jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shreedevi Arun Kumar | B. Bhuvaneshwari | Sukanya Bhunia | Manish K. Jaiswal | Kartikey Verma | Prateek Prateek | Ali Khademhosseini | Raju Kumar Gupta | Akhilesh K. Gaharwar
Source: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology (review, 2020-11-02)
Field: Chemistry — Inorganic Chemistry

<scp>Two‐dimensional</scp> metal organic frameworks for biomedical applications Two-dimensional (2D) metal organic frameworks (MOFs), are an emerging class of layered nanomaterials with well-defined structure and modular composition. The unique pore structure, high flexibility, tunability, and ability to introduce desired functionality within the structural framework, have led to potential use of MOFs in biomedical applications. This article critically reviews the application of 2D MOFs for therapeutic delivery, tissue engineering, bioimaging, and biosensing. Further, discussion on the challenges and strategies in next generation of 2D MOFs are also included. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology. Nanotechnology ; Metal-organic framework ; Flexibility (engineering) ; Modular design ; Nanomaterials ; Materials science ; Nanoscopic scale ; Computer science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Supramolecular Self-Assembly in Materials
Authors: Arun Pal | Santanu Chand | David G. Madden | Douglas Franz | Logan Ritter | Brian Space | Teresa Curtin | Shyam Chand Pal | Madhab C. Das
Source: ACS Applied Materials & Interfaces (article, 2020-08-17)
Field: Chemistry — Inorganic Chemistry

Immobilization of a Polar Sulfone Moiety onto the Pore Surface of a Humid-Stable MOF for Highly Efficient CO<sub>2</sub> Separation under Dry and Wet Environments through Direct CO<sub>2</sub>–Sulfone Interactions The stability of microporous metal-organic frameworks (MOFs) in moist environments must be taken into consideration for their practical implementations, which has been largely ignored thus far. Herein, we synthesized a new moisture-stable Zn-MOF, <b>{[Zn</b><sub><b>2</b></sub><b>(SDB)</b><sub><b>2</b></sub><b>(L)</b><sub><b>2</b></sub><b>]·2DMA}</b><sub><b><i>n</i></b></sub>, <b>IITKGP-12</b>, by utilizing a bent organic linker 4,4'-sulfonyldibenzoic acid (H<sub>2</sub>SDB) containing a polar sulfone group (-SO<sub>2</sub>) and a N, N-donor spacer (<b>L</b>) with a Brunauer-Emmett-Teller surface area of 216 m<sup>2</sup> g<sup>-1</sup>. This material displays greater CO<sub>2</sub> adsorption capacity over N<sub>2</sub> and CH<sub>4</sub> with high IAST selectivity, which is also validated by breakthrough experiments with longer breakthrough times for CO<sub>2</sub>. Most importantly, the separation performance is largely unaffected in the presence of moisture of simulated flue gas stream. Temperature-programmed desorption (TPD) analysis shows the ease of the regeneration process, and the performance was verified for multiple cycles. In order to understand the structure-function relationship at the atomistic level, grand canonical Monte Carlo (GCMC) calculation was performed, indicating that the primary binding site for CO<sub>2</sub> is between the sulfone moieties in <b>IITKGP-12</b>. CO<sub>2</sub> is attracted to the bonded structure (V-shape) of the sulfone moieties in a perpendicular fashion, where C<sub>CO<sub>2</sub></sub> is aligned with S, and the CO<sub>2</sub> axis bisects the SO<sub>2</sub> axis. Thus, the strategic approach to immobilize the polar sulfone moiety with a high number of inherent stronger M-N coordination and the absence of coordination unsaturation made this MOF potential toward practical CO<sub>2</sub> separation applications. Materials science ; Sulfone ; Moiety ; Polar ; Chemical engineering ; Metal-organic framework ; Nanotechnology ; Adsorption ; Polymer chemistry ; Organic chemistry ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Industrial Gas Emission Control
Authors: Г. Э. Бекмухамедов | A. V. Sukhov | Aidar M. Kuchkaev | Dmitry G. Yakhvarov
Source: Catalysts (article, 2020-05-01)
Field: Chemistry — Organic Chemistry

Ni-Based Complexes in Selective Ethylene Oligomerization Processes Linear alpha-olefins are widely used in the petrochemical industry and the world demand for these compounds increases annually. At present, the main method for producing linear alpha-olefins is the homogeneous catalytic ethylene oligomerization. This review presents modern nickel catalysts for this process, mainly systems for obtaining of one of the most demanded oligomer—1-butene—which is used for the production of linear low density polyethylene (LLDPE) and high density polyethylene (HDPE). The dependence of the catalytic performance on the composition and the structure of the used activated complexes, the electronic and coordination states of the nickel center was considered. Linear low-density polyethylene ; Ethylene ; Petrochemical ; Nickel ; Catalysis ; Materials science ; Polyethylene ; High-density polyethylene ; Oligomer ; Homogeneous ; Chemical engineering ; Polymer chemistry ; Organic chemistry ; Chemistry ; Metallurgy ; Thermodynamics ; Composite material ; Engineering Organometallic Complex Synthesis and Catalysis ; Synthetic Organic Chemistry Methods ; Carbon dioxide utilization in catalysis
Authors: Natalie Elia
Source: FEBS Journal (review, 2020-07-08)
Field: Chemistry — Organic Chemistry

Using unnatural amino acids to selectively label proteins for cellular imaging: a cell biologist viewpoint Twenty-five years ago, GFP revolutionized the field of cell biology by enabling scientists to visualize, for the first time, proteins in living cells. However, when it comes to current, state-of-the-art imaging technologies, fluorescent proteins (such as GFP) have several limitations that result from their size and photophysics. Over the past decade, an elegant, alternative approach, which is based on the direct labeling of proteins with fluorescent dyes and is compatible with live-cell and super-resolution imaging applications, has been introduced. In this approach, an unnatural amino acid that can covalently bind a fluorescent dye is incorporated into the coding sequence of a protein. The protein of interest is thereby site-specifically fluorescently labeled inside the cell, eliminating the need for protein- or peptide-labeling tags. Whether this labeling approach will change cell biology research is currently unclear, but it clearly has the potential to do so. In this short review, a general overview of this approach is provided, focusing on the imaging of site-specifically labeled proteins in mammalian tissue culture cells, and highlighting its advantages and limitations for cellular imaging. Green fluorescent protein ; Chemical biology ; Fluorescent protein ; Förster resonance energy transfer ; Computational biology ; Fluorescence ; Amino acid ; Cell ; Live cell imaging ; Chemistry ; Biology ; Biochemistry ; Biophysics ; Cell biology ; Gene Click Chemistry and Applications ; Biotin and Related Studies ; Monoclonal and Polyclonal Antibodies Research
Authors: Nilesh Varadan Orupattur | Samir H. Mushrif | Vinay Prasad
Source: Computational Materials Science (article, 2020-01-16)
Field: Materials Science — Materials Chemistry

Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods Bottleneck ; Biochemical engineering ; Catalysis ; Field (mathematics) ; Molecular dynamics ; Chemistry ; Computer science ; Nanotechnology ; Ab initio ; Computational chemistry ; Materials science ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Topic Modeling ; Catalysis and Oxidation Reactions
Authors: JCS Kadupitiya | Fanbo Sun | Geoffrey Fox | Vikram Jadhao
Source: Journal of Computational Science (article, 2020-03-11)
Field: Materials Science — Materials Chemistry

Machine learning surrogates for molecular dynamics simulations of soft materials Molecular dynamics ; Computer science ; Soft matter ; Artificial neural network ; Inference ; Artificial intelligence ; Biological system ; Machine learning ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Block Copolymer Self-Assembly
Authors: Evan Komp | Stéphanie Valleau
Source: The Journal of Physical Chemistry A (article, 2020-09-16)
Field: Materials Science — Materials Chemistry

Machine Learning Quantum Reaction Rate Constants The ab initio calculation of exact quantum reaction rate constants comes at a high cost due to the required dynamics of reactants on multidimensional potential energy surfaces. In turn, this impedes the rapid design of the kinetics for large sets of coupled reactions. In an effort to overcome this hurdle, a deep neural network (DNN) was trained to predict the logarithm of quantum reaction rate constants multiplied by their reactant partition function-rate products. The training dataset was generated in-house and contains ∼1.5 million quantum reaction rate constants for single, double, symmetric and asymmetric one-dimensional potentials computed over a broad range of reactant masses and temperatures. The DNN was able to predict the logarithm of the rate product with a relative error of 1.1%. Furthermore, when comparing the difference between the DNN prediction and classical transition state theory at temperatures below 300 K a relative percent error of 31% was found with respect to the exact difference. Systems beyond the test set were also studied, these included the H + H<sub>2</sub> reaction, the diffusion of hydrogen on Ni(100), the Menshutkin reaction of pyridine with CH<sub>3</sub>Br in the gas phase, the reaction of formalcyanohydrin with HS<sup>-</sup> in water and the F + HCl reaction. For these reactions, the DNN predictions were accurate at high temperatures and in good agreement with the exact rates at lower temperatures. This work shows that one can take advantage of a DNN to gain insight on reactivity in the quantum regime. Quantum ; Reaction rate constant ; Computer science ; Physics ; Quantum mechanics ; Kinetics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Francesco Pellegrino | Raluca Isopescu | Letizia Pellutiè | Fabrizio Sordello | Andrea Mario Rossi | Erik Ortel | Gianmario Martra | Vasile‐Dan Hodoroaba | Valter Maurino
Source: Scientific Reports (article, 2020-11-03)
Field: Materials Science — Materials Chemistry

Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles In the present work a series of design rules are developed in order to tune the morphology of TiO<sub>2</sub> nanoparticles through hydrothermal process. Through a careful experimental design, the influence of relevant process parameters on the synthesis outcome are studied, reaching to the develop predictive models by using Machine Learning methods. The models, after the validation and training, are able to predict with high accuracy the synthesis outcome in terms of nanoparticle size, polydispersity and aspect ratio. Furthermore, they are implemented by reverse engineering approach to do the inverse process, i.e. obtain the optimal synthesis parameters given a specific product characteristic. For the first time, it is presented a synthesis method that allows continuous and precise control of NPs morphology with the possibility to tune the aspect ratio over a large range from 1.4 (perfect truncated bipyramids) to 6 (elongated nanoparticles) and the length from 20 to 140 nm. Nanoparticle ; Dispersity ; Inverse ; Process (computing) ; Computer science ; Aspect ratio (aeronautics) ; Range (aeronautics) ; Nanotechnology ; Materials science ; Biological system ; Mathematics ; Geometry Machine Learning in Materials Science ; TiO2 Photocatalysis and Solar Cells ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Francis D. Mayer | Pooya Hosseini-Benhangi | Carlos M. Sánchez‐Sánchez | Edouard Asselin | Előd Gyenge
Source: Communications Chemistry (article, 2020-11-06)
Field: Chemistry — Electrochemistry

Scanning electrochemical microscopy screening of CO2 electroreduction activities and product selectivities of catalyst arrays The electroreduction of CO<sub>2</sub> is one of the most investigated reactions and involves testing a large number and variety of catalysts. The majority of experimental electrocatalysis studies use conventional one-sample-at-a-time methods without providing spatially resolved catalytic activity information. Herein, we present the application of scanning electrochemical microscopy (SECM) for simultaneous screening of different catalysts forming an array. We demonstrate the potential of this method for electrocatalytic assessment of an array consisting of three Sn/SnO<sub>x</sub> catalysts for CO<sub>2</sub> reduction to formate (CO2RF). Simultaneous SECM scans with fast scan (1 V s<sup>-1</sup>) cyclic voltammetry detection of products (HCOO<sup>-</sup>, CO and H<sub>2</sub>) at the Pt ultramicroelectrode tip were performed. We were able to consistently distinguish the electrocatalytic activities of the three compositionally and morphologically different Sn/SnO<sub>x</sub> catalysts. Further development of this technique for larger catalyst arrays and matrices coupled with machine learning based algorithms could greatly accelerate the CO<sub>2</sub> electroreduction catalyst discovery. Ultramicroelectrode ; Scanning electrochemical microscopy ; Catalysis ; Electrocatalyst ; Cyclic voltammetry ; Electrochemistry ; Formate ; Materials science ; Nanotechnology ; Chemistry ; Scanning electron microscope ; Voltammetry ; Chemical engineering ; Electrode ; Organic chemistry ; Physical chemistry Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts ; Machine Learning in Materials Science
Authors: Maryam Pardakhti | Pariksheet Nanda | Ranjan Srivastava
Source: The Journal of Physical Chemistry C (article, 2020-01-31)
Field: Materials Science — Materials Chemistry

Impact of Chemical Features on Methane Adsorption by Porous Materials at Varying Pressures Superior performance in methane uptake capacity prediction by hypothetical metal organic frameworks has previously been accomplished using a novel combination of structural and chemical features with machine learning (ML) algorithms. This concept is extended for additional microcrystalline materials, focusing on 69 839 covalent organic frameworks (COFs) and 17 846 porous polymer networks (PPNs). For each material category, data was divided into train (80%) and test (20%) sets. Using the random forest (RF) algorithm, 10-fold cross-validation was carried out to evaluate the robustness of prediction for structural and chemical descriptors. Structural features included surface area, density, and void fraction. Chemical descriptors included the number and type of each atom, electronegativity, and degree of unsaturation among others. When chemical descriptors for adsorption at low pressures were included, significant improvements for predictions were observed compared to solely using structural descriptors. Specifically, adding chemical features increased the R2 value from 0.66 to 0.87 for COFs and from 0.83 to 0.93 for PPNs. These results indicate that inclusion of chemical descriptors improves prediction across materials and pressures. While physisorption is the main driver for adsorption at these pressures, these results also imply contribution of surface chemical motifs on adsorption phenomena. Physisorption ; Adsorption ; Electronegativity ; Porosity ; Methane ; Materials science ; Biological system ; Chemistry ; Organic chemistry ; Composite material Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: W. Ma | E. J. Kautz | A. Baskaran | A. Chowdhury | V. Joshi | B. Yener | D. J. Lewis
Source: Journal of Applied Physics (article, 2020-10-02)
Field: Materials Science — Materials Chemistry

Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships We investigate the methods of microstructure representation for the purpose of predicting processing condition from microstructure image data. A binary alloy (uranium–molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions. Here, we test different microstructure representations and evaluate model performance based on the F1 score. A F1 score of 95.1% was achieved for distinguishing between micrographs corresponding to ten different thermo-mechanical material processing conditions. We find that our newly developed microstructure representation describes image data well, and the traditional approach of utilizing area fractions of different phases is insufficient for distinguishing between multiple classes using a relatively small, imbalanced original dataset of 272 images. To explore the applicability of generative methods for supplementing such limited datasets, generative adversarial networks were trained to generate artificial microstructure images. Two different generative networks were trained and tested to assess performance. Challenges and best practices associated with applying machine learning to limited microstructure image datasets are also discussed. Our work has implications for quantitative microstructure analysis and development of microstructure–processing relationships in limited datasets typical of metallurgical process design studies. Generative grammar ; Representation (politics) ; Discriminative model ; Microstructure ; Image processing ; Image (mathematics) ; Process (computing) Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Generative Adversarial Networks and Image Synthesis
Authors: Alexandra M. Goryaeva | Clovis Lapointe | Chendi Dai | Julien Dérès | Jean‐Bernard Maillet | Mihai‐Cosmin Marinica
Source: Nature Communications (article, 2020-09-17)
Field: Materials Science — Materials Chemistry

Reinforcing materials modelling by encoding the structures of defects in crystalline solids into distortion scores This work revises the concept of defects in crystalline solids and proposes a universal strategy for their characterization at the atomic scale using outlier detection based on statistical distances. The proposed strategy provides a generic measure that describes the distortion score of local atomic environments. This score facilitates automatic defect localization and enables a stratified description of defects, which allows to distinguish the zones with different levels of distortion within the structure. This work proposes applications for advanced materials modelling ranging from the surrogate concept for the energy per atom to the relevant information selection for evaluation of energy barriers from the mean force. Moreover, this concept can serve for design of robust interatomic machine learning potentials and high-throughput analysis of their databases. The proposed definition of defects opens up many perspectives for materials design and characterization, promoting thereby the development of novel techniques in materials science. Characterization (materials science) ; Distortion (music) ; Outlier ; Computer science ; Encoding (memory) ; Measure (data warehouse) ; Work (physics) ; Scale (ratio) ; Ranging ; Data mining ; Atom (system on chip) ; Selection (genetic algorithm) ; Artificial intelligence ; Materials science ; Nanotechnology ; Physics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Xiaoyuan Wang | Binbin Chen | Jie Zhang | Ze‐Rui Zhou | Jian Lv | Peng Geng | Ruo‐Can Qian
Source: Chemical Communications (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Exploiting deep learning for predictable carbon dot design In this study, we developed a deep convolution neural network (DCNN) model for predicting the optical properties of carbon dots (CDs), including spectral properties and fluorescence color under ultraviolet irradiation. These results demonstrate the powerful potential of DCNN for guiding the synthesis of CDs. Ultraviolet ; Deep learning ; Carbon fibers ; Spectral properties ; Fluorescence ; Convolution (computer science) ; Convolutional neural network ; Materials science ; Computer science ; Layer (electronics) ; Irradiation ; Optoelectronics ; Nanotechnology ; Artificial intelligence ; Artificial neural network ; Optics ; Physics ; Algorithm ; Composite number ; Astrophysics Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis
Authors: Sathiya Prabhakaran S.P. | G. Swaminathan | Viraj V. Joshi
Source: Fuel (article, 2020-11-06)
Field: Materials Science — Materials Chemistry

Thermogravimetric analysis of hazardous waste: Pet-coke, by kinetic models and Artificial neural network modeling Thermogravimetric analysis ; Coke ; Petroleum coke ; Hazardous waste ; Combustion ; Materials science ; Waste management ; Chemical engineering ; Chemistry ; Metallurgy ; Organic chemistry Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Petroleum Processing and Analysis
Authors: Mohammad Yazdani-Asrami | Mehran Taghipour‐Gorjikolaie | Wenjuan Song | Min Zhang | Weijia Yuan
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models Current is no longer sinusoidal in modern electric networks because of widespread use of power electronic-based equipments and nonlinear loads. Usually AC loss is calculated for pure sinusoidal current, while it is no longer accurate when current is nonsinusoidal. On the other hand, efficiency of cooling system in large scale power devices is dependent on accurate estimation and prediction of the heat load caused by AC loss in design stage. Therefore, estimation of nonsinusoidal AC loss of high temperature superconducting (HTS) material would be of great interest for designers of large-scale superconducting devices. In this paper, at first nonsinusoidal AC loss of a typical HTS tape was calculated under distorted currents using H-formulation finite element method. Then, a range of artificial intelligence (AI) models were implemented to predict AC loss of a typical HTS tape. In order to find the best and more adaptive AI model for nonsinusoidal AC loss prediction, different regression models are evaluated using Support Vector Machine regression model, Generalized Linear regression model, Decision Tree regression model, Feed Forward Neural Network based model, Adaptive Neuro Fuzzy Inference System based model, and Radial Basis Function Neural Network (RBFNN) based model. In order to evaluate robustness of developed models cross-validation technique is implemented on experimental data. To compare the performance of different AI models, four prediction measures were used: Theil's U coefficients (U_Accuracy and U_Quality), Root Mean Square Error (RMSE) and Regression value (R-value). Obtained results show that best performance belongs to RBFNN based model and then ANFIS based model. U coefficients and RMSE values are obtained less than 0.005 and R-Value is become close to one by using RBFNN based model for testing data, which indicates high accuracy prediction performance. Artificial neural network ; Mean squared error ; Robustness (evolution) ; Computer science ; Root mean square ; Adaptive neuro fuzzy inference system ; Linear regression ; Control theory (sociology) ; Artificial intelligence ; Machine learning ; Mathematics ; Fuzzy logic ; Engineering ; Statistics ; Fuzzy control system ; Electrical engineering Magnetic Properties and Applications ; Physics of Superconductivity and Magnetism ; Thermal Analysis in Power Transmission