jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maxim Ziatdinov | Sergei V. Kalinin
Source: Microscopy and Microanalysis (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

AtomAI: Open-source software for applications of deep learning to microscopy data An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the 'Save PDF' action button. Open source ; Computer science ; Open source software ; Software ; Content (measure theory) ; Artificial intelligence ; Operating system ; Mathematics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Universiti Tun Hussein Onn Malaysia | Z. Z. Low | T. C. Lee | Mohamad Ali Selimin | N. A. Pagan | Evergreen Fibreboard Berhad | Juliana Abdul Halip
Source: Journal of Sustainable Materials Processing and Management (article, 2021-12-08)
Field: Materials Science — Polymers and Plastics

Physical Properties of Rice Husk-Pine Wood Particleboard Rice husk is one types of sustainable and economical agricultural wastes that could produce particleboard as the substitute for solid wood. The problems that encourage this research are due to the increasing of deforestation and the increment demand for solid wood in various industries. Malaysia produces a significant amount of agricultural biomass waste every year. It is noteworthy to mention that agricultural wastes can be fully used in an environmentally friendly way. This study aims to evaluate the properties of particleboard made from rice husk at different fibre loading and mixture. Different formulated composition of rice husk and pine wood shaving was prepared for the production of particleboard by hot pressing at temperature 160 °C for 6 min using Urea-Formaldehyde (UF) resin with additional of ammonium chloride (NH4Cl) as a hardener. The results showed the density values and moisture content of particleboards ranging from 0.547 to 0.660 g/cm3 and 8.072 to 8.929 %, respectively. The thickness swelling and water absorption were increased as the increment of soaking time. Besides, the colour of the rice husk-pine particleboards has no significant difference and changes except for particleboard made from 100% rice husk. While the more proportion of pine wood shaving has the better compaction of particleboards, and the addition of grinded rice husk also make the particleboards have fewer voids. This study is expected to reduce deforestation activities and to maximize the usage of agricultural wastes. Husk ; Pulp and paper industry ; Absorption of water ; Urea-formaldehyde ; Biomass (ecology) ; Waste management ; Sawdust ; Environmental science ; Materials science ; Agronomy ; Composite material ; Adhesive ; Botany ; Engineering Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Lignin and Wood Chemistry
Authors: Zizhen Zhou | Dewei Chu | Claudio Cazorla
Source: Scientific Reports (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Ab initio description of oxygen vacancies in epitaxially strained $$\hbox {SrTiO}_{{3}}$$ at finite temperatures Epitaxially grown [Formula: see text] (STO) thin films are material enablers for a number of critical energy-conversion and information-storage technologies like electrochemical electrode coatings, solid oxide fuel cells and random access memories. Oxygen vacancies ([Formula: see text]), on the other hand, are key defects to understand and tailor many of the unique functionalities realized in oxide perovskite thin films. Here, we present a comprehensive and technically sound ab initio description of [Formula: see text] in epitaxially strained (001) STO thin films. The novelty of our first-principles study lies in the incorporation of lattice thermal excitations on the formation energy and diffusion properties of [Formula: see text] over wide epitaxial strain conditions ([Formula: see text]%). We found that thermal lattice excitations are necessary to obtain a satisfactory agreement between first-principles calculations and the available experimental data for the formation energy of [Formula: see text]. Furthermore, it is shown that thermal lattice excitations noticeably affect the energy barriers for oxygen ion diffusion, which strongly depend on [Formula: see text] and are significantly reduced (increased) under tensile (compressive) strain. The present work demonstrates that for a realistic theoretical description of oxygen vacancies in STO thin films is necessary to consider lattice thermal excitations, thus going beyond standard zero-temperature ab initio approaches. Epitaxy ; Ab initio quantum chemistry methods ; Ab initio ; Materials science ; Lattice (music) ; Condensed matter physics ; Thin film ; Oxide ; Lattice energy ; Nanotechnology ; Crystallography ; Chemistry ; Physics ; Crystal structure ; Molecule ; Quantum mechanics Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Ferroelectric and Piezoelectric Materials
Authors: Ole‐Christian Galbo Engstrøm | Erik Schou Dreier | Kim Steenstrup Pedersen
Source:  (article, 2021-10-01)
Field: Chemistry — Analytical Chemistry

