrier so that the diene undergoes the series of complex motions including roaming, somersaults, twisting, and twisting somersaults around the fullerene until it aligns itself to pass over the barrier. These complicated processes could be easily missed in typically performed quantum chemical simulations with shorter and fewer trajectories. After passing the barrier, the bonds take longer to form than in the case of the ethene reaction: the consequence of the markedly different topology of the PES region between reactants and products. These effects are not captured by static intrinsic reaction coordinate (IRC) calculations that do not reveal any difference in the barrier widths. Alder ; Diels–Alder reaction ; Dynamics (music) ; Computer science ; Chemistry ; Ecology ; Biology ; Psychology ; Organic chemistry ; Catalysis Fullerene Chemistry and Applications ; Advanced Physical and Chemical Molecular Interactions ; Advanced Thermodynamics and Statistical Mechanics
Authors: Yi-Fan Hou | Quanhao Zhang | Pavlo O. Dral
Source: ChemRxiv (preprint, 2024-05-22)
Field: Chemistry — Organic Chemistry

Surprising new dynamics phenomena in Diels–Alder reaction of C60 uncovered with AI Our recently developed physics-informed active learning allowed us to perform extensive AI-accelerated quasi-classical molecular dynamics investigation of the time-resolved mechanism of two Diels–Alder reactions. This investigation revealed that despite the high similarity between static transition state geometries in reactions with ethene and fullerene C60 as dienophiles, the dynamics around the transitions are remarkably different. In a substantial fraction (10%) of reactive trajectories, the larger C60 non-covalently attracts the 1,3-dimethyl-butadiene long before the barrier so that the diene undergoes the series of complex motions including roaming, somersaults, twisting, and twisting somersaults around the fullerene until it aligns itself to pass over the barrier. These complicated processes could be easily missed in typically performed quantum chemical simulations with shorter and fewer trajectories. After passing the barrier, the bonds take longer to form than in the case of the ethene reaction: the consequence of the markedly different topology of the PES region between reactants and products. These effects are not captured by static intrinsic reaction coordinate (IRC) calculations that do not reveal any difference in the barrier widths. Diels–Alder reaction ; Alder ; Dynamics (music) ; Cognitive science ; Chemistry ; Biology ; Physics ; Psychology ; Ecology ; Organic chemistry ; Catalysis Fullerene Chemistry and Applications ; Advanced Physical and Chemical Molecular Interactions ; Advanced Thermodynamics and Statistical Mechanics
Authors: Josiah Roberts | Biswas Rijal | Simon Divilov | Jon‐Paul Maria | William G. Fahrenholtz | Douglas E. Wolfe | Donald W. Brenner | Stefano Curtarolo | Eva Zurek
Source: arXiv (Cornell University) (preprint, 2024-01-03)
Field: Materials Science — Materials Chemistry

Machine Learned Interatomic Potentials for Ternary Carbides trained on the AFLOW Database Large density functional theory (DFT) databases are a treasure trove of energies, forces and stresses that can be used to train machine learned interatomic potentials for atomistic modeling. Herein, we employ structural relaxations from the AFLOW database to train moment tensor potentials (MTPs) for four carbide systems: HfTaC, HfZrC, MoWC and TaTiC. The resulting MTPs are used to relax ~6300 random symmetric structures, and are subsequently improved via active learning to generate robust potentials (RP) that can relax a wide variety of structures, and accurate potentials (AP) designed for the relaxation of low-energy systems. This protocol is shown to yield convex hulls that are indistinguishable from those predicted by AFLOW for the HfTaC, HfZrC and TaTiC systems, and in the case of the MoWC system to predict thermodynamically stable structures that are not found within AFLOW, highlighting the potential of the employed protocol within crystal structure prediction. Relaxation of over three hundred Mo$_{1-x}$W$_x$C stoichiometry crystals first with the RP then with the AP yields formation enthalpies that are in excellent agreement with those obtained via DFT. Relaxation (psychology) ; Ternary operation ; Tensor (intrinsic definition) ; Stoichiometry ; Interatomic potential ; Density functional theory ; Pentamer ; Materials science ; Computer science ; Statistical physics ; Molecular dynamics ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Physical chemistry ; Geometry ; Nuclear magnetic resonance Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions