jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanyan Chen | Jie Sun | Wei Kang | Qian Wang
Source: Materials (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Phonon Transport and Thermoelectric Properties of Imidazole-Graphyne The pentagon has been proven to be an important structural unit for carbon materials, leading to different physical and chemical properties from those of hexagon-based allotropes. Following the development from graphene to penta-graphene, a breakthrough has very recently been made for graphyne-for example, imidazole-graphyne (ID-GY) was formed by assembling experimentally synthesized pentagonal imidazole molecules and acetylenic linkers. In this work, we study the thermal properties and thermoelectric performance of ID-GY by combining first principle calculations with the Boltzmann transport theory. The calculated lattice thermal conductivity of ID-GY is 10.76 W/mK at 300 K, which is only one tenth of that of <i>γ</i>-graphyne (106.24 W/mK). A detailed analysis of the harmonic and anharmonic properties, including the phonon group velocity, phonon lifetime, atomic displacement parameter, and bond energy curves, reveals that the low lattice thermal conductivity can be attributed to the low Young's modulus, low Debye temperature, and high Grüneisen parameter. Furthermore, at room temperature, ID-GY can reach a high <i>ZT</i> value of 0.46 with a 5.8 × 10<sup>12</sup> cm<sup>-2</sup> hole concentration, which is much higher than the value for many other carbon-based materials. This work demonstrates that changing structural units from hexagonal to pentagonal can significantly reduce the lattice thermal conductivity and enhance the thermoelectric performance of carbon-based materials. Graphyne ; Materials science ; Thermal conductivity ; Thermoelectric effect ; Phonon ; Graphene ; Condensed matter physics ; Thermoelectric materials ; Nanotechnology ; Thermodynamics ; Composite material ; Physics Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Guangxin Liu | Zhang Dinghua | Yao Changfeng
Source: Journal of Mechanical Engineering (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Research Progress of the Microstructure on Machined Surface of Titanium Alloys 摘要： 钛合金是航空发动机关键构件的主选材料，切削加工是其制造工艺中最基本的加工方法之一。切削加工产生的热-机载荷会导致钛合金表层微观组织发生复杂变化，从而在加工表面形成表面变质层。表层微观组织严重影响着工件的物理、力学性能，因此，研究钛合金切削表层微观组织对改善航空发动机关键构件的使役性能与可靠性有重要意义。基于表面变质层的形成机理，工艺因子对表层微观组织的影响规律以及微观组织的模拟与预测三个方面的研究成果，介绍钛合金切削表层微观组织的研究现状。最后，分析现有研究存在的一些不足以及未来的研究方向。 Microstructure ; Materials science ; Metallurgy ; Titanium alloy ; Titanium ; Alloy Titanium Alloys Microstructure and Properties ; Material Properties and Applications ; Metallurgy and Material Forming
Authors: Sávio José Vieira Zaccaro | Adhimar Flávio Oliveira | R. M. Rubinger | Crediana Chris de Siqueira | Roberto Affonso da Costa
Source: Research Society and Development (article, 2021-08-14)
Field: Materials Science — Surfaces, Coatings and Films

Determination of thickness and refractive index of SiO2 thin films using the cross-entropy global optimization method Silicon dioxide (SiO2) is a material that is abundant in nature and has wide application in semiconductor and insulating devices. In this work, a set of six SiO2 samples were grown on a Sigma-Aldrich Silicon substrate, varying the growth time and temperature. This set of samples were grown using times of 10 and 12h and temperatures of 800, 900, and 1000 ºC, under ambient atmosphere. After film growth, reflectance measurements were performed on the films and the substrate, using the Stellarnet UV-VIS-NIR spectrophotometer between 194 and 1081.5 nm. These measurements were modeled using a global optimization method, called Cross-entropy, together with the Bootstrapping resampling technique, seeking to robustly and statistically determine the thin film refractive index as a function of the wavelength and its thickness. To estimate the refractive index of the SiO2 thin film, the Cauchy model was used. For the substrate, reflectance measurements were used. The method proved to be efficient, presenting thickness values that were validated according to growth parameters and literature data. This method proved to be an important and low-cost tool, compared to traditional methods, to help in the steps of building thin films for semiconductor and insulating devices, thus improving their physical properties and enabling the development of new devices. Refractive index ; Materials science ; Thin film ; Silicon ; Substrate (aquarium) ; Semiconductor ; Optics ; Optoelectronics ; Nanotechnology Optical Coatings and Gratings ; Thin-Film Transistor Technologies ; Photonic Crystals and Applications
Authors: Jenna L. Wardini | Hasti Vahidi | Huiming Guo | William J. Bowman
Source: Frontiers in Chemistry (review, 2021-11-29)
Field: Materials Science — Materials Chemistry

Probing Multiscale Disorder in Pyrochlore and Related Complex Oxides in the Transmission Electron Microscope: A Review Transmission electron microscopy (TEM), and its counterpart, scanning TEM (STEM), are powerful materials characterization tools capable of probing crystal structure, composition, charge distribution, electronic structure, and bonding down to the atomic scale. Recent (S)TEM instrumentation developments such as electron beam aberration-correction as well as faster and more efficient signal detection systems have given rise to new and more powerful experimental methods, some of which (e.g., 4D-STEM, spectrum-imaging, in situ/operando (S)TEM)) facilitate the capture of high-dimensional datasets that contain spatially-resolved structural, spectroscopic, time- and/or stimulus-dependent information across the sub-angstrom to several micrometer length scale. Thus, through the variety of analysis methods available in the modern (S)TEM and its continual development towards high-dimensional data capture, it is well-suited to the challenge of characterizing isometric mixed-metal oxides such as pyrochlores, fluorites, and other complex oxides that reside on a continuum of chemical and spatial ordering. In this review, we present a suite of imaging and diffraction (S)TEM techniques that are uniquely suited to probe the many types, length-scales, and degrees of disorder in complex oxides, with a focus on disorder common to pyrochlores, fluorites and the expansive library of intermediate structures they may adopt. The application of these techniques to various complex oxides will be reviewed to demonstrate their capabilities and limitations in resolving the continuum of structural and chemical ordering in these systems. Scanning transmission electron microscopy ; Nanotechnology ; Pyrochlore ; Transmission electron microscopy ; Materials science ; Characterization (materials science) ; Atomic units ; Chemical physics ; Chemistry ; Physics ; Phase (matter) Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: R. N. Plotnikova
Source: Proceedings of the Voronezh State University of Engineering Technologies (article, 2021-06-03)
Field: Materials Science — Polymers and Plastics

Investigation of the properties of the brominated phthalate-containing system and determination of its application areas The structure of a multicomponent system including phthalic acid esters has been studied using the methods of infrared spectroscopy and proton magnetic resonance. The obtained spectrograms are analyzed, indicating the presence of peaks corresponding to certain radicals present in the assumed structure of the main organic compounds of the multicomponent system. The presence of bromine atoms in the radicals of the molecules that make up the phthalate-containing system is established. The structure of the molecules of the basic component of the brominated system is shown. Spectral studies have confirmed the classical model of bromine addition at the site of double carbon-carbon bonds in the phthalate radical. It is established that the brominated multicomponent physico-chemical system is represented to a greater extent by esters of o-phthalic acid. The solubility parameter of the system under study is determined by the calculation method. The solubility parameter 35.3 (kJ/m3)0.5 was experimentally confirmed for a brominated phthalate-containing system. It is shown that the Small formula used to calculate the parameters of individual substances can be used to estimate the parameters of complex multicomponent systems. Polymer materials with the minimum values of deviations in the solubility parameter with the system under study, for which ~±1.5, are determined. It is established that the system under study can be used as an effective plasticizer of polybutyl acrylate, polyisoprene, polyvinyl chloride, and polyvinyl acetate. Phthalic acid ; Phthalate ; Bromine ; Plasticizer ; Solubility ; Chemistry ; Molecule ; Polyvinyl chloride ; Hildebrand solubility parameter ; Organic chemistry ; Polymer ; Infrared spectroscopy ; Physical chemistry Polymer Synthesis and Characterization ; Thermal and Kinetic Analysis ; Polymer Science and PVC
Authors: Tao Pang | Haitao Zhang | Liliang Wen | Jun Tang | Bing Zhou | Qianxu Yang | Yong Li | Jiajun Wang | Aiming Chen | Zhongda Zeng
Source: Journal of Analytical Methods in Chemistry (article, 2021-01-20)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of a Weak Correlation between Complicated Data on the Basis of Principal Component Analysis The mining of weak correlation information between two data matrices with high complexity is a very challenging task. A new method named principal component analysis-based multiconfidence ellipse analysis (PCA/MCEA) was proposed in this study, which first applied a confidence ellipse to describe the difference and correlation of such information among different categories of objects/samples on the basis of PCA operation of a single targeted data. This helps to find the number of objects contained in the overlapping and nonoverlapping areas of ellipses obtained from PCA runs. Then, a quantitative evaluation index of correlation between data matrices was defined by comparing the PCA results of more than one data matrix. The similarity and difference between data matrices was further quantified through comprehensively analyzing the outcomes. Complicated data of tobacco agriculture were used as an example to illustrate the strategy of the proposed method, which includes rich features of climate, altitude, and chemical compositions of tobacco leaves. The number of objects of these data reached 171,516 with 14, 4, and 5 descriptors of climate, altitude, and chemicals, respectively. On the basis of the new method, the complex but weak relationship between these independent and dependent variables were interestingly studied. Three widely used but conventional methods were applied for comparison in this work. The results showed the power of the new method to discover the weak correlation between complicated data. Principal component analysis ; Ellipse ; Basis (linear algebra) ; Correlation ; Similarity (geometry) ; Mathematics ; Pattern recognition (psychology) ; Covariance matrix ; Data mining ; Matrix (chemical analysis) ; Computer science ; Statistics ; Artificial intelligence ; Geometry Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Remote Sensing and Land Use
Authors: Hiroaki Ito | Takashi Matsui | Ryo Konno | Makoto Itakura | Yoshio Kodera
Source: Scientific Reports (article, 2021-12-03)
Field: Chemistry — Spectroscopy

LC–MS peak assignment based on unanimous selection by six machine learning algorithms Recent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)-MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm. Algorithm ; Computer science ; Artificial intelligence ; Identification (biology) ; Mass spectrometry ; Reduction (mathematics) ; Proteome ; Machine learning ; Selection (genetic algorithm) ; Bioinformatics ; Mathematics ; Chemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Lennart Martens | Robbin Bouwmeester | Ralf Gabriels | Niels Hulstaert | Sven Degroeve
Source: Research Square (preprint, 2021-03-12)
Field: Chemistry — Spectroscopy

DeepLC can predict retention times for peptides that carry as-yet unseen modifications Carry (investment) ; Computer science ; Computational biology ; Chemistry ; Biology ; Business ; Finance Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Yang Li | Shahar Kvatinsky | Lior Kornblum
Source: Frontiers in Physics (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

Harnessing Conductive Oxide Interfaces for Resistive Random-Access Memories Two-dimensional electron gases (2DEGs) can be formed at some oxide interfaces, providing a fertile ground for creating extraordinary physical properties. These properties can be exploited in various novel electronic devices such as transistors, gas sensors, and spintronic devices. Recently several works have demonstrated the application of 2DEGs for resistive random-access memories (RRAMs). We briefly review the basics of oxide 2DEGs, emphasizing scalability and maturity and describing a recent trend of progression from epitaxial oxide interfaces (such as LaAlO 3 /SrTiO 3 ) to simple and highly scalable amorphous-polycrystalline systems (e.g., Al 2 O 3 /TiO 2 ). We critically describe and compare recent RRAM devices based on these systems and highlight the possible advantages and potential of 2DEGs systems for RRAM applications. We consider the immediate challenges to revolve around scaling from one device to large arrays, where further progress with series resistance reduction and fabrication techniques needs to be made. We conclude by laying out some of the opportunities presented by 2DEGs based RRAM, including increased tunability and design flexibility, which could, in turn, provide advantages for multi-level capabilities. Resistive random-access memory ; Scalability ; Materials science ; Nanotechnology ; Fabrication ; Amorphous solid ; Optoelectronics ; Non-volatile memory ; Resistive touchscreen ; Oxide ; Flexibility (engineering) ; Engineering physics ; Computer science ; Electrical engineering ; Physics ; Engineering ; Chemistry ; Voltage Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing ; Ferroelectric and Piezoelectric Materials
Authors: Giulia Grassa Lima | João Batista Maia Rocha Neto | Hernandes F. Carvalho | Marisa Masumi Beppu
Source: Polysaccharides (article, 2021-05-30)
Field: Materials Science — Surfaces, Coatings and Films

Control of Surface Properties of Hyaluronan/Chitosan Multilayered Coatings for Tumor Cell Capture Prostate cancer (PCa) is a slow-growing neoplasm that has, when diagnosed in its early stages, great chances of cure. During initial tumor development, current diagnostic methods fail to have the desired accuracy, thus, it is necessary to develop or improve current detection methods and prognostic markers for PCa. In this scenario, films composed of hyaluronic acid (HA) and chitosan (CHI) have demonstrated significant capture potential of prostate tumor cells (PC3 line), exploring HA as a CD44 receptor ligand and direct mediator in cell-film adhesion. Here, we present a strategy to control structural and cell adhesion properties of HA/CHI films based on film assembly conditions. Films were built via Layer-by-layer (LbL) deposition, where the pH conditions (3.0 and 5.0) and number of bilayers (3.5, 10.5, and 20.5) were controlled. The characterization of these films was carried out using profilometry, ultraviolet-visible (UV-VIS), atomic force microscopy (AFM) and contact angle measurements. Multilayer HA/CHI films produced at pH 3.0 gave optimum surface wettability and availability of free carboxyl groups. In turn, at pH 5.0, the coverings were thinner and presented a smoother surface. Films prepared with 3.5 bilayers showed greater tumor cell capture regardless of the pH condition, while films containing 10.5 and 20.5 bilayers presented a significant swelling process, which compromised their cell adhesion potential. This study shows that surface chemistry and morphology are critical factors for the development of biomaterials designed for several cell adhesion applications, such as rapid diagnostic, cell signaling, and biosensing mechanisms. Chitosan ; Contact angle ; Adhesion ; Materials science ; Wetting ; Cell adhesion ; Hyaluronic acid ; Nanotechnology ; CD44 ; Layer (electronics) ; Biomedical engineering ; Chemical engineering ; Biophysics ; Cell ; Chemistry ; Composite material ; Medicine ; Biochemistry ; Biology Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Surface Modification and Superhydrophobicity
Authors: Tatiana Zolotoukhina | Momoko Yamada | Shingo Iwakura
Source: Biosensors (article, 2021-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Vibrational Spectra of Nucleotides in the Presence of the Au Cluster Enhancer in MD Simulation of a SERS Sensor Surface-enhanced Raman scattering (SERS) nanoprobes have shown tremendous potential in in vivo imaging. The development of single oligomer resolution in the SERS promotes experiments on DNA and protein identification using SERS as a nanobiosensor. As Raman scanners rely on a multiple spectrum acquisition, faster imaging in real-time is required. SERS weak signal requires averaging of the acquired spectra that erases information on conformation and interaction. To build spectral libraries, the simulation of measurement conditions and conformational variations for the nucleotides relative to enhancer nanostructures would be desirable. In the molecular dynamic (MD) model of a sensing system, we simulate vibrational spectra of the cytosine nucleotide in FF2/FF3 potential in the dynamic interaction with the Au20 nanoparticles (NP) (EAM potential). Fourier transfer of the density of states (DOS) was performed to obtain the spectra of bonds in reaction coordinates for nucleotides at a resolution of 20 to 40 cm<sup>-1</sup>. The Au<sub>20</sub> was optimized by ab initio density functional theory with generalized gradient approximation (DFT GGA) and relaxed by MD. The optimal localization of nucleotide vs. NP was defined and the spectral modes of both components vs. interaction studied. Bond-dependent spectral maps of nucleotide and NP have shown response to interaction. The marker frequencies of the Au<sub>20</sub>-nucleotide interaction have been evaluated. Nucleotide ; Chemistry ; Density functional theory ; Raman spectroscopy ; Raman scattering ; Spectral line ; Relaxation (psychology) ; Ab initio ; Molecular dynamics ; Computational chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Ismail Hossain | Md. Hasib Uddin | Altab Hossain | Mohammad Abdul Jalil | Zulhash Uddin
Source: Tekstilec (article, 2021-03-28)
Field: Materials Science — Polymers and Plastics

Modelling the Effect of Resin-Finishing Process Variables on the Dimensional Stability and Bursting Strength of Viscose Plain Knitted Fabric Using a Fuzzy Expert System The application of cross-linking resin is an effective method for improving and controlling dimensional stability, such as the shrinkage of viscose single jersey knits. However, such treatment often leads to a significant deterioration in the bursting strength of treated fabrics. In this regard, resin treatment using a softening agent can be an additional potential solution for retaining the bursting strength of treated fabrics. Resin treatment is one kind of chemical finishing process that inhibits cellulosic textile fibre swelling during wetting, provides fibre resistance to deformation and prevents shrinkage. The key objective of this study was to model the effect of resin-finishing process variables for predicting the shrinkage control and bursting strength of viscose single jersey knitted fabrics. The MATLAB (Version 8.2.0.701) fuzzy expert system was used to model the optimum resin and softener concentrations, as well as the best curing time for the prediction of maximum shrinkage control with a minimum loss in fabric bursting strength. The optimal process variables were found to be a resin concentration of 75 g/l, a softener concentration of 45 g/l and a curing time of 225 seconds. The fuzzy expert model developed in this study was validated using experimental data. It was found that the model has the ability and accuracy to predict fabric shrinkage and bursting strength effectively in the non-linear field. Viscose ; Shrinkage ; Bursting ; Composite material ; Materials science ; Curing (chemistry) ; Softening Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Material Properties and Processing
Authors: Johannes Gedeon | Jonathan Schmidt | M. J. P. Hodgson | Jack Wetherel | Carlos L. Benavides-Riveros | Miguel A. L. Marques
Source: Research Square (preprint, 2021-07-13)
Field: Materials Science — Materials Chemistry

Machine learning the derivative discontinuity of density-functional theory Abstract Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial and fundamental aspects, such as the explicit dependency of the functionals on the particle number or the infamous derivative discontinuity at integer particle numbers. Here we propose a solution to these problems by training a neural network as the universal functional of density-functional theory that (i) depends explicitly on the number of particles with a piece-wise linearity between the integer numbers and (ii) reproduces the derivative discontinuity of the exchange-correlation energy. This is achieved by using an ensemble formalism, a training set containing fractional densities, and an explicitly discontinuous formulation. Discontinuity (linguistics) ; Integer (computer science) ; Formalism (music) ; Density functional theory ; Mathematics ; Functional theory ; Derivative (finance) ; Statistical physics ; Computer science ; Physics ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods