jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Noha Almoisheer | F. A. Alseroury | Rajeev Kumar | M. Aslam | M.A. Barakat
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Adsorption and anion exchange insight of indigo carmine onto CuAl-LDH/SWCNTs nanocomposite: kinetic, thermodynamic and isotherm analysis Two-dimensional layered materials are gaining much attention in the field of wastewater purification. Herein, we report the synthesis and characterization of an anion selective copper-aluminum-layered double hydroxide/single-walled carbon nanotubes (CuAl-LDH/SWCNTs) composite for the scavenging of organic dye indigo carmine (IC) from aqueous solution. A facile urea hydrolysis method was used for the controlled growth of the metal hydroxides over the SWCNTs. Structural characterization of the prepared materials was investigated using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and X-ray diffraction (XRD) techniques. The obtained results revealed that the CuAl-LDH/SWCNTs composite has a higher potential for the removal of IC in comparison to CuAl-LDH and SWCNTs. The enhanced adsorption capacity of the composite revealed that deposition of CuAl-LDH over SWCNTs increases the active adsorption sites and promotes the interactions between the composite and IC dye <i>via</i> anion exchange, electrostatic, π-π, hydrogen bonding <i>etc.</i> Moreover, adsorption kinetics, isotherms, and thermodynamic studies have been also proposed to illustrate the mechanism of the IC adsorption onto the CuAl-LDH/SWCNTs composite. Thermodynamic parameters showed that the adsorption of IC dye onto the CuAl-LDH/SWCNTs composite was exothermic and spontaneous in nature. Intra-particle diffusion was determined to be the rate-limiting step and adsorption of IC followed the Langmuir isotherm model with the maximum monolayer adsorption capacity 294.117 mg g<sup>-1</sup> at 20 °C. The results suggest that the CuAl-LDH/SWCNTs composite is a potential material for IC adsorption in aqueous solution. Indigo carmine ; Adsorption ; Langmuir adsorption model ; Aqueous solution ; Materials science ; Chemical engineering ; Nanocomposite ; Ion exchange ; Monolayer ; Carbon nanotube ; X-ray photoelectron spectroscopy ; Composite number ; Chemistry ; Inorganic chemistry ; Nuclear chemistry ; Organic chemistry ; Nanotechnology ; Composite material ; Ion Layered Double Hydroxides Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: Maria Marinescu
Source: BMC Chemistry (review, 2019-12-01)
Field: Materials Science — Materials Chemistry

Recent advances in the use of benzimidazoles as corrosion inhibitors These review highlights recent research in the field of benzimidazole compounds that their role as corrosion inhibitors, the structure of the compounds, electrochemical studies, the experimental conditions, the proposed mechanisms as well as the quantum theoretical studies that predict the structure of the compounds with inhibition properties. Benzimidazole ; Corrosion ; Cathodic protection ; Electrochemistry ; Chemistry ; Anode ; Combinatorial chemistry ; Salt (chemistry) ; Inorganic chemistry ; Materials science ; Organic chemistry ; Physical chemistry ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Inorganic and Organometallic Chemistry
Authors: Adeela Rehman | Soo‐Jin Park
Source: Journal of CO2 Utilization (article, 2019-08-31)
Field: Materials Science — Materials Chemistry

Environmental remediation by microporous carbon: An efficient contender for CO2 and methylene blue adsorption Microporous material ; Adsorption ; Melamine ; Methylene blue ; Aqueous solution ; Nitrogen ; Volume (thermodynamics) ; Carbon fibers ; Chemical engineering ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Nuclear chemistry ; Chromatography ; Organic chemistry ; Composite material Covalent Organic Framework Applications ; Membrane Separation and Gas Transport ; Adsorption and biosorption for pollutant removal
Authors: John Keyte | Ketan Pancholi | James Njuguna
Source: Frontiers in Materials (article, 2019-10-09)
Field: Materials Science — Materials Chemistry

Recent Developments in Graphene Oxide/Epoxy Carbon Fiber-Reinforced Composites The two-dimensional macro molecule graphene and its derivatives have widely been investigated for their application as nanofiller in carbon fibre-reinforced composites (CFRC). Research has progressed from techniques that simply mix graphene as a mixing constituent within the composite material to more complex examples where graphene is covalently bonded to fibre, matrix or both via multiple reaction steps. This field of research is multi-disciplinary whereby branches of materials, engineering, polymer science, physics and chemistry often overlap. From the materials engineering perspective, the desire is to discover the novel materials targeting industrial applications and obtain a full understanding of the graphene oxide chemistry and interaction of graphene oxide with a polymer matrix. To date, most of the research is targeted at (i) improving the fibre / matrix interface properties and / or (ii) improving the dispersion of nanofiller within the matrix; both of these factors ultimately improve composite performance. Organising that information critically can lead to emergence of a generalization of material design. Therefore, the objective of this work is to critically review current state of art in the field of graphene oxide / epoxy CFRCs and propose the design rules based on current scientific trend and common themes for future works. Graphene ; Oxide ; Materials science ; Carbon nanotube ; Epoxy ; Composite material ; Nanotechnology Graphene research and applications ; Fiber-reinforced polymer composites ; Graphene and Nanomaterials Applications
Authors: Julius Hillenbrand | Markus Leutzsch | Alois Fürstner
Source: Angewandte Chemie International Edition (article, 2019-08-26)
Field: Chemistry — Organic Chemistry

