jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenbo Zhang | Yuan Li | Han Li | Chen Wang | Guanjun Zhang
Source: 2021 IEEE 4th International Electrical and Energy Conference (CIEEC) (article, 2021-05-28)
Field: Chemistry — Analytical Chemistry

Gaussian Process Regression for Quantitative DP Analysis of Oil-paper Insulation by NIRS Detection Oil-paper insulation is the key insulation structure of the transformers, whose aging condition is closely related to the operations of the equipment. The degree of polymerization (DP) is the direct parameter characterizing the aging condition of oil-paper insulation. Recently, the near infrared spectroscopy (NIRS) measurement powered by quantitative analysis is used to evaluate DP of the insulating papers mainly. While the applications of NIRS for DP evaluation is constrained because the present spectral quantitative analysis method is not accurate and stable enough, especially for onsite tests. In this paper, we propose a Gaussian process regression (GPR) method to predict DP of the oil- paper insulation in laboratory as well as in field. Firstly, the basic principles of GPR algorithm are illustrated. A GPR model for DP prediction is established based on the spectra of differently aged insulating paper samples which are prepared in laboratory. The GPR model show a high prediction accuracy both for training set and testing set. The established GPR model is finally applied on the DP prediction of insulating papers originating from a de-tanked transformer. The accurate predicted DP and the reliable aging assessment results indicate that the established model can be implemented on site. Ground-penetrating radar ; Kriging ; Transformer ; Transformer oil ; Gaussian ; Computer science ; Gaussian process ; Process (computing) ; Regression analysis ; Environmental science ; Engineering ; Machine learning ; Electrical engineering ; Physics ; Voltage Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Fault Detection and Control Systems
Authors: Yunzhe Wang | Shanping Liu | Peter Lile | Sam Walton Norwood | Alberto Hernández | Sukriti Manna | Tim Mueller
Source:  (preprint, 2021-08-20)
Field: Materials Science — Materials Chemistry

Accelerated Prediction of Atomically Precise Cluster Structures Using On-the-fly Machine Learning The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but predicting the stable structures of clusters can be computationally expensive. In this work, we present a procedure for rapidly predicting low-energy structures of nanoclusters by combining a genetic algorithm with interatomic potentials actively learned on-the-fly. Applying this approach to aluminum clusters with 21 to 55 atoms, we have identified structures with lower energy than any reported in the literature for 25 out of the 35 sizes. Our benchmarks indicate that the active learning procedure accelerated the average search speed by more than an order of magnitude relative to genetic algorithm searches using only density functional calculations. This work demonstrates a feasible way to systematically discover stable structures for large nanoclusters and provides insights into the transferability of machine-learned interatomic potentials for nanoclusters. Nanoclusters ; Cluster (spacecraft) ; Transferability ; Computer science ; On the fly ; Work (physics) ; Algorithm ; Machine learning ; Artificial intelligence ; Materials science ; Physics ; Nanotechnology ; Quantum mechanics Machine Learning in Materials Science ; Nanocluster Synthesis and Applications ; Boron and Carbon Nanomaterials Research
Authors: Sweta Sarangi | Suresh Kumar Yatirajula | ‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬Vinod Kumar Saxena
Source: Journal of Coatings Technology and Research (article, 2021-07-29)
Field: Materials Science — Biomaterials

Evaluation of linear and nonlinear rheology of microfibrillated cellulose Materials science ; Rheology ; Shear thinning ; Viscoelasticity ; Composite material ; Dynamic mechanical analysis ; Cellulose ; Shear rate ; Shear modulus ; Chemical engineering ; Polymer Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Material Properties and Processing
Authors: Luca Scarpa
Source: Nonlinearity (preprint, 2021-06-01)
Field: Materials Science — Materials Chemistry

