jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Le Tran Huu Phuc | HyeJun Jeon | Nguyen Tam Nguyen Truong | Jung Jae Hak
Source: Electronics (article, 2019-06-08)
Field: Materials Science — Materials Chemistry

Improving the Dipping Step in Czochraski Process Using Haar-Cascade Algorithm Czochralski crystal growth has become a popular technique to produce pure single crystals. Many methods have also been developed to optimize this process. In this study, a charge-coupled device camera was used to record the crystal growth progress from beginning to end. The device outputs images which were then used to create a classifier using the Haar-cascade and AdaBoost algorithms. After the classifier was generated, artificial intelligence (AI) was used to recognize the images obtained from good dipping and calculate the duration of this operating. This optimization approach improved a Czochralski which can detect a good dipping step automatically and measure the duration with high accuracy. Using this development, the labor cost of the Czochralski system can be reduced by changing the contribution of human specialists’ mission. Cascade ; AdaBoost ; Classifier (UML) ; Artificial intelligence ; Algorithm ; Computer science ; Pattern recognition (psychology) ; Engineering Crystallization and Solubility Studies ; Minerals Flotation and Separation Techniques ; Calcium Carbonate Crystallization and Inhibition
Authors: Ram B. Khattri | Daniel L. Morris | Stephanie M. Bilinovich | Erendra Manandhar | Kahlilah R. Napper | Jacob W. Sweet | David A. Modarelli | Thomas C. Leeper
Source: Molecules (article, 2019-12-30)
Field: Materials Science — Materials Chemistry

Identifying Ortholog Selective Fragment Molecules for Bacterial Glutaredoxins by NMR and Affinity Enhancement by Modification with an Acrylamide Warhead Illustrated here is the development of a new class of antibiotic lead molecules targeted at <i>Pseudomonas aeruginosa</i> glutaredoxin (PaGRX). This lead was produced to (a) circumvent efflux-mediated resistance mechanisms via covalent inhibition while (b) taking advantage of species selectivity to target a fundamental metabolic pathway. This work involved four components: a novel workflow for generating protein specific fragment hits via independent nuclear magnetic resonance (NMR) measurements, NMR-based modeling of the target protein structure, NMR guided docking of hits, and synthetic modification of the fragment hit with a vinyl cysteine trap moiety, i.e., acrylamide warhead, to generate the chimeric lead. Reactivity of the top warhead-fragment lead suggests that the ortholog selectivity observed for a fragment hit can translate into a substantial kinetic advantage in the mature warhead lead, which bodes well for future work to identify potent, species specific drug molecules targeted against proteins heretofore deemed undruggable. Warhead ; Chemistry ; Heteronuclear single quantum coherence spectroscopy ; Moiety ; Biochemistry ; Docking (animal) ; Small molecule ; Glutaredoxin ; Combinatorial chemistry ; Cysteine ; Biophysics ; Stereochemistry ; Nuclear magnetic resonance spectroscopy ; Enzyme ; Biology ; Glutathione Enzyme Structure and Function ; Protein Structure and Dynamics ; Peptidase Inhibition and Analysis
Authors: Davide Sala | Linda Cerofolini | Marco Fragai | Andrea Giachetti | Claudio Luchinat | Antonio Rosato
Source: Computational and Structural Biotechnology Journal (article, 2019-12-26)
Field: Chemistry — Spectroscopy

A protocol to automatically calculate homo-oligomeric protein structures through the integration of evolutionary constraints and NMR ambiguous contacts Protein assemblies are involved in many important biological processes. Solid-state NMR (SSNMR) spectroscopy is a technique suitable for the structural characterization of samples with high molecular weight and thus can be applied to such assemblies. A significant bottleneck in terms of both effort and time required is the manual identification of unambiguous intermolecular contacts. This is particularly challenging for homo-oligomeric complexes, where simple uniform labeling may not be effective. We tackled this challenge by exploiting coevolution analysis to extract information on homo-oligomeric interfaces from NMR-derived ambiguous contacts. After removing the evolutionary couplings (ECs) that are already satisfied by the 3D structure of the monomer, the predicted ECs are matched with the automatically generated list of experimental contacts. This approach provides a selection of potential interface residues that is used directly in monomer-monomer docking calculations. We validated the protocol on tetrameric L-asparaginase II and dimeric Sod1. Monomer ; Nuclear magnetic resonance spectroscopy ; Bottleneck ; Coevolution ; Chemistry ; Biological system ; Intermolecular force ; Computer science ; Computational chemistry ; Polymer ; Molecule ; Biology ; Stereochemistry ; Evolutionary biology Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Lei Wang | Cihui Yang | Jing Wen | Bang-Shu Xiong
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Electro-Optical Operation of Electrical Probe Phase-Change Memory With Ultra-High Electrically Conductive Capping Layer An electrical probe phase-change memory using an ultra-high electrically conductive capping layer allows for a successful write of the amorphous bit without thermally damaging the device. Due to the severe current spreading effect, the recorded bit, however, cannot be sensed based on the conventional electrical means. We, therefore, developed a numerical optical model to calculate the device transmission resulting from a focused spot of a mobile scanning near-field optical microscopy. The feasibility of using this novel approach to distinguish bits from their background is also demonstrated. Materials science ; Electrical conductor ; Optoelectronics ; Layer (electronics) ; Transmission (telecommunications) ; Amorphous solid ; Phase change ; Phase-change memory ; Electric field ; Computer science ; Nanotechnology ; Telecommunications ; Composite material ; Engineering physics Phase-change materials and chalcogenides ; Quantum Dots Synthesis And Properties ; Photonic and Optical Devices
Authors: Yuexing Han | Chuanbin Lai | Bing Wang | Tianyi Hu | Dongli Hu | Hui Gu
Source: Image Analysis & Stereology (article, 2019-04-11)
Field: Materials Science — Ceramics and Composites

SEGMENTATION AND ANALYSIS METHOD FOR TWO-PHASE CERAMIC (HfB2-B4C) BASED ON THE DETECTION OF VIRTUAL BOUNDARIES Microstructure of a material stores the genesis of the material and shows various properties of the material. To efficiently analyse the microstructure of a material, the segmentation of different phases or constituents is an important step. However, in general, due to the microstructure’s complexity, most of segmentation is manually done by human experts. It is challenging to automatically segment the material phases and the microstructure. In this work, we propose a method which combines the the dilation operator, GLCM (gray-level co-occurrence matrix), Hough transform and DBSCAN (density-based spatial clustering of applications with noise) for phases segmentation in the examples of certain material of eutectic HfB2-B4C ceramics. In the segmented regions, the further analysis for the microstructural elements is done with DBSCAN. The experimental results show that the proposed method achieves 95.75% segmentation accuracy for segmenting phases and 86.64% correct classification rate for the microstructure in the segmented phases. These experimental results show that our method is effective for the difficult task of the both segmentation and classification of the microstructural characteristics. Segmentation ; Artificial intelligence ; Computer science ; DBSCAN ; Microstructure ; Pattern recognition (psychology) ; Dilation (metric space) ; Cluster analysis ; Computer vision ; Materials science ; Fuzzy clustering ; Mathematics ; Geometry ; Composite material Advanced ceramic materials synthesis ; X-ray Diffraction in Crystallography ; Advanced materials and composites
Authors: Vijetha Ponnam | T. Subbaiah | Vineet Aniya | Alka Kumari | B. Satyavathi | Ramesh Naidu Mandapati | C Postigo | D Barcela | A Chowdhury | S Jahan | M Islam | M Moniruzzaman | M Alam | M Zaman | N Karim | S Gan | L Suntio | W Shiu | D Mackay | J Seiber | D Glotfelty | V Gupta | I Ali | S Suhas | A Marican | E Durn-Lara | J Kearnsa | L Wellbornb | R Summersa | D Knappe | B Chen | D Zhou | L Zhu | S Hale | J Lehmann | D Rutherford | A Zimmerman | R Bachmann | V Shitumbanuma | A O'toole | K Sundqvist | H Arp | G Cornelissen | S Mayakaduwa | I Herath | Y Ok | D Mohan | M Vithanage | L Velayutham | K Damodaran | G Zhang | Q Zhang | K Sun | X Liu | W Zheng | Y Zhao | M Keiluweit | M Kleber | Z Pan | B Xing | J Li | Y Li | M Wu | Z Zhang | J L | K Foo | K Chang | C Chen | L Jun-Hong | J Hsiend | Y Wang | F Zhao | Y Shih | Z Xinga | S Chen | J Salman | V Njoku | B Hameed | X Yu | G Ying | R Kookana | G Memon | M Bhanger | M Akhtar | A Mittal | J Mittal | A Malviya | D De | S Santosha | V Aniya | A Sreeramoju | B Satyavathi | C Aharoni | M Ungarish | A Gunay | E Arslankaya | I Tosun
Source: Biointerface Research in Applied Chemistry (article, 2019-10-15)
Field: Chemistry — Analytical Chemistry

