jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luis Chacon | Vijay B. Mohan Ramu | Savio J. Poovathingal
Source: AIAA AVIATION 2022 Forum (article, 2022-06-20)
Field: Materials Science — Materials Chemistry

A supervised learning model to predict length-scale dependent permeability of porous carbon composites View Video Presentation: https://doi.org/10.2514/6.2022-4007.vid Permeability of a porous material is computed using direct simulation Monte Carlo (DSMC) technique. The computation of permeability using DSMC technique is a computationally intensive and a tedious process. Surrogate modeling of physical simulations involved in DSMC offers a significantly cheaper alternative to compute permeability. The main objective of this work is to develop a supervised learning model which approximates the physical simulations involving a single gas species through a porous material and captures the length-scale dependency of the material's permeability. This length-scale dependency can then be integrated in material response codes to better simulate the physics of the material instead of assuming a constant value for the property. This work focuses on the development of an analytical function which relates the permeability of the porous material with the simulation conditions and length-scale of the micro-structure. The analytical function was realised using support vector regression (SVR) which was found to be a robust technique in order to capture the complex relationship between temperature, average pressure and length-scale of the micro-structure. The predicted values were found to have a maximum relative error of about 20 percent with the majority of the relative errors being less than 7 percent. The analytical function was validated against a wide range of inputs beyond the scope of trained values to justify the use of the developed supervised learning model. The capability of the developed model to capture the length scale dependency on permeability was emphasized by noting the difference in permeability predicted and it's accuracy for data points at either edges of the training domain. Permeability (electromagnetism) ; Composite material ; Materials science ; Porosity ; Porous medium ; Membrane ; Chemistry High voltage insulation and dielectric phenomena ; Composite Material Mechanics ; Thermal properties of materials
Authors: Linda S. Schadler | Wei Chen | L. Catherine Brinson | Ravishankar Sundararaman | Prajakta Prabhune | Akshay Iyer
Source: ECS Transactions (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

(Invited) Combining Machine Learning, DFT, EFM, and Modeling to Design Nanodielectric Behavior Predicting and designing the properties of polymer nanodielectrics is challenging due to the number of parameters controlling properties and the breadth of scale (from electronic to mm). This paper summarizes a preliminary study using elongated semiconducting nanoparticles with an extrinsic interface that enhanced carrier trapping to attempt to find a parameter space that allows for improved permittivity and breakdown strength without increasing loss. We combine finite element modeling of dielectric constant with a Monte Carlo multi-scale simulation of carrier hopping to predict break down strength. Filler dispersion, filler geometry, isotropy and interface trapping properties are explicitly taken into account to compute design objectives associated with dielectric constant and mobility. Ultimately, we trained a latent variable Gaussian Process (LVGP) metamodel that can take both qualitative (e.g., orientation and dispersion states) and quantitative variables (e.g., microstructure descriptors) as inputs to predict properties over a broader range with observed tradeoffs. Isotropy ; Materials science ; Permittivity ; Dielectric ; Range (aeronautics) ; Monte Carlo method ; Dispersion (optics) ; Statistical physics ; Gaussian ; Computer science ; Condensed matter physics ; Composite material ; Optics ; Physics ; Optoelectronics ; Mathematics Machine Learning in Materials Science
Authors: Daniela Amaral Silva | M. Kenneth Cor | Afaseneh Lavasanifar | Neal M. Davies | Raimar Löbenberg
Source: Pharmacy Education (article, 2022-05-28)
Field: Chemistry — Analytical Chemistry

Using GastroPlus to teach complex biopharmaceutical concepts Context: In response to the COVID-19 pandemic, many educational adjustments had to be made to move in-person teaching to online classrooms. This report showcases the use of the software GastroPlus in an undergraduate-level pharmacy course. Programme description: This course aimed for the students to learn how to perform a mechanistically based simulation to predict the oral absorption pattern, pharmacokinetics, and biopharmaceutics properties of compounds in humans. The computer simulation offered the opportunity to teach concepts about bioavailability providing all kinds of experience with major biopharmaceutic determinants that affect systemic drug exposure. Evaluation: The advantage of this approach was seen by the enhanced performance on the biopharmaceutics questions on the final exam compared with the previous year where the laboratory was not implemented: An increase from 2019 (where no laboratory was implemented) through 2021 incorrect scores from 52, 76 to 75%, respectively. Conclusion: There is great benefit in using computer programs and simulations as a technique to enhance active learning and to educate pharmacy students in salient aspects of biopharmaceutics. Biopharmaceutics ; Biopharmaceutical ; Pharmacy ; Context (archaeology) ; Biopharmaceutics Classification System ; Medical education ; Computer science ; Pharmacology ; Bioavailability ; Medicine ; Chemistry ; Nursing Analytical Methods in Pharmaceuticals ; Pharmaceutical studies and practices
Authors: Felix Schäfer | Sebastian Egger | D. Steiner | Maxime Carré | Rüdiger‐A. Eichel
Source: Journal of Power Sources (article, 2022-03-29)
Field: Materials Science — Materials Chemistry

Soft-sensor based operation of a solid oxide fuel cell system with anode exhaust gas recirculation Stack (abstract data type) ; Solid oxide fuel cell ; Exhaust gas ; Anode ; Natural gas ; Inert gas ; Process engineering ; Specific energy ; Exhaust gas recirculation ; Nuclear engineering ; Automotive engineering ; Materials science ; Chemistry ; Engineering ; Waste management ; Thermodynamics ; Chemical engineering ; Computer science ; Electrode Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Gas Sensing Nanomaterials and Sensors
Authors: M. A. Helal | Seiji Kojima
Source: Materials (review, 2022-09-28)
Field: Materials Science — Materials Chemistry

Brillouin Scattering and First-Principles Studies of BaMO3 (M = Ti, Zr, and Cu) Perovskites Perovskite oxides with the general formula ABO<sub>3</sub> comprise a large number of families among the structures of oxide-based materials, and currently, several perovskite structures have been identified. From a variety of compositions and structures, various functions are observed in perovskite compounds, and therefore, they became very useful for various applications in the electronic and medical industries. One of the most puzzling issues for perovskite compounds is the understanding of the vibration and relaxation dynamics in the gigahertz range. In that sense, the micro-Brillouin scattering system is a very effective tool to probe the gigahertz dynamics, and also, first-principles calculations can be used to describe the phonon structure with different atomic contributions. The micro-Brillouin scattering system and first-principles calculations provide the fundamental information on a variety of vibration and relaxation processes related to structural phase transitions under different external conditions such as temperature, electric field, and pressure. This review article summarizes the Brillouin scattering and first-principles studies on BaMO<sub>3</sub> (M = Ti, Zr, and Cu). Through a detailed analysis of the existing results, we summarize the existing limitations and future perspectives in these research areas, which may propel the development of different perovskite ferroelectrics and extend their practical application areas. Perovskite (structure) ; Brillouin zone ; Brillouin scattering ; Relaxation (psychology) ; Materials science ; Scattering ; Phonon ; Field (mathematics) ; Variety (cybernetics) ; Phase (matter) ; Condensed matter physics ; Nanotechnology ; Chemical physics ; Engineering physics ; Chemistry ; Physics ; Computer science ; Optics ; Crystallography ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Perovskite Materials and Applications ; Multiferroics and related materials
Authors: Katrina Mackey | Martin McHugh | Gerard P. McGlacken
Source: Chemistry Teacher International (article, 2022-09-15)
Field: Chemistry — Physical and Theoretical Chemistry

Some people and personalities of organic chemistry: a teaching hook for mid-level university students Abstract Organic chemistry often represents a key impasse for students during their third level science education. Both intrinsic and extrinsic factors contribute to perceived difficulties in learning the subject. Moreover, the teaching of multi-step organic synthesis at third level has well-documented challenges. At University College Cork (UCC), we have adopted a strategy to engage students at the mid to late degree stage using an innovative teaching hook, to inspire interest and engagement. The approach was taken to outline chronologically some of the seminal breakthroughs in synthesis, and organic chemistry more generally, from the 1950 s to a few of the current leaders of today. Importantly the focus is on the people, the circumstance and the stories surrounding them. As a relevance-based hooking strategy grounded in a storytelling pedagogy, the goal was to inform students of the potential in organic chemistry and thus spark their interest for the subsequent lectures. Over multiple interventions, feedback from students has been highly positive. We posit that the design framework behind the ‘The People and Personalities’ could be adapted to many disciplines in a similarly successful manner. Overall, this approach proved inspirational for students, and was a most timely intervention in their degree program. Relevance (law) ; Personality psychology ; Storytelling ; Psychology ; SPARK (programming language) ; Chemistry ; Student engagement ; Psychological intervention ; Pedagogy ; Mathematics education ; Computer science ; Personality ; Narrative ; Social psychology ; Political science ; Literature ; Art Various Chemistry Research Topics ; Innovative Teaching Methods ; Science Education and Pedagogy
Authors: Supakorn Tirapat | Teerapong Senjuntichai
Source: Engineering Journal (article, 2022-10-31)
Field: Materials Science — Materials Chemistry

Influence of Surface Stresses on the Deflection of Circular Nanoplate with Two-Parameter Elastic Substrate This paper presents the influence of surface energy effects on the deflection of circular nanoplate with two-parameter elastic substrate. The governing equation for axisymmetric bending of the nanoplate, based on the Gurtin-Murdoch surface elasticity theory, resting on a Winkler-Pasternak elastic foundation is derived from a variational approach based on the concept of minimum total potential energy. The analytical general solution to the governing equation is then obtained in terms of the modified Bessel functions. Finally, closed-form solutions for deflections, bending moment and transverse shear in the nanoplate subjected to normally distributed loading are presented explicitly for the boundary conditions of simple, clamped, and free edges. A set of numerical solutions are selected to demonstrate the influence of surface material parameters and the substrate moduli on the deflection and bending moment profiles of a silicon nanoplate on Winkler-Pasternak foundation. It is found that the nanoplate clearly shows size-dependent behaviors, and becomes stiffer with the existence of surface stresses. Deflection (physics) ; Materials science ; Composite material ; Structural engineering ; Substrate (aquarium) ; Surface (topology) ; Geometry ; Optics ; Engineering ; Physics ; Mathematics ; Geology Material Properties and Failure Mechanisms ; Material Properties and Applications ; Microstructure and mechanical properties
Authors: Eric Largy | Bruno Aliès | Guillaume Condesse | Alexandra Gaubert | Thomas Livingston | Karen Gaudin
Source: Analytical and Bioanalytical Chemistry (article, 2022-08-23)
Field: Chemistry — Physical and Theoretical Chemistry

Teaching with simulation tools to introduce the basics of analytical chemistry instrumentation Instrumentation (computer programming) ; Analytical Chemistry (journal) ; Medical laboratory ; Chemistry ; Engineering ; Management science ; Computer science ; Environmental chemistry ; Programming language Various Chemistry Research Topics ; Experimental Learning in Engineering
Authors: Ali Rıza Şekerci | Ali Rıza Erdem
Source: Journal of Educational Sciences & Psychology (article, 2022-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

The development and implementation of a two-tier multiple choice test related to the concepts of oxidation-reduction reactions and galvanic cells In recent years there has been growing interest in research concerning students’ understanding of electrochemistry concepts. The two-tier multiple choice tests have played an important role in helping researchers determine students’ level of understanding and in identifying specific misconceptions. The aim of current research was to develop a two-tier multiple choice test related to the concepts of oxidation-reduction reactions and galvanic cells, and to examine the applicability of this test. For this aim, primarily the students’ misconceptions about related issues were identified in consideration of the literature. Then, a two-tier multiple choice test consisting of 19 items was developed based on the misconceptions identified. As a result, a valid and reliable two-tier multiple choice test was confirmed for the identification of 11th grade students’ misconceptions about the concepts of oxidation-reduction reactions and galvanic cells. In addition, the students’ misconceptions were discussed by evaluating answers given during the test. Test (biology) ; Multiple choice ; Galvanic cell ; Identification (biology) ; Reduction (mathematics) ; Mathematics education ; Computer science ; Psychology ; Chemistry ; Mathematics ; Statistics Various Chemistry Research Topics ; Science Education and Pedagogy ; Educational Strategies and Epistemologies
Authors: Kyosuke Yamaguchi | Ryoji Asahi | Yutaka Sasaki
Source: Advanced Engineering Informatics (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Superconductivity information extraction from the literature: A new corpus and its evaluations This paper describes a superconductivity corpus for materials informatics (SC-CoMIcs) tailored for the extraction of superconductive material information from the literature. Currently, few corpora exist for materials informatics in contrast to the situation for biomedical informatics. In particular, there is no sizable corpus that can be used to assist with the search for superconducting materials. The SC-CoMIcs consists of 1,000 abstracts with manually annotated named entities, main materials, and relations/events associated with superconductivity. In particular, the main material is a selling point of our corpus, which can be regarded as a hub that binds implicitly related physical entities and properties in abstracts. We conduct named entity recognition, main material identification, and relation/event extraction experiments to determine the effectiveness of the corpus. The experimental results show that we can obtain F1 scores of approximately 74%–95%, 84% and 73%–97% for named entity recognition, main material identification and relation extraction tasks, respectively. We also demonstrate that the extracted doping information is consistent with that of the well-known Hume–Rothery rules, which implies that the constructed corpus can provide an opportunity to revisit or find physical chemical rules from the literature. Identification (biology) ; Computer science ; Information extraction ; Informatics ; Natural language processing ; Point (geometry) ; Information retrieval ; Relation (database) ; Named-entity recognition ; Extraction (chemistry) ; Relationship extraction ; Artificial intelligence ; Data mining ; Task (project management) ; Engineering ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Nihang Fu | Lai Wei | Yuqi Song | Qinyang Li | Rui Xin | Sadman Sadeed Omee | Rongzhi Dong | Edirisuriya M. Dilanga Siriwardane | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2022-06-27)
Field: Materials Science — Materials Chemistry

Materials Transformers Language Models for Generative Materials Design: a benchmark study Pre-trained transformer language models on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequence generation. However, no such models have been applied to learn the composition patterns of inorganic materials. Here we train a series of seven modern transformer language models (GPT, GPT-2, GPT-Neo, GPT-J, BLMM, BART, and RoBERTa) using the expanded formulas from material deposited in the ICSD, OQMD, and Materials Projects databases. Six different datasets with/out non-charge-neutral or balanced electronegativity samples are used to benchmark the performances and uncover the generation biases of modern transformer models for the generative design of materials compositions. Our extensive experiments showed that the causal language models based materials transformers can generate chemically valid materials compositions with as high as 97.54\% to be charge neutral and 91.40\% to be electronegativity balanced, which has more than 6 times higher enrichment compared to a baseline pseudo-random sampling algorithm. These models also demonstrate high novelty and their potential in new materials discovery has been proved by their capability to recover the leave-out materials. We also find that the properties of the generated samples can be tailored by training the models with selected training sets such as high-bandgap materials. Our experiments also showed that different models each have their own preference in terms of the properties of the generated samples and their running time complexity varies a lot. We have applied our materials transformer models to discover a set of new materials as validated using DFT calculations. Transformer ; Novelty ; Generative grammar ; Computer science ; Language model ; Electronegativity ; Benchmark (surveying) ; Artificial intelligence ; Natural language processing ; Engineering ; Voltage ; Chemistry ; Electrical engineering Machine Learning in Materials Science
Authors: Lucien Murhula Namegabe | Aladin Ombeni Mahano | Sérigne Omar Sarr
Source: American Journal of Analytical Chemistry (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Development, Validation and Application of a Spectrofluorimetric Method for the Quantification of Nevirapine in Pharmaceutical Formulations Tablets and Suspensions The development of the spectrofluorimetric method can be considered a promising alternative that is relatively less expensive and sufficiently reliable. In the current literature, no method for the analysis of nevirapine by spectro-fluorimetric has been reported. The proposed method is based on the transformation of naturally non-fluorescent nevirapine into a fluorescent derivative after chemical synthesis. Maximum excitation and emission wavelengths are 290 nm and 357 nm respectively. The analytical performance of the method demonstrates linearity in the concentration range 1.5 × 10-2 and 13.5 × 10-2 μg/mL with a correlation coefficient (r) greater than 0.999. The detection (LOD) and quantification (LOQ) limits found are 1.97 × 10-3 μg/mL and 5.48 × 10-3 μg/mL respectively. Recovery is achieved with 99.9% and 100.3% trueness, intra-day precision with a coefficient of variation of repeatability (CVr) of 0.99% and inter-day precision with a coefficient of variation of precision (CVR) of 1.7%. The method has been successfully applied in the analysis of 10 batches of nevirapine tablets and suspensions. Nevirapine ; Repeatability ; Correlation coefficient ; Coefficient of variation ; Chromatography ; Chemistry ; Linearity ; Detection limit ; Analytical Chemistry (journal) ; Derivative (finance) ; Accuracy and precision ; Fluorescence ; Mathematics ; Human immunodeficiency virus (HIV) ; Optics Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Electrochemical sensors and biosensors
Authors: David S. Bell
Source: LCGC North America (article, 2022-02-01)
Field: Chemistry — Inorganic Chemistry

The Current Status of Metal-Organic Frameworks (MOFs) for Use in Liquid Chromatography Metal-organic frameworks (MOFs) are self-assembled combinations of metals and inorganic ligands that result in a relatively young class of highly ordered, porous materials. Because of the number of structural and chemical possibilities, high surface area, controlled pore volume, and favorable thermal properties, MOFs are being investigated in several fields, including chromatography. Because of the enormous interest in a 2018 “Column Watch” article on the subject and the high level of research in the field, this article explores recent (2019–present) activity specifically toward the application of MOFs for liquid chromatography (LC). Metal-organic framework ; Porosity ; Materials science ; Column chromatography ; Nanotechnology ; Chromatography ; Chemistry ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Metal complexes synthesis and properties
Authors: Saptasree Bose | Victoria Padilla | Alexandra Salinas | Fariha Ahmad | Timothy P. Lodge | Christopher J. Ellison | Karen Lozano
Source: Polymer Reviews (article, 2022-10-13)
Field: Materials Science — Biomaterials

Hierarchical Design Strategies to Produce Internally Structured Nanofibers Nanofibers have attracted significant interest due to their unique properties such as high specific surface area, high aspect ratio, and spatial interconnectivity. Nanofibers can exhibit multifunctional properties and unique opportunities for promising applications in a wide variety of fields. Hierarchical design strategies are being used to prescribe the internal structure of nanofibers, such as core-sheath, concentric layers, particles distributed randomly or on a lattice, and co-continuous network phases. This review presents a comprehensive overview of design strategies being used to produce the next generation of nanofiber systems. It includes a description of nanofiber processing methods and their effects on the nano- and microstructure. Physico-chemical effects, such as self-assembly and phase separation, on the ultimate morphology of fibers made from designed emulsions, polymer blends, and block copolymers, are then described. This review concludes with perspectives on existing challenges and future directions for hierarchical design strategies to produce internally structured nanofibers. Materials science ; Nanofiber ; Nanotechnology Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications