jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peng Yu | Dajun Ren | Xiaoqing Zhang | Shuqin Zhang | Xiangyi Gong | Yongliang Chen | Lei Zhang | Pu Liu
Source: Journal of Applied Polymer Science (article, 2024-12-26)
Field: Chemistry — Analytical Chemistry

Synthesis and Selective Adsorption Performance of Cu(II) Imprinted SA/CMC/AM Microspheres in Multi‐Ion Coexistence Environments ABSTRACT To achieve efficient and selective removal of Cu 2+ from a multi‐ion coexistence environment, Cu 2+ ‐imprinted SA/CMC/AM microspheres (IMSCA) were synthesized. The adsorption capacity and removal efficiency of the material under different preparation and adsorption conditions were investigated. The surface morphology and functional groups of IMSCA were characterized and analyzed using SEM, FTIR, and XRD. SEM images revealed a relatively smooth surface of IMSCA with specific pores and a relatively uniform structure. Special peaks appeared in FTIR spectrum of IMSCA, indicating the possibility of the imprinting process and the presence of imprinting sites. The semicrystalline structure exhibited by the imprinted microspheres in XRD characterization further reinforces the likelihood of imprinting sites. In terms of adsorption kinetics, the adsorption process followed a pseudo–second‐order kinetic model, suggesting that chemical adsorption was dominant during the IMSCA adsorption process. In terms of adsorption isotherms, the Langmuir model better fitted the experimental data, suggesting that the adsorption process was monolayer adsorption. The imprinted microspheres exhibited better selective adsorption in mixed solutions of multiple metal ions. After 10 adsorption–desorption cycles, the adsorption capacity of IMSCA remained at approximately 90%. Compared with other imprinted materials, IMSCA exhibits a higher adsorption capacity, a faster elution rate, and superior reusability. Adsorption ; Fourier transform infrared spectroscopy ; Crystallinity ; Chemical engineering ; Molecular imprinting ; Monolayer ; Selective adsorption ; Desorption ; Langmuir adsorption model ; Materials science ; Selectivity ; Kinetics ; Imprinting (psychology) ; Metal ions in aqueous solution ; Chemistry ; Ion ; Catalysis ; Organic chemistry ; Nanotechnology ; Composite material Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal ; Carbon and Quantum Dots Applications
Authors: Claudia Alice Crișan | Horațiu Vermeșan
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Natural gums as green corrosion inhibitors for mild steel In this study the corrosion inhibition efficiency of Guar gum and Arabic gum are investigated on mild steel in concentrated chlorohydric acid. Weight loss and electrochemical methods, potentiondynamic polarization and electrochemical impedance spectroscopy were used to determine the optimum concentration of each gum. The results show that there is a direct correlation between concentration of inhibitor and corrosion inhibition efficiency, therefore the best efficiency was obtained at the highest concentration, 3.3 g/l. When comparing the two polysaccharides Guar gums has a better inhibition effect than Arabic gum at the same concentration. The inhibition mechanism was analysed by fitting the electrochemical results to an adsorption isotherm. This research offers a green alternative to the synthetic inhibitors used in today’s industry during acid pickling in the manufacturing process of mild steel. Both substances are naturally obtained and considered green corrosion inhibitors due to their negligible impact on the environment. Future research on the topic will include more natural compounds tests in the same experimental conditions and comparing them to market inhibitors in terms of efficiency. Natural (archaeology) ; Corrosion ; Metallurgy ; Materials science ; Chemistry ; Geography ; Archaeology Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Metal Extraction and Bioleaching
Authors: Hamid Reza Abbasi | Masoud Babaei | Arash Rabbani | Constantinos Theodoropoulos
Source: ChemRxiv (preprint, 2024-02-20)
Field: Materials Science — Materials Chemistry

Significance of advanced porous configurations in enhancing solid oxide fuel cell performance: a comparative analysis between conventional porous anodes and state-of-the-art variants Three different configurations of the porous anode of solid oxide fuel cell (SOFC) have been computationally synthesized and analyzed with respect to their electrochemical performance and topological properties. In addition to the conventional Ni/YSZ configuration of SOFC, which is usually fabricated using screen-printing, two novel designs (fibrous and lattice microstructures) are synthesized and tested. For each class of configurations, a numerical framework has been developed that can generate microstructures with specified topological characteristics. Finite volume method is employed to investigate transport phenomena and electrochemical reactions occurring within the active layer of porous anode cermet. Utilization of synthetically generated microstructures enables the direct study of certain microstructural and image acquisition attributes that may be inherently fixed within the confines and limits of a destructive 3D imaging. For instance, results showed how the choice of voxel size can greatly alter the measured Triple Phase Boundary (TPB) and the electrochemical performance. The investigation further demonstrated that lattice and fibrous structures produce an increased current density of 4.8 and 1.4 times, respectively, compared to the established conventional configuration. Finally, gradient microstructures have demonstrated the capacity to enhance electrochemical performance when subjected to careful fabrication methodologies. Porosity ; Solid oxide fuel cell ; Anode ; Materials science ; Fuel cells ; Oxide ; Porous medium ; Chemical engineering ; State (computer science) ; Nanotechnology ; Computer science ; Engineering ; Chemistry ; Composite material ; Metallurgy ; Electrode ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Oishika Jash | Anand Srivastava | Sundaram Balasubramanian
Source: ACS Omega (article, 2024-07-03)
Field: Chemistry — Inorganic Chemistry

HP35 Protein in the Mesopore of MIL-101(Cr) MOF: A Model to Study Cotranslocational Unfolding The immobilization of enzymes in metal-organic framework (MOF) cages is important in biotechnology. In this context, the mechanism of translocation of proteins through the cavities of the MOF and the roles played by confinement and MOF chemistry in giving rise to stable protein intermediates that are otherwise transiently populated in the physiological environment are important questions to be addressed. These unexplored aspects are examined with villin headpiece (HP35) as a model protein confined within a mesopore of MIL-101(Cr) using molecular dynamics simulations. At equilibrium, the protein is located farther from the center of the cavity and closer to the MOF surface. Molecular interactions with the MOF partially unfold helix-1 at its N-terminus. Umbrella sampling simulations inform the range of conformations that HP35 undertakes during translocation from one cavity to another and associated changes in free energy. Relative to its equilibrium state within the cavity, the free energy barrier for the unfolded protein at the cage window is estimated to be 16 kcal/mol. This study of MOF-based protein conformation can also be a general approach to observing intermediates in folding-unfolding pathways. Mesoporous material ; Chemical engineering ; Materials science ; Chemistry ; Engineering ; Biochemistry Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; Machine Learning in Materials Science
Authors: Shinyoung Kang | Jihan Kim
Source: arXiv (Cornell University) (preprint, 2024-05-20)
Field: Materials Science — Materials Chemistry

Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing In this study, we explore the potential of using quantum natural language processing (QNLP) to inverse design metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 450 hypothetical MOF structures consisting of 3 topologies, 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and $CO_{2}$ Henry's constant values. We then compare various QNLP models (i.e. the bag-of-words, DisCoCat (Distributional Compositional Categorical), and sequence-based models) to identify the most effective approach to process the MOF dataset. Using a classical simulator provided by the IBM Qiskit, the bag-of-words model is identified to be the optimum model, achieving validation accuracies of 88.6% and 78.0% for binary classification tasks on pore volume and $CO_{2}$ Henry's constant, respectively. Further, we developed multi-class classification models tailored to the probabilistic nature of quantum circuits, with average test accuracies of 92% and 80% across different classes for pore volume and $CO_{2}$ Henry's constant datasets. Finally, the performance of generating MOF with target properties showed accuracies of 93.5% for pore volume and 87% for $CO_{2}$ Henry's constant, respectively. Although our investigation covers only a fraction of the vast MOF search space, it marks a promising first step towards using quantum computing for materials design, offering a new perspective through which to explore the complex landscape of MOFs. Inverse ; Natural (archaeology) ; Computer science ; Linguistics ; Geology ; Philosophy Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Alexander New | Nam Q. Le | Michael J. Pekala | Christopher D. Stiles
Source: arXiv (Cornell University) (preprint, 2024-08-30)
Field: Materials Science — Materials Chemistry

Self-supervised learning for crystal property prediction via denoising Accurate prediction of the properties of crystalline materials is crucial for targeted discovery, and this prediction is increasingly done with data-driven models. However, for many properties of interest, the number of materials for which a specific property has been determined is much smaller than the number of known materials. To overcome this disparity, we propose a novel self-supervised learning (SSL) strategy for material property prediction. Our approach, crystal denoising self-supervised learning (CDSSL), pretrains predictive models (e.g., graph networks) with a pretext task based on recovering valid material structures when given perturbed versions of these structures. We demonstrate that CDSSL models out-perform models trained without SSL, across material types, properties, and dataset sizes. Property (philosophy) ; Artificial intelligence ; Computer science ; Supervised learning ; Machine learning ; Noise reduction ; Pattern recognition (psychology) ; Artificial neural network ; Philosophy X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Chenghan Li | Or Sharir | Shunyue Yuan | Garnet Kin‐Lic Chan
Source: arXiv (Cornell University) (preprint, 2024-02-19)
Field: Materials Science — Surfaces, Coatings and Films

