jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mariana R. Carvalho | F. Raquel Maia | Sílvia Vieira | Rui L. Reis | Joaquím M. Oliveira
Source: Global Challenges (article, 2018-05-24)
Field: Materials Science — Biomaterials

Tuning Enzymatically Crosslinked Silk Fibroin Hydrogel Properties for the Development of a Colorectal Cancer Extravasation 3D Model on a Chip Microfluidic devices are now the most promising tool to mimic in vivo like scenarios such as tumorigenesis and metastasis due to its ability to more closely mimic cell's natural microenvironment (such as 3D environment and continuous perfusion of nutrients). In this study, the ability of 2% and 3% enzymatically crosslinked silk fibroin hydrogels with different mechanical properties are tested in terms of colorectal cancer cell migration, under different microenvironments in a 3D dynamic model. Matrigel is used as control. Moreover, a comprehensive comparison between the traditional Boyden chamber assay and the 3D dynamic microfluidic model in terms of colorectal cancer cell migration is presented. The results show profound differences between the two used biomaterials and the two migration models, which are explored in terms of mechanical properties of the hydrogels as well as the intrinsic characteristics of the models. Moreover, the developed 3D dynamic model is validated by demonstrating that hVCAM-1 plays a major role in the extravasation process, influencing extravasation rate and traveled distance. Furthermore, the developed model enables precise visualization of cancer cell migration within a 3D matrix in response to microenvironmental cues, shedding light on the importance of biophysical properties in cell behavior. Fibroin ; Self-healing hydrogels ; Matrigel ; Materials science ; Extravasation ; Microfluidics ; Cell migration ; Nanotechnology ; SILK ; Biophysics ; Cell ; Biomedical engineering ; Chemistry ; Pathology ; Medicine ; Biology ; Composite material Silk-based biomaterials and applications ; 3D Printing in Biomedical Research ; Cellular Mechanics and Interactions
Authors: Marco Bortoli | Shah Masood Ahmad | Trevor A. Hamlin | F. Matthias Bickelhaupt | Laura Orian
Source: Physical Chemistry Chemical Physics (article, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Nature and strength of chalcogen–π bonds Chalcogen-π interactions occur between a covalently bound chalcogen atom that enters into a non-covalent interaction with an unsaturated moiety, a bonding motif found in various structures, such as, proteins. In this work, we have systematically explored and analyzed chalcogen-π interactions in model systems X2DA (with D = O, S, Se, Te; X = halogen; A = acetylene, ethylene and 2-butyne), using relativistic density functional theory (DFT). The nature and trends in stability of the chalcogen-π bonds are analyzed and interpreted in terms of quantitative MO theory in combination with a matching canonical energy decomposition analysis (EDA) scheme. We find that chalcogen-π bonds increase in strength as the X-D electronegativity difference becomes greater. Moreover, 2-butyne was found to participate in the strongest non-covalent interaction due to enhanced orbital interactions. Chalcogen ; Computational chemistry ; Series (stratigraphy) ; Decomposition ; Chemistry ; Density functional theory ; Chemical physics ; Materials science ; Crystallography ; Organic chemistry Crystallography and molecular interactions ; Organic and Molecular Conductors Research ; Organometallic Compounds Synthesis and Characterization
Authors: Bryce Thurston | Andrew L. Ferguson
Source: Molecular Simulation (article, 2018-05-09)
Field: Materials Science — Biomaterials

Machine learning and molecular design of self-assembling -conjugated oligopeptides Self-assembling oligopeptides present a means to fabricate biocompatible supramolecular aggregates with engineered electronic and optical functionality. We conducted molecular dynamics simulations of self-assembling synthetic oligopeptides with Asp-X-X-X--X-X-X-Asp architectures. Dimerisation and trimerisation free energies were computed for a range of Asp-X-X-X amino acid sequences, and for perylenediimide (PDI) and naphthalenediimide (NDI) conjugated cores that mediate hydrophobic stacking and electron delocalisation within the self-assembled nanostructure. The larger PDI cores elevated oligomerisation free energies by a factor of 2-3 relative to NDI and also improved alignment of the oligopeptides within the stack. Training of a quantitative structure–property relationship (QSPR) model revealed key physicochemical determinants of the oligomerisation free energies and produced a predictive model for the oligomerisation thermodynamics. Oligopeptides with moderate dimerisation and trimerisation free energies of (-25) produced aggregates with the best in-register parallel stacking, and we used this criterion within our QSPR model to perform high-throughput virtual screening to identify promising candidates for the spontaneous assembly of ordered nanoaggregates. We identified a small number of oligopeptide candidates for direct testing in large scale molecular simulations, and predict a novel chemistry DAVG-PDI-GVAD previously unstudied by experiment or simulation to produce well-aligned nanoaggregates expected to possess good optical and electronic functionality. Stacking ; Oligopeptide ; Conjugated system ; Supramolecular chemistry ; Molecular dynamics ; Chemistry ; Quantitative structure–activity relationship ; Nanostructure ; Combinatorial chemistry ; Self-assembly ; Peptide ; Nanotechnology ; Chemical physics ; Computational chemistry ; Materials science ; Molecule ; Stereochemistry ; Organic chemistry ; Polymer Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Luminescence and Fluorescent Materials
Authors: Wenwen Li | Yasunobu Ando
Source: Physical Chemistry Chemical Physics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Comparison of different machine learning models for the prediction of forces in copper and silicon dioxide Recently, the machine learning (ML) force field has emerged as a powerful atomic simulation approach because of its high accuracy and low computational cost. However, there have been relatively fewer applications to multicomponent materials. In this study, we construct and compare ML force fields for both an elemental material (Cu) and binary material (SiO2) with varied inputs and regression models. The atomic environments are described by structural fingerprints that take into account the bond angle, and then, different ML techniques, including linear regression, a neural network and a mixture model method, are used to learn the structure-force relationship. We found that using angular structural fingerprints and a mixture model method significantly improves the accuracy of ML force fields. Additionally, we discuss an effective structural fingerprint auto-selection method based on the least absolute shrinkage and selection operator and the genetic algorithm. The atomic simulations conducted for ML force fields are in excellent agreement with ab initio calculations. As a result of the simulation with our ML force field for the structural and vibrational properties of amorphous SiO2, simulated annealing with a slow cooling rate improved the ring statistics in the amorphous structure and the phonon density of states. Force field (fiction) ; Simulated annealing ; Artificial neural network ; Ab initio ; Field (mathematics) ; Amorphous solid ; Copper ; Linear regression ; Binary number ; Computer science ; Materials science ; Statistical physics ; Artificial intelligence ; Chemistry ; Algorithm ; Machine learning ; Physics ; Mathematics ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Zi‐Kui Liu
Source: Journal of Phase Equilibria and Diffusion (article, 2018-07-02)
Field: Materials Science — Materials Chemistry

Ocean of Data: Integrating First-Principles Calculations and CALPHAD Modeling with Machine Learning CALPHAD ; Thermodynamics ; Metastability ; Robustness (evolution) ; Statistical physics ; Computer science ; Chemistry ; Phase diagram ; Physics ; Phase (matter) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Suman Kumari | Hanuma Reddy Tiyyagura | Timothy Douglas | Elbeshary A.A. Mohammed | Annemie Adriaens | Regina Fuchs−Godec | M. Mohan | André G. Skirtach
Source: Materials & Design (article, 2018-07-07)
Field: Materials Science — Materials Chemistry

ANN prediction of corrosion behaviour of uncoated and biopolymers coated cp-Titanium substrates Materials science ; Biopolymer ; Titanium ; Corrosion ; Substrate (aquarium) ; Nyquist plot ; Chemical engineering ; Electrochemistry ; Composite material ; Biological system ; Metallurgy ; Dielectric spectroscopy ; Electrode ; Chemistry ; Polymer Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Advanced Machining and Optimization Techniques
Authors: Ping Li | Ningling Zhou | Hua Qiu | Manfred F. Maitz | Juan Wang | Nan Huang
Source: Science China Materials (review, 2018-01-10)
Field: Materials Science — Biomaterials

In vitro and in vivo cytocompatibility evaluation of biodegradable magnesium-based stents: a review Biocompatibility ; In vivo ; Biomedical engineering ; Materials science ; In vitro ; Magnesium ; Nanotechnology ; Medicine ; Chemistry ; Biology ; Metallurgy ; Biotechnology ; Biochemistry Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Aluminum Alloys Composites Properties
Authors: Ali Nahvi | Mohammad Kazem Sadoughi | Ali Arabzadeh | Alireza Sassani | Chao Hu | Hali̇l Ceylan | Sunghwan Kim
Source: Journal of Computational Design and Engineering (article, 2018-12-04)
Field: Materials Science — Surfaces, Coatings and Films

Multi-objective Bayesian optimization of super hydrophobic coatings on asphalt concrete surfaces Abstract Conventional snow removal strategies add direct and indirect expenses to the economy through profit lost due to passenger delays costs, pavement durability issues, contaminating the water runoff, and so on. The use of superhydrophobic (super-water-repellent) coating methods is an alternative to conventional snow and ice removal practices for alleviating snow removal operations issues. As an integrated experimental and analytical study, this work focused on optimizing superhydrophobicity and skid resistance of hydrophobic coatings on asphalt concrete surfaces. A layer-by-layer (LBL) method was utilized for spray depositing polytetrafluoroethylene (PTFE) on an asphalt concrete at different spray times and variable dosages of PTFE. Water contact angle and coefficient of friction at the microtexture level were measured to evaluate superhydrophobicity and skid resistance of the coated asphalt concrete. The optimum dosage and spay time that maximized hydrophobicity and skid resistance of flexible pavement while minimizing cost were estimated using a multi-objective Bayesian optimization (BO) method that replaced the more costly experimental procedure of pavement testing with a cheap-to-evaluate surrogate model constructed based on kriging. In this method, the surrogate model is iteratively updated with new experimental data measured at proper input settings. The result of proposed optimization method showed that the super water repellency and coefficient of friction were not uniformly increased for all the specimens by increasing spray time and dosage. In addition, use of the proposed multi-objective BO method resulted in hydrophobicity and skid resistance being maximally augmented by approximately 23% PTFE dosage at a spray time of 5.5 s. Highlights Effects of spray time and dosage on the hydrophobicity and friction of asphalt were investigated. A layer-by-layer method was utilized for spray depositing polytetrafluoroethylene on an asphalt concrete. The optimum dosage and spay time were estimated by using a multi-objective Bayesian optimization method. An acquisition function that can tackle problems involving multiple objective functions was proposed. The optimum hydrophobicity and skid resistance were achieved with 23% PTFE dosage and at a spray time of 5.5 s. Skid (aerodynamics) ; Materials science ; Durability ; Asphalt concrete ; Superhydrophobic coating ; Asphalt ; Rut ; Composite material ; Coating ; Snow removal ; Snow ; Geotechnical engineering ; Engineering ; Geology Surface Modification and Superhydrophobicity ; Smart Materials for Construction ; Icing and De-icing Technologies
Authors: Lei Feng | Susu Zhu | Chu Zhang | Yidan Bao | Pan Gao | Yong He
Source: Molecules (article, 2018-11-08)
Field: Chemistry — Analytical Chemistry

Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874⁻1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties. Hyperspectral imaging ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Pixel ; Mathematics ; Identification (biology) ; Wavelet ; Computer science ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Trung Hai Nguyen | Ariën S. Rustenburg | S.G. Krimmer | Hexi Zhang | John D. Clark | Paul Novick | Kim Branson | Vijay S. Pande | John D. Chodera | David D. L. Minh
Source: PLoS ONE (article, 2018-09-13)
Field: Chemistry — Physical and Theoretical Chemistry

Bayesian analysis of isothermal titration calorimetry for binding thermodynamics Isothermal titration calorimetry (ITC) is the only technique able to determine both the enthalpy and entropy of noncovalent association in a single experiment. The standard data analysis method based on nonlinear regression, however, provides unrealistically small uncertainty estimates due to its neglect of dominant sources of error. Here, we present a Bayesian framework for sampling from the posterior distribution of all thermodynamic parameters and other quantities of interest from one or more ITC experiments, allowing uncertainties and correlations to be quantitatively assessed. For a series of ITC measurements on metal:chelator and protein:ligand systems, the Bayesian approach yields uncertainties which represent the variability from experiment to experiment more accurately than the standard data analysis. In some datasets, the median enthalpy of binding is shifted by as much as 1.5 kcal/mol. A Python implementation suitable for analysis of data generated by MicroCal instruments (and adaptable to other calorimeters) is freely available online. Isothermal titration calorimetry ; Bayesian probability ; Enthalpy ; Thermodynamics ; Calorimetry ; Chemistry ; Gibbs sampling ; Statistical physics ; Statistics ; Computer science ; Mathematics ; Physics thermodynamics and calorimetric analyses ; Computational Drug Discovery Methods ; Field-Flow Fractionation Techniques
Authors: Laura Guarina | Chiara Calorio | Daniela Gavello | Ekaterina Moreva | P. Traina | A. Battiato | S. Ditalia Tchernij | J. Forneris | Min‐Tao Gai | Federico Picollo | P. Olivero | Marco Genovese | Emilio Carbone | Andrea Marcantoni | Valentina Carabelli
Source: Scientific Reports (article, 2018-01-29)
Field: Materials Science — Materials Chemistry

Nanodiamonds-induced effects on neuronal firing of mouse hippocampal microcircuits Fluorescent nanodiamonds (FND) are carbon-based nanomaterials that can efficiently incorporate optically active photoluminescent centers such as the nitrogen-vacancy complex, thus making them promising candidates as optical biolabels and drug-delivery agents. FNDs exhibit bright fluorescence without photobleaching combined with high uptake rate and low cytotoxicity. Focusing on FNDs interference with neuronal function, here we examined their effect on cultured hippocampal neurons, monitoring the whole network development as well as the electrophysiological properties of single neurons. We observed that FNDs drastically decreased the frequency of inhibitory (from 1.81 Hz to 0.86 Hz) and excitatory (from 1.61 to 0.68 Hz) miniature postsynaptic currents, and consistently reduced action potential (AP) firing frequency (by 36%), as measured by microelectrode arrays. On the contrary, bursts synchronization was preserved, as well as the amplitude of spontaneous inhibitory and excitatory events. Current-clamp recordings revealed that the ratio of neurons responding with AP trains of high-frequency (fast-spiking) versus neurons responding with trains of low-frequency (slow-spiking) was unaltered, suggesting that FNDs exerted a comparable action on neuronal subpopulations. At the single cell level, rapid onset of the somatic AP ("kink") was drastically reduced in FND-treated neurons, suggesting a reduced contribution of axonal and dendritic components while preserving neuronal excitability. Excitatory postsynaptic potential ; Postsynaptic Current ; Inhibitory postsynaptic potential ; Postsynaptic potential ; Neuroscience ; Hippocampal formation ; Electrophysiology ; Biophysics ; Photobleaching ; Premovement neuronal activity ; Slice preparation ; Cellular neuroscience ; Chemistry ; Biology ; Fluorescence ; Physics ; Biochemistry Diamond and Carbon-based Materials Research ; Neuroscience and Neural Engineering ; Advanced Memory and Neural Computing
Authors: Shveta Mahajan | S. K. Mittal | Amitava Das
Source: Journal of Food Science and Technology (article, 2018-07-09)
Field: Chemistry — Analytical Chemistry

Machine vision based alternative testing approach for physical purity, viability and vigour testing of soybean seeds (Glycine max) Germination ; Context (archaeology) ; Seed testing ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: María Cruz‐Alonso | Ana Lores‐Padín | Eva Valencia | Héctor González‐Iglesias | Beatriz Fernández | Rosario Pereiro
Source: Analytical and Bioanalytical Chemistry (review, 2018-10-11)
Field: Chemistry — Spectroscopy

Quantitative mapping of specific proteins in biological tissues by laser ablation–ICP-MS using exogenous labels: aspects to be considered Inductively coupled plasma mass spectrometry ; Chemistry ; Laser ablation ; Mass spectrometry ; Quantitative analysis (chemistry) ; Elemental analysis ; Biological fluids ; Analytical Chemistry (journal) ; Nanotechnology ; Chromatography ; Laser ; Materials science Mass Spectrometry Techniques and Applications ; Analytical chemistry methods development ; Advanced Proteomics Techniques and Applications
Authors: Yousef Mohammadi | Mohammad Reza Saeb | Alexander Penlidis | Esmaiel Jabbari | Philippe Zinck | Florian J. Stadler | Krzysztof Matyjaszewski
Source: Macromolecular Theory and Simulations (article, 2018-02-27)
Field: Chemistry — Organic Chemistry

Intelligent Monte Carlo: A New Paradigm for Inverse Polymerization Engineering Abstract Traditional computational methods simulate the microstructure of polymer chains from input reaction conditions, but a need exists for predicting optimum reaction conditions in a computationally demanding multivariable space leading to the synthesis of predesigned microstructures and architectures. Herein, the intelligent Monte Carlo (IMC) approach, able to predict optimum reaction conditions for synthesizing copolymers with predefined, complex microstructures as input is introduced. This is rendered possible by a combination of kinetic Monte Carlo (KMC) simulation with artificial intelligence concepts, which enables a reasonably enhanced convergence to optimum reactions conditions. Chain shuttling polymerization is chosen as a first test case due to its complexity and the intricate multiblock microstructures that are formed; whose tailoring requires multiple parameters. The IMC approach locates optimum reaction conditions for the synthesis of olefinic multiblock copolymers with specific microstructures. This approach provides a new platform for identifying complex reaction conditions to “produce” and “tailor‐make” materials with precisely predefined microstructures and facilitates the development of meaningful structure‐property relationships. Monte Carlo method ; Kinetic Monte Carlo ; Microstructure ; Convergence (economics) ; Computer science ; Materials science ; Inverse ; Polymerization ; Polymer ; Statistical physics ; Mathematics ; Physics ; Composite material Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Arnab Kabiraj | Santanu Mahapatra
Source: Communications Chemistry (article, 2018-11-07)
Field: Materials Science — Materials Chemistry

High-throughput first-principles-calculations based estimation of lithium ion storage in monolayer rhenium disulfide Abstract Two-dimensional materials are promising candidates for lithium ion battery anodes due to their large surface to volume ratio. The distorted T′ phase of the rhenium disulfide crystal makes the study of lithium binding more complex than for other two-dimensional materials with symmetric crystal structures. Here we explore the lithium ion storage capacity of monolayer rhenium disulfide by first-principles based calculations. We employ hardware-accelerator-assisted high-throughput calculations, using a van der Waals density-functional-theory based ‘structure search’ technique, to emulate the lithiation process. Exploring 2000 structures, each containing 49 to 98 atoms, we find the most stable lithiated structures for various lithium concentrations. We then design a delithiation algorithm and apply it to those lithiated structures for the estimation of the reversible specific capacity. Despite possessing high molar mass, a reasonably high specific capacity (214.13 mAh/g) and open-circuit voltage (0.8 V), in agreement with experimental results, make rhenium disulfide a promising alternative anode material. Rhenium ; Monolayer ; Lithium (medication) ; Anode ; Density functional theory ; van der Waals force ; Materials science ; Ion ; Crystal structure ; Lithium-ion battery ; Battery (electricity) ; Chemistry ; Inorganic chemistry ; Physical chemistry ; Computational chemistry ; Crystallography ; Nanotechnology ; Molecule ; Electrode ; Organic chemistry ; Thermodynamics ; Physics 2D Materials and Applications ; Advancements in Battery Materials ; Ferroelectric and Negative Capacitance Devices