Molybdenum Alkylidyne Complexes with Tripodal Silanolate Ligands: The Next Generation of Alkyne Metathesis Catalysts A new type of molybdenum alkylidyne catalysts for alkyne metathesis is described, which is distinguished by an unconventional podand topology. These structurally well-defined complexes are easy to make on scale and proved to be tolerant toward numerous functional groups; even certain protic substituents were found to be compatible. The new catalysts were characterized by X-ray crystallography and by spectroscopic means, including <sup>95</sup> Mo NMR. Molybdenum ; Alkyne ; Metathesis ; Catalysis ; Chemistry ; Combinatorial chemistry ; Organic chemistry Synthetic Organic Chemistry Methods ; Catalytic Alkyne Reactions ; Chemical Synthesis and Analysis
Authors: Soji Shimizu
Source: Chemical Communications (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

aza-BODIPY synthesis towards vis/NIR functional chromophores based on a Schiff base forming reaction protocol using lactams and heteroaromatic amines aza-BODIPY is a class of heteroatom-containing BODIPY analogues targeting near infrared (NIR) chromophores and fluorophores. As a synthetic strategy towards aza-BODIPY structures, we have, recently, developed a Schiff base forming reaction using readily available lactams and heteroaromatic amines. Absorption and fluorescence of a series of compounds cover the whole range of the ultraviolet (UV)/visible (vis)/NIR regions. In addition, some compounds exhibit solid state emission, aggregation-induced emission enhancement, tunable fluorescence in the vis/NIR regions and non-linear optical properties. Furthermore, simple dimerization of aza-BODIPY chromophores caused unusual panchromatic absorption, whereas in combination with an N-confused porphyrin skeleton, multi-state NH tautomerism was achieved. In this Feature Article, wide applicability of this Schiff base forming reaction and optical and electrochemical properties of aza-BODIPY analogues thus synthesized are summarized including recent applications, such as bioimaging, photothermal cancer therapy and organic photovoltaics. Schiff base ; BODIPY ; Chromophore ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Stereochemistry ; Fluorescence Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Nanoplatforms for cancer theranostics
Authors: Adam J. Young | Markus Sauer | Guilherme M. D. M. Rúbio | Akiko Satô | Annette Foelske | Christopher J. Serpell | Jia Min Chin | Michael R. Reithofer
Source: Nanoscale (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

One-step synthesis and XPS investigations of chiral NHC–Au(0)/Au(<scp>i</scp>) nanoparticles Although N-heterocyclic carbenes (NHCs) have been demonstrated as suitable ligands for the stabilisation of gold nanoparticles (AuNPs) through a variety of methods, the manner in which such AuNPs form is yet to be fully elucidated. We report a simple and fast one-step synthesis of uniform chiral (l/d)-histidin-2-ylidene stabilised gold nanoparticles using the organometallic Au(i) complex as a well defined starting material. The resulting nanoparticles have an average size of 2.35 ± 0.43 nm for the L analog whereas an average size of 2.25 ± 0.39 nm was found for the D analog. X-ray photoelectron spectroscopy analyses reveal the presence of Au(i) and Au(0) in all NHC stabilised AuNPs. In contrast, measured X-ray photoelectron spectra of dodecanethiol protected gold nanoparticles showed only the presence of a Au(0) species. This observation leads us to postulate that AuNPs synthesised from organometallic NHC-Au(i) complexes exhibit a monolayer of Au(i) surrounding a Au(0) core. This work highlights the importance of synthetic method choice for NHC-stabilized AuNPs, as this could determine Au oxidation states and resulting AuNP properties such as catalytic activities and stabilities. Nanoparticle ; X-ray photoelectron spectroscopy ; Colloidal gold ; Chemistry ; Medicinal chemistry ; Stereochemistry ; Combinatorial chemistry ; Nanotechnology ; Physics ; Materials science Catalytic Cross-Coupling Reactions ; Nanomaterials for catalytic reactions ; MXene and MAX Phase Materials
Authors: Akinori Saeki | Kakaraparthi Kranthiraja
Source: Japanese Journal of Applied Physics (article, 2019-10-18)
Field: Materials Science — Materials Chemistry

A high throughput molecular screening for organic electronics via machine learning: present status and perspective Abstract Organic electronics such as organic field-effect transistors (OFET), organic light-emitting diodes (OLED), and organic photovoltaics (OPV) have flourished over the last three decades, largely due to the development of new conjugated materials. Their designs have evolved through incremental modification and stepwise inspiration by researchers; however, a complete survey of the large molecular space is experimentally intractable. Machine learning (ML), based on the rapidly growing field of artificial intelligence technology, offers high throughput material exploration that is more efficient than high-cost quantum chemical calculations. This review describes the present status and perspective of ML-based development (materials informatics) of organic electronics. Although the complexity of OFET, OLED, and OPV makes revealing their structure-property relationships difficult, a cooperative approach incorporating virtual ML, human consideration, and fast experimental screening may help to navigate growth and development in the organic electronics field. OLED ; Organic electronics ; Organic field-effect transistor ; Nanotechnology ; Organic solar cell ; Electronics ; Throughput ; Materials science ; Transistor ; Organic semiconductor ; Computer science ; Neuromorphic engineering ; Artificial intelligence ; Field-effect transistor ; Optoelectronics ; Telecommunications ; Electrical engineering ; Engineering ; Polymer ; Artificial neural network Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Anja Aarva | Volker L. Deringer | Sami Sainio | Tomi Laurila | A. Miguel
Source: Chemistry of Materials (article, 2019-10-28)
Field: Materials Science — Materials Chemistry

Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra Carbon-based nanomaterials are a promising platform for diverse technologies, but their rational design requires a more detailed chemical control over their structure and properties than is currently available. A long-standing challenge for the field has been in the interpretation and use of experimental X-ray spectra, especially for the amorphous and disordered forms of carbon. Here, we outline a unified approach to simultaneously and quantitatively analyze experimental X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS) spectra of carbonaceous materials. We employ unsupervised machine learning to identify the most representative chemical environments and deconvolute experimental data according to these spectral contributions. To fit experimental spectra we rely on ab initio references and use all the information available: to fit experimental XAS spectra, the whole XAS fingerprint (reference) spectra of certain sites are taken into account, rather than just peak positions, as is currently the standard procedure. We argue that, even for predominantly pure-carbon materials, carbon K-edge and oxygen K-edge spectra should not be interpreted separately, since the presence of even small amounts of functional groups at the surface manifests itself on the X-ray spectroscopic signatures of both elements in an interlinked manner. Finally, we introduce the idea of carrying out simultaneous fits of XAS and XPS spectra, to reduce the number of degrees of freedom and arbitrariness of the fits. This work opens up a new direction, tightly integrating experiment and simulation, for understanding and ultimately controlling the functionalization of carbon nanomaterials at the atomic level. X-ray absorption spectroscopy ; X-ray photoelectron spectroscopy ; Spectral line ; Carbon fibers ; Absorption spectroscopy ; Amorphous carbon ; Spectroscopy ; Density functional theory ; Ab initio ; Nanomaterials ; Amorphous solid ; Chemistry ; Materials science ; Chemical physics ; Analytical Chemistry (journal) ; Nanotechnology ; Computational chemistry ; Physics ; Nuclear magnetic resonance ; Optics ; Crystallography ; Quantum mechanics Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Yuanjie Zhi | Dongmei Fu | Dawei Zhang | Tao Yang | Xiaogang Li
Source: Metals (article, 2019-03-26)
Field: Materials Science — Materials Chemistry