Image Super-resolution Inspired Electron Density Prediction Drawing inspiration from the domain of image super-resolution, we view the electron density as a 3D grayscale image and use a convolutional residual network to transform a crude and trivially generated guess of the molecular density into an accurate ground-state quantum mechanical density. We find that this model outperforms all prior density prediction approaches. Because the input is itself a real-space density, the predictions are equivariant to molecular symmetry transformations even though the model is not constructed to be. Due to its simplicity, the model is directly applicable to unseen molecular conformations and chemical elements. We show that fine-tuning on limited new data provides high accuracy even in challenging cases of exotic elements and charge states. Our work suggests new routes to learning real-space physical quantities drawing from the established ideas of image processing. Resolution (logic) ; Electron ; Image (mathematics) ; Electron density ; Computer science ; Artificial intelligence ; Computer vision ; Physics ; Nuclear physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques
Authors: Sami M. Ibn Shamsah
Source: PLoS ONE (article, 2024-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetocaloric effect modeling of dysprosium-transition metal based intermetallic alloys for magnetic refrigeration application using hybrid genetic algorithm based support vector regression intelligent method Intermetallic alloy containing rare earth dysprosium ions with the associated unfilled 4f shell electrons and sub-lattice of 3d-transition metal, results into fascinating magnetic properties which are useful for green refrigeration technological application. Magnetocaloric effect remains the fundamental principle upon which magnetic refrigeration technology is based while this cooling technology has advantages of cost effectiveness, high efficiency and environmental friendliness as compared with the existing conventional gas compression systems. Maximum magnetic entropy change (which controls the hugeness of magnetocaloric effect) of intermetallic alloy Dy-T-X (where T = transition metal and X = any other metal or nonmetal) is modeled in this work using hybrid genetic algorithm based support vector regression (GSVR) computational intelligent method with applied magnetic field, ionic concentration and ionic radii descriptors. The developed GSVR-G model with kernel Gaussian function outperforms GSVR-P model with polynomial function with improvement of 85.23%, 78.82% and 78.67% on the basis of the computed correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) on testing sample, respectively. The developed model further investigates the influence of applied external magnetic field on magnetocaloric effect of DyCuAl intermetallic alloy. The developed models in this work circumvent experimental challenges of magnetocaloric effect determination while the recorded precision of the developed model further opens doors for possible exploration of these intermetallic compounds for addressing environmental challenges associated with the present system of cooling. Magnetic refrigeration ; Intermetallic ; Materials science ; Algorithm ; Dysprosium ; Support vector machine ; Thermodynamics ; Taguchi methods ; Mean squared error ; Alloy ; Computer science ; Magnetic field ; Machine learning ; Mathematics ; Metallurgy ; Physics ; Magnetization ; Statistics ; Composite material Magnetic and transport properties of perovskites and related materials ; Optical properties and cooling technologies in crystalline materials
Authors: Antonija Grubišić‐Čabo | Marcos H. D. Guimarães | D. Afanasiev | José Aguilar | Irene Aguilera | Mazhar N. Ali | Semonti Bhattacharyya | Yaroslav M. Blanter | Rixt Bosma | Zhiyuan Cheng | Zhiying Dan | Saroj P. Dash | Joaquín Medina Dueñas | J. Fernández‐Rossier | Marco Gibertini | Sergii Grytsiuk | Maurits J. A. Houmes | Anna Isaeva | Chrystalla Knekna | Arnold H. Kole | Samer Kurdi | José L. Lado | Samuel Mañas‐Valero | J. M. J. Lopes | Damiano Marian | MengXing Na | Falk Pabst | Sergio Barquero Pierantoni | Mexx Regout | Riccardo Reho | Malte Rösner | David Sanz | Toeno van der Sar | Jagoda Sławińska | Matthieu J. Verstraete | Muhammad Waseem | Herre S. J. van der Zant | Zeila Zanolli | David Soriano‐Paños
Source: arXiv (Cornell University) (preprint, 2024-12-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Roadmap on Quantum Magnetic Materials Fundamental research on two-dimensional (2D) magnetic systems based on van der Waals materials has been gaining traction rapidly since their recent discovery. With the increase of recent knowledge, it has become clear that such materials have also a strong potential for applications in devices that combine magnetism with electronics, optics, and nanomechanics. Nonetheless, many challenges still lay ahead. Several fundamental aspects of 2D magnetic materials are still unknown or poorly understood, such as their often-complicated electronic structure, optical properties, and magnetization dynamics, and their magnon spectrum. To elucidate their properties and facilitate integration in devices, advanced characterization techniques and theoretical frameworks need to be developed or adapted. Moreover, developing synthesis methods which increase critical temperatures and achieve large-scale, high-quality homogeneous thin films is crucial before these materials can be used for real-world applications. Therefore, the field of 2D magnetic materials provides many challenges and opportunities for the discovery and exploration of new phenomena, as well as the development of new applications. This Roadmap presents the background, challenges, and potential research directions for various relevant topics in the field on the fundamentals, synthesis, characterization, and applications. We hope that this work can provide a strong starting point for young researchers in the field and provide a general overview of the key challenges for more experienced researchers. Quantum ; Physics ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Magnetic properties of thin films ; Advanced Physical and Chemical Molecular Interactions
Authors: Riny Yolandha Parapat | Yuni Maulani | Gina Nur Fatimah | Freddy Haryanto | Minoo Tasbihi | Michael Schwarze | Reinhard Schomäcker
Source: E3S Web of Conferences (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Eco-friendly Nanocatalysts: Unleashing Non-Precious Metal Potential for Methylene Blue Remediation Water pollution stemming from textile dyes, particularly methylene blue (MB), represents a formidable challenge to both the environment and human well-being. In response to this pressing issue, scientists have redirected their focus toward eco-friendly nanocatalysts as a viable remediation strategy. Nanocatalysts crafted from precious metals like platinum, palladium, and gold have indisputably demonstrated their efficacy in combatting water pollution. Nonetheless, their restricted availability and substantial costs have spurred investigations into non-precious metal-based nanocatalysts. Research outcomes have revealed that non-noble metal (FeNi)-based nanocatalysts, synthesized using natural reductants, exhibit highly promising performance in degrading methylene blue. These alternatives not only deliver impressive efficiency but also incur lower production expenses compared to their noble metal counterparts. The ecological benefits inherent in these nanocatalysts offer a sustainable means of mitigating water pollution attributed to textile dyes. Through detailed synthesis experiments, precipitation-assisted method demonstrated a slightly superior yield of 69.11% with catalytic activity of 16260 μmol MB/g NiFe . Nanomaterial-based catalyst ; Methylene blue ; Environmental remediation ; Environmentally friendly ; Precious metal ; Metal ; Business ; Environmental science ; Nanotechnology ; Waste management ; Chemistry ; Materials science ; Engineering ; Metallurgy ; Organic chemistry ; Catalysis ; Contamination ; Biology ; Ecology ; Photocatalysis Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Environmental remediation with nanomaterials
Authors: Habeebunissa | Alice Antony
Source: Open Journal of Composite Materials (review, 2024-11-29)
Field: Materials Science — Biomaterials

Effects of Nano-Coating, Nanocomposites, Biocomposites to Develop UV Protective Knit Fabrics: A Synergistic Review Nanotechnology is transforming the textile industry by embedding UV-blocking and antimicrobial agents into fabric fibres at the molecular level. This study explores the development of biocomposites and nanocomposite materials for UV protection and microbial resistance in clothing. Nanoscale UV-blocking agents enhance the protection of textiles against harmful ultraviolet radiation. Recent studies on composites such as ZnO/carboxymethyl chitosan, polyacrylonitrile with UV absorbers and TiO2 nanoparticles, and lignin-TiO composites have shown significant improvements in UV protection and some antibacterial activity. Techniques such as electrospinning, hydrothermal synthesis, and natural fibre welding were used to create these composites, focusing on ZnO and TiO2 nanoparticles for dual functionality. Research on nanoscale UV-blocking agents could revolutionise sun protection in clothing and offer better safety against ultraviolet radiation. Multifunctional composites with UV-blocking and antibacterial properties could advance the use of protective clothing in various industries and outdoor activities. Emphasising natural fibres and sustainable materials aligns with the global trend towards eco-friendly solutions, leading to more environmentally friendly products. This literature review aims to comprehensively review and analyze current research on UV protective knit fabrics using nanotechnology, nanocomposites, and biocomposites. It seeks to identify research gaps, evaluate different approaches, and provide insights for future developments in this field. Materials science ; Nanocomposite ; Composite material ; Coating ; Nano- Electrospun Nanofibers in Biomedical Applications ; Dyeing and Modifying Textile Fibers ; Natural Fiber Reinforced Composites
Authors: Nina Gumbiowski | Κateryna Loza | Matthias Epple
Source: Chemie in unserer Zeit (article, 2024-11-08)
Field: Materials Science — Surfaces, Coatings and Films

Partikelanalyse leicht gemacht: Künstliche Intelligenz und Elektronenmikroskopie Zusammenfassung Methoden der Künstlichen Intelligenz (KI) bieten die Möglichkeit, große Datenmengen in kurzer Zeit zu verarbeiten und analysieren, was schnellere, effizientere und neuartige Arbeitsabläufe ermöglicht. Gleichermaßen sollte man sich als Nutzer aber auch der Grenzen dieser computergestützten Anwendungen bewusst sein. Am Beispiel von elektronenmikroskopischen Aufnahmen wird gezeigt, wie ein KI‐basiertes Programm zunächst mit echten Daten trainiert wird, so dass es dann Partikel selbständig mit hoher Genauigkeit erkennen und auswerten kann. Chemistry Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis ; Corrosion Behavior and Inhibition