jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiangyu Chen | Hao Liu | Maoyuan Li | Shifa Wang | M Khalilzadeh | S Kim | H Jang | M Han | S Wang | X Chen | X Pan | S Tang | L Fang | I Sanchis | E Diaz | A Pizarro | X Han | M Sun | X Chai | T Cheng | Q Ma | H Gao | M Petrovi | S Ranev | N Velinov | C Chen | L Karuppasamy | L Gurusamy | S Anandan | J Guan | Y Jia | T Chang | Z Ansarian | A Khataee | S Arefi-Oskoui | Gms Elshafei | F Yehia | G Eshaq | Jin | M Li | Z Yin | H Sun | X Yang | L Zhao | Odi Prasiwi | T Saraswati | M Anwar | Z Hu | Q He | M Ge | P Kaur | S Singh | V Kumar | S Prathap | W Madhuri | S Meena | B Lahijani | K Hedayati | M Goodarzi | Sem Ghahfarokhi | Z Rostami | I Kazeminezhad | N Asiabani | G Nabiyouni | S Khaghani | H Yang | Y Wang | H Lv | X Zhou | X Zu | G Sun | C Zhang
Source: Journal of Modern Polymer Chemistry and Materials (article, 2022-12-30)
Field: Materials Science — Materials Chemistry

Hexagonal Lead Ferrite Magnetic Separation Catalysts: Synthesis, Optical Characterization, Ultrasonic Catalytic Activity and Performance Prediction Objective: This study was performed to explore a new method to synthesize PbFe 12 O 19 magnetic separation catalysts and analyze its ultrasonic catalytic activity for the degradation of rhodamine B (RhB) dye under ultrasonic vibration. Materials science ; Catalysis ; Ferrite (magnet) ; Hexagonal crystal system ; Magnetic separation ; Characterization (materials science) ; Chemical engineering ; Ultrasonic sensor ; Metallurgy ; Nanotechnology ; Composite material ; Crystallography ; Chemistry ; Acoustics ; Engineering ; Physics ; Organic chemistry Magnetic Properties and Synthesis of Ferrites
Authors: Hirotoshi Hirai | Ryosuke Jinnouchi
Source: RSC Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Discovering surface reaction pathways using accelerated molecular dynamics and network analysis tools We present an automated method that maps surface reaction pathways with no experimental data and with minimal human interventions. In this method, bias potentials promoting surface reactions are applied to enable statistical samplings of the surface reaction within the timescale of <i>ab initio</i> molecular dynamics (MD) simulations, and elementary reactions are extracted automatically using an extension of the method constructed for gas- or liquid-phase reactions. By converting the extracted elementary reaction data to directed graph data, MD trajectories can be efficiently mapped onto reaction pathways using a network analysis tool. To demonstrate the power of the method, it was applied to the steam reforming of methane on the Rh(111) surface and to propane reforming on the Pt(111) and Pt<sub>3</sub>Sn(111) surfaces. We discover new energetically favorable pathways for both reactions and reproduce the experimentally-observed materials-dependence of the surface reaction activity and the selectivity for the propane reforming reactions. Dynamics (music) ; Molecular dynamics ; Computer science ; Surface (topology) ; Chemistry ; Biological system ; Computational chemistry ; Physics ; Mathematics ; Biology Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Mass Spectrometry Techniques and Applications
Authors: Anand Narayanan Krishnamoorthy | Christian Wölke | Diddo Diddens | M. Maiti | Youssef Mabrouk | Peng Yan | Mariano Grünebaum | Martin Winter | Andreas Heuer | Isidora Cekić-Lasković
Source: ChemRxiv (preprint, 2022-02-07)
Field: Materials Science — Materials Chemistry

Data-Driven Analysis of High-Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity An in-house, unique, custom-developed high-throughput experimentation facility, used for discovery of novel and optimization of existing electrolyte formulations for diverse cell chemistries and targeted applications, follows a high-throughput formulation-characterization-performance-elucidation-optimization-evaluation chain based on a set of previously established requirements. Here, we propose a scalable data-driven workflow to predict ionic conductivities of non-aqueous battery electrolytes based on linear and Gaussian regression, considering a dataset acquired from one-of-a-kind high-throughput electrolyte formulation to high-throughput conductivity measurement sequence. Deeper insight into various compositional effects is gained from a generalized Arrhenius analysis, in which conductivities, activation energies and deviations from Arrhenius behavior are determined separately. Each observable displays a specific dependence on the electrolyte salt concentration. The conductivity is fully insensitive to the addition of electrolyte additives for otherwise constant molar composition. We also discuss and interpret qualitative trends predicted by the data-driven model in light of physical features such as viscosity or ion association effects. Electrolyte ; Conductivity ; Arrhenius equation ; Throughput ; Molar conductivity ; Battery (electricity) ; Materials science ; Ionic conductivity ; Computer science ; Chemistry ; Thermodynamics ; Ion ; Activation energy ; Physics ; Electrode ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Advanced Battery Technologies Research
Authors: Zhen Liu | Tetiana Zubatiuk | Adrián E. Roitberg | Olexandr Isayev
Source: ChemRxiv (preprint, 2022-06-24)
Field: Materials Science — Materials Chemistry

Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials Computational programs accelerate the chemical discovery processes but often need proper 3-dimensional molecular information as part of the input. Getting optimal molecular structures is challenging because it requires enumerating and optimizing a huge space of stereoisomers and conformers. We developed the python-based Auto3D package for generating the low-energy 3D structures using SMILES as the input. Auto3D is based on state-of-the-art algorithms and can automize the isomer enumeration and duplicate filtering process, 3D building process, geometry optimization and ranking process. Tested on 50 molecules with multiple unspecified stereocenters, Auto3D is guaranteed to find the stereo-configuration that yields the lowest-energy conformer. With Auto3D we provide an extension of the ANI model. The new model, dubbed ANI-2xt, is trained on a tautomer-rich dataset. ANI-2xt is benchmarked with DFT methods on geometry optimization, electronic and Gibbs free energy calculations. Compared with ANI-2x, ANI-2xt provides a 42% error reduction for tautomeric reaction energy calculations when using the gold-standard coupled-cluster calculation as the reference. ANI-2xt can accurately predict the energies with a ~10^6 factor speedup compared to DFT. Python (programming language) ; Computer science ; Speedup ; Energy minimization ; Conformational isomerism ; Algorithm ; Computational science ; Computational chemistry ; Chemistry ; Molecule ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Hafeera Shabbir | Stephen P. Vicchio | Paul J. Meza-Morales | Rachel B. Getman
Source: The Journal of Physical Chemistry C (article, 2022-03-16)
Field: Chemistry — Inorganic Chemistry

Role of Molecular Simulations in the Design of Metal–Organic Frameworks for Gas-Phase Thermocatalysis: A Perspective Metal organic frameworks (MOFs) are highly tunable porous crystalline solids with spatially and electronically isolated catalytically active sites that have been demonstrated for a variety of thermo-, redox-, and photocatalytic reactions. Their tunable natures and relatively well-defined active sites make them advantageous for catalyst design, and molecular simulations have proven highly valuable in this endeavor. However, complexities in the MOF structure require advanced simulation strategies that accurately capture quantum chemistry at the strongly correlated transition metal cation active sites, compositional and structural changes caused by finite temperature and pressure reaction conditions, and transport effects in variously sized pore environments. Luckily, several groups have started using such simulation strategies, paving the way for rich opportunities to design MOF catalysts. Herein, we highlight such examples and provide a perspective on the needs for molecular simulations in the design of MOF catalysts moving forward. Catalysis ; Metal-organic framework ; Nanotechnology ; Materials science ; Molecular dynamics ; Redox ; Porosity ; Gas phase ; Perspective (graphical) ; Phase (matter) ; Transition metal ; Phase transition ; Design elements and principles ; Chemical physics ; Chemistry ; Computer science ; Computational chemistry ; Systems engineering ; Organic chemistry ; Physics ; Adsorption ; Thermodynamics ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: F.I. Espinosa-Lagunes | J. C. Cruz | R. E. Vega-Azamar | I. Murillo-Borbonio | Julieta Torres-González | Ricardo A. Escalona-Villalpando | M.P. Gurrola | J. Ledesma‐García | L.G. Arríaga
Source: Materials for Renewable and Sustainable Energy (article, 2022-06-21)
Field: Chemistry — Electrochemistry

Copper nanoparticles suitable for bifunctional cholesterol oxidation reaction: harvesting energy and sensor Abstract This study reports the performance of simple low-cost synthesized bifunctional Cu/Cu 2 O nanoparticles (NPs) used as a catalyst for energy-harvesting applications through of a microfluidic fuel cell (µFC), and further, as cholesterol (Chol) sensor. TEM characterization of the NPs showed spheres between 4 and 10 nm, while XRD and XPS analysis confirmed the composition and preferential crystallographic plane of Cu/Cu 2 O. In addition, 25.26 m 2 g −1 surface area was obtained, which is greater than those commercial materials. NPs showed high activity toward the cholesterol oxidation reaction when were used as a sensor, obtaining a linear interval between 0.5 and 1 mM and 850 µA mM −1 mg −1 of sensitivity and 8.9 µM limit of quantification LOQ. These values are comparable to results previously reported. Moreover, Cu/Cu 2 O NPs were used as anode in a µFC with 0.96 V of cell voltage and 6.5 mA cm −2 and 1.03 mW cm −2 of current and power density, respectively. This performance is the highest currently reported for cholesterol application as an alternative fuel, and the first one reported for a microfluidic fuel cell system as far as is known. Results showed that the obtained Cu-based NPs presented an excellent performance for the dual application both µFC and sensor, which has potential applications in biomedicine and as an alternative energy source. Bifunctional ; Materials science ; X-ray photoelectron spectroscopy ; Nanoparticle ; Detection limit ; Catalysis ; Chemical engineering ; Anode ; Nanotechnology ; Electrode ; Chemistry ; Chromatography ; Organic chemistry ; Physical chemistry Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Kiran Vaddi | Olga Wodo
Source: Energies (article, 2022-06-23)
Field: Chemistry — Electrochemistry

Active Knowledge Extraction from Cyclic Voltammetry Cyclic Voltammetry (CV) is an electro-chemical characterization technique used in an initial material screening for desired properties and to extract information about electro-chemical reactions. In some applications, to extract kinetic information of the associated reactions (e.g., rate constants and turn over frequencies), CV curve should have a specific shape (for example an S-shape). However, often the characterization settings to obtain such curve are not known a priori. In this paper, an active search framework is defined to accelerate identification of characterization settings that enable knowledge extraction from CV experiments. Towards this goal, a representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either S-shape or not S-shape. Using an active search with BMS oracle, we report a linear target identification in a six-dimensional search space (comprised of thermodynamic, mass transfer, and solution variables as dimensions). Our framework has the potential to be a powerful virtual screening technique for molecular catalysts, bi-functional fuel cell catalysts, and other energy conversion and storage systems. Cyclic voltammetry ; A priori and a posteriori ; Chemical space ; Biological system ; Characterization (materials science) ; Computer science ; Virtual screening ; Representation (politics) ; Identification (biology) ; Chemistry ; Materials science ; Computational chemistry ; Nanotechnology ; Physical chemistry ; Molecular dynamics ; Electrode Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: Junshan Lai | Jiajing Li | Isaiah O. Betinol | Yutao Kuang | Jolene P. Reid
Source: ChemRxiv (preprint, 2022-08-09)
Field: Chemistry — Inorganic Chemistry

A Statistical Modeling Approach to Catalyst Generality Assessment in Enantioselective Synthesis Selecting the optimal catalyst to impart high levels of enantioselectivity in a new transformation is challenging because the ideal molecular requirements of the catalyst for one reaction do not always simply translate to another. In these reaction scenarios practitioners typically use the most general catalyst structure as a starting point for optimization. However, for many reaction systems and catalyst chemotypes the most general catalyst structure may be largely unknown presenting a significant limitation in catalyst application to new reaction space. Herein, we demonstrate that comprehensive statistical models can be applied to identify the most general catalyst for many chemical systems. These inclusive statistical models that encompass many reaction types can provide information about the relevant structural requirements necessary for high enantioselectivity across a broad reaction range. By validating this approach on diverse regions of organocatalyzed reaction space we discovered structurally distinct catalysts can in some cases provide similar levels of enantioselectivity. The second curious finding determined that the best and most popular catalyst systems may not be equivalent. Validation of this approach on a multi-catalytic dearomatization reaction resulted in the discovery that our general catalyst findings allowed for streamlined reaction development for highly complex transformations. Catalysis ; Generality ; Enantioselective synthesis ; Space (punctuation) ; Biochemical engineering ; Computer science ; Combinatorial chemistry ; Chemistry ; Organic chemistry ; Engineering Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis ; Surface Chemistry and Catalysis
Authors: Agus Paul Setiawan Kaban | Wahyu Mayangsari | Moch. Syaiful Anwar | Ahmad Maksum | Taufik Aditiyawarman | Johny Wahyuadi Soedarsono | Aga Ridhova | Rini Riastuti
Source: Eastern-European Journal of Enterprise Technologies (article, 2022-10-30)
Field: Materials Science — Materials Chemistry

Unraveling the study of liquid smoke from rice husks as a green corrosion inhibitor in mild steel under 1 M HCl The work provides the more comprehensive development of Liquid Smoke from Rice Husks Ash (RHA). Notably, the study focuses on the interaction between the primary molecules of inhibitor and mild steel, including thermodynamic calculation and surface treatment upon addition of inhibitor. The electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PP) characterization were utilized to evaluate the anticorrosion of RHA. The Raman Spectroscopy pre and post-addition of RHA’s inhibitor were used to compare the adsorbed functional group of inhibitors. Moreover, the thermodynamic calculation of the inhibitor’s adsorption determines the types of adsorption of the inhibitor. As a result of the adsorption process, the Scanning Electronic Microscope-Energy Dispersive X-Ray (SEM-EDX) aided by The Atomic Force Microscopy (AFM) and Contact Angle Test was implemented to unveil the surface treatment and the change of elemental composition after the addition of an 80 ppm inhibitor. The PP and EIS results show a significant depression of the current density at ‒2.75 μA·cm2 in 80 ppm solution with the highest inhibition efficiency of 99.82 %. The superior inhibition correlates to the adsorption of Si–OH, C–C, C–O–C, &gt;C=O, complex structure, and –OH at wavenumber 458, 662, 1095, 1780, and 3530 cm-1. The LS shows a significant surface area of protection of 0.9982 and high adsorption constant (Kads) at 11.648. The calculated ΔGads of ‒6.59 kJ/mol unveils the chemisorption in nature. At the same time, a combination of 20 and 80 ppm solution is predicted adsorbed horizontally to reduce the contact between the solution and substrate, as shown in SEM and AFM results. It also increases the contact angle and their corresponding hydrophobicity Adsorption ; Chemisorption ; Dielectric spectroscopy ; Raman spectroscopy ; Chemistry ; Corrosion inhibitor ; Contact angle ; Corrosion ; Electrochemistry ; Analytical Chemistry (journal) ; Nuclear chemistry ; Materials science ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Composite material ; Electrode Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Rayan Zaiter | Mohammad Kassem | Daniele Fontanari | Anton Sokolov | Takeshi Usuki | Maria Bokova | Alex C. Hannon | Chris J. Benmore | Fabrice Cousin | И.А. Ожередов | Е. Бычков
Source: Materials & Design (article, 2022-03-15)
Field: Materials Science — Materials Chemistry

Unexpected role of metal halides in a chalcogenide glass network Chalcogenide glasses containing light alkali and Group 11 (Cu, Ag) halides MY (Y = Cl, Br, I) exhibit high ionic conductivity while their heavy alkali counterparts show promising properties for optical applications. The structural role of metal halides in chalcogenide glass networks remains either essentially unknown (alkali halides) or controversial (CuY, AgY). In addition, possible structural changes as a function of MY content have not been reported. Using pulsed neutron diffraction, high-energy X-ray scattering, Raman spectroscopy and FPMD modeling, we show unexpected role of silver halides as unconventional modifiers taking two contrasting glass compositions: (AgI)0.1(As2S3)0.9 (critical percolation domain) and (AgBr)0.5(As2S3)0.5 (modifier-controlled region) as an example. The latter alloy seems to be a promising precursor for thermoelectric applications. The deep insight into the glass structure on the short- and intermediate-range scale, including an enhanced chemical disorder, enables a rational design of these functional materials. Chalcogenide ; Halide ; Materials science ; Alkali metal ; Raman spectroscopy ; Ionic conductivity ; Ionic bonding ; Chalcogenide glass ; Inorganic chemistry ; Chemical engineering ; Physical chemistry ; Ion ; Optics ; Optoelectronics ; Chemistry ; Organic chemistry Phase-change materials and chalcogenides ; Glass properties and applications ; Solid-state spectroscopy and crystallography
Authors: Chidambaram P.K | Chinnasami Sivaji | Ashwini Murugan | M Ramachandran
Source: Journal on Materials and its Characterization (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Performance Analysis of Materials Selection Using Weighted Product Method (WPM) Materials selection. The choice of materials is a sorted one is the process by which engineers remove one or a small number of items that can be found most suitable. Successful engineering design material selection is critical to the process. As much as possible your preparation must be strong and durable that’s what you want. Security to consider there are also implications. You see, with poor material, there are many more dangerous failures arising from examination a very common occurrence in industries. The weighted product method is a multi-criteria decision-making process there are many alternatives, and based on several criteria we must determine the best alternative. Alternative: Specific strength (Mpa), Specific modulus (Gpa), Corrosion resistance, Cost category. Evaluation Preference: AISI 1020, AISI 1040, ASTM A242, AISI 4130, AISI 316. From the result, it is seen that AISI 4130 is got the first rank whereas is ASTM A242 is having the lowest rank. As a result, AISI 4130 is ranked first, while ASTM A242 is ranked lowest. Material selection ; Selection (genetic algorithm) ; Product (mathematics) ; Rank (graph theory) ; Preference ; Process (computing) ; Computer science ; Forensic engineering ; Engineering ; Materials science ; Mathematics ; Statistics ; Composite material ; Machine learning Material Selection and Properties ; Management and Optimization Techniques ; Manufacturing Process and Optimization
Authors: Sang-Il Kim | Dug-Joong Kim | Do-Hyung Kim | Dongmin Jang | Jinwoo Jang | Seung-Yeong Lee | Hak-Sung Kim
Source: International Journal of Precision Engineering and Manufacturing-Green Technology (article, 2022-11-17)
Field: Materials Science — Surfaces, Coatings and Films

Corrosion Lifetime Estimation of Printed Circuit Board in Marine Atmosphere Environment Using Multiphysics Simulation Printed circuit board ; Corrosion ; Multiphysics ; Atmosphere (unit) ; Environmental science ; On board ; Component (thermodynamics) ; Marine engineering ; Computer science ; Materials science ; Engineering ; Structural engineering ; Metallurgy ; Meteorology ; Aerospace engineering ; Finite element method ; Electrical engineering ; Physics Surface Modification and Superhydrophobicity ; Engineering Applied Research ; Recycling and Waste Management Techniques
Authors: Rui Wang | Lingyu Zhu | Subrahmanyam Pattamatta | David J. Srolovitz | Zhaoxuan Wu
Source: arXiv (Cornell University) (preprint, 2022-09-21)
Field: Materials Science — Materials Chemistry

The Taming of the Screw: Dislocation Cores in BCC Metals and Alloys Body-centred cubic (BCC) transition metals tend to be brittle at low temperatures, posing significant challenges in processing and major concerns for damage tolerance. The brittleness is largely dictated by the screw dislocation core; the nature and control of which remain a puzzle after nearly a century. Here, we introduce a physics-based material index \{chi}, the energy difference between BCC and face-centred-cubic structures, that guides manipulation of core properties. The lattice friction and nucleation barrier have near-linear scaling with \{chi} and the core transforms from non-degenerate to degenerate when \{chi} drops below a threshold in BCC alloys. \{chi} is related to solute valence electron concentrations and can be quantitatively predicted by first-principles calculations, providing a robust path for design of ductile and tough BCC alloys. Materials science ; Condensed matter physics ; Brittleness ; Nucleation ; Dislocation ; Scaling ; Degenerate energy levels ; Lattice (music) ; Valence (chemistry) ; Metallurgy ; Physics ; Thermodynamics ; Geometry Microstructure and mechanical properties ; Advanced Materials Characterization Techniques ; Aluminum Alloy Microstructure Properties