Predicting Protein Content in Grain Using Hyperspectral Deep Learning We assess the possibility of performing regression analysis on hyperspectral images utilizing the entire spatio-spectral data cube in convolutional neural networks using protein regression analysis of bulk wheat grain kernels as a test case. By introducing novel modifications of the well-known convolutional neural network, ResNet-18, we are able to significantly increase its performance on hyperspectral images. Our modifications consist of firstly applying a 3D convolution layer enabling learning of spectral derivatives that 2D spatial convolution is unable to learn, and secondly, the application of a (1 × 1) 2D convolution layer that downsamples the spectral dimension. Analysis of the responses learned by the convolution kernels in our modifications reveals meaningful representations of the input data cube that reduce noise and enable the subsequent ResNet-18 to perform more accurate regression analysis. Hyperspectral imaging ; Artificial intelligence ; Computer science ; Content (measure theory) ; Deep learning ; Pattern recognition (psychology) ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Taufik Fathurahman | Putu Harry Gunawan | Esa Prakasa | Junji Sugiyama
Source: Journal of the Korean Wood Science and Technology (article, 2021-09-01)
Field: Chemistry — Organic Chemistry

Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution. Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Pixel ; Sample (material) ; Process (computing) ; Contextual image classification ; Identification (biology) ; Artificial neural network ; Image (mathematics) ; Texture (cosmology) ; Botany Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Industrial Vision Systems and Defect Detection
Authors: Nele‐Johanna Hempel | Matthias Manne Knopp | J. Axel Zeitler | Ragna Berthelsen | Korbinian Löbmann
Source: Journal of Pharmaceutical Sciences (article, 2021-05-19)
Field: Chemistry — Organic Chemistry

Microwave-Induced in Situ Drug Amorphization Using a Mixture of Polyethylene Glycol and Polyvinylpyrrolidone The use of a mixture of polyethylene glycol (PEG) and polyvinylpyrrolidone (PVP) was investigated for microwave-induced in situ amorphization of celecoxib (CCX) inside compacts. Such amorphization requires the presence of a dipolar excipient in the formulation to ensure heating of the compact by absorption of the microwaves. Previously, the hygroscopic nature of PVP was exploited for this purpose. By exposing PVP-based compacts for set time intervals at defined relative humidity, controlled water sorption into the compacts was achieved. In the present study, PEG was proposed as the microwave absorbing excipient instead of water, to avoid the water sorption step. However, it was found that PEG alone melted upon exposure to microwave radiation and caused the compact to deform. Furthermore, CCX was found to recrystallize upon cooling in PEG-based formulations. Hence, a mixture of PEG and PVP was used, where the presence of PVP preserved the physical shape of the compact, and the physical state of the amorphous solid dispersion. To study the impact of the polymer mixture, different compact compositions of CCX, PEG and PVP were prepared. When exposing the compacts to microwave radiation, it was found that the PEG:PVP ratio was critical for in situ amorphization and that complete amorphization was only achieved above a certain temperature threshold. Polyvinylpyrrolidone ; Polyethylene glycol ; Materials science ; PEG ratio ; Amorphous solid ; Excipient ; Chemical engineering ; PEG 400 ; Absorption (acoustics) ; Polymer ; Microwave ; Dispersion (optics) ; Polymer chemistry ; Organic chemistry ; Composite material ; Chemistry ; Chromatography ; Optics Microwave-Assisted Synthesis and Applications ; Drug Solubulity and Delivery Systems ; Synthesis and properties of polymers
Authors: Kang Xia | Qun Chen | Hao Gao | Xiaolei Feng | Jianan Yuan | Cong Liu | Simon A. T. Redfern | Jian Sun
Source: Physical Review Materials (article, 2021-11-29)
Field: Materials Science — Materials Chemistry

Icosahedral silicon boride: A potential hybrid photovoltaic-thermoelectric for energy harvesting Boron-rich compounds have attracted significant attention due to their promising and diverse physical properties, which include ultrahardness, resistance to oxidation and corrosion, and even superconductivity. Here, using a crystal structure search method based on first-principles calculations, we find a boron-rich silicon compound $\mathrm{Si}{\mathrm{B}}_{12}$ that is stable under moderate pressure of around 20 GPa, and which we predict is recoverable to ambient pressure. This silicon boride, with space group Pnnm, is structurally related to the $\ensuremath{\gamma}\text{\ensuremath{-}}{\mathrm{B}}_{28}$ boron phase. Specifically, the $\mathrm{Si}{\mathrm{B}}_{12}$ structure is formed by replacing the ${\mathrm{B}}_{2}$ pairs in $\ensuremath{\gamma}\text{\ensuremath{-}}{\mathrm{B}}_{28}$ with silicon atoms. Our calculations show that this Pnnm $\mathrm{Si}{\mathrm{B}}_{12}$ phase exhibits good thermal stability at moderate pressures above 20 GPa and temperatures to 900 K. We suggest this structure has dynamic stability at ambient pressure and remains stable to temperatures as high as 2000 K. Impressively, this $\mathrm{Si}{\mathrm{B}}_{12}$ phase possesses good light absorption and thermoelectrical properties, which are enhanced by its small and indirect band gap, doubly degenerate bands, and low lattice thermal conductivity. Our predictions should stimulate further investigations of this class of boron-rich semiconductors, especially in view of their superior photovoltaic and thermoelectric properties which may be beneficial in energy applications. Boride ; Materials science ; Boron ; Thermoelectric effect ; Silicon ; Band gap ; Thermal stability ; Icosahedral symmetry ; Semiconductor ; Crystallography ; Nanotechnology ; Optoelectronics ; Metallurgy ; Thermodynamics ; Chemical engineering ; Physics ; Nuclear physics Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Superconductivity in MgB2 and Alloys
Authors: Tim Englert | Florian Gruber | Jan Stiedl | Simon Green | Timo Jacob | Karsten Rebner | Wulf Grählert
Source: Sensors (article, 2021-08-19)
Field: Chemistry — Analytical Chemistry

Use of Hyperspectral Imaging for the Quantification of Organic Contaminants on Copper Surfaces for Electronic Applications To correctly assess the cleanliness of technical surfaces in a production process, corresponding online monitoring systems must provide sufficient data. A promising method for fast, large-area, and non-contact monitoring is hyperspectral imaging (HSI), which was used in this paper for the detection and quantification of organic surface contaminations. Depending on the cleaning parameter constellation, different levels of organic residues remained on the surface. Afterwards, the cleanliness was determined by the carbon content in the atom percent on the sample surfaces, characterized by XPS and AES. The HSI data and the XPS measurements were correlated, using machine learning methods, to generate a predictive model for the carbon content of the surface. The regression algorithms elastic net, random forest regression, and support vector machine regression were used. Overall, the developed method was able to quantify organic contaminations on technical surfaces. The best regression model found was a random forest model, which achieved an R<sup>2</sup> of 0.7 and an RMSE of 7.65 At.-% C. Due to the easy-to-use measurement and the fast evaluation by machine learning, the method seems suitable for an online monitoring system. However, the results also show that further experiments are necessary to improve the quality of the prediction models. Hyperspectral imaging ; Random forest ; Support vector machine ; Regression ; Artificial intelligence ; Environmental science ; Computer science ; Remote sensing ; Machine learning ; Mathematics ; Statistics ; Geology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Industrial Vision Systems and Defect Detection
Authors: Matthias Köck | Thomas Lindel | J. Junker
Source: Molecules (article, 2021-08-10)
Field: Chemistry — Spectroscopy

Incorporation of 4J-HMBC and NOE Data into Computer-Assisted Structure Elucidation with WebCocon Over the past decades, different software programs have been developed for the Computer-Assisted Structure Elucidation (CASE) with NMR data using with various approaches. WebCocon is one of them that has been continuously improved over the past 20 years. Here, we present the inclusion of 4J<sub>CH</sub> correlations (4J-HMBC) in the HMBC interpretation of Cocon and NOE data in WebCocon. The 4J-HMBC data is used during the structure generation process, while the NOE data is used in post-processing of the results. The marine natural product oxocyclostylidol was selected to demonstrate WebCocon's enhanced HMBC data processing capabilities. A systematic study of the 4J<sub>CH</sub> correlations of oxocyclostylidol was performed. The application of NOEs in CASE is demonstrated using the NOE correlations of the diterpene pyrone asperginol A known from the literature. As a result, we obtained a conformation that corresponds very well to the existing X-ray structure. Chemistry ; Stereochemistry Molecular spectroscopy and chirality ; Microbial Natural Products and Biosynthesis ; Plant biochemistry and biosynthesis
Authors: Tomasz Pawlak | Marcin Oszajca | Małgorzata Szczesio | Marek J. Potrzebowski
Source: Molecular Pharmaceutics (article, 2021-12-03)
Field: Chemistry — Spectroscopy

Solid-State Study of the Structure, Dynamics, and Thermal Processes of Safinamide Mesylate─A New Generation Drug for the Treatment of Neurodegenerative Diseases Safinamide mesylate (<b>SM</b>), the pure active pharmaceutical ingredient (API) recently used in Parkinson disease treatment, recrystallized employing water-ethanol mixture of solvents (vol/vol 1:9) gives a different crystallographic form compared to <b>SM</b> in Xadago tablets. Pure <b>SM</b> crystallizes as a hemihydrate in the monoclinic system with the <i>P</i>2<sub>1</sub> space group. Its crystal and molecular structure were determined by means of cryo X-ray crystallography at 100 K. <b>SM</b> in the Xadago tablet exists in anhydrous form in the orthorhombic crystallographic system with the <i>P</i>2<sub>1</sub>2<sub>1</sub>2<sub>1</sub> space group. The water migration and thermal processes in the crystal lattice were monitored by solid-state NMR spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. <b>SM</b> in Xadago in the high-humidity environment undergoes phase transformation to the <i>P</i>2<sub>1</sub> form which can be easily reversed just by heating up to 80 °C. For the commercial form of the API, there is also a reversible thermal transformation observed between <i>Z</i>' = 1 ↔ <i>Z</i>' = 3 crystallographic forms in the 0-20 °C temperature range. Analysis of molecular motion in the crystal lattice proves that the observed conformational polymorphism is forced by intramolecular dynamics. All above-mentioned processes were analyzed and described employing the NMR crystallography approach with the support of advanced theoretical calculations. Orthorhombic crystal system ; Monoclinic crystal system ; Crystal structure ; Differential scanning calorimetry ; Crystallography ; Polymorphism (computer science) ; Thermogravimetric analysis ; Chemistry ; Materials science ; Organic chemistry ; Thermodynamics Advanced NMR Techniques and Applications ; Coordination Chemistry and Organometallics ; Solid-state spectroscopy and crystallography
Authors: Wei Liu | Xue Xu | Changhong Liu | Lei Zheng
Source: Journal of Food Quality (article, 2021-01-19)
Field: Chemistry — Analytical Chemistry

Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging The detection of authenticity is essential to the development and management of Thai jasmine rice industry. In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). Three varieties of rice that were similar to Thai jasmine rice in appearance were selected to perform the classification and quantitative prediction experiments by multispectral images. For the classification experiment, four varieties of rice samples could be easily classified with accuracy achieved to 92% by the BPNN model. For the quantitative prediction of adulteration proportion experiments, the results showed that, among the different chemometric methods, LS-SVM achieved the best prediction performance comparing the results of coefficient of determination, root-mean-square error (RMSEP), bias, and residual predictive deviation (RPD). It can be concluded that multispectral imaging technology with chemometric methods can be applied in the rapid and nondestructive detection of authenticity of Thai jasmine rice. Multispectral image ; Principal component analysis ; Partial least squares regression ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Residual ; Mathematics ; Computer science ; Statistics ; Algorithm Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Yuki Matsuura | Yuhki Tsukada | Toshiyuki Koyama
Source: Physical Review Materials (article, 2021-11-08)
Field: Materials Science — Materials Chemistry

Adjoint model for estimating material parameters based on microstructure evolution during spinodal decomposition Data assimilation techniques are attracting increasing attention because they enable researchers to estimate material parameters by integrating an advanced computational model with experimental data of microstructure evolution. In this study, we develop an adjoint model to integrate a phase-field model for spinodal decomposition with time-series measurement data of compositional field maps to estimate the unknown parameters in the phase-field model. As a case study, simultaneous estimation of six parameters (Gibbs energy parameters, gradient energy coefficient, etc.) in the phase-field model is considered. To confirm the effectiveness of the developed adjoint model, numerical tests called ``twin experiments'' are conducted using synthetic measurement data prepared in advance through phase-field simulation. In the twin experiments, the optimum estimates of six model parameters of interest are shown to coincide with true values, indicating that several model parameters can be successfully estimated. Furthermore, the effects of the standard deviation of measurement noise $(\ensuremath{\sigma})$ and the time interval of measurements $(\mathrm{\ensuremath{\Delta}}{t}_{\mathrm{meas}.}^{\ensuremath{'}})$ on uncertainties of optimum estimates of parameters $({\ensuremath{\sigma}}^{\mathrm{est}})$ are examined by conducting twin experiments. It is shown that there is a positive correlation between $\ensuremath{\sigma}$ and ${\ensuremath{\sigma}}^{\mathrm{est}}$ or between $\mathrm{\ensuremath{\Delta}}{t}_{\mathrm{meas}.}^{\ensuremath{'}}$ and ${\ensuremath{\sigma}}^{\mathrm{est}}$, which is essential information for designing experiments to estimate model parameters. The developed adjoint model is assumed to be useful for estimating unknown parameters (e.g., Gibbs energy parameters of a nonequilibrium phase) using time-series measurement data of microstructure evolution during spinodal decomposition. Spinodal decomposition ; Sigma ; Phase field models ; Energy (signal processing) ; Field (mathematics) ; Spinodal ; Thermodynamics ; Phase (matter) ; Materials science ; Statistical physics ; Mathematics ; Physics ; Statistics ; Pure mathematics ; Quantum mechanics Solidification and crystal growth phenomena ; High Temperature Alloys and Creep ; Microstructure and Mechanical Properties of Steels
Authors: Yang Yuanduo | Yang Li | Yiang Li | Baicun Wang | Ao Sansan | Zhen Luo
Source: Journal of Mechanical Engineering (article, 2021-01-01)
Field: Materials Science — General Materials Science

Advances in Ultrasonic Welding of Carbon Fiber Reinforced Thermoplastic Composites 摘要： 碳纤维增强热塑性复合材料（Carbon fiber reinforced thermoplastic composite，CFRTP）由于其优越的力学性能、较低的加工成本及可回收性，逐渐成为继铝合金、高强钢之后的新一代轻量化材料。国内外科研机构和材料企业都投入巨资和人力竞相开展相关研究，部分高校、企业已开始探索将CFRTP应用于机身与车身的制造与装配。超声波焊接是最适合焊接热塑性材料的方法之一，也是实现大规模装配CFRTP零部件的关键使能技术之一。从CFRTP超声波焊接基本过程、接头形式、数值模拟、质量监控和异种材料连接五个方面进行分析，系统讨论CFRTP超声波焊接最新研究成果，展望CFRTP超声波焊接中有待解决的共性问题，以期为CFRTP零部件的大规模装配应用提供参考。 Composite material ; Thermoplastic composites ; Materials science ; Ultrasonic welding ; Ultrasonic sensor ; Welding ; Thermoplastic ; Acoustics Material Properties and Applications ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes