jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mykhaylo Petryk | Ihor Boyko | Mariya Petryk | Jacques Fraissard | Ivan Mudryk
Source:  (article, 2020-02-06)
Field: Chemistry — Inorganic Chemistry

Modeling of adsorption and desorption of hydrocarbons in nanoporous catalytic zeolite media using nonlinear Langmuir isotherm The theoretical bases of mathematical modeling of nonisothermal adsorbtion and desorbtion in nanoporous catalytic zeolite media for the Langmuir’s nonlinear isotherm are given. They most fully determine the mechanism of adsorption equilibrium for nanoporous systems of ZSM-5 zeolite. The effective scheme of linearization of a nonlinear model is implemented. High-speed analytical solutions of the system of linearized boundary problems of adsorption and desorption in nanoporous media are justified and obtained using the Hevisides operational method. Zeolite ; Adsorption ; Nanoporous ; Langmuir ; Langmuir adsorption model ; Desorption ; Sorption isotherm ; Catalysis ; Chemistry ; Chemical engineering ; Materials science ; Inorganic chemistry ; Chromatography ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis
Authors: Mariano Campoy‐Quiles
Source: DIGITAL.CSIC (Spanish National Research Council (CSIC)) (article, 2020-08-05)
Field: Materials Science — Materials Chemistry

High throughput investigations in organic solar cells using material libraries based on gradients Molecular based photovoltaics can play a key role in the transition to a sustainable energy paradigm, as is the most sustainable solar cell technology (in terms of energy investment on return), and simultaneously is amenable to be solution processed into any shape, can be as light as a feather, and can be produced with the largest colour pallet and transparency degree imaginable. Indeed, there is an infinite number of molecules that can be synthesised to match our needs. The bottleneck then becomes how to identify and screen potential candidates, or, in other words, how to find the needle in the haystack.&#13;\n&#13;\nIn this talk I will first describe a novel methodology for the fast evaluation of donor/acceptor systems for photovoltaics. The new approach is based on the fabrication of samples with gradients in the relevant parameters of interest that represent a large fraction of the corresponding parameter space [1-5]. In particular, we fabricate gradients in thickness [1, 2], microstructure [1, 2], composition [2, 3] and apply hyperspectral imaging to correlate material and device properties. The method can be used both, for evaporated [4] and solution processed binary [1, 2] and ternary [3] systems, is up to 100 faster than conventional optimization protocols, uses less than 50 mg of each active layer material and generates hundreds to millions of data points per system [3]. Then I will show how this machinery can be used to find design rules for the optimum composition in non-fullerene acceptors based devices. If time allows, I will illustrate how samples with controlled gradients in relevant parameters can be employed to introduce novel device concepts (e.g. microcavity based minituarized spectrometers [6]) and to perform accurate fundamental studies. Throughput ; Process engineering ; Environmental science ; Computer science ; Chemistry ; Nanotechnology ; Materials science ; Engineering ; Telecommunications Machine Learning in Materials Science ; Nanowire Synthesis and Applications
Authors: Sahajpreet Singh
Source: International Journal of Engineering Applied Sciences and Technology (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

MONTE CARLO SIMULATION OF RADIOACTIVE DECAY Most of the f-block element's nucleus is not stable, to gain stability they break down in two or more daughter nucleus. For radioactive decay simulation, the fact nuclei decay is sufficient. We will use the Monte Carlo method which is based on random numbers, although radioactive decay is also a random process as we don't know accurately which one nuclei will be going to decay next. To do real-time experiments to study radioactivity is dangerous and very costly. The emitted radiations in this process are harmful to human beings and nature but although as its application it has uses in especially cancer treatment. Monte Carlo method ; Radioactive decay ; Statistical physics ; Nuclear physics ; Computer science ; Physics ; Mathematics ; Statistics Graphite, nuclear technology, radiation studies ; Radiative Heat Transfer Studies
Authors: Sergey Solovyev
Source: International Journal for Computational Civil and Structural Engineering (article, 2020-03-24)
Field: Materials Science — Materials Chemistry

A PROBABLISTIC APPROACH TO EVALUATION OF THE ULTIMATE LOAD ON FLEXURAL RC ELEMENT ON CRACK LENGTH The fracture mechanics of concrete and reinforced concrete is a promising direction in the development of methods for reinforced concrete structural elements design and inspection. At the same time, probabilistic methods of design and behavior analysis of structural elements are of particular interest. The article describes a probabilistic approach to load-bearing capacity and reliability analysis of flexural reinforced concrete elements based on the crack length criterion. The functional relationship between the critical stress intensity coefficient of concrete and the design compressive strength of concrete is given. The article presents a method for the reliability analysis of flexural reinforced concrete elements at the operational stage with limited statistical data about the critical stress intensity coefficient of concrete. The ultimate value of the failure probability (or reliability index) should be set for each object individually based on the value of the acceptable risk. Structural engineering ; Flexural strength ; Reliability (semiconductor) ; Reinforced concrete ; Probabilistic logic ; Computer science ; Bearing capacity ; Compressive strength ; Materials science ; Engineering ; Composite material ; Artificial intelligence Structural mechanics and materials ; Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability
Authors: Haobo Zhang | Jianhua Jiang | Hongchuan Qin | Xi Li | Jian Li
Source:  (preprint, 2020-03-10)
Field: Materials Science — Materials Chemistry

Experiment Based Thermoelectric Characteristics Analysis of Solid Oxide Fuel Cell Systems Under Hydrogen and Methane Solid oxide fuel cell (SOFC) has attracted more and more attention due to its power generation characteristics of high efficiency, low emission, and strong fuel adaptability. In order to ensure the stable and efficient operation of the system, it is urgent to study the thermoelectric characteristics and control laws of SOFC systems under different fuels for different fuel types can result in different thermoelectric characteristics. Therefore, based on the 200-hour experimental data obtained from an external steam-reforming SOFC independent power generation system, the temperature distribution and electrical characteristics of the system at different power stages are studied. Moreover, the thermoelectric characteristics of the independent SOFC systems under pure hydrogen and methane fuel types with the same output power are further compared. The results show that, the stack voltage and the fuel utilization rate of the methane fed SOFC system is lower than that of the hydrogen fueled SOFC system. And the temperature gradient on the fuel side of the methane fed SOFC system is relatively large, while the temperature gradient on the air side of the hydrogen fueled SOFC is relatively large. The analysis process plays a significant role in system control strategy design, which improves the fuel adaptability of the system. Methane ; Solid oxide fuel cell ; Hydrogen ; Oxide ; Thermoelectric effect ; Fuel cells ; Materials science ; Hydrogen fuel ; Chemical engineering ; Environmental science ; Nuclear engineering ; Chemistry ; Engineering ; Physics ; Physical chemistry ; Metallurgy ; Thermodynamics ; Organic chemistry Advancements in Solid Oxide Fuel Cells ; Perovskite Materials and Applications ; Fuel Cells and Related Materials
Authors: Jonathon J. O’Brien | John A. Wrobel | Shunqiang Li | Ling Xie | Sherri R. Davies | Harsha P. Gunawardena | Bahjat F. Qaqish | Xian Chen | Matthew J. Ellis
Source: UNC Libraries (article, 2020-04-18)
Field: Chemistry — Spectroscopy

QuantFusion: Novel Unified Methodology for Enhanced Coverage and Precision in Quantifying Global Proteomic Changes in Whole Tissues Single quantitative platforms such as label-based or label-free quantitation (LFQ) present compromises in accuracy, precision, protein sequence coverage, and speed of quantifiable proteomic measurements. To maximize the quantitative precision and the number of quantifiable proteins or the quantifiable coverage of tissue proteomes, we have developed a unified approach, termed QuantFusion, that combines the quantitative ratios of all peptides measured by both LFQ and label-based methodologies. Here, we demonstrate the use of QuantFusion in determining the proteins differentially expressed in a pair of patient-derived tumor xenografts (PDXs) representing two major breast cancer (BC) subtypes, basal and luminal. Label-based in-spectra quantitative peptides derived from amino acid-coded tagging (AACT, also known as SILAC) of a non-malignant mammary cell line were uniformly added to each xenograft with a constant predefined ratio, from which Ratio-of-Ratio estimates were obtained for the label-free peptides paired with AACT peptides in each PDX tumor. A mixed model statistical analysis was used to determine global differential protein expression by combining complementary quantifiable peptide ratios measured by LFQ and Ratio-of-Ratios, respectively. With minimum number of replicates required for obtaining the statistically significant ratios, QuantFusion uses the distinct mechanisms to “rescue” the missing data inherent to both LFQ and label-based quantitation. Combined quantifiable peptide data from both quantitative schemes increased the overall number of peptide level measurements and protein level estimates. In our analysis of the PDX tumor proteomes, QuantFusion increased the number of distinct peptide ratios by 65%, representing differentially expressed proteins between the BC subtypes. This quantifiable coverage improvement, in turn, not only increased the number of measurable protein fold-changes by 8% but also increased the average precision of quantitative estimates by 181% so that some BC subtypically expressed proteins were rescued by QuantFusion. Thus, incorporating data from multiple quantitative approaches while accounting for measurement variability at both the peptide and global protein levels make QuantFusion unique for obtaining increased coverage and quantitative precision for tissue proteomes. Computer science ; Computational biology ; Environmental science ; Biological system ; Biology Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications
Authors: Jonas Schöpf
Source: Digital Library of the University of Innsbruck (University of Innsbruck) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

The Weighted Path Order in TTT2 In computer science it is a fundamental question whether a program terminates. This plays, for example, a critical role in formal verification as we need termination for total correctness of algorithms. In general, termination is undecidable and thus a complex and hard task. However, term rewriting, a model of computation, provides a set of many useful methods for proving termination. One such method is the weighted path order which unifies several other well-known termination methods. In this thesis I present an implementation of the weighted path order in the automatic termination tool Tyrolean Termination Tool 2 (TTT2) and a way to certify its proofs using the certifier CeTA. Furthermore, I demonstrate a new implementation of a method called maximal polynomials with its usable rules technique and two new backends to incorporate specific external tools into the Tyrolean Termination Tool 2. Path (computing) ; Order (exchange) ; Computer science ; Mathematics ; Business ; Computer network Nuclear Materials and Properties ; Catalysis and Oxidation Reactions
Authors: Casey Y. Lee | Hany Soliman | Nadia Bragagnolo | Arjun Sahgal | Benjamin Geraghty | Albert P. Chen | Ruby Endre | William J. Perks | Jay Detsky | Eric Leung | Michael W.Y. Chan | Chris Heyn | Charles H. Cunningham
Source: medRxiv (preprint, 2020-11-10)
Field: Chemistry — Spectroscopy

Predicting Response to Radiotherapy of Intracranial Metastases with Hyperpolarized <sup>13</sup> C MRI Abstract Purpose Stereotactic radiosurgery (SRS) is used to manage intracranial metastases in a significant fraction of patients. Local progression after SRS can often only be detected with increased volume of enhancement on serial MRI scans which may lag true progression by weeks or months. Experimental Design Patients with intracranial metastases (N = 11) were scanned using hyperpolarized 13 C MRI prior to treatment with stereotactic radiosurgery (SRS). The status of each lesion was then recorded at six months post-treatment follow-up (or at the time of death). Results The positive predictive value of 13 C-lactate signal, measured pre-treatment, for prediction of progression of intracranial metastases at six months post-treatment with SRS was 0.8 p &lt; 0.05, and the AUC from an ROC analysis was 0.77 p &lt; 0.05. The distribution of 13 C-lactate z -scores was different for intracranial metastases from different primary cancer types (F = 2.46, p = 0.1). Conclusions Hyperpolarized 13 C imaging has potential as a method for improving outcomes for patients with intracranial metastases, by identifying patients at high risk of treatment failure with SRS and considering other therapeutic options such as surgery. Radiosurgery ; Medicine ; Nuclear medicine ; Radiation therapy ; Radiology ; Predictive value ; Lesion ; Magnetic resonance imaging ; Internal medicine ; Surgery Advanced NMR Techniques and Applications ; Brain Metastases and Treatment ; Lung Cancer Research Studies
Authors: Mehmet Parlak | Timuçin Everest | Yakup Çıkılı
Source: ÇOMÜ Ziraat Fakültesi Dergisi (article, 2020-12-21)
Field: Materials Science — Biomaterials

Şekerpancarı ve Sarımsak Tarımı Yapılan Toprakların Karbon Stokları ile Bazı Fizikokimyasal Özellikleri: Kırklareli- Babaeski ve Balıkesir- Altıeylül Örnek Çalışmaları Küresel iklim değişikliğiyle mücadele etmenin yollarından birisi de toprakların karbon stoklarını artırmaktır. Bu araştırmanın amacı şekerpancarı (Beta vulgaris L.) ve sarımsak (Allium sativum L.) tarlalarındaki toprakların organik karbon stokları ile bazı fiziksel ve kimyasal özelliklerini belirlemektir. Toprak örnekleri 0-20 cm derinlikten alınmış ve tekstür, hacim ağırlığı, pH, elektriksel iletkenlik, kireç, toplam N, alınabilir P, K, Fe, Cu, Mn, Zn gibi fiziksel ve kimyasal özellikleri belirlenmiştir. Şekerpancarı topraklarında organik karbon stokları 33.57 ton/ha ve sarımsak topraklarında ise bu değer 30.70 ton/ha olarak saptanmıştır. Şekerpancarı ve sarımsak topraklarının genellikle tınlı bünyeli (kumlu killi tın ve killi tın), nötr ve hafif alkali pH’da ve tuzsuz oldukları belirlenmiştir. Şekerpancarı topraklarının %44.5’i az kireçli ve %22.2’si kireçli sınıfında iken, sarımsak topraklarının %11.1’inin kireçli sınıfında oldukları saptanmıştır. Organik madde içerikleri bakımından şekerpancarı topraklarının %66.7’sinin ve sarımsak topraklarının ise %33.3’ünün az sınıfında oldukları belirlenmiştir. Şekerpancarı topraklarının %55.6’sının toplam N içeriği, %44.4’ünün alınabilir P içeriği, %11.1’i alınabilir Cu içeriği, %70.3’ünün alınabilir Zn içeriği bakımından ve sarımsak topraklarının ise %16.7’sinin toplam N içeriği, %22.3’ünün alınabilir P içeriği, %61.1’inin alınabilir Zn içeriği bakımından yetersiz sınıfında oldukları saptanmıştır. Hem organik karbon stoklarını hem de yetersiz seviyedeki besin maddesi miktarlarını artırmak için ahır gübresiyle birlikte gübre uygulamaları yapılmalıdır. Nuclear chemistry ; Chemistry ; Physics Clay minerals and soil interactions ; Plant Micronutrient Interactions and Effects ; Soil Carbon and Nitrogen Dynamics
Authors: Lessa Kay Grunenfelder
Source:  (article, 2020-09-10)
Field: Chemistry — Physical and Theoretical Chemistry

Active Learning in an Introductory Materials Science Course Abstract A lecture-based introductory materials science course was converted to an active learning experience without altering the scheduling of the course, classroom location, or faculty contact hours. Group lab activities, using simple and inexpensive materials, were incorporated into the course to enhance student engagement and understanding. Throughout a 15 week semester, 10 labs were performed. Each lab was completed and submitted during a class period, with students working in groups of 4. Periodic video lectures were posted prior to class to prepare students for specific in-class activities. No core content was removed from the original lecture-based version of the course. Each lab activity was a modular unit enabling adoption of the course structure by multiple professors. The in-class labs are described here, along with preliminary data on student experience and outcomes. Metrics of analysis include exam performance, student feedback, and observations of student engagement. The redesigned course has been taught twice, by two different professors. Data from lab-based courses is compared to data from previous iterations of the standard lecture-based model, as taught by the same faculty members. Class (philosophy) ; Course (navigation) ; Computer science ; Mathematics education ; Active learning (machine learning) ; Modular design ; Student engagement ; Student activities ; Multimedia ; Medical education ; Psychology ; Engineering ; Medicine ; Artificial intelligence Various Chemistry Research Topics ; Experimental Learning in Engineering ; Nanotechnology research and applications
Authors: Anh Tran | John Mitchell | Laura Swiler | Timothy Wildey
Source: arXiv (Cornell University) (preprint, 2020-04-24)
Field: Materials Science — Materials Chemistry

An active learning high-throughput microstructure calibration framework\n for solving inverse structure-process problems in materials informatics Determining a process-structure-property relationship is the holy grail of\nmaterials science, where both computational prediction in the forward direction\nand materials design in the inverse direction are essential. Problems in\nmaterials design are often considered in the context of process-property\nlinkage by bypassing the materials structure, or in the context of\nstructure-property linkage as in microstructure-sensitive design problems.\nHowever, there is a lack of research effort in studying materials design\nproblems in the context of process-structure linkage, which has a great\nimplication in reverse engineering. In this work, given a target\nmicrostructure, we propose an active learning high-throughput microstructure\ncalibration framework to derive a set of processing parameters, which can\nproduce an optimal microstructure that is statistically equivalent to the\ntarget microstructure. The proposed framework is formulated as a noisy\nmulti-objective optimization problem, where each objective function measures a\ndeterministic or statistical difference of the same microstructure descriptor\nbetween a candidate microstructure and a target microstructure. Furthermore, to\nsignificantly reduce the physical waiting wall-time, we enable the\nhigh-throughput feature of the microstructure calibration framework by adopting\nan asynchronously parallel Bayesian optimization by exploiting high-performance\ncomputing resources. Case studies in additive manufacturing and grain growth\nare used to demonstrate the applicability of the proposed framework, where\nkinetic Monte Carlo (kMC) simulation is used as a forward predictive model,\nsuch that for a given target microstructure, the target processing parameters\nthat produced this microstructure are successfully recovered.\n Context (archaeology) ; Microstructure ; Linkage (software) ; Computer science ; Bayesian optimization ; Process (computing) ; Materials informatics ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Manufacturing Process and Optimization
Authors: Shi Jun Ang | Wujie Wang | Daniel Schwalbe‐Koda | Simon Axelrod | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2020-04-23)
Field: Materials Science — Materials Chemistry

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires ab initio molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higherlevels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems. Ab initio ; Potential energy surface ; Molecular dynamics ; Chemistry ; Transition state theory ; Chemical physics ; Solvation ; Transition state ; Intramolecular force ; Statistical physics ; Computational chemistry ; Physics ; Molecule ; Quantum mechanics ; Reaction rate constant Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular Junctions and Nanostructures