jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: А. Е. Галашев
Source: Electrochemical Materials and Technologies (review, 2024-12-05)
Field: Materials Science — Materials Chemistry

First principle study of the physical properties of actinide oxides. A review Reprocessing spent nuclear fuel creates the conditions for converting nuclear energy to a clean and sustainable energy. Thermal conductors in reactors are molten salts, the safe use of which is based on an accurate knowledge of their thermophysical properties. Due to the difficulty of experimentally measuring these properties, computational methods are coming to the fore. The main component of SNF is uranium dioxide, the change in the structure, energy and electronic properties of which when oxygen is removed from the system and plutonium is added to it was studied using DFT + U calculations. The influence of noble metals present in spent fuel on the process of direct reduction of irradiated nuclear fuel is considered. This work also examines the effects of doping thorium with uranium when Th-U fuel is used in a nuclear reactor. The paper presents data on the calculation of heat capacity, thermal conductivity, elastic moduli and phase transitions occurring in UO2 at high pressures. The thermodynamic stability of actinide oxides is discussed in detail. The ways of improving DFT calculations to increase the accuracy of determining the properties of these nuclear materials are analyzed. Actinide ; Materials science ; Physics ; Nuclear physics Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear materials and radiation effects
Authors: Adhira Satria Pratama | Gumawa Windu Manggada | Magdy Abdelghany Elsayed | Shixue Zhou
Source: DISTILAT JURNAL TEKNOLOGI SEPARASI (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

HIGH-ENERGY MILLING OF MAGNESIUM WITH ZEOLITE FOR HYDROGEN STORAGE Magnesium has received great attention as a potential material for hydrogen storage due to its environmental friendliness, high gravimetric hydrogen storage capacity, and low cost. However, the material's effectiveness is limited by its high thermodynamic stability, which makes hydrogen desorption challenging, and low reaction kinetics, which slows the rate of hydrogen absorption and desorption. To enhance the performance of magnesium as a hydrogen storage material, this work innovatively aims to examine the impact of time and high energy milling speed on particle size as well as the milling aid effect of zeolite 5A. Experiment results showed that after ball milling with 4 h milling time and 270 r/min milling speed, the size of magnesium crystallites calculated from XRD data is in the range of 32.8-49.3 nm, while the size before ball milling was 72.7 nm. The kinetic measurement results showed that the Mg+10 wt.% zeolite 5A+10 wt.% Ni sample with 4 h milling time and 270 r/min milling speed at 330°C and 2.5 MPa had the best absorption and desorption capacity of 4.14 wt.% and 3.91 wt.%, respectively, in less than 25 min. The synergistic effect of milling time, milling speed, and the addition of zeolite 5A with Ni has a significant role in reducing the size of Mg particles, which affects the absorption and desorption performance of magnesium. Hydrogen storage ; Ball mill ; Gravimetric analysis ; Desorption ; Materials science ; Particle size ; Magnesium ; Zeolite ; Hydrogen ; Magnesium hydride ; Crystallite ; Chemical engineering ; Metallurgy ; Catalysis ; Chemistry ; Adsorption ; Organic chemistry ; Alloy Hydrogen Storage and Materials ; Magnesium Alloys: Properties and Applications ; Hybrid Renewable Energy Systems
Authors: Ganesh Sivaraman | Chris J. Benmore
Source: arXiv (Cornell University) (preprint, 2024-03-01)
Field: Materials Science — Materials Chemistry

Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted structures derived from atom-atom pair potentials in disordered materials, has been a longstanding challenge in condensed matter physics. This perspective gives a brief overview of the traditional approaches employed over the past several decades. Namely, the use of approximate interatomic pair potentials that relate 3-dimensional structural models to the measured structure factor and its associated pair distribution function. The use of machine learned interatomic potentials has grown in the past few years, and has been particularly successful in the cases of ionic and oxide systems. Recent advances in large scale sampling, along with a direct integration of scattering measurements into the model development, has provided improved agreement between experiments and large-scale models calculated with quantum mechanical accuracy. However, details of local polyhedral bonding and connectivity in meta-stable disordered systems still require improvement. Here we leverage MACE-MP-0; a newly introduced equivariant foundation model and validate the results against high-quality experimental scattering data for the case of molten iron(II) oxide (FeO). These preliminary results suggest that the emerging foundation model has the potential to surpass the traditional limitations of classical interatomic potentials. Foundation (evidence) ; Equivariant map ; Scattering ; Computer science ; Materials science ; Physics ; Mathematics ; Optics ; Political science ; Pure mathematics ; Law X-ray Diffraction in Crystallography
Authors: Niklas Dobberstein | Astrid Maaß | Jan Hamaekers
Source: Research Square (preprint, 2024-01-18)
Field: Materials Science — Materials Chemistry

LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design Abstract Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transformer (GPT) models. In our efforts to develop such a tool for exploring the organic chemical space in search of potentially electro-active compounds, we present LLamol, a single novel generative transformer model based on the LLama 2 architecture, which was trained on a 13M superset of organic compounds drawn from diverse public sources. To allow for a maximum flexibility in usage and robustness in view of potentially incomplete data, we introduce Stochastic Context Learning as a new training procedure. We demonstrate that the resulting model adeptly handles single- and multi-conditional organic molecule generation with up to four conditions, yet more are possible. The model generates valid molecular structures in SMILES notation while flexibly incorporating three numerical and/or one token sequence into the generative process, just as requested. The generated compounds are very satisfactory in all scenarios tested. In detail, we showcase the model's capability to utilize token sequences for conditioning, either individually or in combination with numerical properties, making LLamol a potent tool for de novo molecule design, easily expandable with new properties. Scientific Contribution: We developed a novel generative transformer model, LLamol, based on the LLama 2 architecture that was trained on a diverse set of 13M organic compounds. It introduces Stochastic Context Learning (SCL) as a new training procedure, allowing for flexible and robust generation of valid organic molecules with up to multiple conditions that can be combined in various ways, making it a potent tool for de novo molecular design. Generative grammar ; Computer science ; Transformer ; Security token ; Artificial intelligence ; Generative model ; Notation ; Robustness (evolution) ; Machine learning ; Theoretical computer science ; Engineering ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Akira Miura | Koki Muraoka | Kotaro Maki | Saori I. Kawaguchi | Kazuhiro Hikima | Hiroyuki Muto | Atsunori MATSUDA | Ichirō Yamane | Toshihiro Shimada | Hiroaki ITO | Yoshikazu Mizuguchi | Chikako Moriyoshi | Hiroshi NAKAJIMA | Shigeo Mori | Hiroshi Ôike | Akira Nakayama | Wenhao Sun | Nataly Carolina Rosero-Navarro | Kiyoharu Tadanaga
Source: Institutional Repositories DataBase (IRDB) (article, 2024-09-01)
Field: Materials Science — Materials Chemistry

Stress-Induced Martensitic Transformation in Na3YCl6 Martensitic transformation with volume expansion plays a crucial role in enhancing the mechanical properties of steel and partially stabilized zirconia. We believe that a similar concept could be applied to unexplored nonoxide materials. Herein, we report the stress-induced martensitic transformation of monoclinic Na3YCl6 with an ∼3.4% expansion. In situ synchrotron X-ray diffraction and atomistic simulations showed that anisotropic crystallographic transformation from monoclinic to rhombohedral Na3YCl6 occurs exclusively under uniaxial pressure; no effect is observed under hydrostatic pressure conditions. The uniaxially pressed powder compact of monoclinic Na3YCl6 showed a large indentation impression and low Young’s modulus, in contrast to its high bulk modulus, suggesting that these unique mechanical properties are induced by the martensitic transformation. Monoclinic crystal system ; Diffusionless transformation ; Martensite ; Indentation ; Hydrostatic pressure ; Diffraction ; Anisotropy ; Deformation (meteorology) Thermal Expansion and Ionic Conductivity ; Inorganic Chemistry and Materials ; High-pressure geophysics and materials
Authors: Saurav Bharadwaj | Akshita Midha | Shikha Sharma | G. S. Sidhu | Rajesh Kumar
Source: arXiv (Cornell University) (review, 2024-03-07)
Field: Chemistry — Analytical Chemistry

Optical Screening of Citrus Leaf Diseases Using Label-Free Spectroscopic Tools: A Review Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants. Environmental science ; Computer science ; Chemistry ; Medicine Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Water Quality Monitoring and Analysis
Authors: Thi Nhan Tran | Nguyen Vo Anh Duy | Nguyen Hoang Hieu | Truc Nguyen | To Van Nguyen | Thi Viet Bac Phung | Peter Schall | Minh Triết Đặng
Source: arXiv (Cornell University) (preprint, 2024-06-18)
Field: Materials Science — Materials Chemistry

