jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alex Bryan | Ethan Wales | Samarth Vedante | Andreu Blanquer | Dipesh Neupane | Sanjay Mishra | Lucie Bačáková | Tomoko Fujiwara | Jessica Amber Jennings | Joel D. Bumgardner
Source: Marine Drugs (article, 2022-09-29)
Field: Materials Science — Biomaterials

Evaluation of Magnesium-Phosphate Particle Incorporation into Co-Electrospun Chitosan-Elastin Membranes for Skin Wound Healing Major challenges facing clinicians treating burn wounds are the lack of integration of treatment to wound, inadequate mechanical properties of treatments, and high infection rates which ultimately lead to poor wound resolution. Electrospun chitosan membranes (ESCM) are gaining popularity for use in tissue engineering applications due to their drug loading ability, biocompatibility, biomimetic fibrous structure, and antimicrobial characteristics. This work aims to modify ESCMs for improved performance in burn wound applications by incorporating elastin and magnesium-phosphate particles (MgP) to improve mechanical and bioactive properties. The following ESCMs were made to evaluate the individual components' effects; (C: chitosan, CE: chitosan-elastin, CMg: chitosan-MgP, and CEMg: chitosan-elastin-MgP). Membrane properties analyzed were fiber size and structure, hydrophilic properties, elastin incorporation, MgP incorporation and in vitro release, mechanical properties, degradation profiles, and in vitro cytocompatibility with NIH3T3 fibroblasts. The addition of both elastin and MgP increased the average fiber diameter of CE (~400 nm), CMg (~360 nm), and CEMg (565 nm) compared to C (255 nm). Water contact angle analysis showed elastin incorporated membranes (CE and CEMg) had increased hydrophilicity (~50°) compared to the other groups (C and CMg, ~110°). The results from the degradation study showed mass retention of ~50% for C and CMg groups, compared to ~ 30% seen in CE and CEMg after 4 weeks in a lysozyme/PBS solution. CMg and CEMg exhibited burst-release behavior of ~6 µg/ml or 0.25 mM magnesium within 72 h. In vitro analysis with NIH3T3 fibroblasts showed CE and CEMg groups had superior cytocompatibility compared to C and CMg. This work has demonstrated the successful incorporation of elastin and MgP into ESCMs and allows for future studies on burn wound applications. Chitosan ; Elastin ; Biocompatibility ; Membrane ; Electrospinning ; Wound healing ; Chemistry ; Materials science ; Contact angle ; Surface modification ; In vitro ; Biomedical engineering ; Chemical engineering ; Nuclear chemistry ; Biophysics ; Polymer ; Biochemistry ; Composite material ; Organic chemistry ; Surgery Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Hemostasis and retained surgical items
Authors: Su-Jin Lee | Byungjoon Min | Junhyeok Bang
Source: Scientific Reports (article, 2022-04-12)
Field: Materials Science — Materials Chemistry

Substrate effect on hydrogen evolution reaction in two-dimensional Mo2C monolayers The physical and chemical properties of atomically thin two-dimensional (2D) materials can be modified by the substrates. In this study, the substrate effect on the electrocatalytic hydrogen evolution reaction (HER) in 2D Mo<sub>2</sub>C monolayers was investigated using first principles calculations. The isolated Mo<sub>2</sub>C monolayer shows large variation in HER activity depending on hydrogen coverage: it has relatively low activity at low hydrogen coverage but high activity at high hydrogen coverage. Among Ag, Au, Cu, and graphene substrates, the HER activity is improved on the Ag and Cu substrates especially at low hydrogen coverage, while the effects of the Au and graphene substrates on the HER activity are insignificant. The improvement is caused by the charge redistribution in the Mo<sub>2</sub>C layer on the substrate, and therefore the HER activity becomes high for any hydrogen coverage on the Ag and Cu substrates. Our results suggest that, in two-dimensional electrocatalysis, the substrate has a degree of freedom to tune the catalytic activity. Monolayer ; Graphene ; Hydrogen ; Substrate (aquarium) ; Catalysis ; Electrocatalyst ; Materials science ; Redistribution (election) ; Chemical engineering ; Chemical physics ; Chemistry ; Nanotechnology ; Photochemistry ; Electrode ; Physical chemistry ; Organic chemistry ; Electrochemistry MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; 2D Materials and Applications
Authors: Paola Di Pietro | Chiara Bigi | Sandeep Kumar Chaluvadi | Daniel Knez | Piu Rajak | Regina Ciancio | Jun Fujii | Francesco Mercuri | S. Lupi | G. Rossi | F. Borgatti | A. Perucchi | P. Orgiani
Source: Advanced Electronic Materials (article, 2022-03-11)
Field: Materials Science — Materials Chemistry

Oxygen‐Driven Metal–Insulator Transition in SrNbO<sub>3</sub> Thin Films Probed by Infrared Spectroscopy Abstract The occurrence of oxygen‐driven metal–insulator‐transition (MIT) in SrNbO 3 (SNO) thin films epitaxially grown on (110)‐oriented DyScO 3 has been reported. SNO films are fabricated by the pulsed laser deposition technique at different partial O 2 pressure to vary the oxygen content and their structural, optical, and transport properties are probed. SNO unit cell has been found to shrink vertically as the oxygen content increases but keeping the epitaxial matching with the substrate. The results of Fourier‐transform infra‐red spectroscopy show that highly oxygenated SNO samples (i.e., grown at high oxygen pressure) show distinct optical conductivity behavior with respect to oxygen deficient films, hence demonstrating the insulating character of the formers with respect to those fabricated with lower pressure conditions. Tailoring the optical absorption and conductivity of strontium niobate epitaxial films across the MIT will favor novel applications of this material. Materials science ; Epitaxy ; Thin film ; Pulsed laser deposition ; Oxygen ; Partial pressure ; Conductivity ; Substrate (aquarium) ; Analytical Chemistry (journal) ; Spectroscopy ; Metal–insulator transition ; Infrared ; Fourier transform infrared spectroscopy ; Optoelectronics ; Metal ; Nanotechnology ; Chemical engineering ; Optics ; Chemistry Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials
Authors: Paweł Stelmachowski | Karolina Kadela | Gabriela Grzybek | Monika Gołda‐Cępa | Krzysztof Kruczała | Andrzej Kotarba
Source: Carbon (article, 2022-07-30)
Field: Materials Science — Materials Chemistry

Post-plasma oxidation in water of graphene paper surface Plasma activation is increasingly used to tailor the electronic properties of carbon materials by surface functionalization, especially with various oxygen groups. However, the actual application of these materials may be some time after the modification was performed, and what is more, in a different environment, e.g. in an aqueous solution. The presented research addresses the problem of instability of plasma-induced changes of graphene paper material where the desired surface property – work function – was verified just after plasma treatment and after bringing the plasma-treated surface to a stable state. In this study, we optimised plasma treatment for the maximum work function changes, and then we determined the speciation of the resulting oxygen groups before and after immersion of the plasma-treated samples in water. We found that the plasma-modified graphene paper's functionalization degree and electronic properties highly depend on the post-plasmatic reactions in water, which increase the concentration of surface oxygen groups. The observed reactivity is rationalized in terms of plasma-emitted irradiation in the ultraviolet range resulting in an enhanced delocalized radical species generation. These radicals react upon contact with water and increase the number of oxygen functional groups. Surface modification ; Graphene ; Delocalized electron ; Plasma ; Radical ; Work function ; Oxygen ; Aqueous solution ; Materials science ; Chemical engineering ; Chemistry ; Photochemistry ; Nanotechnology ; Organic chemistry ; Layer (electronics) ; Physical chemistry Graphene research and applications ; Diamond and Carbon-based Materials Research ; Graphene and Nanomaterials Applications
Authors: Anil Rajak | Jürgen Rühe
Source: Advanced Materials Interfaces (article, 2022-09-06)
Field: Materials Science — Surfaces, Coatings and Films

High Hysteresis Suspended Wetting State: A Wetting Regime for Controlled Trapping of Drops on Micro‐Trench Covered Surfaces Abstract Wetting of simple liquids on rough surfaces is usually described either by the Cassie–Baxter model where drops are suspended over the roughness features with air trapped below, or by the Wenzel model where the drops are impaled on the roughness features. According to the Wenzel model, drops can be trapped on the surfaces, whereas in the Cassie–Baxter model, the drops can be easily released upon adding a small tilt. Surfaces with very regular surface pattern, such as closely spaced micro‐trenches, can exhibit the so‐called High Hysteresis Suspended State (HySS). In this state the drops assume a spherical shape with air trapped below them, a characteristic feature of the Cassie–Baxter state, but show a very high contact angle hysteresis (up to &gt;70°), typical for the Wenzel regime. The HySS regime is studied as a function of the surface geometry, applied pressure and the mass of the drop. Through roll‐off measurements it is shown that surfaces with controlled trapping conditions can be generated, where drops can be captured, however, upon addition of a small amount of energy (e.g., induced by tilt) can be released. The parameters that can be used to control the “depth” of the trap are elucidated. Wetting ; Materials science ; Trapping ; Hysteresis ; Drop (telecommunication) ; Wetting transition ; Surface finish ; Tilt (camera) ; Trench ; Contact angle ; Mechanics ; Surface roughness ; Composite material ; Optics ; Condensed matter physics ; Geometry ; Physics ; Mechanical engineering Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Nanomaterials and Printing Technologies
Authors: Yuxuan Chen | Yuke Li | Zheng-Da Hu | Zexiang Wang | Zhenxing Li | Jicheng Wang
Source: Photonic Sensors (article, 2022-11-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Performance Quality Factor Based Sensor With Diagonal Cylinder Metasurface of the Bound State in the Continuum Abstract High-quality-factor (high- Q -factor) electromagnetic resonance plays an important role in sensor applications. Previously proposed gas refractive index sensors are often limited by the large cavity length or microscale fabrication process in practical applications. Recently, ultra-high Q factor resonance based on the bound state in the continuum (BIC) has provided a feasible approach to solve these problems. In this paper, we propose a metasurface structure consisting of a single size tetramer cylinder. It supports dual band toroidal dipole (TD) resonances driven by BIC. The physical mechanism of double TD resonances is clarified by the multipole decomposition of the metasurface band structure and far-field scattering power. The sensor structure based on this achieves a sensitivity of 518.3 MHz/RIU, and the maximum line width does not exceed 680 kHz. The high- Q -factor electromagnetic resonance has the advantages of polarization independence and simplicity to manufacture. These findings will open up an avenue to develop the ultrasensitive sensor in the gigahertz regime. Multipole expansion ; Q factor ; Physics ; Dipole ; Resonance (particle physics) ; Scattering ; Optics ; Computational physics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Francesco Paolo Pinnola | Marzia Sara Vaccaro | Raffaele Barretta | Francesco Marotti de Sciarra | Giuseppe Ruta
Source: Archive of Applied Mechanics (article, 2022-05-19)
Field: Materials Science — Materials Chemistry

Elasticity problems of beams on reaction-driven nonlocal foundation Abstract A challenging task in nonlocal continuum mechanics consists in formulating constitutive relations leading to well-posed structural problems. Several strategies have been adopted to overcome issues inherent applicability of Eringen’s pure nonlocal theory to nanostructures, such as local/nonlocal mixtures of elasticity and integral models involving modified averaging kernels. These strategies can be applied to the ill-posed problem of flexure of a beam on Wieghardt nonlocal foundation without considering any fictitious boundary forces of constitutive type. A consistent formulation of nonlocal elastic foundation underlying a Bernoulli–Euler beam is thus conceived in the present paper by requiring that transverse displacements are convex combination of reaction-driven local and nonlocal phases governed by Winkler and Wieghardt laws, respectively. The proposed integral mixture is proven to be equivalent to a more convenient differential problem, equipped with nonlocal boundary conditions, which can be effectively exploited to solve nonlocal problems of beams resting on mixture reaction-driven continuous foundation. Effectiveness of the developed nonlocal approach is illustrated by analytically solving simple elasto-static problems of structural mechanics. Boundary value problem ; Elasticity (physics) ; Euler's formula ; Constitutive equation ; Mathematics ; Beam (structure) ; Continuum mechanics ; Regular polygon ; Classical mechanics ; Mathematical analysis ; Applied mathematics ; Physics ; Structural engineering ; Geometry ; Finite element method ; Engineering Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: Alastair Marshall | Thomas Reisser | Phila Rembold | Christoph Müller | Jochen Scheuer | Martin Gierse | Tim R. Eichhorn | Jakob M. Steiner | P. Hautle | Tommaso Calarco | Fedor Jelezko | Martin B. Plenio | Simone Montangero | Ilai Schwartz | Matthias M. Müller | Philipp Neumann
Source: Physical Review Research (article, 2022-12-12)
Field: Chemistry — Spectroscopy

Macroscopic hyperpolarization enhanced with quantum optimal control Hyperpolarization of nuclear spins enhances nuclear magnetic resonance signals, which play a key role for imaging and spectroscopy in the natural and life sciences. This signal amplification unlocks previously inaccessible techniques, such as metabolic imaging of cancer cells. In this paper, electron spins from the photoexcited triplet state of pentacene-doped naphthalene crystals are used to polarize surrounding protons. As existing strategies are rendered less effective by experimental constraints, they are replaced with optimal control pulses designed with redcrab. In contrast to previous optimal control approaches, which consider one or two effective nuclei, this closed-loop optimization is macroscopic. A 26% improvement in signal and 15% faster polarization rate are observed. Additionally, a strategy called autonomously optimized repeated linear sweep (ARISE) is introduced to efficiently tailor existing hyperpolarization sequences in the presence of experimental uncertainty to enhance their performance. ARISE is expected to be broadly applicable in many experimental settings. Hyperpolarization (physics) ; Quantum ; Physics ; Quantum mechanics ; Nuclear magnetic resonance ; Nuclear magnetic resonance spectroscopy Advanced NMR Techniques and Applications ; Atomic and Subatomic Physics Research ; Quantum Information and Cryptography
Authors: Keith A. Brown
Source: npj Computational Materials (article, 2022-05-02)
Field: Materials Science — Materials Chemistry

MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research Research and games both require the participant to make a series of choices. Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research. While this process may seem opaque to researchers outside the field of machine learning, examining active learning in games provides an accessible way to showcase the process and its virtues. Here, we examine active learning through the lens of the game Wordle to both explain the active learning process and describe the types of research questions that arise when using active learning for materials research. Active learning (machine learning) ; Process (computing) ; Computer science ; Field (mathematics) ; Artificial intelligence ; Machine learning ; Data science ; Mathematics ; Programming language Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Machine Learning and Algorithms
Authors: D. Parajuli | N. Murali | K. Samatha
Source: Bibechana (article, 2022-09-12)
Field: Materials Science — Materials Chemistry

Correlation between the Magnetic and DC resistivity studies of Cu substituted Ni and Zn in Ni-Zn ferrites Cu substituted Ni0.5-xCuxZn0.5Fe2O4 (x = 0, 0.05, 0.1, 0.15 and 0.2) samples is synthesized using the sol-gel auto-combustion process. They have a cubic spinel structure with crystallite size in the range of 29.01–42.68 nm. The increment in the copper content increases the DC conductivity. The electrical resistivity decrease with an increase in the temperature i.e. it has a negative temperature coefficient with resistance similar to semiconductors. The remnant ratios R obtained from VSM show their isotropic nature forming single domain ferrimagnetic particles. The results are compared with Ni0.5CuxZn0.5-xFe2O4 (x = 0 to 0.25). The resultant material Cu substituted Zn is more significant than that of Ni as indicated by its results and previous literature. BIBECHANA 19 (2022) 61-67 Ferrimagnetism ; Electrical resistivity and conductivity ; Spinel ; Crystallite ; Materials science ; Analytical Chemistry (journal) ; Copper ; Metal ; Metallurgy ; Magnetization ; Chemistry ; Magnetic field Magnetic Properties and Synthesis of Ferrites ; Semiconductor materials and interfaces ; Magnetic Properties and Applications
Authors: David A. Leonard | Pavel Ryabchuk | Muhammad Anwar | S. Dastgir | Kathrin Junge | Matthias Beller
Source: ChemSusChem (article, 2022-01-03)
Field: Chemistry — Organic Chemistry

A Convenient and Stable Heterogeneous Nickel Catalyst for Hydrodehalogenation of Aryl Halides Using Molecular Hydrogen Hydrodehalogenation is an effective strategy for transforming persistent and potentially toxic organohalides into their more benign congeners. Common methods utilize Pd/C or Raney-nickel as catalysts, which are either expensive or have safety concerns. In this study, a nickel-based catalyst supported on titania (Ni-phen@TiO<sub>2</sub> -800) is used as a safe alternative to pyrophoric Raney-nickel. The catalyst is prepared in a straightforward fashion by deposition of nickel(II)/1,10-phenanthroline on titania, followed by pyrolysis. The catalytic material, which was characterized by SEM, TEM, XRD, and XPS, consists of nickel nanoparticles covered with N-doped carbon layers. By using design of experiments (DoE), this nanostructured catalyst is found to be proficient for the facile and selective hydrodehalogenation of a diverse range of substrates bearing C-I, C-Br, or C-Cl bonds (>30 examples). The practicality of this catalyst system is demonstrated by the dehalogenation of environmentally hazardous and polyhalogenated substrates atrazine, tetrabromobisphenol A, tetrachlorobenzene, and a polybrominated diphenyl ether (PBDE). Halide ; Aryl ; Nickel ; Catalysis ; Chemistry ; Hydrogen ; Heterogeneous catalysis ; Combinatorial chemistry ; Organic chemistry ; Alkyl Nanomaterials for catalytic reactions ; Environmental remediation with nanomaterials ; Asymmetric Hydrogenation and Catalysis
Authors: Lei Gan
Source: International Journal of Thermophysics (article, 2022-05-06)
Field: Materials Science — Materials Chemistry

Predicting Density and Viscosity for Liquid Metals and Alloys Using Machine Learning Overfitting ; Artificial neural network ; Viscosity ; Random forest ; Machine learning ; Linear regression ; Materials science ; Artificial intelligence ; Ternary operation ; Computer science Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Theoretical and Computational Physics
Authors: Boyu Zhang | Musen Zhou | Jian Wu | Fuchang Gao
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Predicting the Materials Properties Using a 3D Graph Neural Network With Invariant Representation Accurate prediction of physical properties is critical for discovering and designing novel materials. Machine learning technologies have attracted significant attention in the materials science community for their potential for large-scale screening. Graph Convolution Neural Network (GCNN) is one of the most successful machine learning methods because of its flexibility and effectiveness in describing 3D structural data. Most existing GCNN models focus on the topological structure but overly simplify the three-dimensional geometric structure. However, in materials science, the 3D-spatial distribution of atoms is crucial for determining the atomic states and interatomic forces. This paper proposes an adaptive GCNN with a novel convolution mechanism that simultaneously models atomic interactions among all neighboring atoms in three-dimensional space. We apply the proposed model to two distinctly challenging materials properties prediction problems. The first is Henry&#x2019;s constant for gas adsorption in Metal-Organic Frameworks (MOFs), which is notoriously difficult because of its high sensitivity to atomic configurations. The second is the ion conductivity in solid-state crystal materials, which is difficult because of the few labeled data available for training. The new model outperforms existing graph-based models on both data sets, suggesting that the critical three-dimensional geometric information is indeed captured. Computer science ; Graph ; Convolution (computer science) ; Convolutional neural network ; Artificial neural network ; Theoretical computer science ; Representation (politics) ; Invariant (physics) ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: H.R. Abedi | Keyvan Safaei Baghbaderani | Ala’aldin Alafaghani | Mohammadreza Nematollahi | Fatemeh Kordizadeh | Moataz M. Attallah | Ala Qattawi | Mohammad Elahinia
Source: Journal of Materials Engineering and Performance (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Neural Network Modeling of NiTiHf Shape Memory Alloy Transformation Temperatures Artificial neural network ; Transformation (genetics) ; Shape-memory alloy ; Range (aeronautics) ; Materials science ; Computer science ; Process (computing) ; Artificial intelligence ; Pattern recognition (psychology) Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing