jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pedro Jesús Rodríguez de Rivera | Miriam Rodríguez de Rivera | Fabiola Socorro | Manuel Rodríguez de Rivera
Source: Journal of Thermal Analysis and Calorimetry (article, 2024-05-02)
Field: Chemistry — Physical and Theoretical Chemistry

Monitoring of some minor human skin lesions using a skin calorimeter Abstract The growing interest of human skin thermal properties is motivating the development of new instruments, either by contact or by remote sensing. In this work, we show the ability of a skin calorimeter to monitor the temporal evolution of the heat capacity and the equivalent thermal resistance of the skin, in two small skin lesions. The first one consists of a forehead wound of 10 × 2 mm 2 . The other injury consists of a second degree burn on the volar wrist area, of 10 × 20 mm 2 . We studied the temporal evolution of the thermal properties of both injuries. The variation of heat capacity was significant. For first injury, heat capacity decreased by 21% and full recovery was achieved after ten days. For the second case, the heat capacity decrease was 55% and recovery was achieved after 3 weeks. These skin recoveries are monitored by the measured heat capacity value. The returning of the heat capacity to its normal value coincides with the recovery from the injury. Human skin ; Calorimeter (particle physics) ; Materials science ; Chemistry ; Biology ; Optics ; Physics thermodynamics and calorimetric analyses ; Insect and Arachnid Ecology and Behavior ; Neurobiology and Insect Physiology Research
Authors: Hui Chu | Hanjun Zhao | Peng Li | Yong‐Xin Guo
Source: Nature Communications (article, 2024-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Urban skies safeguarded: innovative drone detection with programmable metasurface periscope Programmable metasurfaces (PMSs) exhibit great potentials in target detection techniques, because they can take actions to change channel propagation characteristics which introduces further degrees of freedom for system optimizations. However, responses of most traditional PMSs are sensitive to incident angles of impinging electromagnetic waves, resulting in a failure of angular estimation to dynamic targets coming from different directions. Herein, by proposing a fully resonant structure and introducing a mode-alignment technology, we report an isotropic angle-insensitive PMS whose phase response is stable with respect to different incident angles in both elevation- and azimuth-planes. A radar scheme that uses such a PMS as a periscope is also demonstrated, to detect drones in a non-line-of-sight (N-LOS) scenario which usually happens in an urban environment. Our proposed scheme enables those targets even falling in shadow areas caused by high buildings to be successfully detected and tracked, which shows promising potentials in N-LOS target detections. Drone ; Azimuth ; Computer science ; Isotropy ; Radar ; Shadow (psychology) ; Physics ; Optics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Wireless Communication Technologies
Authors: Nadia B. Haro Mares | Sonja C. Döller | Till Wissel | Markus M. Hoffmann | M. Vogel | Gerd Buntkowsky
Source: Molecules (review, 2024-04-08)
Field: Chemistry — Spectroscopy

Structures and Dynamics of Complex Guest Molecules in Confinement, Revealed by Solid-State NMR, Molecular Dynamics, and Calorimetry This review gives an overview of current trends in the investigation of confined molecules such as water, small and higher alcohols, carbonic acids, ethylene glycol, and non-ionic surfactants, such as polyethylene glycol or Triton-X, as guest molecules in neat and functionalized mesoporous silica materials employing solid-state NMR spectroscopy, supported by calorimetry and molecular dynamics simulations. The combination of steric interactions, hydrogen bonds, and hydrophobic and hydrophilic interactions results in a fascinating phase behavior in the confinement. Combining solid-state NMR and relaxometry, DNP hyperpolarization, molecular dynamics simulations, and general physicochemical techniques, it is possible to monitor these confined molecules and gain deep insights into this phase behavior and the underlying molecular arrangements. In many cases, the competition between hydrogen bonding and electrostatic interactions between polar and non-polar moieties of the guests and the host leads to the formation of ordered structures, despite the cramped surroundings inside the pores. Molecular dynamics ; Molecule ; Hydrogen bond ; Steric effects ; Chemical physics ; Chemistry ; Solid-state nuclear magnetic resonance ; Ionic bonding ; Computational chemistry ; Organic chemistry ; Nuclear magnetic resonance Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Zeolite Catalysis and Synthesis
Authors: Pallavi Kumari | P. Ballone | Candelas Paniagua | Radwa H. Abou‐Saleh | Yoselin Benitez‐Alfonso
Source: Biomacromolecules (article, 2024-02-27)
Field: Materials Science — Biomaterials

Cellulose–Callose Hydrogels: Computational Exploration of Their Nanostructure and Mechanical Properties Polysaccharides play a crucial role in virtually all living systems. They also represent the biocompatible and fully sustainable component of a variety of nanoparticles, which are of increasing interest in biomedicine, food processing, cosmetics, and structural reinforcement of polymeric materials. The computational modeling of complex polysaccharide phases will assist in understanding the properties and behavior of all these systems. In this paper, structural, bonding, and mechanical properties of 10 wt % cellulose-callose hydrogels (β-glucans coexisting in plant cell walls) were investigated by atomistic simulations. Systems of this kind have recently been introduced in experiments revealing unexpected interactions between the polysaccharides. Starting from initial configurations inspired by X-ray diffraction data, atomistic models made of ∼1.6 × 10<sup>6</sup> atoms provide a qualitatively consistent view of these hydrogels, displaying stability, homogeneity, connectivity, and elastic properties beyond those of a liquid suspension. The simulation shows that the relatively homogeneous distribution of saccharide nanofibers and chains in water is not due to the solubility of cellulose and callose, but to the formation of a number of cross-links among the various sample components. The broad distribution of strength and elasticity among the links implies a degree of anharmonicity and irreversible deformation already evident at low external load. Besides the qualitative agreement with experimental observations, the simulation results display also quantitative disagreements in the estimation of elastic coefficients, such as the Young's modulus, that require further investigation. Complementary simulations of dense cellulose-callose mixtures (no hydrogels) highlight the role of callose in smoothing the contact surface of different nanofibers forming larger bundles. Cellulose-callose structures in these systems displayed an enhanced water uptake and delayed dye release when compared to cellulose alone, highlighting potential new applications as drug delivery scaffolds. The simulation trajectories provide a tuning and testing ground for the development of coarse-grained models that are required for the large scale investigation of mechanical properties of cellulose and callose mixtures in a watery environment. Self-healing hydrogels ; Cellulose ; Nanostructure ; Chemistry ; Chemical engineering ; Polymer science ; Nanotechnology ; Materials science ; Polymer chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Advanced Materials and Mechanics ; Polysaccharides Composition and Applications
Authors: Xiaowei Xin | Junhua Jia | Shunpeng Pang | Ruotong Hu | Huili Gong | Xiaoyan Gao | Xiangqian Ding
Source: Optics Express (article, 2024-01-23)
Field: Chemistry — Analytical Chemistry

