jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Donatella Paolino | Andra Tudose | Christian Celia | Luisa Di Marzio | Felisa Cilurzo | Constantin Mircioiu
Source: Materials (article, 2019-02-26)
Field: Chemistry — Organic Chemistry

Mathematical Models as Tools to Predict the Release Kinetic of Fluorescein from Lyotropic Colloidal Liquid Crystals In this study, we investigated the release kinetic of fluorescein from colloidal liquid crystals made from monoglyceride and different non-ionic surfactants. The crystals were physicochemically characterized and the release experiments were carried out under the sink conditions, while mathematical models were described as extrapolations from solutions of the diffusion equation, in different initial and boundary conditions imposed by pharmaceutical formulations. The diffusion equation was solved using Laplace and Fourier transformed functions for release kinetics from infinite reservoirs in a semi-infinite medium. Solutions represents a general square root law and can be applied for the release kinetic of fluorescein from lyotropic colloidal liquid crystals. Akaike, Schwartz, and Imbimbo criteria were used to establish the appropriate mathematical model and the hierarchy of the performances of different models applied to the release experiments. The Fisher statistic test was applied to obtain the significance of differences among mathematical models. Differences of mathematical criteria demonstrated that small or no significant statistic differences were carried out between the various applied models and colloidal formulations. Phenomenological models were preferred over the empirical and semi-empirical ones. The general square root model shows that the diffusion-controlled release of fluorescein is the mathematical models extrapolated for lyotropic colloidal liquid crystals. Colloid ; Fluorescein ; Materials science ; Diffusion ; Mathematical model ; Thermodynamics ; Chemistry ; Mathematics ; Physics ; Physical chemistry ; Optics ; Fluorescence ; Statistics Surfactants and Colloidal Systems ; Protein Interaction Studies and Fluorescence Analysis ; Analytical Chemistry and Chromatography
Authors: Wei Zhang | Riccardo Mazzarello | E. Ma
Source: MRS Bulletin (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

Phase-change materials in electronics and photonics Electronics ; Photonics ; Materials science ; Nanotechnology ; Phase change ; Engineering physics ; Electronic materials ; Optoelectronics ; Engineering ; Electrical engineering Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Chalcogenide Semiconductor Thin Films
Authors: Shaolong Zhu | Jinyu Zhang | Maoni Chao | Xinjuan Xu | Puwen Song | Jinlong Zhang | Zhongwen Huang
Source: Molecules (article, 2019-12-30)
Field: Chemistry — Analytical Chemistry

A Rapid and Highly Efficient Method for the Identification of Soybean Seed Varieties: Hyperspectral Images Combined with Transfer Learning Convolutional neural network (CNN) can be used to quickly identify crop seed varieties. 1200 seeds of ten soybean varieties were selected, hyperspectral images of both the front and the back of the seeds were collected, and the reflectance of soybean was derived from the hyperspectral images. A total of 9600 images were obtained after data augmentation, and the images were divided into a training set, validation set, and test set with a 3:1:1 ratio. Pretrained models (AlexNet, ResNet18, Xception, InceptionV3, DenseNet201, and NASNetLarge) after fine-tuning were used for transfer training. The optimal CNN model for soybean seed variety identification was selected. Furthermore, the traditional machine learning models for soybean seed variety identification were established by using reflectance as input. The results show that the six models all achieved 91% accuracy in the validation set and achieved accuracy values of 90.6%, 94.5%, 95.4%, 95.6%, 96.8%, and 97.2%, respectively, in the test set. This method is better than the identification of soybean seed varieties based on hyperspectral reflectance. The experimental results support a novel method for identifying soybean seeds rapidly and accurately, and this method also provides a good reference for the identification of other crop seeds. Hyperspectral imaging ; Convolutional neural network ; Artificial intelligence ; Identification (biology) ; Test set ; Transfer of learning ; Set (abstract data type) ; Mathematics ; Pattern recognition (psychology) ; Reflectivity ; Computer science ; Crop ; Agronomy ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Brandon Bocklund | Richard Otis | Aleksei Egorov | Abdulmonem Obaied | Irina Roslyakova | Zi-Kui Liu
Source: MRS Communications (article, 2019-06-01)
Field: Materials Science — Materials Chemistry

ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu–Mg Unary operation ; CALPHAD ; Markov chain Monte Carlo ; Gibbs sampling ; Gibbs free energy ; Bayesian probability ; Monte Carlo method ; Markov chain ; Parallel tempering Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Penghui Cao | Michael P. Short | Sidney Yip
Source: Proceedings of the National Academy of Sciences (article, 2019-09-04)
Field: Materials Science — Materials Chemistry

Potential energy landscape activations governing plastic flows in glass rheology While glasses are ubiquitous in natural and manufactured materials, the atomic-level mechanisms governing their deformation and how these mechanisms relate to rheological behavior are still open questions for fundamental understanding. Using atomistic simulations spanning nearly 10 orders of magnitude in the applied strain rate we probe the atomic rearrangements associated with 3 characteristic regimes of homogeneous and heterogeneous shear flow. In the low and high strain-rate limits, simulation results together with theoretical models reveal distinct scaling behavior in flow stress variation with strain rate, signifying a nonlinear coupling between thermally activated diffusion and stress-driven motion. Moreover, we find the emergence of flow heterogeneity is closely correlated with extreme values of local strain bursts that are not readily accommodated by immediate surroundings, acting as origins of shear localization. The atomistic mechanisms underlying the flow regimes are interpreted by analyzing a distance matrix of nonaffine particle displacements, yielding evidence of various barrier-hopping processes on a fractal potential energy landscape (PEL) in which shear transformations and liquid-like regions are triggered by the interplay of thermal and stress activations. Rheology ; Energy landscape ; Strain rate ; Materials science ; Fractal ; Chemical physics ; Displacement (psychology) ; Thermal ; Mechanics ; Strain (injury) ; Plasticity ; Stress (linguistics) ; Particle (ecology) ; Nonlinear system ; Fractal dimension ; Glass transition ; Statistical physics ; Thermodynamics ; Composite material ; Chemistry ; Polymer ; Physics ; Geology ; Mathematics Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Glass properties and applications
Authors: Pengpeng Chen | Wei Bai | Yinyin Bao
Source: Journal of Materials Chemistry C (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Fluorescent chemodosimeters for fluoride ions<i>via</i>silicon-fluorine chemistry: 20 years of progress Critical commentary on and a brief summary of representative F<sup>−</sup>fluorescent probes, imaging agents and nanosensors based on Si–F chemistry are provided, followed by pointing out the limitations of current systems and possible future directions. Fluorescence ; Fluoride ; Fluorine ; Silicon ; Nanosensor ; Ion ; Nanotechnology ; Materials science ; Inorganic chemistry ; Chemistry ; Engineering physics ; Optoelectronics ; Optics ; Physics ; Organic chemistry Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques
Authors: Haodong Chen | Yujun Li | Xiongfeng Ma | Lishuang Guo | Yunxin He | Zhongying Ren | Zhengcheng Kuang | Xiling Zhang | Zhigang Zhang
Source: Scientific Reports (article, 2019-01-08)
Field: Materials Science — Materials Chemistry

Analysis of potential strategies for cadmium stress tolerance revealed by transcriptome analysis of upland cotton In recent years, heavy metal pollution has become a more serious global problem, and all countries are actively engaged in finding methods to remediate heavy metal-contaminated soil. We conducted transcriptome sequencing of the roots of cotton grown under three different cadmium concentrations, and analysed the potential strategies for coping with cadmium stress. Through Gene Ontology analysis, we found that most of the genes differentially regulated under cadmium stress were associated with catalytic activity and binding action, especially metal iron binding, and specific metabolic and cellular processes. The genes responsive to cadmium stress were mainly related to membrane and response to stimulus. The KEGG pathways enriched differentially expressed genes were associated with secondary metabolite production, Starch and sucrose metabolism, flavonoid biosynthesis, phenylalanina metalism and biosynthesis, in order to improve the activity of antioxidant system, repair systems and transport system and reduction of cadmium toxicity. There are three main mechanisms by which cotton responds to cadmium stress: thickening of physical barriers, oxidation resistance and detoxification complexation. Meanwhile, identified a potential cotton-specific stress response pathway involving brassinolide, and ethylene signaling pathways. Further investigation is needed to define the specific molecular mechanisms underlying cotton tolerance to cadmium stress. In this study potential coping strategies of cotton root under cadmium stress were revealed. Our findings can guide the selection of cotton breeds that absorb high levels of cadmium. Cadmium ; Transcriptome ; Metabolic pathway ; Metabolomics ; Mechanosensitive channels ; Biochemistry ; Biology ; Chemistry ; Gene ; Cell biology ; Gene expression ; Bioinformatics Nanoparticles: synthesis and applications ; Plant Stress Responses and Tolerance
Authors: Zongrui Jiang | Lei Ge | Linzhou Zhuang | Mengran Li | Zhanke Wang | Zhonghua Zhu
Source: ACS Applied Materials & Interfaces (article, 2019-11-04)
Field: Chemistry — Inorganic Chemistry

Fine-Tuning the Coordinatively Unsaturated Metal Sites of Metal–Organic Frameworks by Plasma Engraving for Enhanced Electrocatalytic Activity Metal-organic frameworks (MOFs) have recently emerged as promising electrocatalysts because of their atomically dispersed metal sites and porous structures. The active sites of MOF catalysts largely exist as coordinatively unsaturated metal sites (CUMSs). In this study, facile microwave-induced plasma engraving is applied to fine-tune the CUMSs of cobalt-based MOF (Co-MOF-74) without destroying its phase integrity by controlling the plasma-engraving species, intensity, and duration. The electrochemical activity of the engraved MOF is found to be quantitatively correlated to the coordination geometry of the metal centers corresponding to CUMSs. Specifically, the hydrogen plasma-engraved Co-MOF-74 shows an enhanced catalytic activity of oxygen evolution reaction, which exhibits a low overpotential (337 mV at 15 mA cm<sup>-2</sup>), high turnover frequency (0.0219 s<sup>-1</sup>), and large mass activity (54.3 A g<sup>-1</sup>). The developed CUMS control strategy and the revealed CUMSs activity correlation can inspire the further microstructure tuning of MOFs for various applications. Engraving ; Overpotential ; Materials science ; Catalysis ; Metal-organic framework ; Metal ; Cobalt ; Porosity ; Chemical engineering ; Etching (microfabrication) ; Electrochemistry ; Nanotechnology ; Electrode ; Organic chemistry ; Physical chemistry ; Composite material ; Metallurgy ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Yohei Miwa | Kenjiro Taira | Junosuke Kurachi | Taro Udagawa | Shoichi Kutsumizu
Source: Nature Communications (article, 2019-04-23)
Field: Materials Science — Polymers and Plastics

A gas-plastic elastomer that quickly self-heals damage with the aid of CO2 gas Abstract Self-healing materials are highly desirable because they allow products to maintain their performance. Typical stimuli used for self-healing are heat and light, despite being unsuitable for materials used in certain products as heat can damage other components, and light cannot reach materials located within a product or device. To address these issues, here we show a gas-plastic elastomer with an ionically crosslinked silicone network that quickly self-heals damage in the presence of CO 2 gas at normal pressures and room temperature. While a strong elastomer generally exhibits slow self-healing properties, CO 2 effectively softened ionic crosslinks in the proposed elastomer, and network rearrangement was promoted. Consequently, self-healing was dramatically accelerated by ~10-fold. Moreover, self-healing was achieved even at −20 °C in the presence of CO 2 and the original mechanical strength was quickly re-established during the exchange of CO 2 with air. Elastomer ; Self-healing ; Materials science ; Silicone ; Composite material ; Self-healing material Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Yu Saito | Shin Kento | Kei Terayama | Shaan Desai | Masaru Onga | Yuji Nakagawa | Yuki M. Itahashi | Yoshihiro Iwasa | Makoto Yamada | Koji Tsuda
Source: npj Computational Materials (article, 2019-12-17)
Field: Materials Science — Materials Chemistry

Deep-learning-based quality filtering of mechanically exfoliated 2D crystals Abstract Two-dimensional (2D) crystals are attracting growing interest in various research fields such as engineering, physics, chemistry, pharmacy, and biology owing to their low dimensionality and dramatic change of properties compared to the bulk counter parts. Among the various techniques used to manufacture 2D crystals, mechanical exfoliation has been essential to practical applications and fundamental research. However, mechanically exfoliated crystals on substrates contain relatively thick flakes that must be found and removed manually, limiting high-throughput manufacturing of atomic 2D crystals and van der Waals heterostructures. Here, we present a deep-learning-based method to segment and identify the thickness of atomic layer flakes from optical microscopy images. Through carefully designing a neural network based on U-Net, we found that our neural network based on U-net trained only with the data based on realistically small number of images successfully distinguish monolayer and bilayer MoS 2 and graphene with a success rate of 70–80%, which is a practical value in the first screening process for choosing monolayer and bilayer flakes of all flakes on substrates without human eye. The remarkable results highlight the possibility that a large fraction of manual laboratory work can be replaced by AI-based systems, boosting productivity. Monolayer ; Nanotechnology ; Graphene ; Exfoliation joint ; Bilayer ; Materials science ; Curse of dimensionality ; Artificial neural network ; Artificial intelligence ; van der Waals force ; Limiting ; Computer science ; Mechanical engineering ; Engineering ; Chemistry 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Graphene research and applications
Authors: Alireza Rahnama | Guilherme Zepon | Seetharaman Sridhar
Source: International Journal of Hydrogen Energy (article, 2019-02-22)
Field: Materials Science — Materials Chemistry

Machine learning based prediction of metal hydrides for hydrogen storage, part II: Prediction of material class Artificial neural network ; Artificial intelligence ; Hydrogen storage ; Multiclass classification ; Computer science ; Machine learning ; Feature selection ; Hydrogen ; Support vector machine ; Chemistry Hydrogen Storage and Materials ; Nuclear Materials and Properties ; Hybrid Renewable Energy Systems
Authors: Xuanjun Wu | Sichen Xiang | Jiaqi Su | Weiquan Cai
Source: The Journal of Physical Chemistry C (article, 2019-03-22)
Field: Chemistry — Inorganic Chemistry

Understanding Quantitative Relationship between Methane Storage Capacities and Characteristic Properties of Metal–Organic Frameworks Based on Machine Learning Metal–organic frameworks (MOFs) are one category of emerging porous materials, which are promising competitors applied in gas storage and separation due to their high porosity and high surface area. It is still time consuming to search for optimal materials for methane storage from a large number of candidates by traditional methods such as molecular simulations and quantum mechanics. Recently, machine learning (ML) algorithms were gradually used to accelerate the discovery of high-performance MOFs. In this work, Henry’s coefficient besides other characteristic parameters was computed and appended into the previously reported data set of hypothetical metal–organic frameworks (hMOFs) for methane storage. The new data set with 37 features and 130 397 samples was then randomly split into a training set and a test set in the ratio of 7:3, which were applied for ML training and testing with three different algorithms, including support vector machine, random forest regression (RFR), and gradient boosting regression tree (GBRT). The results indicate that the GBRT model demonstrates the best generalization ability to predict nontrained data set, whereas the RFR model results in the best predictive power in the training set. The analysis of feature importance from machine learning algorithms confirms that the high generalization ability of the GBRT model is attributed to the model extracting more information from a wider range of features. The RFR model results in the highest prediction accuracy with Pearson correlation coefficient (r2) of 0.9984 and root mean square error (RMSE) of 3.93 in the training set of absolute gravimetric uptakes. The GBRT model results in the highest prediction accuracy with r2 of 0.9908 and RMSE of 9.40 in the test set of absolute gravimetric uptakes, which is the highest prediction accuracy among the up-to-date reports. According to volumetric capacities for methane storage, the optimal hMOFs exhibit ϕ of 0.65–0.88, liquid-crystal display of ∼7.5 Å, VSA of ∼2250 m2 cm–3, etc. Test set ; Random forest ; Mean squared error ; Gradient boosting ; Support vector machine ; Correlation coefficient ; Machine learning ; Ensemble learning ; Artificial intelligence ; Metal-organic framework ; Data set ; Computer science ; Linear regression ; Methane ; Generalization ; Data mining ; Mathematics ; Statistics ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis ; Carbon dioxide utilization in catalysis
Authors: Ian M. Pendleton | Gary Cattabriga | Zhi Li | Mansoor Ani Najeeb | Sorelle A. Friedler | Alexander J. Norquist | Emory M. Chan | Joshua Schrier
Source: MRS Communications (article, 2019-06-04)
Field: Materials Science — Materials Chemistry

Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management Automation ; Computer science ; Pipeline (software) ; Software ; Process (computing) ; Abstraction ; Abstraction layer ; Software engineering ; Data collection ; Programming language ; Engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Tao Ren | Michael F. Modest | Alexander Fateev | Gavin Sutton | Weijie Zhao | Florin Rusu
Source: Applied Energy (article, 2019-06-14)
Field: Chemistry — Spectroscopy

Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements Infrared ; Combustion ; Rayleigh scattering ; Combustor ; Spectrometer ; Analytical Chemistry (journal) ; Fourier transform infrared spectroscopy ; Computational physics ; Fourier transform ; Emission spectrum ; Spectral line ; Chemistry ; Materials science ; Optics ; Physics ; Mathematics ; Mathematical analysis ; Physical chemistry Spectroscopy and Laser Applications ; Spectroscopy and Chemometric Analyses ; Combustion and flame dynamics
Authors: Jean‐Marie Aubry | Jesús F. Ontiveros | Jean‐Louis Salager | Véronique Nardello‐Rataj
Source: Advances in Colloid and Interface Science (review, 2019-12-30)
Field: Chemistry — Organic Chemistry

Use of the normalized hydrophilic-lipophilic-deviation (HLDN) equation for determining the equivalent alkane carbon number (EACN) of oils and the preferred alkane carbon number (PACN) of nonionic surfactants by the fish-tail method (FTM) Alkane ; Carbon number ; Pulmonary surfactant ; Chemistry ; Ternary operation ; Alkyl ; Carbon fibers ; Cloud point ; Thermodynamics ; Hydrocarbon ; Aggregation number ; Boiling point ; Organic chemistry ; Materials science ; Critical micelle concentration ; Micelle ; Aqueous solution Surfactants and Colloidal Systems ; Environmental Chemistry and Analysis ; Analytical Chemistry and Chromatography
Authors: Kaushal Kumar Sharma | Ajit Kembhavi | Aniruddha Kembhavi | T. Sivarani | Sheelu Abraham | Kaustubh Vaghmare
Source: Monthly Notices of the Royal Astronomical Society (article, 2019-11-06)
Field: Chemistry — Analytical Chemistry

Application of convolutional neural networks for stellar spectral classification ABSTRACT Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regression problems like the determination of stellar atmospheric parameters Teff, $\rm {\log g}$, and [Fe/H]. We propose an automated approach for the classification of stellar spectra in the optical region using convolutional neural networks (CNNs). Traditional machine learning (ML) methods with ‘shallow’ architecture (usually up to two hidden layers) have been trained for these purposes in the past. However, deep learning methods with a larger number of hidden layers allow the use of finer details in the spectrum which results in improved accuracy and better generalization. Studying finer spectral signatures also enables us to determine accurate differential stellar parameters and find rare objects. We examine various machine and deep learning algorithms like artificial neural networks, Random Forest, and CNN to classify stellar spectra using the Jacoby Atlas, ELODIE, and MILES spectral libraries as training samples. We test the performance of the trained networks on the Indo-U.S. Library of Coudé Feed Stellar Spectra (CFLIB). We show that using CNNs, we are able to lower the error up to 1.23 spectral subclasses as compared to that of two subclasses achieved in the past studies with ML approach. We further apply the trained model to classify stellar spectra retrieved from the SDSS data base with SNR &amp;gt; 20. Stellar classification ; Convolutional neural network ; Artificial intelligence ; Physics ; Artificial neural network ; Deep learning ; Pattern recognition (psychology) ; LAMOST ; Astronomical spectroscopy ; Spectral line ; Random forest ; Astrophysics ; Computer science ; Machine learning ; Stars ; Astronomy Spectroscopy and Chemometric Analyses ; Stellar, planetary, and galactic studies ; Astronomical Observations and Instrumentation