Prediction and Knowledge Mining of Outdoor Atmospheric Corrosion Rates of Low Alloy Steels Based on the Random Forests Approach The objective of this paper is to develop an approach to forecast the outdoor atmospheric corrosion rate of low alloy steels and do corrosion-knowledge mining by using a Random Forests algorithm as a mining tool. We collected the corrosion data of 17 low alloy steels under 6 atmospheric corrosion test stations in China over 16 years as the experimental datasets. Based on the datasets, a Random Forests model is established to implement the purpose of the corrosion rate prediction and data-mining. The results showed that the random forests can achieve the best generalization results compared to the commonly used machine learning methods such as the artificial neural network, support vector regression, and logistic regression. In addition, the results also showed that regarding the effect to the corrosion rate, environmental factors contributed more than chemical compositions in the low alloy steels, but as exposure time increases, the effect of the environmental factors will gradually become less. Furthermore, we give the effect changes of six environmental factors (Cl− concentration, SO2 concentration, relative humidity, temperature, rainfall, and pH) on corrosion with exposure time increasing, and the results illustrated that pH had a significant contribution to the corrosion of the entire process. The paper also dealt with the problem of the corrosion rate forecast, especially for changing environmental factors situations, and obtained the qualitative and quantitative results of influences of each environmental factor on corrosion. Corrosion ; Random forest ; Logistic regression ; Alloy ; Relative humidity ; Support vector machine ; Environmental science ; Generalization ; Wind speed ; Metallurgy ; Artificial neural network ; Process (computing) ; Humidity ; Materials science ; Data mining ; Computer science ; Machine learning ; Meteorology ; Mathematics Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Mehrshad Mehrpouya | Annamaria Gisario | Atabak Rahimzadeh | Mohammadreza Nematollahi | Keyvan Safaei Baghbaderani | Mohammad Elahinia
Source: The International Journal of Advanced Manufacturing Technology (article, 2019-11-15)
Field: Materials Science — Materials Chemistry

A prediction model for finding the optimal laser parameters in additive manufacturing of NiTi shape memory alloy Shape-memory alloy ; Nickel titanium ; Pseudoelasticity ; Austenite ; Selective laser melting ; Materials science ; Artificial neural network ; Process (computing) ; Aerospace ; Mechanical engineering ; Computer science ; Transformation (genetics) ; Martensite ; Metallurgy ; Artificial intelligence ; Engineering ; Microstructure Shape Memory Alloy Transformations ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Yaolong Zhang | Xueyao Zhou | Bin Jiang
Source: The Journal of Physical Chemistry Letters (article, 2019-02-25)
Field: Materials Science — Materials Chemistry

Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks Direct dynamics simulations become increasingly popular in studying reaction dynamics for complex systems where analytical potential energy surfaces (PESs) are unavailable. Yet, the number and/or the propagation time of trajectories are often limited by high computational costs, and numerous energies and forces generated on-the-fly become wasted after simulations. We demonstrate here an example of reusing only a very small portion of existing direct dynamics data to reconstruct a 90-dimensional globally accurate reactive PES describing the interaction of CO<sub>2</sub> with a movable Ni(100) surface based on a machine learning approach. In addition to reproducing previous results with much better statistics, we predict scattering probabilities of CO<sub>2</sub> at the state-to-state level, which is extremely demanding for direct dynamics. We propose this unified way to investigate gaseous and gas-surface reactions of medium size, initiating with hundreds of preliminary direct dynamics trajectories, followed by low-cost and high-quality simulations on full-dimensional analytical PESs. Bridging (networking) ; Dynamics (music) ; Computer science ; Biological system ; Physics ; Acoustics ; Biology Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Saba Kalantary | Ali Jahani | Reza Pourbabaki | Zahra Beigzadeh
Source: RSC Advances (article, 2019-01-01)
Field: Materials Science — Biomaterials

Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies Prediction of the diameter of a nanofiber is very difficult, owing to complexity of the interactions of the parameters which have an impact on the diameter and the fact that there is no comprehensive method to predict the diameter of a nanofiber. Therefore, the aim of this study was to compare the multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) models to develop mathematical models for the diameter prediction of poly(ε-caprolactone) (PCL)/gelatin (Gt) nanofibers. Four parameters, namely, the weight ratio, applied voltage, injection rate, and distance, were considered as input data. Then, a prediction of the diameter for the nanofiber model (PDNFM) was developed using data mining techniques such as MLP, RBFNN, and SVM. The PDNFM<sub>MLP</sub> is introduced as the most accurate model to predict the diameter of PCL/Gt nanofibers on the basis of costs and time-saving. According to the results of the sensitivity analysis, the value of the PCL/Gt weight ratio is the most significant input which influences PDNFM<sub>MLP</sub> in PCL/Gt electrospinning. Therefore, the PDNFM model, using a decision support system (DSS) tool can easily predict the diameter of PCL/Gt nanofibers prior to electrospinning. Nanofiber ; Electrospinning ; Gelatin ; Support vector machine ; Radial basis function ; Materials science ; Multilayer perceptron ; Computer science ; Biological system ; Composite material ; Artificial intelligence ; Artificial neural network ; Polymer ; Chemistry Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Water Quality Monitoring Technologies
Authors: Albert T. Chen | Alexander Franks | Nikolai Slavov
Source: PLoS Computational Biology (article, 2019-07-01)
Field: Chemistry — Spectroscopy

DART-ID increases single-cell proteome coverage Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net. Dart ; Computational biology ; Proteome ; Biology ; Computer science ; Bioinformatics Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Mass Spectrometry Techniques and Applications