Electric field enhances the electronic and diffusion properties of penta-graphene nanoribbons for application in lithium-ion batteries: a first-principles study Enhancing the electronic and diffusion properties of lithium-ion batteries is crucial for improving the performance of the fast-growing energy storage devices. Recently, fast-charging capability of commercial-like lithium-ion anodes with the least modification of the current manufactoring technology is of great interest. Here we use first principles methods with density functional theory and the climbing image-nudged elastic band method to evaluate the impact of an external electric field on the stability, electronic and diffusion properties of penta-graphene nanoribbons upon lithium adsorption. We show that by adsorbing a lithium atom, these semiconductor nanoribbons become metal with a formation energy of - 0.22 (eV). The lithium-ion mobility of this material is comparable to that of a common carbon graphite layer. Under a relatively small vertical electric field, the structural stability of these lithium-ion systems is even more stable, and their diffusion coefficient is enhanced significantly of ~719 times higher than that of the material in the absence of an applied electric field and ~521 times higher than in the case of commercial graphitic carbon layers. Our results highlight the role of an external electric field as a novel switch to improve the efficiency of lithium-ion batteries with penta-graphene nanoribbon electrodes and open a new horizon for the use of more environmentally friendly pentagonal materials as anode materials in lithium-ion battery industry. Lithium (medication) ; Graphene nanoribbons ; Graphene ; Ion ; Diffusion ; Electric field ; Materials science ; Nanotechnology ; Field (mathematics) ; Chemical physics ; Chemistry ; Physics ; Thermodynamics ; Organic chemistry ; Mathematics ; Quantum mechanics Graphene research and applications ; Advancements in Battery Materials ; Quantum-Dot Cellular Automata
Authors: Kai Zheng
Source: Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (article, 2024-01-01)
Field: Materials Science — Biomaterials

Theoretical Studies of Flexo-effects in Inorganic Janus Nanotubes Janus ; Nanotechnology ; Chemistry ; Materials science Supramolecular Self-Assembly in Materials ; Micro and Nano Robotics ; Pickering emulsions and particle stabilization
Authors: Baoxia Li | Wenzhuo Chen | Shaohuang Bian | A Lusi | Xiaojiang Tang | Yang Liu | Junwei Guo | Dan Zhang | Cheng Yang | Feng Huang
Source: Electronics (article, 2024-03-04)
Field: Chemistry — Spectroscopy

Recognition of Ethylene Plasma Spectra 1D Data Based on Deep Convolutional Neural Networks As a commonly used plasma diagnostic method, the spectral analysis methodology generates a large amount of data and has a complex quantitative relationship with discharge parameters, which result in low accuracy and time-consuming operation of traditional manual spectral recognition methods. To quickly and efficiently recognize the discharge parameters based on the collected spectral data, a one-dimensional (1D) deep convolutional neural network was constructed, which can learn the data features of different classes of ethylene plasma spectra to obtain the corresponding discharge parameters. The results show that this method has a higher recognition accuracy of higher than 98%. This model provides a new idea for plasma spectral diagnosis and its related application. Convolutional neural network ; Pattern recognition (psychology) ; Plasma ; Spectral line ; Artificial intelligence ; Computer science ; Spectral analysis ; Artificial neural network ; Physics ; Spectroscopy Mass Spectrometry Techniques and Applications ; Gas Sensing Nanomaterials and Sensors ; Water Quality Monitoring and Analysis
Authors: Ikeda, Arisa | Ryo Higuchi | Tomohiro Yokozeki | Katsuhiro Endo | Yuta Kojima | Misato Suzuki | Mayu Muramatsu
Source: arXiv (Cornell University) (preprint, 2024-10-28)
Field: Materials Science — Materials Chemistry

Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties In this study, we develop a conditional diffusion model that proposes the optimal process parameters and predicts the microstructure for the desired mechanical properties. In materials development, it is costly to try many samples with different parameters in experiments and numerical simulations. The use of data-driven inverse design method can reduce the cost of materials development. This study develops an inverse analysis model that predicts process parameters and microstructures. This method can be used for any material, but in this study it is applied to polymeric material, which is the matrix resin of carbon fiber reinforced thermoplastics as an example. Matrix resins contain a mixture of dendrites, which are crystalline phases, and amorphous phases even after crystal growth is complete, and it is important to consider the microstructures consisting of the crystalline structure and the remaining amorphous phase to achieve the desired mechanical properties. Typically, the temperature during forming affects the microstructures, which in turn affect the macroscopic mechanical properties. The trained diffusion model can propose not only the processing temperature but also the microstructure when Young's modulus and Poisson's ratio are given. The capability of our conditional diffusion model to represent complex dendrites is also noteworthy. Dendrite (mathematics) ; Materials science ; Microstructure ; Process (computing) ; Matrix (chemical analysis) ; Diffusion ; Composite material ; Statistical physics ; Thermodynamics ; Computer science ; Geometry ; Mathematics ; Physics Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Aluminum Alloy Microstructure Properties
Authors: João Paulo Cerqueira Felix | Wanderson Souza Araújo | João Marcos T. Palheta | Jônatas Favotto Dalmedico | Flávio Pereira de Oliveira | Alexandre C. Dias | Diego Guedes‐Sobrinho | Celso R. C. Rêgo | Renato L. T. Parreira | Maurício J. Piotrowski
Source: ACS Nanoscience Au (article, 2024-12-16)
Field: Chemistry — Organic Chemistry

