jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Daniel Koury
Source: Digital Scholarship - UNLV (University of Nevada Reno) (article, 2019-05-29)
Field: Materials Science — Materials Chemistry

Characterization of the mechanism of bi-layer oxide growth on austenitic stainless steels 316L and D9 in oxygen-controlled Lead-Bismuth Eutectic (Lbe) Lead Bismuth Eutectic (LBE) has been proposed for use in programs for accelerator-based and reactor-based transmutation of nuclear waste. LBE is a leading candidate material as a spallation target (in accelerator-based transmutation) and an option for the sub-critical blanket coolant. The corrosion by LBE of annealed and cold-rolled 316L stainless steels, and the modified austenitic stainless steel alloy D9, has been studied using Scanning Electron Microscopy (SEM), Electron Probe Micro Analysis (EPMA), and X-ray Photoelectron Spectroscopy (XPS). Exposed and unexposed samples have been compared and the differences studied. Small amounts of surface contamination are present on the samples and have been removed by ion-beam sputtering. The unexposed samples reveal typical stainless steel characteristics: a chromium oxide passivation surface layer and metallic iron and nickel. The exposed samples show protective iron oxide and chromium oxide growths on the surface. Oxygen takes many forms on the exposed samples, including oxides of iron and chromium, carbonates, and organic acids from subsequent handling after exposure to LBE. Different types of surface preparation have lead to considerably different modes of corrosion. The cold-rold samples were resistant to thick oxide growth, having only a thin (&lt; 1 mum), dense chromium-rich oxide. The annealed 316L and D9 samples developed thick, bi-layered oxides, the inner layer consisting of chromium-rich oxides (likely spinel) and the outer layer consisting mostly of iron oxides. The cold-rolled samples were able to maintain a thin chromium oxide layer because of the surface work performed on it, as ample diffusion pathways provided an adequate supply of chromium atoms. The annealed samples grew thick oxides because iron was the primary diffusant, as there are fewer fast-diffusion pathways and therefore an amount of chromium insufficient to maintain a chromium based oxide. Even the thick oxide, however, can prolong the life of a steel in LBE, provided proper oxygen control. The mechanisms responsible for the differences in the oxidation behaviors are discussed. Eutectic system ; Metallurgy ; Materials science ; Bismuth ; Layer (electronics) ; Austenite ; Oxide ; Oxygen ; Characterization (materials science) ; Microstructure ; Composite material ; Chemistry ; Nanotechnology Nuclear Materials and Properties ; High-Temperature Coating Behaviors ; Nuclear reactor physics and engineering
Authors: Melih Soner Çeliktaş | Merve Uyan
Source: Mugla Journal of Science and Technology (article, 2019-08-29)
Field: Materials Science — Polymers and Plastics

PLANT FIBER REINFORCED BIOCOMPOSITE: PROPERTIES AND APPLICATIONS Son yıllarda farklı endüstriyel uygulamalar için geleneksel polimer kompozitlerin yerine yenilenebilir kaynakların kullanımına olan ilgi artmaktadır. En az bir bileşen biyobazlı veya biyobozunur olan polimer kompozitlere biyokompozit adı verilmektedir. Son zamanlarda bitki liflerinin sahip oldukları mekanik, termal, optik ve biyobozunurluk gibi benzersiz özellikleri sebebiyle biyokompozit ürünlerdekullanılmaktadır. Bu malzemelere olan yüksek ilginin bir diğer nedeni ise ucuz ve düşük ortam etkisine sahip olmalarıdır. Bitki liflerinin bol miktarda bulunabilirliği ve erişilebilirliği ortaya çıkan yeni ilginin ana nedenleridir. Doğal kaynaklardan oluşan yüksek performanslı malzemeler sayesinde, malzeme bilimi alanındaki biyoteknolojide önemli kazanımlar dünya çapında artış göstermektedir. Bunlara ilaveten, dünyadaki çevre sorunlarına karşı artanfarkındalık, araştırmacıları ve üreticileri biyokompozit malzemeler alanında çaba göstermeye yöneltmektedir. Bu kapsamda farklı polimerlerle bitki liflerinin birlikte kullanıldığı mühendislik ve teknolojik uygulamalar için gerçekleştirilmiş birçok tasarım ve uyarlama çalışması bulunmaktadır. Doğal kaynağa dayalı yüksek performanslı malzemelerin bir sonucu olarak malzeme biliminde önemli başarımlar elde edilmiştir. Bu çalışmada, çeşitli bitki liflerinin biyokompozit malzeme üretmek için kullanımı, lif türlerinin mekanik özellikler üzerindeki etkileri ve üretim sürecinde biyokompozitlerin yapısı ile bitki lif takviyeli güçlendirilmiş kompozitlerin gelecek eğilimleri üzerine tartışılmaktadır. Physics ; Horticulture ; Chemistry ; Biology Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties
Authors: Gangshan Wu | Qiyou Jiang | Yuhao Bai | Chongfeng Tian | Wei Pan | Baohua Zhang
Source:  (preprint, 2019-12-20)
Field: Chemistry — Analytical Chemistry

Nitrogen status assessment for multiple cultivars of strawberries using portable NIR spectrometers combined with cultivar recognition and multivariate analysis <title>Abstract</title> Background The assessment of nitrogen status non-destructively in strawberry was performed to indicate its growth and provide guidance for precise management of N fertilizer using near-infrared reflectance (NIR) spectroscopy with leaf spectral reflectance. The Leaf soil plant analysis development (SPAD) value was thought as an indicator data that indirectly reflects nitrogen status in strawberry. However, the variation of cultivars would lead to differences in the cell structure and light scattering and/or reflection effects of strawberry leaves when assessed strawberry N status, which caused that no single leaf SPAD threshold value or same N demand can be applied for all strawberry cultivars. As a result, accurate detection of SPAD values and N status in strawberries with multiple cultivars is still challenging.Results In this study, Individual-cultivar model, hybrid-cultivar model and multi-cultivar model were developed for the determination of SPAD values, and the performance of the models in lessening the impact of cultivar variation were studied and compared. Individual-cultivar model was constructed on the basis of the single cultivar of strawberry leaf; hybrid-cultivar model was developed by merging the spectrum reflectance data and SPAD values of all studied leaf samples, and multi-cultivar model was built in combination with cultivar identification, individual-cultivar models, and model search strategy.Conclusion The results indicated that multi-cultivar model was more superior to the other two models for SPAD values estimation of strawberry leaves from different cultivars, with the overall Rp and RMSEP value respectively being 0.966 and 0.468. We demonstrate that the leaf N content per strawberry is profoundly affected by cultivar variation, and establishing a multi-cultivar model might be more useful in monitoring nitrogen status and guiding N fertilization recommendations for different strawberry cultivars. Cultivar ; Spectrometer ; Nitrogen ; Horticulture ; Multivariate statistics ; Environmental science ; Agronomy ; Biology ; Chemistry ; Mathematics ; Statistics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Katja Venko | Marjana Novič
Source: Molecules (article, 2019-02-27)
Field: Chemistry — Organic Chemistry

An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high interest of researchers, consumers and drug manufacturers. The hydrophilic nature of phenols makes a cell membrane penetration difficult, which imply an alternative way of uptake via membrane transporters. However, the structural and functional data of membrane transporters are limited, thus the in silico modelling is really challenging and urgent tool in elucidation of transporter ligands. Focus of this research was a particular transporter bilitranslocase (BTL). BTL has a broad tissue expression (vascular endothelium, absorptive and excretory epithelia) and can transport wide variety of poly-aromatic compounds. With available BTL data (pKi [mmol/L] for 120 organic compounds) a robust and reliable QSAR models for BTL transport activity were developed and extrapolated on 300 phenolic compounds. For all compounds the transporter profiles were assessed and results show that dietary phenols and some drug candidates are likely to interact with BTL. Moreover, synopsis of predictions from BTL models and hits/predictions of 20 transporters from Metrabase and Chembench platforms were revealed. With such joint transporter analyses a new insights for elucidation of BTL functional role were acquired. Regarding limitation of models for virtual profiling of transporter interactions the computational approach reported in this study could be applied for further development of reliable in silico models for any transporter, if in vitro experimental data are available. Transporter ; In silico ; Chemistry ; Phenols ; Computational biology ; Biochemistry ; Biology ; Gene Free Radicals and Antioxidants ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Roman A. Eremin | Pavel N. Zolotarev | Tilmann Leisegang | Pavlo Solokha
Source: AIP conference proceedings (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A machine learning approach for predicting formation enthalpy: A case study of Mackay-type approximants of icosahedral quasicrystals We present a machine learning approach for evaluating thermodynamic properties within model compositional/configurational spaces (CCSs) of complex intermetallics with Mackay-type building units. Recently, by performing high-throughput density functional theory calculations, we determine and analyze the formation energies for the recently synthesized 1/1 approximant of icosahedral quasicrystals in the Sc-Pd system possessing structural disorder. Here, sequentially increasing training sets are used in regression models based on compositional and topological descriptors for predicting formation enthalpies. We investigate different learning strategies. The obtained R2 dependencies show higher learning rates for the models using real features (composition and contact counters of defects) with small training set sizes, whereas using categorical features (topological types of defect structure fragments) result in a slightly higher prediction quality when the training set size increases. In connection with the obtained results, the proposed approach is considered as a tool for investigations of disordering phenomena in complex intermetallics as well as for a subsequent searching for new stable phases of Mackay-type approximants of quasicrystals. Quasicrystal ; Icosahedral symmetry ; Intermetallic ; Type (biology) ; Categorical variable ; Connection (principal bundle) ; Enthalpy ; Materials science ; Statistical physics ; Topology (electrical circuits) ; Artificial intelligence ; Machine learning ; Computer science ; Mathematics ; Thermodynamics ; Physics ; Alloy ; Geometry ; Combinatorics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Quasicrystal Structures and Properties
Authors: Črtomir Donik
Source: Materiali in tehnologije (article, 2019-12-19)
Field: Materials Science — Surfaces, Coatings and Films

Do the latest scanning electron microscopes with a variety of analytical and imaging techniques provide us with realistic images and compositions? The scanning electron microscope (SEM) has become a must-have for any research institution, university, or industry involved in material production, treatment, processing, research or even distribution. The SEM has evolved into probably the most common analytical tool in the materials research community, with its imaging, elemental analytics, phase analyses, local micro-stress analyses, in-situ cross-section preparation, lamella preparation for TEM, etc. Since the number of SEMs has drastically increased, due to its availability and its wide range of uses, increasing numbers of poorly trained SEM operators are involved in materials research and development. The resulting problem is that all analytical instruments always show "something" and the operator must know the limitations of the technique. The skilled and properly trained operator understands the physics behind the SEM and its associated analytical techniques, such as EDS, WDS, SIMS, EBSD, BEI, and ECCI. Materials science ; Variety (cybernetics) ; Scanning electron microscope ; Nanotechnology ; Microscope ; Engineering physics ; Optics ; Artificial intelligence ; Computer science ; Engineering ; Physics ; Composite material Electron and X-Ray Spectroscopy Techniques
Authors: Maroš Halama | Emily Haluschak | Peter Hanzes | Gabriela Baranová
Source: E3S Web of Conferences (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

The effect of defect size and soil aggressivity on corrosion of underground oil &amp; gas pipelines Nowadays, the oil &amp; gas industry has limited number of non-destructive corrosion techniques for assessment of life-time of existing infrastructures, especially on low and medium pressure pipelines exposed to aggressive soil environment where defect in isolation and presence of water formed ideal conditions for corrosion attack. Efficient non-destructive corrosion monitoring can be achieved using mobile potentiostat through the selection of appropriate monitoring techniques and special kind of sensor. OCP potential is monitored for 10 minutes and actual corrosion rate of pipeline with corrosion products in soil mixture was determined using Stern polarization technique in non-invasive arrangement. Using global database with range of minimum and maximum corrosion rate limits obtained from terrain measurements we can determine active or passive role of corrosion products and assess risk joint with aggressiveness of soil in location of interest. Finally, values were divided between high active, medium active and passive corrosion. Output is alarm, which can help in decision if reconstruction of underground pipelines where isolation failed in time is sufficient or not. When done properly, corrosion monitoring using non-invasive “in-situ” technique can serve as an early warning system before the onset of corrosion related failures. Corrosion ; Pipeline transport ; Environmental science ; Polarization (electrochemistry) ; Petroleum engineering ; Materials science ; Environmental engineering ; Metallurgy ; Engineering ; Chemistry Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Non-Destructive Testing Techniques
Authors: Mаrtin Dejanov | Darinka Ilieva-Stefanova | Iva Chelik
Source: MATEC Web of Conferences (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Assessment the quality of apricots in the process of drying with neural networks and support vector machines The paper presents an analysis of the assessment the quality of apricots during the drying process using two types of classifires: ANNs and SVMs. The quality of apricots is categorized in three classes according to the color and b-carotene content through the process of drying. The classification is made by using ‘CIE Lab’ color model and spectral characteristics in the VIS range. Neural networks are BPN and PNN, and classifiers are kernel and linear SVM. The spectral characteristics are pre-processed with SNV, MSC, First derivative and PCA. According to the results for color features, BPN and SVM with “rbf” kernel have the best performance while PNN has the worst performance. When using spectral characteristics the BPN network performs well: e avg = 4.1% and e max = 12.1% but the SVM linear (e avg = 3.4%, e max =5.3%) and SVM with “rbf” kernel (e avg = 2.4%, e max =5.2%) classifiers have better results. As a conclusion, it could be said that classifiers using spectral features perform well with errors at about 2-5%. Classification with color features is an alternative method, which is less complex, cheaper and with acceptable errors. Support vector machine ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Kernel (algebra) ; Computer science ; Polynomial kernel ; Radial basis function kernel ; Mathematics ; Machine learning ; Kernel method Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Jakob Schiøtz | Jacob Madsen | Bjarke J. G. Østergaard | Anders Siig Dreisig | Pei Liu | Stig Helveg | Ole Winther | Jens Kling | Jakob Birkedal Wagner | Thomas W. Hansen
Source: Microscopy and Microanalysis (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

Using Neural Networks to Identify Atoms in HRTEM Images Journal Article Using Neural Networks to Identify Atoms in HRTEM Images Get access Jakob Schiøtz, Jakob Schiøtz DTU Physics, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Jacob Madsen, Jacob Madsen DTU Physics, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Bjarke J G Østergaard, Bjarke J G Østergaard DTU Physics, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Anders S Dreisig, Anders S Dreisig DTU Physics, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Pei Liu, Pei Liu DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Stig Helveg, Stig Helveg Haldor Topsøe A/S, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Ole Winther, Ole Winther DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Jens Kling, Jens Kling DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Jakob Birkedal Wagner, Jakob Birkedal Wagner DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Thomas Willum Hansen Thomas Willum Hansen DTU Nanolab, Technical University of Denmark, Kgs. Lyngby, Denmark Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 216–217, https://doi.org/10.1017/S1431927619001818 Published: 01 August 2019 High-resolution transmission electron microscopy ; Artificial neural network ; Materials science ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Nanotechnology ; Transmission electron microscopy Machine Learning in Materials Science ; Nuclear Physics and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Soham Banerjee | Chia-Hao Liu | Kirsten M. Ø. Jensen | Pavol Juhás | Jennifer D. Lee | Marcus A. Tofanelli | Christopher J. Ackerson | Christopher B. Murray | Simon J. L. Billinge
Source: PubMed (preprint, 2019-01-25)
Field: Materials Science — Materials Chemistry

Cluster-mining: An approach for determining core structures of metallic nanoparticles from atomic pair distribution function data A novel approach for finding and evaluating structural models of small metallic nanoparticles is presented. Rather than fitting a single model with many degrees of freedom, libraries of clusters from multiple structural motifs are built algorithmically and individually refined against experimental pair distribution functions. Each cluster fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles. Cluster (spacecraft) ; Nanoparticle ; Goodness of fit ; Core (optical fiber) ; Computer science ; Function (biology) ; Data mining ; Key (lock) ; Pair distribution function ; Degrees of freedom (physics and chemistry) ; Materials science ; Statistical physics ; Biological system ; Nanotechnology ; Physics ; Machine learning ; Thermodynamics X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Juan C. Pérez | Motasem Alfarra | Guillaume Jeanneret | Adel Bibi | Ali Thabet | Bernard Ghanem | Pablo Arbeláez
Source: King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (article, 2019-12-11)
Field: Chemistry — Analytical Chemistry

Robust Gabor Networks This work takes a step towards investigating the benefits of merging classical vision techniques with deep learning models. Formally, we explore the effect of replacing the first layers of neural network architectures with convolutional layers that are based on Gabor filters with learnable parameters. As a first result, we observe that architectures utilizing Gabor filters as low-level kernels are capable of preserving test set accuracy of deep convolutional networks. Therefore, this architectural change exalts their capabilities in extracting useful low-level features. Furthermore, we observe that the architectures enhanced with Gabor layers gain advantages in terms of robustness when compared to the regular models. Additionally, the existence of a closed mathematical expression for the Gabor kernels allows us to develop an analytical expression for an upper bound to the Lipschitz constant of the Gabor layer. This expression allows us to propose a simple regularizer to enhance the robustness of the network. We conduct extensive experiments with several architectures and datasets, and show the beneficial effects that the introduction of Gabor layers has on the robustness of deep convolutional networks. Computer science ; Business ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Image Processing Techniques and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research