The stochastic Cahn–Hilliard equation with degenerate mobility and logarithmic potential Abstract We prove existence of martingale solutions for the stochastic Cahn–Hilliard equation with degenerate mobility and multiplicative Wiener noise. The potential is allowed to be of logarithmic or double-obstacle type. By extending to the stochastic framework a regularization procedure introduced by Elliott and Garcke in the deterministic setting, we show that a compatibility condition between the degeneracy of the mobility and the blow-up of the potential allows to confine some approximate solutions in the physically relevant domain. By using a suitable Lipschitz-continuity property of the noise, uniform energy and magnitude estimates are proved. The passage to the limit is then carried out by stochastic compactness arguments in a variational framework. Applications to stochastic phase-field modelling are also discussed. Degenerate energy levels ; Mathematics ; Multiplicative noise ; Martingale (probability theory) ; Logarithm ; Lipschitz continuity ; Multiplicative function ; Mathematical analysis ; Compact space ; Applied mathematics ; Statistical physics ; Physics ; Computer science Solidification and crystal growth phenomena ; Advanced Mathematical Modeling in Engineering ; Stochastic processes and financial applications
Authors: Vineeth Venugopal | Sourav Sahoo | Mohd Zaki | Manish Agarwal | Nitya Nand Gosvami | N. M. Anoop Krishnan
Source: Patterns (preprint, 2021-06-24)
Field: Materials Science — Materials Chemistry

Looking through glass: Knowledge discovery from materials science literature using natural language processing Most of the knowledge in materials science literature is in the form of unstructured data such as text and images. Here, we present a framework employing natural language processing, which automates text and image comprehension and precision knowledge extraction from inorganic glasses' literature. The abstracts are automatically categorized using latent Dirichlet allocation (LDA) to classify and search semantically linked publications. Similarly, a comprehensive summary of images and plots is presented using the caption cluster plot (CCP), providing direct access to images buried in the papers. Finally, we combine the LDA and CCP with chemical elements to present an elemental map, a topical and image-wise distribution of elements occurring in the literature. Overall, the framework presented here can be a generic and powerful tool to extract and disseminate material-specific information on composition-structure-processing-property dataspaces, allowing insights into fundamental problems relevant to the materials science community and accelerated materials discovery. Latent Dirichlet allocation ; Computer science ; Knowledge extraction ; Information retrieval ; Plot (graphics) ; Information extraction ; Comprehension ; Natural language processing ; Topic model ; Domain knowledge ; Dissemination ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Biomedical Text Mining and Ontologies
Authors: Emiliane Daher | Fernando Gomes de Souza | Viviane Brandão | Jean Carelo
Source: Brazilian Journal of Experimental Design Data Analysis and Inferential Statistics (article, 2021-01-01)
Field: Materials Science — Biomaterials

Poly(lactic acid)-PLA: An Analysis Based on Literature Text Mining The most researched and used biodegradable aliphatic polyester worldwide is poly (lactic acid) - PLA. Due to its inherent properties, PLA is a leading biomaterial for several and numerous applications and the most promising biopolymer capable of replacing conventional petroleum-derived polymers. Besides, PLA is also one of the most promising candidates for new developments in the traditional sectors such as packaging and the automotive industry, in the electronics industry, and the biomedical area. To verify the importance of drug release in the various applications of PLA, a text mining of the last ten years was carried out. The text-mining tool was used to identify this polymer's main applications in the last ten years, allowing us to draw a map year by year based on correlation analysis. Firstly, an analysis was made of the main applications of PLA in scientific research, by searching for "PLA", in the ScienceDirect scientific base (https://www.sciencedirect.com/) and using the online tool Voyant Tools. Some words related to drug release were selected, checking how many times they were mentioned over the years. In this way, it was possible to see which years were the most and least used PLA in drug release. It was not possible to observe a gradual increase or decrease in this polymer's use, for this purpose, over time. The text mining presented here proved to be an efficient and fast way to observe the desired theme. Polyester ; Biopolymer ; Computer science ; Theme (computing) ; Biomaterial ; Automotive industry ; Polymer ; Data science ; Nanotechnology ; World Wide Web ; Materials science ; Engineering ; Composite material biodegradable polymer synthesis and properties
Authors: Fernando Gomes de Souza | Thaís Nogueira Barradas | Vinicius de Freitas Caetano | Angela Becerra Lovera
Source:  (preprint, 2021-12-01)
Field: Materials Science — Polymers and Plastics

Nanoparticles and polyaniline electrical conductivity Polyaniline is a conductive polymer that attracts the attention of many researchers around the world. The history of this polymer begins in 1862 when Letheby first reported this material. Since then, a myriad of studies has been conducted on this material, and new works continue to investigate the potential of this material. Polyaniline has been improved with the help of Nanotechnology. The use of nanofillers has been seen as a quick and economical way to modify materials, driving innovations based on new physical and chemical properties from the conductive polymer materials and nanoparticles joining. Several works address the use of different nanoparticles, which leads to the practical impossibility of sifting through all this information. Thus, this work proposes to systematically collect data in the literature and investigate which nanoparticles can increase the electrical conductivity of Polyaniline (PAni). The results obtained demonstrate that among the possible nanofillers, graphene and carbon nanotubes have great prominence. Furthermore, the results of the meta-analysis prove that PAni's conductivity increases when this polymer is modified with the aforementioned nanofillers. Polyaniline ; Conductive polymer ; Materials science ; Carbon nanotube ; Nanotechnology ; Nanoparticle ; Polymer ; Conductivity ; Electrical conductor ; Carbon Nanoparticles ; Polyaniline nanofibers ; Graphene ; Composite material ; Chemistry Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Maximilian Fichtner | Stefan Schuster | Heiko Stark
Source: Scientific Reports (article, 2021-03-02)
Field: Materials Science — Materials Chemistry

Influence of spatial structure on protein damage susceptibility: a bioinformatics approach Aging research is a very popular field of research in which the deterioration or decline of various physiological features is studied. Here we consider the molecular level, which can also have effects on the macroscopic level. The proteinogenic amino acids differ in their susceptibilities to non-enzymatic modification. Some of these modifications can lead to protein damage and thus can affect the form and function of proteins. For this, it is important to know the distribution of amino acids between the protein shell/surface and the core. This was investigated in this study for all known structures of peptides and proteins available in the PDB. As a result, it is shown that the shell contains less susceptible amino acids than the core with the exception of thermophilic organisms. Furthermore, proteins could be classified according to their susceptibility. This can then be used in applications such as phylogeny, aging research, molecular medicine, and synthetic biology. Amino acid ; Protein Data Bank (RCSB PDB) ; Computational biology ; Protein structure ; Biology ; Function (biology) ; Biochemistry ; Bioinformatics ; Chemistry ; Evolutionary biology Enzyme Structure and Function ; Redox biology and oxidative stress ; Genetics, Aging, and Longevity in Model Organisms
Authors: Min Sik Park | Dongseon Lee | Youngchun Kwon | Eunji Kim | Youn-Suk Choi
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Data undersampling models for the efficient rule-based retrosynthetic planning Computer-aided retrosynthetic planning for organic molecules, which is based on a large synthetic database, is a significant part of the recent development of autonomous robotic chemists. As in other AI fields, however, the class imbalance problem in the dataset affects the prediction performance of retrosynthetic paths. Here, we demonstrate that applying undersampling models to the imbalanced reaction dataset can improve the prediction of retrosynthetic templates for target molecules. We report improvements in the top-1 and top-10 prediction accuracies by 13.8% (13.1, 5.4%) and 8.8% (6.9, 2.4%) for undersampling based on the similarity (random, dissimilarity) clustering of molecular structures of products, respectively. These results demonstrate the importance of deep understanding of the statistical distribution, internal structure, and sampling for the training dataset. For practical applications, the target-oriented undersampling model is proposed and confirmed by the improved prediction performance of 9.3 and 4.2% for the top-1 and top-10 accuracies, respectively. Undersampling ; Retrosynthetic analysis ; Computer science ; Cluster analysis ; Data mining ; Artificial intelligence ; Sampling (signal processing) ; Machine learning ; Pattern recognition (psychology) ; Chemistry Machine Learning in Materials Science ; Text and Document Classification Technologies
Authors: Tessei Kawano | Satoshi Iikubo | Yoshito Andou
Source: Research Square (preprint, 2021-06-28)
Field: Materials Science — Biomaterials

The Control of Crystal Structure and Mechanical Property in Regenerated Cellulose Film by Coagulation Conditions Coagulation ; Property (philosophy) ; Cellulose ; Control (management) ; Coagulation system ; Materials science ; Crystal (programming language) ; Crystal structure ; Nanotechnology ; Business ; Chemical engineering ; Chemistry ; Crystallography ; Computer science ; Medicine ; Engineering ; Artificial intelligence ; Internal medicine Advanced Cellulose Research Studies ; Dyeing and Modifying Textile Fibers ; biodegradable polymer synthesis and properties
Authors: Thijs Roebroek | Wim Vandenberg | François Sipieter | Siewert Hugelier | Christophe P. Stove | Jin Zhang | Peter Dedecker
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-01-06)
Field: Materials Science — Materials Chemistry

Simultaneous readout of multiple FRET pairs using photochromism Abstract Förster resonant energy transfer (FRET) is a powerful mechanism to probe associations in situ . Simultaneously performing more than one FRET measurement can be challenging due to the spectral band-width required for the donor and acceptor fluorophores. We present an approach to distinguish overlapping FRET pairs based on the photochromism of the donor fluorophores, even if the involved fluorophores display essentially identical absorption and emission spectra. We develop the theory underlying this method and validate our approach using numerical simulations. To apply our system, we develop rsAKARev, a photochromic biosensor for cAMP-dependent kinase (PKA), and combine it with the spectrally-identical biosensor EKARev, a reporter for ERK kinase activity, to deliver simultaneous readout of both activities in the same cell. We further perform multiplexed PKA, ERK, and calcium measurements by including a third, spectrally-shifted biosensor. Our work demonstrates that exploiting donor photochromism in FRET can be a powerful approach to simultaneously read out multiple associations within living cells. Förster resonance energy transfer ; Photochromism ; Biosensor ; Fluorescence ; Acceptor ; Chemistry ; Materials science ; Optoelectronics ; Biophysics ; Nanotechnology ; Optics ; Physics Photochromic and Fluorescence Chemistry ; Advanced Fluorescence Microscopy Techniques ; Retinal Development and Disorders
Authors: Guillermo Moreno‐Alcántar | Cristian Díaz-Rosas | Alberto Fernández‐Alarcón | Luis Turcio-García | Marcos Flores‐Álamo | Tomás Rocha‐Rinza | Hugo Torrens
Source: Inorganics (article, 2021-02-02)
Field: Materials Science — Materials Chemistry

Fluorination Effects in XPhos Gold(I) Fluorothiolates Gold phosphine derivatives such as thiolates have been recently proposed as catalysts or catalyst precursors. The relevance of the supramolecular environment on the fine-tuning of the catalytical activity on these compounds incentivizes the use of tools that are convenient to characterize in detail the non-covalent landscape of the systems. Herein, we show the molecular and supramolecular diversity caused by the changes in the fluorination pattern in a family of new XPhos goldfluorothiolate derivatives. Furthermore, we studied the supramolecular interactions around the Au centers using quantum chemical topology tools, in particular the quantum theory of atoms in molecules (QTAIM) and the non-covalent interaction index. Our results give detailed insights into the fluorination effects on the strength of intramolecular and intermolecular interactions in these systems. We have also used QTAIM delocalization indexes to define a novel hapticity indicator. Finally, we assessed the trans influence of the fluorothiolates on the phosphine in terms of the change in the δ 31P-NMR. These results show the feasibility of the use of fluorination in the modulation of the electronic properties of Buchwald phosphine gold(I) compounds, and thereby its potential catalytic activity. Supramolecular chemistry ; Delocalized electron ; Intermolecular force ; Intramolecular force ; Phosphine ; Atoms in molecules ; Covalent bond ; Chemistry ; Catalysis ; Chemical physics ; Computational chemistry ; Molecule ; Nanotechnology ; Materials science ; Organic chemistry Nanocluster Synthesis and Applications ; Organometallic Complex Synthesis and Catalysis ; Catalytic Alkyne Reactions
Authors: Martin Molkenthin | Werner M. Nau | Boris J. Nachtsheim
Source: Chemistry - A European Journal (article, 2021-10-22)
Field: Materials Science — Biomaterials

Efficient Hydro‐ and Organogelation by Minimalistic Diketopiperazines Containing a Highly Insoluble Aggregation‐Induced, Blue‐Shifted Emission Luminophore** Abstract We report the synthesis, gelation abilities and aggregation‐induced, blue‐shifted emission (AIBSE) properties of two minimalistic diketopiperazine‐based gelators. Despite containing a highly insoluble luminophore that makes up more than half of their respective molecular masses, efficient hydrogelation by multiple stimuli for one and efficient organogelation for the other compound are reported. Insights into the aggregation and gelation properties were gained through examination of the photophysical and material properties of selected gels, which are representative of the different modes of gelation. The synthesis of the gelators is highly modular and based on readily available amino acid building blocks, allowing the efficient and rapid diversification of these core structures and fine‐tuning of gel properties. Luminophore ; Aggregation-induced emission ; Diketopiperazines ; Chemistry ; Materials science ; Nanotechnology ; Combinatorial chemistry ; Organic chemistry ; Fluorescence ; Luminescence ; Optoelectronics ; Physics Supramolecular Self-Assembly in Materials ; Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: María Dolores Márquez-Medina | Rafael Luque | Alina M. Balu | Francisco Ivars‐Barceló | Carolina Carrillo‐Carrión
Source: Catalysis Science & Technology (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Metal doping of porous materials <i>via</i> a post-synthetic mechano-chemical approach: a general route to design low-loaded versatile catalytic systems Mesoporous silica materials doped with metals (Fe/Zr) presented superior catalytic performance for oxidations. The use of MOFs as metal sources improved the metal distribution, and allowed a fine-tuning of Brønsted–Lewis acidity depending on the MOF. Catalysis ; Doping ; Materials science ; Mesoporous material ; Porosity ; Metal ; Mesoporous silica ; Lewis acids and bases ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Metallurgy Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science ; Polyoxometalates: Synthesis and Applications
Authors: Adam Langlois | P. Blake J. St. Onge | Paul‐Ludovic Karsenti | Aneeta Younus | Simon Rondeau‐Gagné
Source: The Journal of Physical Chemistry B (article, 2021-08-17)
Field: Materials Science — Materials Chemistry

Modulating the Photophysical Properties and Electron Transfer Rates in Diketopyrrolopyrrole-Based Coordination Polymers Molecular self-assembly through noncovalent interactions is a particularly efficient approach to fine-tune the optoelectronic and photophysical properties of electroactive materials. In metal-ligand coordination polymers, the final properties of the assemblies are directly related to the nature of the metal-ligand interaction. To probe for such influence on the photophysical properties of electroactive materials, a series of coordination polymers based on a well-known organic dye, diketopyrrolopyrrole, was prepared through coordination of a terpyridine-containing monomer with various metal sources, including iron, cobalt, zinc, and manganese. The resulting supramolecular polymers were characterized through multiple techniques, including UV-vis and fluorescence spectroscopy, time-correlated single-photon counting, and femtosecond transient absorption spectroscopy to reveal the impact of the metal source on the final photophysical properties of coordination polymers. As expected, important variations were found between different coordination polymers in terms of absorption, fluorescence kinetics, and electron transfer rate. While iron and cobalt-containing polymers showed ultrafast electrons transfer rates, assemblies from manganese were shown to be much less efficient, confirming the importance of metal centers. This detailed fundamental study unravels some important relationships between metal-ligand interactions, supramolecular self-assembly, and photophysical properties, ultimately leading to new avenues for the design of functional polymers based on organic dyes. Supramolecular chemistry ; Polymer ; Terpyridine ; Photochemistry ; Materials science ; Cobalt ; Electron transfer ; Ligand (biochemistry) ; Supramolecular polymers ; Fluorescence ; Ultrafast laser spectroscopy ; Metal ; Monomer ; Coordination complex ; Chemistry ; Spectroscopy ; Molecule ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Luminescence and Fluorescent Materials ; Metal-Organic Frameworks: Synthesis and Applications