jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kairi Morozumi | H. Kanemaru | Akihiro Okada | T. Ichimiya | Takayoshi Kobayashi | Eiji Tokunaga
Source: Applied Sciences (article, 2024-03-01)
Field: Chemistry — Physical and Theoretical Chemistry

Pockels Effect of Interfacial Water on a Mono-Electrode Induced by Current Parallel to the Electrode Surface When an electric field is applied between two electrodes facing each other immersed in a liquid, the interfacial Pockels effect, a refractive index change proportional to the electric field, occurs in the electric double layer at the liquid–electrode interface. Here, we report that the Pockels effect of interfacial water can be observed even when an electric field is applied parallel to the surface of a “single” electrode in an electrolyte solution. This is a non-trivial result since the electric field parallel to the interface should not cause a broken spatial inversion symmetry, which is required for the Pockels effect. The Pockels signal was detected as a change in the transmitted light intensity due to the field-induced spectral shift of the interference fringes of the transparent conductive oxide electrode layer on a glass substrate. The magnitude of the signal increased as it approached the ends of the electrode, and the sign reversed across the center of the electrode. The electric field distribution calculated from the interfacial potential difference due to the in-plane parallel current showed that an electric field perpendicular to the interface was induced, whose distribution was consistent with the position dependence of the Pockels signal. A similar phenomenon was also observed for a single copper electrode, confirming that this is a universal effect. Electrode ; Materials science ; Current (fluid) ; Optoelectronics ; Electrical engineering ; Chemistry ; Engineering Electrostatics and Colloid Interactions ; Electrohydrodynamics and Fluid Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Som Shrestha | Zoriana Demchuk | Felipe Polo‐Garzon | Achutha Tamraparni | Joshua T. Damron | Daryl L. Howard | Tomonori Saito | Alexei P. Sokolov | Diana Hun | Catalin Gainaru
Source: Heliyon (article, 2024-08-01)
Field: Materials Science — Polymers and Plastics

An experimental toolbox for the physical characterization of thermal insulating polymeric foams Recent advancements in polymer science and manufacturing technologies triggered new developments of porous materials used for mitigating heat losses, such as thermal insulating polymeric foams. The major bottleneck in the optimization of these products, however, remains the absence of analytical methods able to scrutinize their large design space reasonably quickly and cost-effectively. This manuscript targets the paucity of data for polymeric foams by illustrating, at a proof-of-principle level, that several well-established analytical methods including optical microscopy, pycnometry, dielectric spectroscopy, thermogravimetric analysis, and nuclear magnetic resonance can be exploited for an extensive, yet logistically efficient, characterization of these materials. The purpose of this study is thus introducing an experimental platform for the characterization of market foam products and for the development of new polymeric foams with pore sizes that are particularly relevant for industrial and residential thermal insulation. Since this work introduces several new methodologies, it may be used as a guide for both laboratory users and specialists in the field, who may further improve the herein proposed experimental concepts. Characterization (materials science) ; Materials science ; Thermal insulation ; Process engineering ; Thermogravimetric analysis ; Nanotechnology ; Porous medium ; Porosity ; Mechanical engineering ; Composite material ; Engineering ; Chemical engineering Polymer Foaming and Composites ; Phase Equilibria and Thermodynamics ; Heat and Mass Transfer in Porous Media
Authors: Atsushi Hashimoto | Ken‐ichiro Suehara | Takaharu Kameoka
Source: Sensors (article, 2024-02-09)
Field: Chemistry — Analytical Chemistry

Development of a Simultaneous Quantification Method for Multiple Modes of Nitrogen in Leaf Models Using Near-Infrared Spectroscopic Measurement By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein-nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites. Near-infrared spectroscopy ; Nitrogen ; Spectrometer ; Absorption (acoustics) ; Nitrate ; Nitric acid ; Analytical Chemistry (journal) ; Chemistry ; Biological system ; Remote sensing ; Materials science ; Optics ; Environmental chemistry ; Physics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Christopher A. Mizzi | B. Maiorov
Source: The Journal of the Acoustical Society of America (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Enabling resonant ultrasound spectroscopy in high magnetic fields Resonant ultrasound spectroscopy (RUS) is a powerful method to determine elastic constants with high accuracy and precision from a single measurement of the mechanical resonances of a sample. Conventionally, the quantitative extraction of elastic moduli with RUS assumes free boundary conditions which can often lead to the adoption of unstable sample positioning between ultrasonic transducers that is incompatible with extreme environments like high magnetic fields. We show that, under specific conditions, introducing a small amount of adhesive between a RUS sample and ultrasonic transducers introduces a perturbation to the free resonance condition which can be accounted for by a simple model. This means elastic constants can be determined to within the uncertainty of conventional RUS, but with significant improvements including sample stability and control of sample orientation. We demonstrate the efficacy of this approach with measurements on a range of materials including room temperature measurements on polycrystalline metals, temperature-dependent measurements of the structural phase transition in strontium titanate single crystals, and magnetic field-dependent measurements of magnetic phase transitions in gadolinium polycrystals up to 14 T. Resonant ultrasound spectroscopy ; Materials science ; Ultrasonic sensor ; Spectroscopy ; Magnetic field ; Transducer ; Magnetometer ; Condensed matter physics ; Phase transition ; Nuclear magnetic resonance ; Elastic modulus ; Acoustics ; Composite material ; Physics Ferroelectric and Piezoelectric Materials ; High-pressure geophysics and materials ; Electronic and Structural Properties of Oxides
Authors: Peter L. Rodríguez‐Kessler
Source: arXiv (Cornell University) (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Revisiting the Global Minimum Structure of the Pt5V Cluster In this work, the most stable structures of Pt5V clusters are investigated using the successive growth algorithm (SCG) in conjunction with density functional theory (DFT) calculations. The resulting structures are evaluated by various functionals, including GGA (PBE), meta-GGA (TPSS) and hybrid (B3PW91, PBE0, PBEh-3c, M06-L), in conjunction with the Def2TZVP basis set. The results based on these functionals show that two different structures compete for the global minimum. The structural and electronic properties of the two most stable structures are discussed. Cluster (spacecraft) ; Economic geography ; Geography ; Computer science Nanocluster Synthesis and Applications
Authors: Samir F. Matar | Vladimir L. Solozhenko
Source: C – Journal of Carbon Research (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Novel Superhard Tetragonal Hybrid sp3/sp2 Carbon Allotropes Cx (x = 5, 6, 7): Crystal Chemistry and Ab Initio Studies Novel superhard tetragonal carbon allotropes C5, C6, and C7, characterized by the presence of sp3- and sp2-like carbon sites, have been predicted from crystal chemistry and extensively studied by quantum density functional theory (DFT) calculations. All new allotropes were found to be cohesive, with crystal densities and cohesive energies decreasing along the C5-C6-C7 series due to the greater openness of the structures resulting from the presence of C=C ethene and C=C=C propadiene subunits, and they were mechanically stable, with positive sets of elastic constants. The Vickers hardness evaluated by different models qualifies all allotropes as superhard, with Hv values ranging from 90 GPa for C5 to 79 GPa for C7. Phonon band structures confirm that the new allotropes are also dynamically stable. The electronic band structures reveal their metallic-like behavior due to the presence of sp2-hybridized carbon. Tetragonal crystal system ; Density functional theory ; Ab initio ; Materials science ; Carbon fibers ; Ab initio quantum chemistry methods ; Crystal (programming language) ; Quantum chemistry ; Crystal structure ; Phonon ; Crystallography ; Computational chemistry ; Chemical physics ; Chemistry ; Molecule ; Condensed matter physics ; Organic chemistry ; Composite material ; Physics ; Composite number Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics ; Fullerene Chemistry and Applications
Authors: Meiping Jiang | Ruiping Wu | Dongqin Liu | Xiaoli Wang
Source: Scientific Reports (article, 2024-03-05)
Field: Materials Science — Biomaterials

Utilizing Ni(II) complex for metal drug-gel particles in cervical cancer treatment and designing novel drugs through machine learning methods Abstract In the present study, a novel coordination polymer (CP) based on Ni(II), namely, [Ni(L)(D-CAM)(H 2 O)] n ( 1 ) (H 2 D-CAM = (1 R ,3 S )-1,2,2-trimethylcyclopentane-1,3-dicarboxylic acid and L = 3,6-bis(benzimidazol-1-yl)pyridazine), has been produced successfully through applying a mixed ligand synthesis method via reacting Ni(NO 3 ) 2 ·6H 2 O with 3,6-bis(benzimidazol-1-yl)pyridazine ligand in the presence of a carboxylic acid co-ligand. Hyaluronic acid (HA) and carboxymethyl chitosan (CMCS) are representatives of natural polysaccharides and have good biocompatibility. Based on the chemical synthesis method, HA/CMCS hydrogel was successfully prepared. SEM showed that the lyophilized gel presented a typical macroporous structure with three-dimensional connected pores, which had unique advantages as a drug carrier. Using paclitaxel as a drug model, we further synthesized a novel paclitaxel-loaded metal gel and evaluated its therapeutic effect on cervical cancer. Finally, novel drugs from the reinforcement learning simulation are suggested to have better biological activity against ovarian cancer due to low affinity energy and stronger interaction strength towards the protein receptor. Pyridazine ; Paclitaxel ; Biocompatibility ; Hyaluronic acid ; Ligand (biochemistry) ; Chemistry ; Combinatorial chemistry ; Chitosan ; Drug ; Nuclear chemistry ; Materials science ; Stereochemistry ; Receptor ; Cancer ; Organic chemistry ; Pharmacology ; Biochemistry ; Biology Nanoparticle-Based Drug Delivery ; Dendrimers and Hyperbranched Polymers ; Nanoplatforms for cancer theranostics
Authors: Junpu Wang | Xiaozhuang Yue | Yao Wang | Liupeng Di | Wenzhi Wang | Jingchao Wei | Fei Yu
Source: Polymers (article, 2024-10-06)
Field: Materials Science — Materials Chemistry

A Three-Dimensional Modeling Approach for Carbon Nanotubes Filled Polymers Utilizing the Modified Nearest Neighbor Algorithm Carbon nanotubes (CNTs) are extensively utilized in the fabrication of high-performance composites due to their exceptional mechanical, electrical, and thermal characteristics. To investigate the mechanical properties of CNTs filled polymers accurately and effectively, a 3D modeling approach that incorporates the microstructural attributes of CNTs was introduced. Initially, a representative volume element model was constructed utilizing the modified nearest neighbor algorithm. During the modeling phase, a corresponding interference judgment method was suggested, taking into account the potential positional relationships among the CNTs. Subsequently, stress-strain curves of the model under various loading conditions were derived through finite element analysis employing the volume averaging technique. To validate the efficacy of the modeling approach, the stress within a CNT/epoxy resin composite with varying volume fractions under different axial strains was computed. The resulting stress-strain curves were in good agreement with experimental data from the existing literature. Hence, the modeling method proposed in this study provides a more precise representation of the random distribution of CNTs in the matrix. Furthermore, it is applicable to a broader range of aspect ratios, thereby enabling the CNT simulation model to more closely align with real-world models. Carbon nanotube ; k-nearest neighbors algorithm ; Materials science ; Polymer ; Algorithm ; Nanotechnology ; Computer science ; Composite material ; Artificial intelligence Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites ; Polymer Nanocomposites and Properties
Authors: Tamara Gavrilović | Vesna Đorđević | Jovana Periša | Mina Medić | Zoran Ristić | Aleksandar Ćirić | Željka Antić | Miroslav D. Dramićanin
Source: Materials (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Luminescence Thermometry with Eu3+-Doped Y2Mo3O12: Comparison of Performance of Intensity Ratio and Machine Learning Temperature Read-Outs Accurate temperature measurement is critical across various scientific and industrial applications, necessitating advancements in thermometry techniques. This study explores luminescence thermometry, specifically utilizing machine learning methodologies to enhance temperature sensitivity and accuracy. We investigate the performance of principal component analysis (PCA) on the Eu<sup>3+</sup>-doped Y<sub>2</sub>Mo<sub>3</sub>O<sub>12</sub> luminescent probe, contrasting it with the traditional luminescence intensity ratio (LIR) method. By employing PCA to analyze the full emission spectra collected at varying temperatures, we achieve an average accuracy (ΔT) of 0.9 K and a resolution (δT) of 1.0 K, significantly outperforming the LIR method, which yielded an average accuracy of 2.3 K and a resolution of 2.9 K. Our findings demonstrate that while the LIR method offers a maximum sensitivity (Sr) of 5‱ K⁻<sup>1</sup> at 472 K, PCA's systematic approach enhances the reliability of temperature measurements, marking a crucial advancement in luminescence thermometry. This innovative approach not only enriches the dataset analysis but also sets a new standard for temperature measurement precision. Luminescence ; Doping ; Materials science ; Intensity (physics) ; Optoelectronics ; Composite material ; Optics ; Physics Luminescence Properties of Advanced Materials ; Optical properties and cooling technologies in crystalline materials ; Perovskite Materials and Applications
Authors: Shen Q. Pan | Zhanyuan Chang
Source: Sensors (article, 2024-08-28)
Field: Chemistry — Organic Chemistry

WD-1D-VGG19-FEA: An Efficient Wood Defect Elastic Modulus Predictive Model As a mature non-destructive testing technology, near-infrared (NIR) spectroscopy can effectively identify and distinguish the structural characteristics of wood. The Wood Defect One-Dimensional Visual Geometry Group 19-Finite Element Analysis (WD-1D-VGG19-FEA) algorithm is used in this study. 1D-VGG19 classifies the near-infrared spectroscopy data to determine the knot area, fiber deviation area, transition area, and net wood area of the solid wood board surface and generates a two-dimensional image of the board surface through inversion. Then, the nonlinear three-dimensional model of wood with defects was established by using the inverse image, and the finite element analysis was carried out to predict the elastic modulus of wood. In the experiment, 270 points were selected from each of the four regions of the wood, totaling 1080 sets of near-infrared data, and the 1D-VGG19 model was used for classification. The results showed that the identification accuracy of the knot area was 95.1%, the fiber deviation area was 92.7%, the transition area was 90.2%, the net wood area was 100%, and the average accuracy was 94.5%. The error range of the elastic modulus prediction of the three-dimensional model established by the VGG19 classification model in the finite element analysis is between 2% and 10%, the root mean square error (RMSE) is about 598. 2, and the coefficient of determination (R2) is 0. 91. This study shows that the combination of the VGG19 algorithm and finite element analysis can accurately describe the nonlinear defect morphology of wood, thus establishing a more accurate prediction model of wood mechanical properties to maximize the use of wood mechanical properties. Finite element method ; Mean squared error ; Knot (papermaking) ; Structural engineering ; Vibration ; Materials science ; Mathematics ; Algorithm ; Biological system ; Composite material ; Engineering ; Acoustics ; Physics ; Statistics ; Biology Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Wood Treatment and Properties
Authors: Anchen Li | Elena Casiraghi | Juho Rousu
Source: Bioinformatics (article, 2024-09-13)
Field: Materials Science — Materials Chemistry

Chemical reaction enhanced graph learning for molecule representation The code is publicly available at https://github.com/coder-ACAC/RLM. Computer science ; Graph ; Representation (politics) ; Theoretical computer science ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Kyohei Hayakawa | Isao Matsui | Yuichi Sekine | Takaharu Maeguchi
Source: MATERIALS TRANSACTIONS (article, 2024-07-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning to Predict the Effect of Stress on Iron Loss and Its Frequency Dependence in Non-Oriented Electrical Steels At present, almost 50% of electrical power is consumed by motors. Thus, increasing the efficiency of motors is an important issue. To achieve more efficient operation, it is vital to improve the accuracy of input data for motor loss design. In this study, we focused on the iron loss of electromagnetic steels, which is assumed to account for a large proportion of motor losses, and examined whether the effect of stress on the iron loss and its frequency dependence could be predicted with high accuracy by machine learning. First, experimental iron loss data are obtained at flux densities of 0.1–1.7 T, frequencies of 50–3000 Hz, and applied stresses from −200 to 200 MPa. No significant deterioration in iron loss behavior is observed in specimens subjected to 3% and 10% pre-strain by tensile loading. These data show that the effect of stress on iron loss varies significantly depending on the excitation conditions. The complex iron loss behaviors are the result of interplay between magnetic wall movement and magnetic domain rotation during the magnetization process. As simple regression of the magnetization process is difficult, we apply three machine learning algorithms to the experimental dataset. The results show that the LightGBM algorithm produces the most accurate predictions of the experimental iron loss values. The contributions of the explanatory variables are found to be consistent with empirical knowledge. This study demonstrates the potential for machine learning to enable improve the accuracy of iron loss data input to motor loss design. Materials science ; Stress (linguistics) ; Metallurgy Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Jinfeng Li | Haorong Li
Source: Electronics (article, 2024-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Susceptibility to Low-Frequency Breakdown in Full-Wave Models of Liquid Crystal-Coaxially-Filled Noise-Shielded Analog Phase Shifters Building on the fully encapsulated architecture of liquid crystal (LC) coaxial phase shifters, which leverages noise-shielding advantages for millimeter-wave wideband reconfigurable applications, this study addresses the less-explored issue of low-frequency breakdown (LFB) susceptibility in modern full-wave solvers. Specifically, it identifies the vulnerability nexus between the tuning states (driven by low-frequency bias voltages) and the constitutive elements of LC-filled coaxial phase shifters—namely, the core line, housing grounding, and radially sandwiched tunable dielectrics—operating at millimeter-wave frequencies (60 GHz WiGig), microwave (1 GHz), and far lower frequency regimes (down to 1 MHz, 1 kHz, and 1 Hz) for long-wavelength or quasi-static conditions, with specialized applications in submarine communications and geophysical exploration. For completeness, the study also investigates the device state prior to LC injection, when the cavity is air-filled. Key computational metrics, such as effective permittivity and characteristic impedance, are analyzed. The results show that at 1 kHz, deviations in effective permittivity exceed four orders of magnitude compared to 1 GHz, while characteristic impedance exhibits deviations of three orders of magnitude. More critically, in the LFB regime, theoretical benchmarks from 1 MHz to 1 kHz and 1 Hz demonstrate an exponential increase in prediction error for both effective permittivity, rising from 16.8% to 1.5 × 104% and 1.5 × 107%, and for characteristic impedance, escalating from 8.1% to 1.15 × 103% and 3.9 × 104%, respectively. Consequently, the prediction error of the differential phase shift, minimal at 60 GHz (0.16%), becomes noticeable at 1 MHz (4.39%), increases sharply to 743.88% at 1 kHz, and escalates dramatically to 2.18 × 1010% at 1 Hz. The findings reveal a pronounced frequency asymmetry in LFB susceptibility for the LC coaxial phase shifter biased at extremely low frequencies. Shielded cable ; Phase (matter) ; Noise (video) ; Acoustics ; Materials science ; Physics ; Electrical engineering ; Engineering ; Computer science Liquid Crystal Research Advancements ; Nonlinear Dynamics and Pattern Formation ; Quantum optics and atomic interactions