jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xue Han | Sihai Yang⧫
Source: Angewandte Chemie International Edition (review, 2023-01-31)
Field: Chemistry — Inorganic Chemistry

Molecular Mechanisms behind Acetylene Adsorption and Selectivity in Functional Porous Materials Since its first industrial production in 1890s, acetylene has played a vital role in manufacturing a wide spectrum of materials. Although current methods and infrastructures for various segments of acetylene industries are well-established, with emerging functional porous materials that enabled desired selectivity toward target molecules, it is of timely interest to develop new efficient technologies to promote safer acetylene processes with a higher energy efficiency and lower carbon footprint. In this Minireview, we, from the perspective of materials chemistry, review state-of-the-art examples of advanced porous materials, namely metal-organic frameworks and decorated zeolites, that have been applied to the purification and storage of acetylene. We also discuss the challenges on the roadmap of translational research in the development of new solid sorbent-based separation technologies and highlight areas which require future research efforts. Acetylene ; Nanotechnology ; Sorbent ; Materials science ; Porosity ; Adsorption ; Carbon footprint ; Process engineering ; Chemistry ; Engineering ; Organic chemistry ; Greenhouse gas Metal-Organic Frameworks: Synthesis and Applications ; Synthesis and Characterization of Pyrroles ; Porphyrin and Phthalocyanine Chemistry
Authors: Jelena Vasić | Dušan Dimić | Marko Antonijević | Edina Avdović | Dejan Milenković | Đura Nakarada | Jasmina M. Dimitrić Marković | Maja Molnar | Melita Lončarić | Drago Bešlo | Zoran Marković
Source: International Journal of Molecular Sciences (article, 2023-07-23)
Field: Chemistry — Organic Chemistry

The Electronic Effects of 3-Methoxycarbonylcoumarin Substituents on Spectral, Antioxidant, and Protein Binding Properties Coumarin derivatives are a class of compounds with pronounced biological activities that depend primarily on the present substituents. Four 3-methoxycarbonylcoumarin derivatives with substituents of different electron-donating/electron-withdrawing abilities (Br, NO<sub>2</sub>, OH, and OMe) were investigated structurally by NMR, IR, and UV-VIS spectroscopies and density functional theory methods. The appropriate level of theory (B3LYP-D3BJ/6-311++G(d,p) was selected after comparing similar compounds' experimental and theoretical structural parameters. The natural bond orbital and quantum theory of atoms in molecules were employed to investigate the intramolecular interactions governing stability. The electronic effects of substituents mostly affected the aromatic ring that the substituents are directly attached to. The antioxidant properties were investigated by electron paramagnetic resonance spectroscopy towards HO<sup>•</sup>, and the percentages of reduction were between 13% (<b>6-Br</b>) and 23% (<b>6-OMe</b>). The protein binding properties towards transport proteins were assessed by spectrofluorimetry, molecular docking, and molecular dynamics (MD). The experimentally determined binding energies were well reproduced by molecular docking, showing that the spontaneity of ibuprofen binding was comparable to the investigated compounds. The flexibility of HSA in MD simulations depended on the substituents. These results proved the importance of electronic effects for the protein binding affinities and antioxidant properties of coumarin derivatives. Chemistry ; Intramolecular force ; Density functional theory ; Docking (animal) ; Computational chemistry ; Electron paramagnetic resonance ; Molecule ; Natural bond orbital ; Binding energy ; HOMO/LUMO ; Stereochemistry ; Organic chemistry Free Radicals and Antioxidants ; Photochemistry and Electron Transfer Studies ; Metal complexes synthesis and properties
Authors: Arijit Halder | Ryan A. Klein | Sarah Shulda | Gavin A. McCarver | Philip A. Parilla | Hiroyasu Furukawa | Craig M. Brown | C. Michael McGuirk
Source: Journal of the American Chemical Society (article, 2023-03-30)
Field: Chemistry — Inorganic Chemistry

Multivariate Flexible Framework with High Usable Hydrogen Capacity in a Reduced Pressure Swing Process Step-shaped adsorption-desorption of gaseous payloads by flexible metal-organic frameworks can facilitate the delivery of large usable capacities with significantly reduced energetic penalties. This is desirable for the storage, transport, and delivery of H<sub>2</sub>, as prototypical adsorbents require large swings in pressure and temperature to achieve usable capacities approaching their total capacities. However, the weak physisorption of H<sub>2</sub> typically necessitates undesirably high pressures to induce the framework phase change. As de novo design of flexible frameworks is exceedingly challenging, the ability to intuitively adapt known frameworks is required. We demonstrate that the multivariate linker approach is a powerful tool for tuning the phase change behavior of flexible frameworks. In this work, 2-methyl-5,6-difluorobenzimidazolate was solvothermally incorporated into the known framework CdIF-13 (<i>sod</i>-Cd(benzimidazolate)<sub>2</sub>), resulting in the multivariate framework <i>sod</i>-Cd(benzimidazolate)<sub>1.87</sub>(2-methyl-5,6-difluorobenzimidazolate)<sub>0.13</sub> (ratio = 14:1), which exhibited a considerably reduced stepped adsorption threshold pressure while maintaining the desirable adsorption-desorption profile and capacity of CdIF-13. At 77 K, the multivariate framework exhibits stepped H<sub>2</sub> adsorption with saturation below 50 bar and minimal desorption hysteresis at 5 bar. At 87 K, saturation of step-shaped adsorption occurs by 90 bar, with hysteresis closing at 30 bar. These adsorption-desorption profiles enable usable capacities in a mild pressure swing process above 1 mass %, representing 85-92% of the total capacities. This work demonstrates that the desirable performance of flexible frameworks can be readily adapted through the multivariate approach to enable efficient storage and delivery of weakly physisorbing species. Adsorption ; Desorption ; USable ; Physisorption ; Saturation (graph theory) ; Chemistry ; Bar (unit) ; Metal-organic framework ; Hysteresis ; Pressure swing adsorption ; Multivariate statistics ; Process (computing) ; Chemical engineering ; Nanotechnology ; Process engineering ; Computer science ; Materials science ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; MXene and MAX Phase Materials ; Hydrogen Storage and Materials
Authors: Paweł Kustroń | Marcin Korzeniowski | Adam Sajbura | Tomasz Piwowarczyk | Paweł Kaczyński | Paweł Sokołowski
Source: Applied Sciences (article, 2023-08-05)
Field: Materials Science — Materials Chemistry

Development of High-Power Ultrasonic System Dedicated to Metal Powder Atomization The article presents the results of the development works and research on the atomization process carried out using two prototype high-power ultrasonic systems. Ultrasonic systems have been designed to develop a new metal powder production process; these materials are increasingly used in modern manufacturing processes such as additive technologies or spraying and surfacing processes. The preliminary studies presented in the article were conducted for water to assess the effectiveness of both systems and to verify the theoretical and structural assumptions. In ultrasonic atomization, the ultrasonic wave causes the phenomenon of cavitation, which leads to the overcoming of the surface tension forces of the liquid and its disintegration into fine droplets. The important parameters that affect the properties of the produced droplets include, among others, the frequency of the sonotrode vibrations and the amplitude of the vibrations of the working plate. As part of the research, the paper presents the process of selecting the sonotrode geometry for two different values of the transducer’s natural frequencies (20 kHz and 70 kHz). In the design process, the finite element method was used to perform a harmonic analysis and develop the geometry of the sonotrode and the working plate. The design assumptions and the design process were presented. The modeled and then ultrasonic waveguides were verified experimentally by measuring the deflection distribution on the working plate surface using a high-precision laser displacement sensor. Then, the work ultimately resulted in conducting atomization tests of water. The obtained aerosols and the mechanism of their formation were studied using a high-speed camera. Finally, using Matlab R2020a software and image analysis scripts, it was possible to analyze the droplet size distribution generated by both systems. It was observed that 50% of the produced droplets were in the range of 35–55 μm for a 20 kHz system, while for a 70 kHz system it was 10–25 μm, which is a very satisfying distribution in terms of metal powder atomization. Ultrasonic sensor ; Mechanical engineering ; Deflection (physics) ; Materials science ; Vibration ; Acoustics ; Transducer ; Finite element method ; Engineering ; Structural engineering ; Optics Ultrasound and Cavitation Phenomena ; Additive Manufacturing and 3D Printing Technologies ; Electrohydrodynamics and Fluid Dynamics
Authors: Maria Politi | Fábio Baum | Kiran Vaddi | Edwin Antonio | Sonya Vasquez | Brittany P. Bishop | Nadya Peek | Vincent C. Holmberg | Lilo D. Pozzo
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A high-throughput workflow for the synthesis of CdSe nanocrystals using a sonochemical materials acceleration platform A sonochemical Materials Acceleration Platform was implemented to synthesize CdSe nanocrystals under 625 unique conditions (in triplicate) in less than 6 weeks. The modularity of the workflow is adaptable to a variety of applications. Workflow ; Modularity (biology) ; Nanocrystal ; Acceleration ; Throughput ; Variety (cybernetics) ; Materials science ; Nanotechnology ; Computer science ; Physics ; Operating system ; Database ; Artificial intelligence Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques ; Laser-Ablation Synthesis of Nanoparticles
Authors: Yannick Ureel | Maarten R. Dobbelaere | Yi Ouyang | Kevin De Ras | Maarten K. Sabbe | Guy Marin | Kevin M. Van Geem
Source: Engineering (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Active Machine Learning for Chemical Engineers: A Bright Future Lies Ahead! By combining machine learning with the design of experiments, thereby achieving so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering. While active machine learning algorithms are maturing, their applications are falling behind. In this article, three types of challenges presented by active machine learning—namely, convincing the experimental researcher, the flexibility of data creation, and the robustness of active machine learning algorithms—are identified, and ways to overcome them are discussed. A bright future lies ahead for active machine learning in chemical engineering, thanks to increasing automation and more efficient algorithms that can drive novel discoveries. Machine learning ; Active learning (machine learning) ; Artificial intelligence ; Computer science ; Robustness (evolution) ; Flexibility (engineering) ; Computational learning theory ; Hyper-heuristic ; Online machine learning ; Automation ; Robot learning ; Engineering ; Robot ; Mobile robot Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Satyam Bhatti | Habib Ullah Manzoor | Bruno Michel | Ruy S. Bonilla | Richard Abrams | Ahmed Zoha | Sajjad Hussain | Rami Ghannam
Source: Advanced Energy and Sustainability Research (review, 2023-08-23)
Field: Materials Science — Materials Chemistry

Revolutionizing Low‐Cost Solar Cells with Machine Learning: A Systematic Review of Optimization Techniques Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low‐cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost‐effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low‐cost solar cell research and development landscape. The review encompasses a variety of ML approaches, such as Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs), which have proven effective in boosting the efficiency, stability, and affordability of solar cells. The findings of this review indicate that GPR combined with BO is the most promising method for developing low‐cost solar cells. These techniques can significantly speed up the discovery of new PV materials and structures while enhancing the efficiency and stability of low‐cost solar cells. The review concludes with insights on the challenges, prospects, and future directions of ML in low‐cost solar cell research and development. Photovoltaic system ; Bayesian optimization ; Boosting (machine learning) ; Computer science ; Artificial intelligence ; Machine learning ; Solar cell ; Biochemical engineering ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Photovoltaic System Optimization Techniques ; Chalcogenide Semiconductor Thin Films
Authors: Henry Heberle | Linlin Zhao | Sebastian Schmidt | Thomas Wolf | Julian Heinrich
Source: Journal of Cheminformatics (article, 2023-01-06)
Field: Materials Science — Materials Chemistry

XSMILES: interactive visualization for molecules, SMILES and XAI attribution scores Data scientists can use XSMILES to understand their models' behavior and compare multiple modeling approaches. The tool provides a set of parameters to adapt the visualization to users' needs and it can be integrated into different platforms. We believe XSMILES can support data scientists to develop, improve, and communicate their models by making it easier to identify patterns and compare attributions through interactive exploratory visualization. Computer science ; Visualization ; Attribution ; Property (philosophy) ; Set (abstract data type) ; Context (archaeology) ; Atom (system on chip) ; Information retrieval ; Data mining ; Psychology ; Programming language ; Epistemology Machine Learning in Materials Science ; Data Visualization and Analytics ; Scientific Computing and Data Management
Authors: Muhammad Shahriyar Islam | Kah‐Yoong Chan | Gregory Soon How Thien | Pei-Ling Low | Chu-Liang Lee | Sew Kin Wong | Ervina Efzan Mhd Noor | Benedict Wen-Cheun Au | Zi-Neng Ng
Source: Polymers (article, 2023-08-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Performances of Polymer-Dispersed Liquid Crystal Films for Smart Glass Applications Polymer-dispersed liquid crystal (PDLC) film is an active smart film penetrating the market due to its unique functionalities. These functional characteristics include switchable tint capabilities, which shield building residents from the sun's harmful ultraviolet (UV) rays, improve energy-saving features, and produce higher cost-efficiency. Although PDLC films are promising in several applications, there is still ambiguity on the performance of PDLC films. Particularly, the sizing effects' (such as film thickness and area) correlation with visible light transmission (VLT), ultraviolet rejection (UVR), infrared rejection (IRR), light intensity, current consumption, and apparent power consumption is not well understood. Therefore, this study investigated the sizing effects of PDLC films, including the thickness effect on VLT, UVR, IRR, light intensity, and area influence on current and apparent power consumptions. The varying applied voltage effect on the light transmittance of the PDLC film was also effectively demonstrated. A 0.1 mm PDLC film was successfully presented as a cost-efficient film with optimal parameters. Consequently, this study paves the way for a clearer understanding of PDLC films (behavior and sizing effects) in implementing economic PDLC films for large-scale adoption in commercial and residential premises. Materials science ; Transmittance ; Liquid crystal ; Ultraviolet ; Optoelectronics ; Sizing ; Polymer ; Liquid-crystal display ; Optics ; Composite material Liquid Crystal Research Advancements ; Semiconductor Lasers and Optical Devices ; Advanced Materials and Mechanics
Authors: Shuxin Zheng | Jiyan He | Chang Liu | Yu Shi | Ziheng Lu | Weitao Feng | Fusong Ju | Jiaxi Wang | Jianwei Zhu | Yaosen Min | He Zhang | Shidi Tang | Hongxia Hao | Peiran Jin | Chi Chen | Frank Noé | Haiguang Liu | Tie‐Yan Liu
Source: arXiv (Cornell University) (preprint, 2023-06-08)
Field: Materials Science — Materials Chemistry

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science. Computer science ; Molecular dynamics ; Deep learning ; Sampling (signal processing) ; Artificial intelligence ; Simulated annealing ; Statistical physics ; Machine learning ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Sean Nations | Tarak Nandi | Ali Ramazani | Shengnian Wang | Yuhua Duan
Source: Journal of Energy Storage (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Metal hydride composition-derived parameters as machine learning features for material design and H2 storage Electronegativity ; Random forest ; Hydrogen storage ; Hydride ; Ensemble learning ; Gradient boosting ; Hydrogen ; Computer science ; Chemistry ; Machine learning ; Materials science Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Mahsa Golmohammadi | Masoud Aryanpour
Source: Materials Today Communications (article, 2023-01-25)
Field: Materials Science — Materials Chemistry

Analysis and evaluation of machine learning applications in materials design and discovery Exploit ; Field (mathematics) ; Computer science ; Focus (optics) ; Work (physics) ; Data science ; Machine learning ; Artificial intelligence ; Sample (material) ; Management science ; Mechanical engineering ; Engineering ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Fuel Cells and Related Materials
Authors: Xiaowei Li | Jinhui Wang | Jiaxuan Ma | Ting Yang | Shuai Yuan | Xiaoyu Liu | Yunduo Feng | Peipeng Jin
Source: Journal of Materials Research and Technology (article, 2023-03-16)
Field: Materials Science — Biomaterials

Thermal deformation behavior of Mg–3Sn–1Mn alloy based on constitutive relation model and artificial neural network The flow stress behavior of Mg–3Sn–1Mn alloy during thermal compression deformation was systematically studied. The thermal compression simulation experiment was carried out at different deformation temperatures and different strain rates in the range of 523–673 K and 0.001-1s−1, respectively. It is found that at low temperatures and high strain rates, a large number of twins were generated at the initial stage of thermal deformation, causing an increase in the corresponding flow stress, which makes the traditional constitutive relation model insensitive to predicting the thermal deformation behavior of Magnesium (Mg) alloys with twinning effect. To better evaluate the rheological behavior of Mg alloys, an artificial neural network (ANN) model based on a feedforward and back-propagation algorithm was developed to predict the thermal deformation behavior of Mg–3Sn–1Mn alloy affected by twinning phenomena. The inputs of the model were deformation temperature, strain rate and strain, the output was the flow stress. The comparative evaluation of the obtained results using statistical standard R2 and relative error R¯. The correlation coefficients predicted by the constitutive model were 0.964 and 0.869 at low and high stress levels, respectively. And the correlation coefficient of the neural network predictive model was 0.992. The result shows that the trained ANN is more accurate than the traditional constitutive relation model in predicting the thermal deformation behavior with the twinning effect. Materials science ; Flow stress ; Crystal twinning ; Deformation (meteorology) ; Constitutive equation ; Strain rate ; Stress (linguistics) ; Compression (physics) ; Alloy ; Composite material ; Metallurgy ; Thermodynamics ; Microstructure ; Finite element method ; Physics Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics