jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuan Liu | Päivi Mäki‐Arvela | Atte Aho | Zuzana Vajglová | В.М. Гунько | Ivo Heinmaa | Narendra Kumar | Kari Eränen | Tapio Salmi | Dmitry Yu. Murzin
Source: Molecules (article, 2018-04-18)
Field: Chemistry — Inorganic Chemistry

Zeta Potential of Beta Zeolites: Influence of Structure, Acidity, pH, Temperature and Concentration Measurements of the zeta potential of solid heterogeneous supports are important for preparation of metal supported catalysts and for shaping zeolites into extrudates. In the current work, different types of heterogeneous support materials such as SiO₂, Al₂O₃, and a range of beta zeolites of different silica- to-alumina ratio were analysed. It was observed that parameters such as temperature, pH and acidity significantly affect the zeta potential. In several instances, depending on the materials’ acidity and microstructure, maxima in zeta potential were observed. The solid materials were thoroughly characterized using XRD, SEM, EDX, TEM, nitrogen physisorption, Al-NMR and FTIR with pyridine before zeta potential measurements. Chemistry ; Zeta potential ; BETA (programming language) ; Inorganic chemistry ; Environmental chemistry ; Chemical engineering Zeolite Catalysis and Synthesis ; Chemical Synthesis and Characterization ; Clay minerals and soil interactions
Authors: Harrison Specht | Guillaume Harmange | David H. Perlman | Edward Emmott | Zachary Niziolek | Bogdan Budnik | Nikolai Slavov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-08-25)
Field: Chemistry — Spectroscopy

Automated sample preparation for high-throughput single-cell proteomics A major limitation to applying quantitative LC-MS/MS proteomics to small samples, such as single cells, are the losses incured during sample cleanup. To relieve this limitation, we developed a Minimal ProteOmic sample Preparation (mPOP) method for culture-grown mammalian cells. mPOP obviates cleanup and thus eliminates cleanup-related losses while expediting sample preparation and simplifying its automation. Bulk SILAC samples processed by mPOP or by conventional urea-based methods indicated that mPOP results in complete cell lysis and accurate relative quantification. We integrated mPOP lysis with the Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) sample preparation, and benchmarked the quantification of such samples on a Q-exactive instrument. The results demonstrate low noise and high technical reproducibility. Then, we FACS sorted single U-937, HEK-293, and mouse ES cells into 96-well plates and analyzed them by automated mPOP and SCoPE-MS. The quantified proteins enabled separating the single cells by cell-type and cell-division-cycle phase. Sample preparation ; Proteomics ; Stable isotope labeling by amino acids in cell culture ; Lysis ; Mass spectrometry ; Chromatography ; Quantitative proteomics ; Sample (material) ; Chemistry ; Proteome ; Throughput ; Computational biology ; Computer science ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Vi Nguyen Thanh Le | Beniamin Apopei | Kamal Alameh
Source: Information Processing in Agriculture (article, 2018-08-09)
Field: Chemistry — Analytical Chemistry

Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes. Support vector machine ; Binary number ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Engineering ; Machine learning ; Mathematics ; Arithmetic Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Face and Expression Recognition
Authors: Zandile Mhlwatika | Blessing A. Aderibigbe
Source: Molecules (review, 2018-08-31)
Field: Materials Science — Polymers and Plastics

Application of Dendrimers for the Treatment of Infectious Diseases Dendrimers are drug delivery systems that are characterized by a three-dimensional, star-shaped, branched macromolecular network. They possess ideal properties such as low polydispersity index, biocompatibility and good water solubility. They are made up of the interior and the exterior layers. The exterior layer consists of functional groups that are useful for conjugation of drugs and targeting moieties. The interior layer exhibits improved drug encapsulation efficiency, reduced drug toxicity, and controlled release mechanisms. These unique properties make them useful for drug delivery. Dendrimers have attracted considerable attention as drug delivery system for the treatment of infectious diseases. The treatment of infectious diseases is hampered severely by drug resistance. Several properties of dendrimers such as their ability to overcome drug resistance, toxicity and control the release mechanism of the encapsulated drugs make them ideal systems for the treatment of infectious disease. The aim of this review is to discuss the potentials of dendrimers for the treatment of viral and parasitic infections. Dendrimer ; Drug delivery ; Drug ; Biocompatibility ; Dispersity ; Nanotechnology ; Drug carrier ; Materials science ; Chemistry ; Pharmacology ; Medicine ; Organic chemistry Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; HIV Research and Treatment
Authors: Philippe Garrigue | Jingjie Tang | Ling Ding | Ahlem Bouhlel | Aura Tintaru | Erik Laurini | Yuanyu Huang | Zhenbin Lyu | Jun Zhang | Samantha Fernandez | Laure Balasse | Wenjun Lan | Eric Mas | Domenico Marson | Yuhua Weng | Xiaoxuan Liu | Suzanne Giorgio | Juan Iovanna | Sabrina Pricl | Benjamin Guillet | Ling Peng
Source: Proceedings of the National Academy of Sciences (article, 2018-10-22)
Field: Materials Science — Polymers and Plastics

Self-assembling supramolecular dendrimer nanosystem for PET imaging of tumors Significance Nanotechnology-based imaging is expected to bring breakthroughs in cancer diagnosis by improving imaging sensitivity and specificity while reducing toxicity. Here, we developed an innovative nanosystem for positron emission tomography (PET) imaging based on a self-assembling amphiphilic dendrimer. This dendrimer assembled spontaneously into uniform supramolecular nanomicelles with abundant PET reporting units on the surface. By harnessing both dendrimeric multivalence and the “enhanced permeation and retention” (EPR) effect, this dendrimer nanosystem effectively accumulated in tumors, leading to exceedingly sensitive and specific imaging of various tumors, especially those that are otherwise undetectable using the clinical gold reference 2-fluorodeoxyglucose ([ 18 F]FDG). This study illustrates the power of nanotechnology based on self-assembling dendrimers to provide an effective platform for bioimaging and related biomedical applications. Dendrimer ; Nanotechnology ; Supramolecular chemistry ; Positron emission tomography ; Amphiphile ; Materials science ; Chemistry ; Biomedical engineering ; Medicine ; Nuclear medicine ; Molecule ; Polymer ; Polymer chemistry ; Copolymer ; Organic chemistry Dendrimers and Hyperbranched Polymers ; Advanced biosensing and bioanalysis techniques ; Radiopharmaceutical Chemistry and Applications
Authors: Ming Liu | Yang Guo | Jeff Wang | Mark Yergin
Source: npj Materials Degradation (article, 2018-07-16)
Field: Materials Science — Materials Chemistry

Corrosion avoidance in lightweight materials for automotive applications Abstract Due to increasing vehicle greenhouse gas emission regulations across the globe, the automotive industry is facing pressure to improving fuel economy. The key to achieving this is reducing vehicle mass and developing alternative energy vehicles, thereby reducing environmental impact. Lightweighting has become one of the most important aspects to consider in the design and manufacture of modern vehicles, while electric vehicles require a different approach to design and build. All the aforementioned factors have a significant impact on materials selection and usage, and corrosion is a vital consideration. In this article, we discuss and summarize some of the challenges and demands of future fuel-efficient vehicles, from the perspective of corrosion avoidance Automotive industry ; Corrosion ; Fuel efficiency ; Greenhouse gas ; Key (lock) ; Efficient energy use ; Risk analysis (engineering) ; Automotive engineering ; Computer science ; Manufacturing engineering ; Engineering ; Environmental science ; Business ; Materials science ; Computer security ; Aerospace engineering Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics
Authors: Fatemeh Madani | Seyedeh Sara Esnaashari | Basil Mujokoro | Farid Abedin Dorkoosh | Masood Khosravani | Mahdi Adabi
Source: Advanced Pharmaceutical Bulletin (article, 2018-03-18)
Field: Materials Science — Biomaterials

Investigation of Effective Parameters on Size of Paclitaxel Loaded PLGA Nanoparticles <b><i>Purpose:</i></b> The size of polymeric nanoparticles is considered as an effective factor in cancer therapy due to enterance into tumor tissue via the EPR effect. The purpose of this work was to investigate the effective parameters on poly(lactic-co-glycolic acid)-paclitaxel (PLGA -PTX) nanoparticles size. <b><i>Methods:</i></b> We prepared PLGA-PTX nanoparticles via single emulsion and precipitation methods with variable paremeters including drug concentration, aqueous to organic phase volume ratio, polymer concentration, sonication time and PVA concentration. <b><i>Results:</i></b> PLGA-PTX nanoparticles were characterized by dynamic light scattering (DLS) and scanning electron microscopy (SEM). The results exhibited that the diameter of nanoparticles enhanced with increasing drug, polymer and PVA concentrations whereas organic to aqueous phase volume ratio and sonication time required to the optimization for a given size. <b><i>Conclusion:</i></b> The precipitation method provides smaller nanoparticles compared to emulsion one. Variable parameters including drug concentration, aqueous to organic phase volume ratio, polymer concentration, sonication time and PVA concentration affect diameter of nanoparticles. Sonication ; PLGA ; Nanoparticle ; Emulsion ; Dynamic light scattering ; Aqueous solution ; Materials science ; Polymer ; Aqueous two-phase system ; Chemical engineering ; Precipitation ; Particle size ; Glycolic acid ; Nuclear chemistry ; Chemistry ; Nanotechnology ; Chromatography ; Composite material ; Organic chemistry ; Lactic acid Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; RNA Interference and Gene Delivery
Authors: Yiying Zhao | Susu Zhu | Chu Zhang | Xuping Feng | Lei Feng | Yong He
Source: RSC Advances (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Application of hyperspectral imaging and chemometrics for variety classification of maize seeds Seed variety classification is important for assessing variety purity and increasing crop yield. A hyperspectral imaging system covering the spectral range of 874-1734 nm was applied for variety classification of maize seeds. A total of 12 900 maize seeds including 3 different varieties were evaluated. Spectral data of 975.01-1645.82 nm were extracted and preprocessed. Discriminant models were developed using a radial basis function neural network (RBFNN). The influence of calibration sample size on classification accuracy was studied. Results showed that with the expansion of calibration sample size, calibration accuracy varied slightly, but prediction accuracy changed from the increasing form to the stable form. Accordingly, the optimal size of the calibration set was determined. Optimal wavelength selection was conducted by loading of principal components (PCs). The RBFNN model developed on optimal wavelengths with the optimal size of the calibration set obtained satisfactory results, with calibration accuracy of 93.85% and prediction accuracy of 91.00%. Visualization of classification map of seed varieties was achieved by applying this RBFNN model on the average spectra of each sample. Besides, the procedure to determine the optimal sample quantity proposed in this study was verified by support vector machine (SVM). The overall results indicated that hyperspectral imaging was a potential technique for variety classification of maize seeds, and would help to develop a real-time detection system for maize seeds as well as other crop seeds. Hyperspectral imaging ; Chemometrics ; Variety (cybernetics) ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Seed and Plant Biochemistry
Authors: Gregor N. C. Simm | Markus Reiher
Source: Journal of Chemical Theory and Computation (article, 2018-09-04)
Field: Materials Science — Materials Chemistry

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes For a theoretical understanding of the reactivity of complex chemical systems, relative energies of stationary points on potential energy hypersurfaces need to be calculated to high accuracy. Due to the large number of intermediates present in all but the simplest chemical processes, approximate quantum chemical methods are required that allow for fast evaluations of the relative energies but at the expense of accuracy. Despite the plethora of benchmark studies, the accuracy of a quantum chemical method is often difficult to assess. Moreover, a significant improvement of a method's accuracy (e.g., through reparameterization or systematic model extension) is rarely possible. Here, we present a new approach that allows for the systematic, problem-oriented, and rolling improvement of quantum chemical results through the application of Gaussian processes. Due to its Bayesian nature, reliable error estimates are provided for each prediction. A reference method of high accuracy can be employed if the uncertainty associated with a particular calculation is above a given threshold. The new data point is then added to a growing data set in order to continuously improve the model and, as a result, all subsequent predictions. Previous predictions are validated by the updated model to ensure that uncertainties remain within the given confidence bound, which we call backtracking. We demonstrate our approach with the example of a complex chemical reaction network. Computer science ; Gaussian process ; Gaussian ; Data science ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Joseph Socci | Amin Osatiashtiani | Georgios Kyriakou | A.V. Bridgwater
Source: Applied Catalysis A General (article, 2018-11-24)
Field: Chemistry — Inorganic Chemistry

The catalytic cracking of sterically challenging plastic feedstocks over high acid density Al-SBA-15 catalysts Catalysis ; Fluid catalytic cracking ; Chemistry ; Microporous material ; Polyolefin ; Product distribution ; Mesoporous material ; Cracking ; Low-density polyethylene ; Chemical engineering ; Physisorption ; Zeolite ; Organic chemistry ; Polyethylene Zeolite Catalysis and Synthesis ; Membrane Separation and Gas Transport ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Janis Timoshenko | Avik Halder | Bing Yang | Söenke Seifert | Michael J. Pellin | Štefan Vajda | Anatoly I. Frenkel
Source: The Journal of Physical Chemistry C (article, 2018-08-26)
Field: Materials Science — Materials Chemistry

Subnanometer Substructures in Nanoassemblies Formed from Clusters under a Reactive Atmosphere Revealed Using Machine Learning Size-selected clusters, soft-landed on an oxide substrate, is a promising and highly tunable material for heterogeneous catalysis. Agglomeration of the deposited clusters, however, leads to changes in the particle properties and structure. The latter for such cluster assemblies can also be different from that in self-standing nanoparticles of similar sizes. To monitor the formation of such complex materials, in situ studies at different length scales are required. Toward that goal, we combined small-angle X-ray scattering (SAXS), X-ray absorption near-edge structure (XANES) spectroscopy, ab initio simulations, and machine learning (artificial neural network) techniques. We detected significant differences between the sizes of particle agglomerates, as probed by SAXS, and the sizes of locally ordered regions, as seen by XANES. We interpret these differences as an evidence for the fractal, grape-cluster-like structure of the agglomerates; thus, XANES and SAXS provide highly complementary structural information. This finding can have a profound effect on our understanding of particle sintering and assembly processes and of structure–properties relationship in ultradispersed metal catalysts in reaction conditions. Small-angle X-ray scattering ; XANES ; Materials science ; Agglomerate ; Cluster (spacecraft) ; Particle (ecology) ; Nanoparticle ; Chemical physics ; X-ray absorption fine structure ; Scattering ; Ab initio ; Particle size ; Absorption (acoustics) ; Chemical engineering ; Nanotechnology ; Spectroscopy ; Chemistry ; Composite material ; Optics ; Organic chemistry ; Computer science ; Physics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Theoretical and Computational Physics
Authors: Jaimyun Jung | Jae Ik Yoon | Hyung Keun Park | Jin You Kim | Hyoung Seop Kim
Source: Computational Materials Science (article, 2018-09-21)
Field: Materials Science — Materials Chemistry

An efficient machine learning approach to establish structure-property linkages Property (philosophy) ; Computer science ; Linkage (software) ; Field (mathematics) ; Construct (python library) ; Microstructure ; Process (computing) ; Range (aeronautics) ; Artificial intelligence ; Gaussian ; Machine learning ; Industrial engineering ; Materials science ; Mathematics ; Engineering ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Chunwang Dong | Gaozhen Liang | Bin Hu | Haibo Yuan | Yongwen Jiang | Hongkai Zhu | Jiangtao Qi
Source: Scientific Reports (article, 2018-07-06)
Field: Chemistry — Analytical Chemistry

Prediction of Congou Black Tea Fermentation Quality Indices from Color Features Using Non-Linear Regression Methods Fermentation is the key process to produce the special color of congou black tea. The machine vision technology is applied to detect the color space changes of black tea's color in RGB, Lab and HSV, and to find out its relevance to black tea's fermentation quality. And then the color feature parameter is used as input to establish physicochemical indexes (TFs, TRs, and TBs) and sensory features' linear and non-linear quantitative evaluation model. Results reveal that color features are significantly correlated to quality indices. Compared with the other two color models (RGB and HSV), CIE Lab model can better reflect the dynamic variation features of quality indices and foliage color information of black tea. The predictability of non-linear models (RF and SVM) is superior to PLS linear model, while RF model presents a slight advantage over the classic SVM model since RF model can better represent the quantitative analytical relationship between image information and quality indices. This research has proved that computer image color features and non-linear method can be used to quantitatively evaluate the changes of quality indices (e.g. sensory quality) and the pigment during black tea's fermentation. Besides, the test is simple, fast, and nondestructive. Black tea ; Linear regression ; Regression ; Statistics ; Regression analysis ; Quality (philosophy) ; Computer science ; Mathematics ; Food science ; Biology Spectroscopy and Chemometric Analyses ; Sensory Analysis and Statistical Methods ; Tea Polyphenols and Effects
Authors: Dominik Lungerich | Olena Papaianina | Mikhail Feofanov | Jia Liu | Mirunalini Devarajulu | Sergey I. Troyanov | Sabine Maier | Konstantin Amsharov
Source: Nature Communications (article, 2018-11-06)
Field: Chemistry — Organic Chemistry

Dehydrative π-extension to nanographenes with zig-zag edges Zig-zag nanographenes are promising candidates for the applications in organic electronics due to the electronic properties induced by their periphery. However, the synthetic access to these compounds remains virtually unexplored. There is a lack in efficient and mild strategies origins in the reduced stability, increased reactivity, and low solubility of these compounds. Herein we report a facile access to pristine zig-zag nanographenes, utilizing an acid-promoted intramolecular reductive cyclization of arylaldehydes, and demonstrate a three-step route to nanographenes constituted of angularly fused tetracenes or pentacenes. The mild conditions are scalable to gram quantities and give insoluble nanostructures in close to quantitative yields. The strategy allows the synthesis of elusive low bandgap nanographenes, with values as low as 1.62 eV. Compared to their linear homologues, the structures have an increased stability in the solid-state, even though computational analyses show distinct diradical character. The structures were confirmed by X-ray diffraction or scanning tunneling microscopy. Zigzag ; Scanning tunneling microscope ; Nanotechnology ; Nanostructure ; Materials science ; Chemistry Synthesis and Properties of Aromatic Compounds ; Graphene research and applications ; Fullerene Chemistry and Applications
Authors: Valeria A. Risso | José M. Sánchez‐Ruiz | S. Banu Ozkan
Source: Current Opinion in Structural Biology (review, 2018-04-13)
Field: Materials Science — Materials Chemistry

Biotechnological and protein-engineering implications of ancestral protein resurrection Extant taxon ; Biology ; Protein engineering ; Computational biology ; Evolutionary biology ; Protein evolution ; Scrutiny ; Flexibility (engineering) ; Functional diversity ; Genetics ; Biochemistry ; Enzyme ; Gene ; Ecology Enzyme Structure and Function ; RNA and protein synthesis mechanisms ; Protein Structure and Dynamics