Combination of near-infrared spectroscopy with Wasserstein generative adversarial networks for rapidly detecting raw material quality for formula products Near-infrared spectroscopy (NIRS) has emerged as a key technique for rapid quality detection owing to its fast, non-destructive, and eco-friendly characteristics. However, its practical implementation within the formulation industry is challenging owing to insufficient data, which renders model fitting difficult. The complexity of acquiring spectra and spectral reference values results in limited spectral data, aggravating the problem of low generalization, which diminishes model performance. To address this problem, we introduce what we believe to be a novel approach combining NIRS with Wasserstein generative adversarial networks (WGANs). Specifically, spectral data are collected from representative samples of raw material provided by a formula enterprise. Then, the WGAN augments the database by generating synthetic data resembling the raw spectral data. Finally, we establish various prediction models using the PLSR, SVR, LightGBM, and XGBoost algorithms. Experimental results show the NIRS-WGAN method significantly improves the performance of prediction models, with R<sup>2</sup> and RMSE of 0.949 and 1.415 for the chemical components of sugar, respectively, and 0.922 and 0.243 for nicotine. The proposed framework effectively enhances the predictive capabilities of various models, addressing the issue caused by limited training data in NIRS prediction tasks. Computer science ; Raw data ; Generalization ; Chemometrics ; Key (lock) ; Near-infrared spectroscopy ; Artificial intelligence ; Generative adversarial network ; Machine learning ; Data mining ; Pattern recognition (psychology) ; Algorithm ; Deep learning ; Mathematics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Industrial Vision Systems and Defect Detection
Authors: Jun Dong | Xinyue Wang | Xuhai Pan | Chenxu Chang
Source: Fuel (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Characteristics of constant pressure dehydrogenation and construction of linear diffusion model for solid hydrogen storage materials: A case study of TiMn1.5 alloy Hydrogen storage ; Dehydrogenation ; Alloy ; Diffusion ; Constant (computer programming) ; Materials science ; Hydrogen ; Thermodynamics ; Fick's laws of diffusion ; Metallurgy ; Chemistry ; Physics ; Computer science ; Organic chemistry ; Catalysis Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Nuclear Materials and Properties
Authors: Mohammad Arishi | Mohammed Kuku
Source: Alexandria Engineering Journal (article, 2024-12-21)
Field: Chemistry — Inorganic Chemistry

Mitigating carbon emissions through AI-driven optimization of zeolite structures: A hybrid model approach The escalating hazard of weather change necessitates pressing advancements in carbon-mitigating technologies. The CO₂ capture with zeolites involves working by physical and chemical adsorption, taking advantage of the high surface area, porosity, and the crystalline structure zeolites possess. Zeolites, with their incredible adsorption properties, have emerged as promising materials for carbon capture. However, optimizing zeolite structures to maximize carbon capture efficiency is a complicated and useful resource-extensive technique. Traditional optimization techniques, at the same time effective, are regularly restricted using their computational demands and incapability to fully discover the considerable design space, leading to suboptimal answers. This study introduces a novel integrated model that mixes Genetic Algorithms (GA) with Generative Adversarial Networks (GANs) to enhance the layout and optimization of zeolite systems for carbon capture. GAN stands for generative adversarial networks, which are actual AIs that create realistic data, whereas GA resembles the process of natural selection to get the best solution. They both enhance the zeolite design in this study. The GA component successfully searches the design space by iteratively choosing and evolving promising zeolite systems, even as the GAN aspect generates new, high-capacity systems based totally on learned styles from present facts. This GA-GAN hybrid approach addresses the constraints of modern-day strategies by enabling a complete exploration of feasible zeolite configurations and improving the probability of identifying the optimal approach. Optimization of the zeolite structure is done by a blend of genetic algorithms in conjunction with Generative Adversarial Networks for improved adsorption capacity, surface area, and selectivity of CO₂ capture. The latest research report has found that the GA-GAN mix model completely outperforms classical optimization methods, a promising tool for advanced carbon capture materials development to this end. The study introduces a brand-new approach to finding the best zeolite designs by merging the characteristics of genetic algorithms and generative adversarial networks. The findings and analysis imply that it is indeed this combined model that will push the carbon capture drive in the future, thus helping global climate change. Zeolite ; Greenhouse gas ; Carbon fibers ; Environmental science ; Waste management ; Environmental engineering ; Computer science ; Engineering ; Chemistry ; Geology ; Algorithm ; Composite number ; Catalysis ; Oceanography Zeolite Catalysis and Synthesis ; Carbon Dioxide Capture Technologies ; CO2 Sequestration and Geologic Interactions
Authors: Tianhao Su | Bin Cao | Shunbo Hu | Musen Li | Tong‐Yi Zhang
Source: Journal of Materials Informatics (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

CGWGAN: crystal generative framework based on Wyckoff generative adversarial network Discovering novel crystals is a highly effective way to develop new materials, though it presents significant challenges. Recently, many artificial intelligence (AI) generative methods have been developed to generate new crystals. In this work, we present a crystal generative framework based on Wyckoff generative adversarial network (CGWGAN) to efficiently discover novel crystals. The CGWGAN includes three modules: a generator of crystal templates, an atom-infill module, and a crystal screening module. The generator uses a generative adversarial network (GAN) to produce crystal templates embedded with asymmetry units (ASUs), space groups, lattice vectors, and the total number of atoms within the lattice cell, ensuring that the generated templates precisely match all requirements of crystals. These templates become crystal candidates after filling in atoms of different chemical elements. These candidates are screened by M3GNet and the passed ones are subjected to density functional theory (DFT)-based calculations to finally verify their stability. As a showcase, the CGWGAN successfully discovers seven novel crystals within the Ba-Ru-O system, demonstrating its effectiveness. This work provides a knowledge-guided Artificial Intelligence generative framework for accelerating crystal discovery. Generative grammar ; Adversarial system ; Generative adversarial network ; Computer science ; Artificial intelligence ; Image (mathematics) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Bashir Kazimi | Karina Ruzaeva | Stefan Sandfeld
Source:  (article, 2024-06-17)
Field: Materials Science — Surfaces, Coatings and Films

Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy In this work, we explore the potential of self-supervised learning with Generative Adversarial Networks (GANs) for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks, including semantic segmentation, denoising, noise & background removal, and super-resolution. Experimentation with varying model complexities and receptive field sizes reveals the remarkable phenomenon that fine-tuned models of lower complexity consistently outperform more complex models with random weight initialization. We demonstrate the versatility of self-supervised pretraining across various downstream tasks in the context of electron microscopy, allowing faster convergence and better performance. We conclude that self-supervised pretraining serves as a powerful catalyst, being especially advantageous when limited annotated data are available and efficient scaling of computational cost is important. Generative grammar ; Adversarial system ; Computer science ; Artificial intelligence ; Electron microscope ; Physics ; Optics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Berk Tekkaya | Michael Dölz | Sebastian Münstermann
Source: International Journal of Mechanical Sciences (article, 2024-08-13)
Field: Materials Science — Metals and Alloys

Multi-scale approach to hydrogen susceptibility based on pipe-forming deformation history Hydrogen-induced-cracking initiates without external loading due to residual stresses. Pipe manufacturing process composed of crimping, U -ing, O -ing, and expansion has a major impact on local hydrogen concentration, as strain pattern evolves from one forming step to another, causing residual stresses that serve as driving force for hydrogen diffusion. The novelty of the presented work lies in the development of a multi-scale approach that links the residual stresses from the macroscopic pipe-forming process with locally dissolved hydrogen atoms in microstructure under the consideration of microstructural heterogeneities to identify areas susceptible to hydrogen-induced-cracking. First, a 3d-pipe-forming-model was built. Second, representative volume elements with lattice defects were generated to analyze hydrogen trapping in microstructure. Third, representative volume elements were placed in the pipe via sub-modeling, so that local loading history of the pipe was assigned to microstructure models. At the end of the pipe-forming process, representative volume elements were loaded with hydrogen on the surface and final hydrogen concentration was simulated based on residual stresses, considering microstructural effects such as grain size/shape, crystallographic texture and hydrogen traps, e.g. dislocations, voids and inclusions. On meso-/macroscale, a combined isotropic–kinematic hardening material model was implemented, while on microscale, a phenomenological crystal-plasticity-hydrogen-diffusion model was coded. According to the multi-scale simulations under the consideration of microstructural effects the bottom center position in the pipe was detected to be critical to hydrogen-induced-cracking as the maximum local hydrogen concentration was predicted at that location. Based on the loading history hydrogen-induced-cracking susceptibility increases from voids to hard and soft non-metallic inclusions. • Monitoring microstructural evolution during the pipe forming process via scale-bridging. • Hydrogen diffusion on microscale considering microstructural characteristics. • Analyzes of residual stresses from pipe forming as driving force for HIC. • According to loading history soft inclusions become more critical than hard ones. • Voids are less critical traps than hard and soft non-metallic inclusions. Deformation (meteorology) ; Scale (ratio) ; Materials science ; Structural engineering ; Geology ; Engineering ; Composite material ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Material Properties and Failure Mechanisms
Authors: Shymaa Adel Elsayed | Nehal A. Barghout | Safaa Ragab | Ehab Abdel‐Latif | H. A. Etman | Mohamed A. Hamed | N. Eddy | Ahmed El Nemr
Source: Journal of the Chinese Chemical Society (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Condensed Fukui function and experimental evaluation of the corrosion inhibition properties of some antipyrinyl‐imidazotriazole and their derivatives for copper in an acidic environment Abstract The effectiveness of synthesized antipyrinyl‐imidazotriazole and its derivatives as inhibitors for the corrosion of copper alloy in 0.5 M H 2 SO 4 solution was tested using weight loss, electrochemical impedance spectroscopy (EIS) and potentiodyanmic polarization techniques. The generated results confirmed that the tested compounds have strong inhibition efficiencies for the protection of the corrosion of copper alloy in 0.5 M H 2 SO 4 . Maximum inhibition efficiencies (IEs) evaluated from electrochemical measurements at inhibitor's concentrations of 0.040 g/L were 85% (5‐(4‐Antipyrinyl)‐3 H ‐imidazo[1,2‐ b ][1,2,4]triazole), 60% (6‐Antipyrinyl‐imidazo[2,1‐ b ]thiazole) and 72% (2‐Antipyrinyl‐7‐ethoxy‐imidazo[2,1‐ b ]benzothiazole). It was observed that the inhibition efficiency was strongly influenced by the flow rate of the solution and was reduced to 79 (5‐(4‐Antipyrinyl)‐3 H ‐imidazo[1,2‐ b ][1,2,4]triazole), 60% (6‐Antipyrinyl‐imidazo[2,1‐ b ]thiazole), and 44% (2‐Antipyrinyl‐7‐ethoxy‐imidazo[2,1‐ b ]benzothiazole) at an agitation speed of 400 rpm. EI from weight loss was comparable with those from PDP of mixed‐type inhibition style and EIS of diffusion model. The low inhibition efficiency for 6‐Antipyrinyl‐imidazo[2,1‐ b ]thiazole was significantly enhanced from 60% to 90% through a synergistic effect of 0.0001 M KI. The Temkin and Frumkin isotherms indicate the physical adsorption of inhibitors on copper surface. Condensed Fukui function calculations reveal a common center for electrophilic attacks in the three molecules: the nitrogen in the bridged pyrrole rings (labeled as N11). Chemistry ; Thiazole ; Copper ; Electrochemistry ; Corrosion ; Benzothiazole ; Adsorption ; Dielectric spectroscopy ; Nuclear chemistry ; Inorganic chemistry ; Stereochemistry ; Organic chemistry ; Physical chemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Meng Guo | Xuyang Li | Jia Du | Xueguo Liu | Nam Hoon Kim | Joong Hee Lee
Source: Carbon letters (article, 2024-02-24)
Field: Materials Science — Materials Chemistry

Enhancement of anticorrosion performance of epoxy coatings with exfoliated graphite nanoplatelets and functionalized graphene Materials science ; Epoxy ; Graphite ; Graphene ; Composite material ; Exfoliated graphite nano-platelets ; Nanotechnology Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics ; Concrete Corrosion and Durability