jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Claudia Malacrida | Luca Scapinello | Roberto Cirilli | Sara Grecchi | Andrea Penoni | Tiziana Benincori | Sabine Ludwigs
Source: ChemElectroChem (article, 2021-06-14)
Field: Materials Science — Polymers and Plastics

In Situ Electrochemical Investigations of Inherently Chiral 2,2′‐Biindole Architectures with Oligothiophene Terminals Abstract The synthesis and characterization of three new inherently chiral N , N′ ‐dipropyl‐3,3′‐diheteroaryl‐2,2′‐biindole monomers, nicknamed Ind 2 T 4 , Ind 2 T 6 and Ind 2 Ph 2 T 4 , which differ in the number of thiophenes as terminals, are reported. In addition to a full monomer characterization, stable electroactive oligomeric films were obtained by electro‐oxidation upon cycling to potentials which activate the thiophene terminals. Cyclic voltammetry, UV‐Vis‐NIR spectroelectrochemistry and in situ conductance measurements show that oligomeric films of Ind 2 T 6 present the best stability and electrochromic switching performance. Enantioselective tests with a chiral ferrocene amine clearly show the potential as chiral selectors for analytical and sensing purposes. Electrochromism ; Monomer ; Cyclic voltammetry ; Electrochemistry ; Thiophene ; Ferrocene ; Enantioselective synthesis ; Amine gas treating ; In situ ; Materials science ; Conductance ; Polymer chemistry ; Characterization (materials science) ; Combinatorial chemistry ; Chemistry ; Electrode ; Organic chemistry ; Nanotechnology ; Physical chemistry ; Polymer ; Catalysis Conducting polymers and applications ; Porphyrin and Phthalocyanine Chemistry ; Electrochemical Analysis and Applications
Authors: Rahul Tripathi | Pritam Bhattacharyya | Sukanta Nandi | Alok Shukla | Abha Misra
Source: Applied Materials Today (article, 2021-03-20)
Field: Materials Science — Materials Chemistry

Molecular switching operation in gate constricted interface of MoS2 and hBN heterostructure Heterojunction ; Materials science ; Molybdenum disulfide ; Mesoscopic physics ; Semiconductor ; Conductance ; Molecular switch ; Optoelectronics ; van der Waals force ; Hexagonal boron nitride ; Nanotechnology ; Condensed matter physics ; Chemistry ; Molecule ; Graphene ; Physics 2D Materials and Applications ; Electronic and Structural Properties of Oxides ; Graphene research and applications
Authors: Atefeh Fazel-Najafabadi | Baptiste Auguié
Source: Materials Advances (article, 2021-12-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Orientation dependence of optical activity in light scattering by nanoparticle clusters The optical properties of nanoparticle clusters vary with the spatial arrangement of the constituent particles, but also the overall orientation of the cluster with respect to the incident light. Cluster (spacecraft) ; Orientation (vector space) ; Light scattering ; Materials science ; Nanoparticle ; Scattering ; Optics ; Nanotechnology ; Physics ; Geometry ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Diego Archilla | Jesús López‐Sánchez | A. Hernando | E. Navarro | Pilar Marín
Source: Nanomaterials (article, 2021-04-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Boosting the Tunable Microwave Scattering Signature of Sensing Array Platforms Consisting of Amorphous Ferromagnetic Fe2.25Co72.75Si10B15 Microwires and Its Amplification by Intercalating Cu Microwires The following work addresses new configurations of sensing array platforms that are composed of Co-based amorphous ferromagnetic microwires (MWs) to obtain an enhanced modulation of the microwave scattering effects through the application of low strength DC or AC magnetic fields. An amorphous MW is an ultrasoft ferromagnetic material (coercivity ~0.2 Oe) with a circumferential magnetic anisotropy that provides a high surface sensitivity when it is subjected to an external magnetic field. Firstly, microwave scattering experiments are performed as a function of the length and number of MWs placed parallel to each other forming an array. Subsequently, three array configurations are designed, achieving high <i>S</i><sub>21</sub> scattering coefficients up to about -50 dB. The influence of DC and AC magnetic fields on <i>S</i><sub>21</sub> has been analyzed in frequency and time domains representation, respectively. In addition, the MWs sensing array has been overlapped by polymeric surfaces and the variations of their micrometric thicknesses also cause strong changes in the <i>S</i><sub>21</sub> amplitude with displacements in the frequency that are associated to the maximum scattering behavior. Finally, a new concept for amplifying microwave scattering is provided by intercalating Cu MWs into the linear Co-based arrays. The designed mixed system that is composed by Co-based and Cu MWs exhibits a higher <i>S</i><sub>21</sub> coefficient when compared to a single Co-based MW system because of higher electrical conductivity of Cu. However, the ability to modulate the resulting electromagnetic scattering is conferred by the giant magneto-impedance (GMI) effects coming from properties of the ultrasoft amorphous MWs. The mixed array platform covers a wide range of sensor applications, demonstrating the feasibility of tuning the <i>S</i><sub>21</sub> amplitude over a wide scattering range by applying AC or DC magnetic fields and tuning the resonant frequency position according to the polymeric slab thickness. Materials science ; Scattering ; Amorphous solid ; Ferromagnetism ; Microwave ; Condensed matter physics ; Coercivity ; Anisotropy ; Magnetoresistance ; Magnetic field ; Giant magnetoimpedance ; Optoelectronics ; Giant magnetoresistance ; Optics ; Physics ; Crystallography Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metallic Glasses and Amorphous Alloys
Authors: Andreas Elsenberg | F. Gärtner | Thomas Klassen
Source: Journal of Thermal Spray Technology (article, 2021-04-19)
Field: Materials Science — Materials Chemistry