Torrefied and unmodified capsicum annuam biochar for the removal of synthetic hazardous pesticide (carbofuran) from watershed The present study explored the applicability of torrefied and unmodified Capsicum Annuam biochar (CABC) for the removal of carbofuran from its aqueous streams. The developed biochar at low temperature pyrolysis (300°C) attained a surface area and pore volume of 32.07 m^2⁄g and 0.064〖cm〗^3/g, respectively. The biochar was characterized for its physico-chemical transformation. It was observed that aromatic and hydroxyl groups present in the CABC were responsible for the sorption of carbofuran. The efficacy of carbofuran removal was examined by varying the process variables such as carbofuran concentration, sorption time, pH and adsorbent dosage. The investigation on isothermal models suggested a monolayer adsorption of carbofuran onto homogeneous surface of CABC. While the sorption kinetics is chemical rate controlling mechanism with abundant sorption sites available on the surface and the initial concentration of carbofuran is low compared to the sorption capacities of the CABC. The mass transfer mechanism was studied. The optimization of parameters with central composite design in response surface methodology showed an optimum adsorption uptake value obtained is 134.84 mg⁄g with a desirability value 0.98 with initial concentration as the most influencing parameter. Carbofuran ; Biochar ; Sorption ; Adsorption ; Chemistry ; Pyrolysis ; Central composite design ; Chromatography ; Environmental chemistry ; Pesticide ; Response surface methodology ; Chemical engineering ; Organic chemistry Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal
Authors: Yun Zhao | Mahamed Lamine Guindo | Xing Xu | Xiang Shi | Miao Sun | Yong He
Source: Engenharia Agrícola (article, 2019-10-01)
Field: Chemistry — Analytical Chemistry

A NOVEL RAISIN SEGMENTATION ALGORITHM BASED ON DEEP LEARNING AND MORPHOLOGICAL ANALYSIS We propose a segmentation algorithm for raisin extraction. The proposed approach consists of the following aspects. Deep learning is used to predict the number of raisins in each connected region, and the shape features such as the roundness, area, X-axis value for the centroid, Y-axis value for the centroid, axis length and perimeter of each region will be used to establish the prediction model. Morphological analysis, based on edge parameters including the polar axis, polar angle and angular velocity, is applied to search for the suitable break points that are useful for identifying the dividing lines between two adjacent raisins. To make our segmentation more accurate, some machine-learning algorithms such as the random forest (RF), support vector machine (SVM) and deep learning (deep neural network, DNN) are applied to predict the number of raisins and to decide whether the raisins need more segmentation. The performance of the three models is compared, and the DNN is the most accurate. Centroid ; Artificial intelligence ; Segmentation ; Roundness (object) ; Artificial neural network ; Deep learning ; Support vector machine ; Pattern recognition (psychology) ; Perimeter ; Computer science ; Algorithm ; Mathematics ; Geometry Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Industrial Vision Systems and Defect Detection
Authors: Sihe Kang | Haiying Deng | Long Chen | Xiaoxuan Zeng | Yimei Liu | Keli Chen
Source: Journal of Spectroscopy (article, 2019-09-22)
Field: Chemistry — Analytical Chemistry

Rapid Identification and Quality Evaluation of Medicinal Centipedes in China Using Near-Infrared Spectroscopy Integrated with Support Vector Machine Algorithm To investigate the feasibility of rapid identification and quality evaluation of Chinese medicinal centipedes using NIR spectroscopy, the qualitative and quantitative analysis models were explored. A PCA-SVC model was optimized to differentiate five species of the genus Scolopendra . When the model was validated with the calibration and prediction sets, the prediction accuracy was 100% and 81.82%, respectively; it can meet the requirement for rapid and preliminary identification. Based on nitrogen content detected by the chemical method, and the dimensionality of spectral data reduced with PLS, the quantitative analysis models were successfully built by PLSR and SVR algorithms. After spectra were pretreated and parameters were optimized, the performance, rationality, and prediction ability of the models were validated and evaluated with RMSECV, RMSEP, RMSEE, R 2 , and RPD. Compared with the features and advantages of these two models, the PLS-SVR model had better performance and stronger prediction capacity, and it was finally regarded as the optimal quantitative analysis model to predict nitrogen content. The relative deviation between the predictive value and the reference was 2.69%, and the average recovery was 99.02%, which indicated it has potential for rapid prediction and evaluation of the quality of medicinal centipedes. This research suggested that NIR spectroscopy can be used as a rapid detection method to identify species and evaluate the quality of medicinal centipedes in China. Biological system ; Principal component analysis ; Near-infrared spectroscopy ; Support vector machine ; Partial least squares regression ; Identification (biology) ; Calibration ; Computer science ; Pattern recognition (psychology) ; Mathematics ; Artificial intelligence ; Machine learning ; Statistics ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Venkata V. B. Yallapragada | Sidney P. Walker | Ciarán Devoy | Stephen Buckley | Yensi Flores Bueso | Mark Tangney
Source: Proteins Structure Function and Bioinformatics (article, 2019-10-07)
Field: Chemistry — Spectroscopy

Function2Form Bridge—Toward synthetic protein holistic performance prediction Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design. Context (archaeology) ; Computer science ; In silico ; Machine learning ; Artificial intelligence ; Data mining ; Biology Advanced Proteomics Techniques and Applications ; Protein purification and stability ; Scientific Computing and Data Management
Authors: Jay R. Walton | Luis A. Rivera−Rivera | Robert R. Lucchese | J. W. Bevan
Source: The Journal of Physical Chemistry A (article, 2019-01-04)
Field: Materials Science — Materials Chemistry

Canonical Approach To Generate Multidimensional Potential Energy Surfaces A new force-based canonical approach for the accurate generation of multidimensional potential energy surfaces is demonstrated. Canonical transformations previously developed for diatomic molecules are used to construct accurate approximations to the 3-dimensional potential energy surface of the water molecule from judiciously chosen (adopting the right coordinate system) 1-dimensional planar slices that are shown to have the same canonical shape as the classical Lennard-Jones potential curve. Spline interpolation is then used to piece together the 1-dimensional canonical potential curves, to obtain the full 3-dimensional potential energy surface of a water molecule with a relative error less than 0.01. This work provides an approach to greatly reduce the computational cost of constructing potential energy surfaces in molecules from ab initio calculations. The canonical transformation techniques developed in this work illuminate a pathway to deepening our understanding of chemical bonding. Potential energy ; Potential energy surface ; Diatomic molecule ; Surface (topology) ; Work (physics) ; Planar ; Canonical coordinates ; Transformation (genetics) ; Interpolation (computer graphics) ; Molecule ; Energy (signal processing) ; Computational chemistry ; Chemistry ; Mathematics ; Physics ; Classical mechanics ; Computer science ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Deisy Chaves | Emanuele Trucco | Juan Barraza | María Trujillo
Source: Computers in Industry (article, 2019-01-03)
Field: Chemistry — Analytical Chemistry

An image processing system for char combustion reactivity characterisation Char ; Combustion ; Reactivity (psychology) ; Image processing ; Process engineering ; Computer science ; Materials science ; Engineering drawing ; Engineering ; Image (mathematics) ; Artificial intelligence ; Chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Measurement and Detection Methods ; Advanced Algorithms and Applications
Authors: Xiaodong Zhang | Long Chen | Yu Bai | Keli Chen
Source: Journal of Spectroscopy (article, 2019-03-10)
Field: Chemistry — Analytical Chemistry

Study on the Processing Technology of Calamine Calcination by Near-Infrared Spectroscopy Near-infrared spectroscopy has been widely used in qualitative and quantitative analysis and online monitoring in the production process of traditional Chinese medicines. The aim was to establish a fast determination model of zinc oxide (ZnO) content in calcined calamine and to explore methods through judging the end point of calamine calcination. Eight batches of calamine samples sourced from hydrozincite with different sizes and textures were calcined at different temperatures. During the calcination process, ZnO contents, X-ray diffraction (XRD) patterns, and near-infrared spectra of the samples were used to analyze their changes rules. The model of determining ZnO content of calcined calamine was established to use near-infrared spectroscopy based on the partial least squares (PLS) regression algorithm. In addition, this paper summarized the change rules of calamine in calcination according to XRD patterns, using the “ K value” quantitative method to define the characteristic T value. When the T value was equal to 1.00 (100%), that is to say, the calamine sample was completely calcined. Then, matching the near-infrared spectroscopy data with the T value and establishing the T value analysis model using the PLS algorithm were performed. Through cross and independent validation and evaluation, it was proved that the two models were very effective and had strong predictive abilities. Finally, the purpose of the online monitoring of the calcination process and controlling the quality of the calcined calamine was achieved. Calcination ; Spectroscopy ; Infrared spectroscopy ; Process analytical technology ; Materials science ; Zinc ; Analytical Chemistry (journal) ; Infrared ; Mineralogy ; Chemistry ; Environmental chemistry ; Chemical engineering ; Metallurgy ; Engineering ; Physics ; Catalysis ; Optics Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis ; Geochemistry and Geologic Mapping
Authors: R. Patrick Xian | Yves Acremann | Steinn Ýmir Ágústsson | Maciej Dendzik | Kevin Bühlmann | Davide Curcio | Dmytro Kutnyakhov | Frederico Pressacco | Michael Heber | Shuo Dong | J. Demšar | W. Würth | Philip Hofmann | Martin Wolf | Laurenz Rettig | Ralph Ernstorfer
Source: arXiv (Cornell University) (preprint, 2019-09-17)
Field: Materials Science — Materials Chemistry

An open-source, distributed workflow for band mapping data in multidimensional photoemission spectroscopy Characterization of the electronic band structure of solid state materials is routinely performed using photoemission spectroscopy. Recent advancements in short-wavelength light sources and electron detectors give rise to multidimensional photoemission spectroscopy, which permits parallel measurements of the electron spectral function in energy, two momentum components and additional physical parameters with single-event detection capability. Processing the photoelectron event streams with a rate of a few to tens of megabytes per second poses a major data challenge in the field. We describe an open-source, distributed workflow for efficient interaction with single-electron events in photoemission band mapping experiments compatible with beamlines at 3$^{\text{rd}}$ and 4$^{\text{th}}$ generation light sources as well as table-top laser sources. The workflow transforms single-event data into calibrated and structured formats for subsequent storage, visualization and further analysis by facility users. The workflow can be archived for reuse and provides a basis for the documentation and comparison of multidimensional band mapping data and their integration into materials science databases. Workflow ; Angle-resolved photoemission spectroscopy ; Inverse photoemission spectroscopy ; Computer science ; Photoemission spectroscopy ; X-ray photoelectron spectroscopy ; Event (particle physics) ; Documentation ; Spectroscopy ; Physics ; Electronic structure ; Database Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science