jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alberto Castellano-Soria | Jesús López‐Sánchez | Aída Serrano | Giulio Gorni | M. Varela | Ignacio Sardinero | N. Carmona | A. Hernando | Pilar Marín | E. Navarro
Source: Journal of Alloys and Compounds (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

Sol-gel synthesis control of iron-cobalt alloy/ferrite core/shell nanoparticles supported by a carbon medium with semi-hard magnetic features We explore a simple and optimized approach for obtaining magnetic nanoparticle-carbon composites by a novel one-pot sol-gel method. In them, various metal cations ratios of Fe3+ and Co2+ are evaluated. The ratio of Fe:Co is a critical parameter that governs the presence of different crystalline phases with tailored magnetic properties. Interestingly, the smallest introduction of Co into the synthesis (19Fe:1Co) provides an abrupt emergence of the body-centered cubic (bcc) Fe-Co alloy, instead of the orthorhombic Pnma structure characteristic of the Fe3C intermetallic compound (1Fe:0Co). Advanced structural and electronic characterizations reveal the formation of Fe-Co/Co-ferrite core/shell nanoparticle structures embedded in a carbon matrix. The sphere-like nanoparticles range from 10 to 45 nm and the shells show a spinel structure with a thickness of 2–3 nm. In addition, X-ray absorption spectroscopy unveils that the oxidation state of Fe and Co cations is close to zero, demonstrating their predominant metallic character. The magnetic properties can be modulated by a precise control of the alloy composition varying the Co content, displaying saturation magnetization values close to ∼137 emu/g. The nanoparticles are mainly single magnetic domain with a considerable coercive field (∼450 Oe), higher than those reported in the literature for Fe-Co nanoparticles. This semi-hard character is due to a notable spring exchange effect emerged by passivating the surface of Fe-Co-bcc cores with a thin Co-ferrite-like shell. Materials science ; Alloy ; Nanoparticle ; Coercivity ; Cobalt ; Intermetallic ; Ferrite (magnet) ; Orthorhombic crystal system ; Chemical engineering ; Magnetization ; Spinel ; Composite material ; Nanotechnology ; Crystallography ; Metallurgy ; Crystal structure ; Condensed matter physics ; Chemistry ; Magnetic field Magnetic Properties and Synthesis of Ferrites ; Advancements in Battery Materials ; Magnetic Properties of Alloys
Authors: Mizuho Yabushita | Ryota Osuga | Toshiyuki Yokoi | Atsushi Muramatsu
Source: Catalysis Science & Technology (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Zeolite-based catalysts for oxidative upgrading of methane: design and control of active sites Various reported active sites in zeolite-based catalysts categorized into three groups in terms of their origin have been overviewed from the viewpoints of their performance, structure, and catalysis. Catalysis ; Methane ; Zeolite ; Oxidative phosphorylation ; Chemistry ; Chemical engineering ; Combinatorial chemistry ; Environmental science ; Organic chemistry ; Engineering ; Biochemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Susumu Fujii | Akihide Kuwabara
Source: Computational Materials Science (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

Interatomic potentials for cubic zirconia and yttria-stabilized zirconia optimized by genetic algorithm Yttria stabilized zirconia (YSZ) is an important engineering ceramic oxide used for various applications, including solid electrolytes in solid oxide fuel cells due to its high ionic conductivity. Accurate and computationally inexpensive interatomic potentials for cubic ZrO2 and YSZ are required to accommodate the large number of defect configurations originating from high concentrations of Y and oxygen vacancies and to statistically understand their properties in realistic time. In this study, a genetic algorithm has been used to optimize empirical interatomic potential parameters for cubic ZrO2 and 10Y2O3 mol% YSZ using energies, forces acting on atoms, and stresses generated by ab initio calculations as training data. The optimized potentials reproduce the structural, mechanical, and thermal properties as well as the ionic conduction properties more accurately than previously reported empirical interatomic potentials. The developed potentials will be useful for a statistical characterization of YSZ properties, combined with more accurate ab initio calculations. Cubic zirconia ; Yttria-stabilized zirconia ; Materials science ; Interatomic potential ; Crystallography ; Chemistry ; Computational chemistry ; Molecular dynamics ; Ceramic ; Metallurgy Nuclear materials and radiation effects ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Andrea N. Giordano | Rahul Rao
Source: Nanomaterials (review, 2023-07-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Beyond the Visible: A Review of Ultraviolet Surface-Enhanced Raman Scattering Substrate Compositions, Morphologies, and Performance The first observation of ultraviolet surface-enhanced Raman scattering (UV-SERS) was 20 years ago, yet the field has seen a slower development pace than its visible and near-infrared counterparts. UV excitation for SERS offers many potential advantages. These advantages include increased scattering intensity, higher spatial resolution, resonance Raman enhancement from organic, biological, and semiconductor analytes, probing UV photoluminescence, and mitigating visible photoluminescence from analytes or substrates. One of the main challenges is the lack of readily accessible, effective, and reproducible UV-SERS substrates, with few commercial sources available. In this review, we evaluate the reported UV-SERS substrates in terms of their elemental composition, substrate morphology, and performance. We assess the best-performing substrates with regard to their enhancement factors and limits of detection in both the ultraviolet and deep ultraviolet regions. Even though aluminum nanostructures were the most reported and best-performing substrates, we also highlighted some unique UV-SERS composition and morphology substrate combinations. We address the challenges and potential opportunities in the field of UV-SERS, especially in relation to the development of commercially available, cost-effective substrates. Lastly, we discuss potential application areas for UV-SERS, including cost-effective detection of environmentally and militarily relevant analytes, in situ and operando experimentation, defect engineering, development of materials for extreme environments, and biosensing. Ultraviolet ; Raman scattering ; Materials science ; Photoluminescence ; Nanotechnology ; Substrate (aquarium) ; Raman spectroscopy ; Nanostructure ; Optoelectronics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Quantum Dots Synthesis And Properties
Authors: Taotao Yu | Jianyu Li | Mingjun Han | Yinghe Zhang | Haipeng Li | Qing Peng | Ho-Kin Tang
Source: Nanomaterials (article, 2023-06-25)
Field: Materials Science — Materials Chemistry

Enhancing the Mechanical Stability of 2D Fullerene with a Graphene Substrate and Encapsulation Recent advancements have led to the synthesis of novel monolayer 2D carbon structures, namely quasi-hexagonal-phase fullerene (qHPC<sub>60</sub>) and quasi-tetragonal-phase fullerene (qTPC<sub>60</sub>). Particularly, qHPC<sub>60</sub> exhibits a promising medium band gap of approximately 1.6 eV, making it an attractive candidate for semiconductor devices. In this study, we conducted comprehensive molecular dynamics simulations to investigate the mechanical stability of 2D fullerene when placed on a graphene substrate and encapsulated within it. Graphene, renowned for its exceptional tensile strength, was chosen as the substrate and encapsulation material. We compared the mechanical behaviors of qHPC<sub>60</sub> and qTPC<sub>60</sub>, examined the influence of cracks on their mechanical properties, and analyzed the internal stress experienced during and after fracture. Our findings reveal that the mechanical reliability of 2D fullerene can be significantly improved by encapsulating it with graphene, particularly strengthening the cracked regions. The estimated elastic modulus increased from 191.6 (qHPC<sub>60</sub>) and 134.7 GPa (qTPC<sub>60</sub>) to 531.4 and 504.1 GPa, respectively. Moreover, we observed that defects on the C60 layer had a negligible impact on the deterioration of the mechanical properties. This research provides valuable insights into enhancing the mechanical properties of 2D fullerene through graphene substrates or encapsulation, thereby holding promising implications for future applications. Materials science ; Graphene ; Fullerene ; Ultimate tensile strength ; Nanotechnology ; Tetragonal crystal system ; Molecular dynamics ; Composite material ; Monolayer ; Phase (matter) ; Computational chemistry ; Chemistry Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Fullerene Chemistry and Applications
Authors: Shrutidhara Sarma | V. Ramgopal Rao
Source: Nanotechnology (review, 2023-10-02)
Field: Materials Science — Materials Chemistry

Emerging synthesis and characterization techniques for hybrid polymer nanocomposites Metallic nanoparticles and carbon nanotubes are two of the most promising nanomaterials, due to their distinctive properties occurring from spatial confinement of electron-hole pairs. The unique combination of metallic nanoparticles and carbon nanotubes (CNTs) in a polymer matrix offers unparalleled advantages, making them highly desirable in various fields. Advanced methods and techniques for synthesizing and characterizing hybrid metal-CNT-polymer nanocomposites have undergone significant progress in recent years, paving their integration into various fields, including aerospace, electronics, energy, water treatment and environmental remediation. These advances have allowed better understanding of nanocomposite properties and imparted ability to tune specific properties through size, shape, and distribution control of the nanofillers within the matrix material or by altering filler properties through functionalization. This study aims to critically judge the emerging tools, techniques and methods used in polymer nanocomposites with specific focus on metal-CNT based hybrid polymer nanocomposites, and suggest new avenues for research in the field. Furthermore, by examining the mechanisms affecting the performance of these composites, we can understand how the inclusion of fillers alters the microstructure and overall behavior of the material. Ultimately, this knowledge could lay the foundation for the development of novel nanocomposites with tailored properties and enhanced performance in a plethora of applications. Materials science ; Nanocomposite ; Carbon nanotube ; Nanotechnology ; Polymer nanocomposite ; Characterization (materials science) ; Nanomaterials ; Polymer ; Nanoparticle ; Surface modification ; Composite material ; Mechanical engineering Carbon Nanotubes in Composites ; Polymer Nanocomposite Synthesis and Irradiation ; Dielectric materials and actuators
Authors: Hussain Ali | Prakash Muthudoss | Manikandan Ramalingam | Lakshmi Kanakaraj | Amrit Paudel | Gobi Ramasamy
Source: AAPS PharmSciTech (review, 2023-01-10)
Field: Chemistry — Analytical Chemistry

Machine Learning–Enabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data An increasingly large dataset of pharmaceutics disciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al.. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robust data on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset ( http://www.models.life.ku.dk/Tablets ) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. HIGHLIGHTS: • We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies • A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets • Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance • The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation • Model extension from lab-scale to pilot-scale successfully demonstrated. Hyperparameter ; Workflow ; Computer science ; Machine learning ; Visualization ; Artificial intelligence ; Artificial neural network ; Multilayer perceptron ; Data mining ; Scale (ratio) ; Database Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Grier M. Jones | Run R. Li | A. Eugene DePrince | Konstantinos D. Vogiatzis
Source: The Journal of Physical Chemistry Letters (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

Data-Driven Refinement of Electronic Energies from Two-Electron Reduced-Density-Matrix Theory The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced-density matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can enable large-scale calculations on such systems, the quality of the solution is limited by the fact that only a subset of known necessary <i>N</i>-representability constraints can be applied to the 2RDM in practical calculations. Here, we demonstrate that violations of partial three-particle (T1 and T2) <i>N</i>-representability conditions, which can be evaluated with knowledge of only the 2RDM, can serve as <i>physics-based</i> features in a machine-learning (ML) protocol for improving energies from v2RDM calculations that consider only two-particle (PQG) conditions. Proof-of-principle calculations demonstrate that the model yields substantially improved energies relative to reference values from configuration-interaction-based calculations. RDM ; Electron ; Matrix (chemical analysis) ; Computer science ; Statistical physics ; Exponential function ; Electronic structure ; Density matrix ; Physics ; Density functional theory ; Quantum mechanics ; Mathematics ; Chemistry ; Mathematical analysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Toyin Omojola
Source: Industrial & Engineering Chemistry Research (article, 2023-08-28)
Field: Chemistry — Inorganic Chemistry

Mechanistic Insights into the Induction Period of Methanol-to-Olefin Conversion over ZSM-5 Catalysts: A Combined Temperature-Programmed Surface Reaction and Microkinetic Modeling Study The induction period of propylene formation from methanol is compared to that of dimethyl ether (DME) over ZSM-5 catalysts of different compositions using a combination of temperature-programmed surface reaction experiments and microkinetic modeling. Transient reactor performance is simulated by solving coupled 1D nonlinear partial differential equations accounting for elementary steps based on the methoxy methyl mechanism and axial dispersion and convection in the reactor. Three binding site ensembles and three active site ensembles are observed. These sites constitute up to 3% (binding sites ∼70%, active sites ∼30%) of the total acid sites during methanol conversion and up to 1% (binding sites ∼30%, active sites ∼70%) of the total acid sites during DME conversion. Over the binding sites, during the induction period of methanol, and DME conversion, the acid site density is the key descriptor. Over the active sites, acid site density is the key descriptor with higher site densities correlating with lower barriers of propylene formation during the induction period of methanol conversion. The barrier to DME desorption is the key descriptor, with lower desorption barriers correlating with lower barriers of propylene formation during the induction period of DME conversion. Barriers to propylene formation are lower during the induction period of methanol conversion (up to 141 kJ mol–1) compared to that of DME conversion (up to 200 kJ mol–1) over ZSM-5 catalysts. Induction period ; Methanol ; Chemistry ; Catalysis ; ZSM-5 ; Dimethyl ether ; Desorption ; Olefin fiber ; Organic chemistry ; Adsorption ; Zeolite Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Daniel Vizoso | Ghatu Subhash | Krishna Rajan | Rémi Dingreville
Source: Chemistry of Materials (article, 2023-01-24)
Field: Materials Science — Materials Chemistry

Connecting Vibrational Spectroscopy to Atomic Structure via Supervised Manifold Learning: Beyond Peak Analysis Vibrational spectroscopy is a nondestructive technique commonly used in chemical and physical analyses to determine atomic structures and associated properties. However, the evaluation and interpretation of spectroscopic profiles based on human-identifiable peaks can be difficult and convoluted. To address this challenge, we present a reliable protocol based on supervised manifold learning techniques meant to connect vibrational spectra to a variety of complex and diverse atomic structure configurations. As an illustration, we examined a large database of virtual vibrational spectroscopy profiles generated from atomistic simulations for silicon structures subjected to different stress, amorphization, and disordering states. We evaluated representative features in those spectra via various linear and nonlinear dimensionality reduction techniques and used the reduced representation of those features with decision trees to correlate them with structural information unavailable through classical human-identifiable peak analysis. We show that our trained model accurately (over 97% accuracy) and robustly (insensitive to noise) disentangles the contribution from the different material states, hence demonstrating a comprehensive decoding of spectroscopic profiles beyond classical (human-identifiable) peak analysis. Dimensionality reduction ; Nonlinear dimensionality reduction ; Spectroscopy ; Representation (politics) ; Molecular vibration ; Computer science ; Artificial intelligence ; Molecular physics ; Materials science ; Chemistry ; Chemical physics ; Statistical physics ; Raman spectroscopy ; Physics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: В. И. Болобов | Il’nur U. Latipov | Valentin S. Zhukov | Grigory Popov
Source: Energies (article, 2023-07-25)
Field: Materials Science — Materials Chemistry

Using the Magnetic Anisotropy Method to Determine Hydrogenated Sections of a Steel Pipeline The paper deals with a non-destructive method of detecting hydrogenated sections of pipelines, which is based on variations of the level of mechanical stresses generated in the surface layers of the steel pipe material during its hydrogenation. The use of a magnetoanisotropic method based on the phenomenon of metal magnetoelastic anisotropy development, which consists in the variation of the magnetic properties of ferromagnetic materials in direction and magnitude under the influence of mechanical stresses, is proposed as a way to register that variation. Based on the results of tensile testing of carbon steel plates with measurement of the difference in principal mechanical stresses (DPMS) occurring in metal, as well as experiments on electrolytic hydrogenation of specimens with measurement of the DPMS signal, it was confirmed that when steel structures are saturated with hydrogen, tensile stresses are generated in the surface layers, the magnitude of which increases as the concentration of hydrogen increases in the metal. In this case, it is assumed that the indicated dependence between the hydrogen concentration in the metal and the stresses arising as a result of hydrogenation is linear. For the example of lamellar specimens made of pipe low-carbon steel, the possibility of using the magnetoanisotropic method for registering sections of underground pipelines with a high content of hydrogen is substantiated, which can become the basis for a method of diagnosing sections of pipelines with broken insulation for the possibility of their further operation. The scientific novelty of this article is the establishment of a relationship between the hydrogen content in the metal, the stresses that arise in this case, and the change in the magnetic properties of ferromagnetic materials, characterized by the magnitude of the DPMS signal. This study contributes to the understanding of the process of hydrogenation of metals, and may be useful in detecting and preventing damage to gas and oil pipelines caused by hydrogen embrittlement as a cause of stress corrosion. Materials science ; Anisotropy ; Hydrogen ; Hydrogen embrittlement ; Stress (linguistics) ; Carbon steel ; Metal ; Composite material ; Lamellar structure ; Ultimate tensile strength ; Pipeline transport ; Ferromagnetism ; Metallurgy ; Corrosion ; Condensed matter physics ; Chemistry Material Properties and Failure Mechanisms ; Non-Destructive Testing Techniques ; Industrial Engineering and Technologies