Highlighting the Potential of Synergistic Cu–Pt Single-Atom Alloy Sub-nanoclusters for Enhanced H<sub>2</sub> Adsorption: A DFT Investigation Single-atom alloy sub-nanoclusters offer promising potential for understanding intricate interfacial phenomena at the atomic level, enabling the rational design of efficient catalysts and nanomaterials for H<sub>2</sub> energy storage, purification, and conversion. Herein, we employed density functional theory calculations improved by van der Waals corrections to investigate H<sub>2</sub> adsorption on pure copper (Cu <sub><i>n</i></sub> ) and copper-platinum (Cu <sub><i>n</i>-1</sub>Pt) sub-nanoclusters. We characterized Cu <sub><i>n</i></sub> sub-nanoclusters ranging from <i>n</i> = 2 to <i>n</i> = 14, identifying the most stable sizes (4, 6, 8, 10, and 12) through a set of stability analysis. Subsequently, we substituted a single Cu atom with Pt to form single-atom alloy Cu <sub><i>n</i>-1</sub>Pt sub-nanoclusters, which showed enhanced stabilization and reactivity compared to pure Cu sub-nanoclusters. While Cu-only sub-nanoclusters exhibited weak side-on interactions with H<sub>2</sub>, resulting in minimal charge transfer and negligible structural changes, CuPt-based sub-nanoclusters showed strong interactions characterized by molecular dissociation (H-H bond breaking) and significant charge transfer from the sub-nanoclusters to the H atoms. These findings highlight the synergistic effects of the Cu-Pt combination and provide valuable insights into the fundamental processes of H<sub>2</sub> adsorption on metal sub-nanoclusters, with significant implications for catalytic applications and materials design in hydrogen-related technologies. Nanoclusters ; Alloy ; Atom (system on chip) ; Adsorption ; Materials science ; Crystallography ; Computational chemistry ; Nanotechnology ; Chemical physics ; Physical chemistry ; Chemistry ; Metallurgy ; Computer science ; Embedded system Nanomaterials for catalytic reactions ; Catalytic Processes in Materials Science ; Nanocluster Synthesis and Applications
Authors: Qifei Ma | Huaizhou Jin | Xiaoxiao Shang | Tamás Párdy | Ott Scheler | Simona Bartkova | Dan Cojoc | Denis Garoli | Shangzhong Jin
Source: arXiv (Cornell University) (preprint, 2024-11-14)
Field: Chemistry — Physical and Theoretical Chemistry

Application of Optical Tweezers in the Study of Emulsions for Multiple Applications Emulsions are ubiquitous in everyday life and find applications in various industries. Optical tweezers (OTs) have emerged as the preferred method for studying emulsion dynamics. In this review, we first introduce the theory of optical trapping and emulsion stability. We then survey applications in the manipulation of emulsions, stability mechanism, the processes of aggregation and coalescence, and important responsive and switchable behaviors. And we overview the instrumentation framework of various OT setups, and evaluate their complexity and cost with a view towards the democratization of this technology. Following this, we delve into basic experimentation methods, the challenges associated with using OTs in emulsion applications. Additionally, we present a promising research outlook, including studies on stability mechanism of emulsions stabilized by compound or mixed emulsifiers or rigid or soft particles, as well as dynamic processes of responsive or functional emulsions. Optical tweezers ; Tweezers ; Computer science ; Nanotechnology ; Materials science ; Optics ; Physics Various Chemistry Research Topics ; Microfluidic and Capillary Electrophoresis Applications
Authors: Meng Wang | Na Jin
Source: Molecular & cellular biomechanics (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

Modeling and simulation of nonlinear dynamical systems for biosensor sensitivity based on carbon nanocomposites Nanomaterials have a wide range of applications in various fields of scientific research due to their unique physical and chemical properties. With the rapid development of science and technology, nanocomposites synthesized from various nanoparticles have obtained many excellent properties due to their synergistic effects. In this paper, a series of carbon-based nanomaterials are proposed for in-depth research, and corresponding biosensors are constructed. In this study, ECL biosensors based on a variety of nanocomposite materials will be used to detect and analyze cells, and also detect other cells that are similar to cells. The experimental data show that the relative standard deviation of the detection results of the two methods is within 8%, and the sensor has high sensitivity, excellent stability and fast response speed. The sensor showed excellent performance in the repeatability test, and the relative standard deviation of repeated detection was less than 2%. This result shows that the sensor has highly consistent detection capabilities, providing important support for its reliability in practical applications. By adding the description of repeatability data, the summary more comprehensively reflects the performance advantages of the sensor. Sensitivity (control systems) ; Nonlinear system ; Biosensor ; Nanocomposite ; Carbon fibers ; Materials science ; Nanotechnology ; Biological system ; Physics ; Engineering ; Electronic engineering ; Composite material ; Biology ; Composite number Carbon Nanotubes in Composites