jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Deepa Narayanan | Sarita G. Bhat | Gaurav Baranwal
Source: The Applied Biology & Chemistry Journal (article, 2021-09-30)
Field: Materials Science — Biomaterials

Characterization of innately decellularised micropattern pseudostem of Musa balbisiana - A non-surface functionalized 3D economic biomaterial scaffold Banana (Musa balbisiana) pseudostem 3D scaffolds have been developed here for primary eukaryotic cell and cell line culture as an economical, sustainable, eco-friendly alternative for surface-functionalized polymeric and plant tissue-based structures. Musa pseudostem 3D micro pattern scaffold (MPM-3Ds) developed by freeze-drying followed by ethylene oxide sterilization yielded 5.6ng of DNA per mg of tissue, confirming its extended decellularised state. Thermogravimetric analysis, contact angle measurement, uniaxial testing, and FTIR determined thermal stability, wettability, tensile strength, and surface functional groups respectively. Micro and macronutrients, sugars, and amino acids that naturally enrich MPM-3Ds were estimated using EDAX, HPLC, and biochemical analysis. The most important finding was, non-surface functionalized MPM-3Ds supported attachment, growth, and differentiation of human mesenchyme stem cells, human primary hepatocytes like cells, primary mouse brain cortical neurons, mouse fibroblast cells, and human pancreatic cancer cells. MPM-3Ds showed in vivo biodegradation and biocompatibility in a preliminary analysis in Sprague Dawley rats. These findings illuminate nature's power to nurture cells in the micropattern cradles of MPM- 3Ds that can support innovative research in stem cell differentiation, drug and cosmetic testing, and biosensor development leading to advanced biomedical research. Scaffold ; Biocompatibility ; Biomaterial ; Tissue engineering ; Chemistry ; Biomedical engineering ; Materials science ; Nanotechnology ; Organic chemistry ; Medicine Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; 3D Printing in Biomedical Research
Authors: Marcin Krynski | Mariana Rossi
Source: arXiv (Cornell University) (preprint, 2021-06-16)
Field: Materials Science — Materials Chemistry

Efficient Gaussian Process Regression for prediction of molecular\n crystals harmonic free energies We present a method to accurately predict the Helmholtz harmonic free\nenergies of molecular crystals in high-throughput settings. This is achieved by\ndevising a computationally efficient framework that employs a Gaussian Process\nRegression model based on local atomic environments. The cost to train the\nmodel with ab initio potentials is reduced by starting the optimisation of the\nframework parameters, as well as the training and validation sets, with an\nempirical potential. This is then transferred to train the model based on\ndensity-functional theory potentials, including dispersion-corrections. We\nbenchmarked our framework on a set of 444 hydrocarbon crystal structures,\ncomprising 38 polymorphs, and 406 crystal structures either measured in\ndifferent conditions or derived from them. Superior performance and high\nprediction accuracy, with mean absolute deviation below 0.04 kJ/mol/atom at 300\nK is achieved by training on as little as 60 crystal structures. Furthermore,\nwe demonstrate the predictive efficiency and accuracy of the developed\nframework by successfully calculating the thermal lattice expansion of aromatic\nhydrocarbon crystals within the quasi-harmonic approximation, and predict how\nlattice expansion affects the polymorph stability ranking.\n Crystal structure prediction ; Helmholtz free energy ; Lattice (music) ; Ab initio ; Density functional theory ; Gaussian ; Crystal (programming language) ; Statistical physics ; Gaussian process ; Lattice energy ; Computation ; Chemistry ; Crystal structure ; Algorithm ; Computer science ; Computational physics ; Physics ; Thermodynamics ; Computational chemistry ; Quantum mechanics ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zekun Ren | Siyu Tian | Juhwan Noh | Felipe Oviedo | Guangzong Xing | Qiaohao Liang | Ruiming Zhu | Armin G. Aberle | Shijing Sun | Xiaonan Wang | Yi Liu | Qianxiao Li | J. Senthilnath | Kedar Hippalgaonkar | Yousung Jung | Tonio Buonassisi
Source: SSRN Electronic Journal (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

An Invertible Crystallographic Representation for &lt;b&gt;General&lt;/b&gt; Inverse Design of Inorganic Crystals with Targeted Properties Invertible matrix ; Inverse ; Crystallography ; Representation (politics) ; Mathematics ; Chemistry ; Materials science ; Pure mathematics ; Geometry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications
Authors: Junxian Geng | Lei Chen | Kuangrong Hao | Hengqian Wang
Source:  (article, 2021-05-22)
Field: Chemistry — Analytical Chemistry

Fractal-based combined kernel function model for the polyester polymerization process The polymerization process plays a significant role in polyester production, and the melt intrinsic viscosity is an important indicator of product quality. This paper introduced the fractal dimension feature selection method and combined the kernel function model based on STL decomposition (Seasonal-Trend decomposition procedure based on Loess) for the polyester polymerization process. The fractal dimension is used to analyze the spatial characteristics of the data and select features to construct the feature space, and the STL decomposition is used to analyze the time characteristics of the data to construct the combined kernel function. The two methods are combined with Gaussian process regression model, and the simulation experiment of industrial data shows the effectiveness of the proposed algorithm. Fractal dimension ; Fractal ; Kernel (algebra) ; Computer science ; Biological system ; Algorithm ; Mathematics ; Mathematical analysis ; Discrete mathematics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Sensory Analysis and Statistical Methods
Authors: Tousif Ahmed | Han Bing | Michael Sulecki | Marigrace Ferrill | Zubaer M. Hossain
Source: Physical review. E (article, 2021-02-15)
Field: Materials Science — Polymers and Plastics

Strength evolution laws in curing of solvent-welded polymers This paper reports the scaling laws to describe the time-evolution behavior of solvent-mediated strength at the interface between two identical thermoplastic polymers below the glass-transition temperature. Our results suggest that the evolution scales as sqrt[t], where t is the curing time. It depends on the time evolution of interfacial stiffness and toughness, each of which scales as sqrt[t]. Employing a combination of experiments and continuum scale simulations, we show that the evolution of strength, stiffness, and toughness is controlled by pure diffusion. It can therefore be treated as a Gaussian process. While the "saturation of strength," which describes the transition of strength evolution into a steady state, does not strictly follow any power-law type behavior, a simple exponential law accurately characterizes both evolution and saturation of strength. This suggests that the longer timescale nonlinear processes (that are overdetermined by the power-law type scaling laws) diminish rapidly in approaching a steady state. Furthermore, the kinetics of the evolution processes is well captured by the dissolution of polymer particles. While dissolution involves a different timescale, it strongly correlates with the solvent-welding process upon normalization. The correlation highlights the equivalence of the dissolution and solvent-joining processes and offers an easier route to determining strength at arbitrary curing times. Additionally, the dissolution rate of polymer particles is shape dependent and governed by the surface-to-volume ratio. Materials science ; Power law ; Dissolution ; Polymer ; Scaling ; Glass transition ; Thermodynamics ; Solvent ; Stiffness ; Composite material ; Law ; Physics ; Chemistry ; Mathematics ; Physical chemistry ; Geometry Polymer crystallization and properties ; Polymer composites and self-healing ; Epoxy Resin Curing Processes
Authors: Nguyen Duong Nguyen | Hieu‐Chi Dam
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-12-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SmFe12 substituted structures with OFM representation, magnetization and formation energy The dataset shows SmFe<sub>12</sub>-based crystalline magnetic materials under the formula SmFe<sub>12-<em>a-b</em></sub>X<em><sub>a</sub></em>Y<em><sub>b</sub></em> with X and Y as the substituted elements from Mo, Zn, Co, Cu, Ti, Al, and Ga; <em>a</em> and <em>b</em> are integer numbers of X and Y compositions, respectively and under restriction <em>a+</em><em>b</em>&lt;4. A structure is created by substituting <em>a</em> iron sites with element X and <em>b</em> iron sites with element Y. The formation energy and magnetization per atom of each structure are estimated from the Vienna ab initio simulation package (VASP) [1]. The optimization process for VASP follows the Open Quantum Materials Database standard [2]. The formation energy and magnetization are estimated with reference from [3]. Details discussion about the dataset are shown in the paper "Nguyen, DN., Kino, H., Miyake, T. <em>et al.</em> Explainable active learning in investigating structure–stability of SmFe<sub>12-α-β</sub>X<sub>α</sub>Y<sub>β</sub> structures X, Y {Mo, Zn, Co, Cu, Ti, Al, Ga}. <em>MRS Bulletin</em> (2022). https://doi.org/10.1557/s43577-022-00372-9" [1] VASP: Vienna Ab initio Simulation Package: https://www.vasp.at/. [2] OQMD: Open Quantum Materials Database: http://www.oqmd.org/. [3] Journal of Applied Physics 120, 203904 (2016); https://doi.org/10.1063/1.4968798 Magnetization ; Representation (politics) ; Materials science ; Condensed matter physics ; Physics ; Magnetic field ; Political science ; Quantum mechanics Magnetic Properties of Alloys ; Geomagnetism and Paleomagnetism Studies ; Magnetic Properties and Applications
Authors: Rebecca Lindsey | Nir Goldman | Laurence E. Fried | Sorin Bastea
Source: ChemRxiv (preprint, 2021-12-12)
Field: Materials Science — Materials Chemistry

Chemistry-Mediated Ostwald Ripening in Carbon-RichC/O Systems at Extreme Conditions There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g. shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e. growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials. Ostwald ripening ; Carbon fibers ; Nanotechnology ; Expansive ; Nanomaterials ; Condensation ; Molecular dynamics ; Chemistry ; Chemical physics ; Materials science ; Computational chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; High-pressure geophysics and materials ; Catalysis and Oxidation Reactions
Authors: Sterling G. Baird | Eric R. Homer | David T. Fullwood | Oliver K. Johnson
Source: ChemRxiv (preprint, 2021-12-24)
Field: Materials Science — Materials Chemistry

Towards a Quantitative Cartography of the Grain Boundary Energy Landscape: Paths and Correlations We apply a newly developed Voronoi fundamental zone (VFZ) framework to gain insights about grain boundary (GB) structure-property relationships in the five degree-of-freedom (5DOF) space of cubic GBs. We analyze the shape and size of a 5DOF fundamental zone (FZ), molecular statics energy uncertainty, property similarity of GBs that are crystallographically \close" (i.e. correlations), and energy pathways through 5DOF space. Considered together, these insights are important for managing tradeoffs between accuracy, complexity, and design considerations for electron backscatter diffraction/serial sectioning, high-energy diffraction microscopy, molecular statics, and density-functional theory. In terms of the shape and size of a 5DOF FZ, we discover that a FZ is smaller than expected at only ∼65° in the largest principal component. Thus, a 10° difference between two GBs, which may have previously been considered small, is actually quite large. We represent a GB by five transformed Cartesian coordinates equipped with a Euclidean distance metric. Using this representation, we find that the FZ has a low aspect-ratio shape (i.e. width, length, height, etc. are similar) which is important for 5DOF numerical differentiation. Semivariogram and numerical optimization methods reveal that grain boundary energy (GBE) in Ni and Fe are globally correlated within ∼6° to 8° in the grain boundary octonion (GBO) sense (multiply by 2 to convert to misorientation angle). For local correlation lengths of high-symmetry GBs of interest, we notice significant variation relative to global correlation lengths and an inverse relationship with the Brandon criterion. We suggest that property data with no more than ± ∼3 % error and point sets with GBs that are no more than ∼3−4° apart should be used and then paired with high-fidelity interpolation strategies. Finally, in terms of dynamic material behavior, geodesic paths through 5DOF space for Ni suggest that, under appropriate conditions, a certain low-energy Σ7 GB may transform into the frequently observed Σ3 coherent-twin GB which may be interesting to verify by experiment or simulation. Misorientation ; Mathematics ; Geometry ; Grain boundary ; Energy landscape ; Boundary (topology) ; Similarity (geometry) ; Physics ; Statistical physics ; Mathematical analysis ; Crystallography ; Computer science ; Chemistry Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Magnetic Properties and Applications
Authors: Colin A. Scholes
Source: Chemical Engineering Education (article, 2021-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Experiencing Process Plant Conditions Through a Pilot Plant-Based Laboratory Class A practical for students to experience a process plant is presented, based on operating a solvent absorption plant for carbon dioxide capture. The student must operate the plant in assigned roles that closely identify with a chemical plant environment, to achieve specific performance targets. Students must overcome technical challenges that require them to translate their theoretical understanding into practical outcomes. This ensures students take ownership of the class and act semi-independently of the demonstrators. Class (philosophy) ; Process (computing) ; Pilot plant ; Process engineering ; Engineering ; Computer science ; Waste management ; Artificial intelligence Various Chemistry Research Topics ; Chemistry and Chemical Engineering ; Experimental Learning in Engineering
Authors: Paolo Micheletti | Jérôme Faist | Tudor Olariu | Urban Senica | Mattias Beck | Giacomo Scalari
Source: APL Photonics (preprint, 2021-10-01)
Field: Chemistry — Spectroscopy

Regenerative terahertz quantum detectors Because of the ultrafast and photon-driven nature of the transport in their active region, we demonstrate that quantum cascade lasers can be operated as resonantly amplified terahertz detectors with wide RF bandwidth. Tunable responsivities up to 50 V/W and noise equivalent powers down to 100 pW/Hz1/2 are demonstrated at 4.7 THz. Constant peak responsivities with respect to the detector temperature are observed up to 80 K. Thanks to the ≈ps intersub-band lifetime, electrical bandwidths larger than 20 GHz can be obtained, allowing the detection of optical beatnotes from quantum cascade THz frequency combs. Terahertz radiation ; Cascade ; Detector ; Physics ; Optoelectronics ; Ultrashort pulse ; Photon ; Quantum ; Noise (video) ; Laser ; Optics ; Quantum mechanics ; Chemistry Spectroscopy and Laser Applications ; Photonic and Optical Devices ; Terahertz technology and applications
Authors: Qiufang Zheng
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-06-01)
Field: Chemistry — Organic Chemistry

Chinese University Students' Perceptions of the Use and Effectiveness of Turnitin in EAP Writing Student plagiarism has been prevalent in academic discourse. This paper investigates through a questionnaire and interviews the use and effectiveness of Turnitin, a major plagiarism detection system, in Chinese university students’ EAP writing in a Sino-foreign joint institute in Shanghai. The study finds except for its online marking tool GradeMark, Turnitin has been well-received by most students. Two most welcomed features are the similarity report and its convenience in submitting assignments. Besides, most students perceived Turnitin as an effective tool in reflecting and reducing plagiarism in their writing. However, the study also shows although students’ general understanding of the Western concept of plagiarism has been greatly improved, some students may still have misconceptions, and lack sufficient knowledge of what constitutes plagiarism, which can be a major factor contributing to the high similarity rate in their writing on Turnitin. Based on these findings, the study puts forward suggestions on how to reduce student plagiarism in EAP writing, in particular pointing out the introduction of Turnitin in EAP writing instruction in EFL contexts may help to cut down plagiarism. Psychology ; Mathematics education ; Perception ; Medical education ; Computer science ; Medicine Ferrocene Chemistry and Applications ; Writing and Handwriting Education
Authors: Fusataka Kuniyoshi | Jun Ozawa | Makoto Miwa
Source: arXiv (Cornell University) (preprint, 2021-06-27)
Field: Materials Science — Materials Chemistry

Analyzing Research Trends in Inorganic Materials Literature Using NLP In the field of inorganic materials science, there is a growing demand to extract knowledge such as physical properties and synthesis processes of materials by machine-reading a large number of papers. This is because materials researchers refer to many papers in order to come up with promising terms of experiments for material synthesis. However, there are only a few systems that can extract material names and their properties. This study proposes a large-scale natural language processing (NLP) pipeline for extracting material names and properties from materials science literature to enable the search and retrieval of results in materials science. Therefore, we propose a label definition for extracting material names and properties and accordingly build a corpus containing 836 annotated paragraphs extracted from 301 papers for training a named entity recognition (NER) model. Experimental results demonstrate the utility of this NER model; it achieves successful extraction with a micro-F1 score of 78.1%. To demonstrate the efficacy of our approach, we present a thorough evaluation on a real-world automatically annotated corpus by applying our trained NER model to 12,895 materials science papers. We analyze the trend in materials science by visualizing the outputs of the NLP pipeline. For example, the country-by-year analysis indicates that in recent years, the number of papers on "MoS2," a material used in perovskite solar cells, has been increasing rapidly in China but decreasing in the United States. Further, according to the conditions-by-year analysis, the processing temperature of the catalyst material "PEDOT:PSS" is shifting below 200 degree, and the number of reports with a processing time exceeding 5 h is increasing slightly. Pipeline (software) ; Computer science ; Artificial intelligence ; Natural language processing ; Reading (process) ; Named-entity recognition ; Parsing ; Scale (ratio) ; Information retrieval ; Engineering ; Linguistics Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: PV Ershov | А. С. Макарова
Source: Medicine of Extreme Situations (article, 2021-09-01)
Field: Chemistry — Organic Chemistry

Current trends in anticancer drug prototype in vitro pharmacology: bibliometric analysis 2019–2021 Identification of novel low molecular weight compounds with antitumor activity is the first important step towards the development of candidate drugs and a popular trend in in vitro pharmacology. The aim of the study was to assess the key trends and rank the scientific priorities in anticancer drug design using bibliometric analysis. The protocol involved using the panel of bibliographic databases (PubMed, Scopus, Cortellis) and analytical web-based tools PubChem, FACTA +, ClustVis, Reaxys, PathwayStudio and VOSviewer software to review a sample of 1657 papers issued 2020–2021.The work was also focused on 70 new promising basic structures and derivatives targeted at inhibiting both individual pro-tumor proteins and signaling cascades. It was found that serine-threonine protein kinases, receptor tyrosine kinases, DNA topoisomerases and tubulins as well as signaling pathways PI3K, mTOR, AKT1, STAT3, HIF-1a, and p53 account for up to 60% of the total structure of cellular targets for the design of anticancer drugs. The increasing scientific interest in innovative inhibitors of tumor-associated protein complexes, transcription factors and metabolic enzymes has been found. The compounds, which belong to heterocycles, glycosides, quinones and terpenes, were mentioned in 71% of papers as the basic structures for antitumor derivatives design. Papers, published in 2019, in which the compounds, such as lapachone, luteolin, quercetin, monastrol, and crisosplenol D are studied in the context of the design of new drug prototypes, have the highest citation rate. The systematic bibliometric approach involving the use of a panel of analytical resources makes it possible to assess R&amp;D trends and scientific priorities in anticancer drug design, thus organically complementing the classic reviews in periodicals. AKT1 ; Context (archaeology) ; Computational biology ; Pharmacology ; Luteolin ; Drug discovery ; DrugBank ; Chemistry ; Drug ; Biochemistry ; Biology ; PI3K/AKT/mTOR pathway ; Signal transduction ; Flavonoid Synthesis and biological activity ; Bioactive Compounds and Antitumor Agents ; Cancer therapeutics and mechanisms