Aerosol Deposition of Ti3SiC2-MAX-Phase Coatings Abstract For the present study on Aerosol Deposition of MAX-phase materials, Ti 3 SiC 2 was chosen as model system due to the availability of property data and commercial powder. The as-received powder was milled to different nominal sizes. For revealing details on coating formation and possible bonding mechanisms, Aerosol Deposition experiments were performed for different particle size batches and process gas pressures. Microstructural analyses reveal that coating formation preferably occurs for particle sizes smaller two microns. Using such small particle sizes, crack-free, dense layers can be obtained. The individual deposition efficiencies for the different particle sizes, particularly the critical size below which deposition gets prominent, vary with process gas flows and associated pressures. Detailed microstructural analyses of coatings by high-resolution scanning electron microscopy reveal plastic deformation and fracture, both attributing to shape adaption to previous spray layers and probably bonding. In correlation to coating thickness or deposition efficiencies, respective results give indications for possible bonding mechanisms and a tentative window of Aerosol Deposition for Ti 3 SiC 2 MAX-phases as spray material. Materials science ; Deposition (geology) ; Aerosol ; Coating ; Scanning electron microscope ; Gas dynamic cold spray ; Particle (ecology) ; Composite material ; Particle size ; Phase (matter) ; Metallurgy ; Chemical engineering ; Mineralogy ; Meteorology MXene and MAX Phase Materials ; Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties
Authors: Alexandru Topor | Dan Liu | Cătălin Maxim | Ghénadie Novitchi | Cyrille Train | Zeid A. ALOthman | Abdullah A. Al‐Kahtani | Liviu Ungur | Le Tuan Anh Ho | Liviu F. Chibotaru | Marius Andruh
Source: Journal of Materials Chemistry C (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design of Fe<sup>III</sup>–Ln<sup>III</sup> binuclear complexes using compartmental ligands: synthesis, crystal structures, magnetic properties, and <i>ab initio</i> analysis Rare examples of phenoxido-bridged Fe III –Ln III binuclear complexes are synthesized, and their dynamic and static magnetic properties are rationalized by ab initio calculations. Ab initio ; Crystallography ; Materials science ; Ab initio quantum chemistry methods ; Crystal structure ; Crystal (programming language) ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Quantum mechanics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Sarah J. D. Nauwelaerts | Nancy H. C. Roosens | Alfred Bernard | Sigrid C. J. De Keersmaecker | Koen De Cremer
Source: Scientific Reports (article, 2021-05-12)
Field: Chemistry — Spectroscopy

Development of a multiplex mass spectrometry method for simultaneous quantification of urinary proteins related to respiratory health Respiratory health of children is a health priority. Club cell protein (CC16) is an interesting biomarker of lung diseases and adverse effects towards the airway epithelium integrity. Osteopontin (OPN) and nuclear factor-kappa B (NF-κB) also play a role in respiratory health. The use of urine as biomarker source is useful in studies involving children but necessitates proper adjustment for physiological confounders influencing the urinary excretion, potentially characterized with beta-2-microglobulin (β2M), retinol binding protein 4 (RBP4) or myoglobin (MYO), as well as adjustment for possible renal dysfunction, characterized by human serum albumin (HSA). The simultaneous quantification of all these proteins in urine could facilitate children's health monitoring. A multiple reaction monitoring method (MRM) was developed and validated for the relative quantification of the seven mentioned urinary proteins. A total of nine proteotypic peptides were selected and used for the relative quantification of the seven proteins. The MRM method was completely validated for all proteins and partially for OPN. LOQ's ranged from 0.3 to 42.8 ng/ml, a good reproducibility and a good linearity were obtained across the analytical measurement range (r<sup>2</sup> > 0.98). The method yielded varying correlations (r<sup>2</sup> of 0.78, 0.71, 0.34 and 0.15 for CC16, β2M, RBP4 and HSA respectively) with available immunoassay data. It also allowed the identification and successful quantification of β2M and RBP4 as a protein candidate for adjustment of renal handling and dysfunction. All proteins were detected in the urine samples except for MYO and NF-κB. Our validated MRM-method is able to simultaneously quantify in urine biomarkers of airway epithelium integrity and biomarkers of variation in renal function and urinary dilution. This will allow to investigate further in future studies if urine can be used as a good surrogate source for biomarkers of airway epithelium integrity, and to understand the complex relationship between cause and effect in children's respiratory health monitoring. Multiplex ; Mass spectrometry ; Urinary system ; Chromatography ; Computational biology ; Medicine ; Bioinformatics ; Chemistry ; Biology ; Internal medicine Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Se Won Yi | Young Min Shin | Jung Bok Lee | Ju Young Park | Dae‐Hyun Kim | Wooyeol Baek | Jeong‐Kee Yoon | Deok Gie Kim | In Sik Shin | Changsoo Kim | Mi‐Lan Kang | Jae Won Yang | Hak‐Joon Sung
Source: Small (article, 2021-03-17)
Field: Materials Science — Biomaterials

Dilation‐Responsive Microshape Programing Prevents Vascular Graft Stenosis Shape memory materials have been successfully applied to minimally invasive implantation of medical devices. However, organ-movement-specific shape programing at a microscale level has never been demonstrated despite significant unmet needs. As vein-to-artery grafting induces vein dilation and stenosis, a polymeric self-enclosable external support (SES) is designed to wrap the vascular out-wall. Its micropores are programmed to increase sizes and interconnections upon dilation. Vessel dilation promotes venous maturation, but overdilation induces stenosis by disturbed blood flow. Therefore, the unique elastic shape-fixity of SES provides a foundation to enable a stable microscale shape transition by maintaining the vein dilation. The shape transition of micropore architecture upon dilation induces beneficial inflammation, thereby regenerating vasa vasorum and directing smooth muscle cell migration toward adventitia with the consequent muscle reinforcement of veins. This game-changer approach prevents the stenosis of vein-to-artery grafting by rescuing ischemic disorders and promoting arterial properties of veins. Vasa vasorum ; Dilation (metric space) ; Adventitia ; Stenosis ; Microscale chemistry ; Artery ; Vein ; Vascular smooth muscle ; Materials science ; Medicine ; Anatomy ; Cardiology ; Surgery ; Internal medicine ; Smooth muscle ; Psychology Electrospun Nanofibers in Biomedical Applications ; Advanced Materials and Mechanics ; Intracranial Aneurysms: Treatment and Complications
Authors: Carlos Madariaga | Roger Schurch | Jorge Alfredo Ardila‐Rey | Osvaldo Muñoz | Sebastián Fingerhuth
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Partial Discharge Electrical Tree Growth Identification by Means of Waveform Source Separation Techniques Electrical trees are one of the main degradation processes leading to failure of high voltage polymeric insulation. Electrical trees grow under the effect of partial discharges (PD), which can be measured and analyzed for condition monitoring of electrical insulation. In this paper, techniques that are normally used for classification of PD and noise separation were explored in their ability to determine the stage of growth of electrical trees: Spectral Power Clustering Technique (SPCT), Time-Frequency (TF) maps and Chromatic Technique (CT). The techniques allowed to analyze PD signals captured in ultra-high frequency (UHF) range with an antenna during tree growth. Laboratory treeing-samples were made of epoxy resin and trees were generated at six different excitation frequencies: 0.1, 10, 50, 150, 250 and 350 Hz. The results showed that two parameters, part of SPCT and TF maps, were sensitive to the tree progression and showed a consistent relation with the length of the tree: the low-frequency power ratio and the equivalent bandwidth. These two parameters were selected to create a new map, proposed for the characterization of electrical tree growth, which is more consistent and robust than the original separation maps. It was found that the low-frequency content of PD pulses proportionally increased with tree propagation. Waveform ; Identification (biology) ; Partial discharge ; Computer science ; Tree (set theory) ; Source separation ; Voltage ; Electrical engineering ; Algorithm ; Mathematics ; Telecommunications ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Douglas Nascimento | Shady S. Refaat | Hermes Loschi | Yuzo Iano | Euclides Lourenço Chuma | Waseem El Sayed | Amr Madi
Source: Sensors and Actuators A Physical (article, 2021-05-12)
Field: Materials Science — Materials Chemistry

Current sensor optimization based on simulated transfer function under partial discharge pulses The time measurement efficiency of the partial discharge (PD) relies on the signal-to-noise ratio (SNR) and gain of the high-frequency current transformer (HFCT) sensor. However, the PD's time measurement efficiency decreases with the noise coupled to the HFCT in onsite measurements. To overcome that setback, this paper proposes one pre-processing, through modelling and simulation, considering the physical effects, features of the electrical circuit and coil construction parameters of the HFCT. The main goal is to reach reasonable high SNR under the strong influence of background noises. This investigation aims to validate the hypothesis of improvement or deterioration of the HFCT signal response through a transfer function optimization. This research effort's contributions are threefold: 1. Generation of PD pulse signal and noise addition; 2. HFCT modelling, simulation, and frequency response analysis; and 3. Models performance evaluation and validation of hypothesis. In conclusion, the pre-processing approach stands out as a means to robustify and provide freedom to the electric utility, making up for an eventual need to redefine the physical and geometrical parameters of the HFCT sensor under specific background noise for maintenance tests purpose. According to a cyber-physical system framework, experiments corroborate the project's goals to contribute to the PD pattern monitoring in onsite measurements and incorporate robustness to signals with low SNRs. Partial discharge ; Robustness (evolution) ; Transfer function ; Current transformer ; Noise (video) ; Signal processing ; Electronic engineering ; SIGNAL (programming language) ; Measurement uncertainty ; Computer science ; Acoustics ; Transformer ; Engineering ; Voltage ; Electrical engineering ; Artificial intelligence ; Digital signal processing ; Physics High voltage insulation and dielectric phenomena ; Magnetic Field Sensors Techniques ; Electrical and Bioimpedance Tomography
Authors: Victor S.C. Fung | Jiaxin Zhang | Eric Juarez | Bobby G. Sumpter
Source: ChemRxiv (preprint, 2021-01-21)
Field: Materials Science — Materials Chemistry

Benchmarking Graph Neural Networks for Materials Chemistry Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are proposed. Benchmarking ; Computer science ; Workflow ; Machine learning ; Hyperparameter ; Artificial intelligence ; Benchmark (surveying) ; Graph ; Flexibility (engineering) ; Strengths and weaknesses ; Artificial neural network ; Data science ; Theoretical computer science ; Database Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Fuel Cells and Related Materials
Authors: Tim James | Dimitar Hristozov
Source: Methods in molecular biology (article, 2021-11-03)
Field: Materials Science — Materials Chemistry

Deep Learning and Computational Chemistry Deep learning ; Artificial intelligence ; Computer science ; Context (archaeology) ; Generative grammar ; The Renaissance ; Transformational leadership ; Artificial neural network ; Machine learning ; Data science ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Geetanjali Singh | Namita Mittal | Satyendra Singh Chouhan
Source: IETE Technical Review (review, 2021-10-12)
Field: Materials Science — Materials Chemistry

A Systematic Review of Deep Learning Approaches for Natural Language Processing in Battery Materials Domain Natural Language Processing (NLP) acts towards the processing of linguistics between human and computer. The application of NLP in the chemical industry has proven to be a boon in the past decade. This paper presents a survey of deep learning (DL) models on NLP fundamentals for battery materials domain-related research. Various DL models like convolutional neural networks, recursive neural networks, recurrent neural networks, long short-term memory networks, and attention networks have been discussed while comparing their efficacy for different NLP tasks. Innovations in presented DL models provide enhanced performance for the NLP-based tasks. This paper will enlighten researchers, to get an insight into the current practices and scope for future developments on battery materials. They can apply NLP techniques for various applications like structural recognition and extraction, understanding of chemical features without a domain expert, and understanding the chemical structure and summarization approaches. Moreover, this paper will help chemists and researchers in the chemical industry to explore various areas to work upon. To empower this, various applications and future scope is discussed in this paper. Automatic summarization ; Computer science ; Scope (computer science) ; Artificial intelligence ; Domain (mathematical analysis) ; Deep learning ; Convolutional neural network ; Natural language processing ; Artificial neural network ; Machine learning ; Data science Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Fault Detection and Control Systems
Authors: Marcus H. Reis | Filipp Gusev | Nicholas G. Taylor | Sang Hun Chung | Matthew Verber | Yueh Z. Lee | Olexandr Isayev | Frank A. Leibfarth
Source: ChemRxiv (preprint, 2021-07-20)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery of 19F MRI Agents Enabled by Automated Copolymer Synthesis Modern polymer science is plagued by the curse of multidimensionality; the large chemical space imposed by including combinations of monomers into a statistical copolymer overwhelms polymer synthesis and characterization technology and limits the ability to systematically study structure–property relationships. To tackle this challenge in the context of 19F MRI agents, we pursued a computer-guided materials discovery approach that combines synergistic innovations in automated flow synthesis and machine learning (ML) method development. A software controlled, continuous polymer synthesis platform was developed to enable iterative experimental–computational cycles that resulted in the synthesis of 397 unique copolymer compositions within a six-variable compositional space. The non-intuitive design criteria identified by ML, which was accomplished by exploring less than 0.9% of overall compositional space, upended conventional wisdom in the design of 19F MRI agents and lead to the identification of >10 copolymer compositions that outperformed state-of-the-art materials. Copolymer ; Context (archaeology) ; Computer science ; Polymer ; Characterization (materials science) ; Identification (biology) ; Monomer ; Artificial intelligence ; Materials science ; Machine learning ; Algorithm ; Nanotechnology Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Innovative Microfluidic and Catalytic Techniques Innovation