jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: D. P. de Andrade Deus | J. M. J. Lopes | R. H. Miwa
Source: arXiv (Cornell University) (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

Stacking order effects on the energetic stability and electronic properties of $n$-doped graphene/h-BN van der Waals heterostructures on SiC(0001) Heterostructures made of stacked 2D materials with different electronic properties are studied for their potential in creating multifunctional devices. Graphene (G) and hexagonal boron nitride (h-BN) van der Waals (vdW) systems have been extensively researched, including recent studies on synthesizing h-BN on graphene/SiC(0001) templates. These studies suggest that h-BN encapsulation could occur in addition to vdW epitaxy. This work presents theoretical research on G/h-BN heterostructures on SiC(0001) with a carbon buffer layer. The results show an energetic preference for h-BN encapsulation below a single layer of graphene: G/h-BN/SiC in bilayer systems and G/h-BN/G/SiC in trilayer systems. Electronic structure calculations reveal that the linear energy band dispersion of graphene is maintained in bilayer systems but with Dirac points at different energy positions due to the varied electron doping level of graphene. In trilayer systems, the doping level of graphene also depends on the stacking order. The electronic band structure of G/h-BN/G/SiC features two Dirac points below the Fermi level but with different energies. Less stable systems like G/G/h-BN/ and h-BN/G/G/SiC display parabolic bands near the Fermi level. Additional structural characterizations were performed based on simulations of C-1s core-level-shift (CLS) and carbon K-edge X-ray absorption near-edge spectroscopy (XANES) to aid future experimental spectroscopy in these graphene/h-BN vdW systems. Graphene ; Materials science ; Heterojunction ; van der Waals force ; Fermi level ; Doping ; Stacking ; XANES ; Bilayer graphene ; Fermi energy ; Condensed matter physics ; Nanotechnology ; Spectroscopy ; Electron ; Optoelectronics ; Chemistry ; Physics ; Molecule Graphene research and applications ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Weijian Zheng | Jun‐Sang Park | Péter Kenesei | Ahsan Ali | Zhengchun Liu | Ian Foster | Nicholas Schwarz | Rajkumar Kettimuthu | Antonino Miceli | Hemant Sharma
Source: arXiv (Cornell University) (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

Rapid detection of rare events from in situ X-ray diffraction data using machine learning High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots over time of the evolving microstructure and attributes. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. Here we present a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. Our technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to 9 times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data into compact, semantic-rich representations of visually salient characteristics (e.g., peak shapes). These characteristics can be a rapid indicator of anomalous events such as changes in diffraction peak shapes. We anticipate that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods that span many decades of length scales. Computer science ; Diffraction ; Cluster analysis ; Set (abstract data type) ; Energy (signal processing) ; Data set ; Representation (politics) ; Artificial intelligence ; Context (archaeology) ; Pattern recognition (psychology) ; Data mining ; Optics ; Physics ; Geology Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Advanced Electron Microscopy Techniques and Applications
Authors: B Shanthi Sudha | M Ramachandran | Vimala Saravanan | Chandrasekar Raja
Source: Aeronautical and Aerospace Engineering (article, 2023-03-03)
Field: Materials Science — Materials Chemistry

Assessment of Selection of Suitable for Spar of the HumanPowered Aircraft Using COPRAS Method Generally speaking, while making decisions, it is more customary to rely on intuition rather than any form of numerical technique. In areas where there are many options and many factors impacting the choice, such as the selection of materials, a more precise approach would be necessary. It is necessary to understand the material's characteristics, price, design principles, and interactions while making mechanical parts. Material science has not only produced traditional materials such as metals, ceramics, and polymers but has also given rise to various novel materials like composites, smart (intelligent) materials, and functionally graded materials. A designer now has access to a wider range of materials than ever before thanks to the growth of knowledge. In these instances, a strategic choice must be made in order to satisfy all the functional specifications of the product while upholding client requests and preferences. The performance criteria of the component must be specified, along with a general description of the properties and processing needs of the primary material, As the first stage of the material selection process. Consequently, certain material types might be ruled out, while others are chosen as potential options for the component. The relevant material properties are subsequently listed and assigned a priority ranking. The best material can then be chosen using optimization techniques. The result of the COPRAS analysis is followed by Al 2024-T4 is the first rank, Carbon 63% Epoxy is last rank. Based on the analysis, the materials ranked highest for usage are as follows: Al 2024-T4 aluminum alloy, E-glass 73%-Epoxy, E-glass 56% Epoxy, and E-glass 65% Polyester. These selections have been carefully assessed and determined to be the most suitable options, taking into account their specific properties and how well they align with the intended application. The Al 2024-T4 aluminum alloy exhibits excellent strength-to-weight ratio and corrosion resistance, making it an ideal choice for structural components. E-glass 73%-Epoxy, E-glass 56% Epoxy, and E-glass 65% Polyester offer a combination of strength, flexibility, and durability, catering to various requirements within the application. The meticulous evaluation of these materials ensures that the chosen ones will deliver optimal performance and reliability in the designated context. Material selection ; Ranking (information retrieval) ; Computer science ; Selection (genetic algorithm) ; Rank (graph theory) ; Component (thermodynamics) ; Intuition ; Process (computing) ; Operations research ; Engineering ; Materials science ; Mathematics ; Artificial intelligence ; Composite material Material Selection and Properties ; Aluminum Alloys Composites Properties ; Graphite, nuclear technology, radiation studies
Authors: Gesiane Mendonça Ferreira | Daniella Herdi Cariello | Filipi França dos Santos | Kelly Cristine da Silveira | Maria Clara Gonçalves | Mônica Calixto de Andrade
Source: VETOR - Revista de Ciências Exatas e Engenharias (article, 2023-06-28)
Field: Materials Science — Materials Chemistry

Synthesis of Hybrid Cellulose Acetate Membranes and Application of the Flynn-Wall-Ozawa Isoconversional Method The synthesis of hybrid materials is a promising alternative in the production of materials with optimized properties. In this context, the classic cellulose acetate membranes are being modified. It is worth mentioning that knowing the thermal stability of cellulosic materials is an essential factor in their use since organic polymers have low thermal resistance, limiting their applicability. This work evaluates the synthesis and the study of the thermal degradation kinetics of hybrid cellulose acetate membranes. The synthesis was carried out using the sol-gel process, with the phase inversion technique, through the reaction of cellulose acetate with organometallic precursors: tetraethyl orthosilicate, TEOS; 3-aminopropyl triethoxysilane, APTES, and titanium isopropoxide, TiPOT, in fixed composition. Thermal characterization was performed with thermogravimetric analysis at different heating rates under a nitrogen atmosphere. The kinetic parameter of thermal decomposition activation energy, Ea, was estimated with the isoconversional method proposed by Flynn-Wall-Ozawa (FWO). The degradation kinetics shows that the change in the chemical composition of the samples directly interferes with their thermal properties. Thus, it is possible to observe that the activation energy found for the reference sample, AC-Pure, in the first and second batches was 233.5 and 219.05 kJ/mol, respectively. The composition modification results in an increase in activation energy, in which sample B0//100-30 recorded 310.31 and 226.05 kJ/mol in the first and second batches; and sample B100//0-30 reached 452.73 and 424.74 kJ/mol. It is clear that the increase in the thermal resistance of the samples is associated with the change in the chemical composition and must be attributed to the increase in the TEOS content in the composition due to the formation and increase in the presence of siloxane groups in the material. Activation energy ; Cellulose acetate ; Thermal stability ; Cellulose ; Thermogravimetric analysis ; Membrane ; Materials science ; Tetraethyl orthosilicate ; Chemical engineering ; Triethoxysilane ; Thermal decomposition ; Chemistry ; Polymer chemistry ; Nuclear chemistry ; Organic chemistry ; Composite material Thermal and Kinetic Analysis ; biodegradable polymer synthesis and properties ; Polymer Nanocomposites and Properties
Authors: Lin-Jie Wang | Pengtu Zhang | Yali Geng | Zaisheng Zhu | Shiling Yuan
Source: Molecules (article, 2023-06-08)
Field: Chemistry — Spectroscopy

Harmonic Vibrational Frequency Simulation of Pharmaceutical Molecules via a Novel Multi-Molecular Fragment Interception Method By means of a computational method based on Density Functional Theory (DFT), using commercially available software, a novel method for simulating equilibrium geometry harmonic vibrational frequencies is proposed. Finasteride, Lamivudine, and Repaglinide were selected as model molecules to study the adaptability of the new method. Three molecular models, namely the single-molecular, central-molecular, and multi-molecular fragment models, were constructed and calculated by Generalized Gradient Approximations (GGAs) with the PBE functional via the Material Studio 8.0 program. Theoretical vibrational frequencies were assigned and compared to the corresponding experimental data. The results indicated that the traditional single-molecular calculation and scaled spectra with scale factor exhibited the worst similarity for all three pharmaceutical molecules among the three models. Furthermore, the central-molecular model with a configuration closer to the empirical structure resulted in a reduction of mean absolute error (MAE) and root mean squared error (RMSE) in all three pharmaceutics, including the hydrogen-bonded functional groups. However, the improvement in computational accuracy for different drug molecules using the central-molecular model for vibrational frequency calculation was unstable. Whereas, the new multi-molecular fragment interception method showed the best agreement with experimental results, exhibiting MAE and RMSE values of 8.21 cm<sup>-1</sup> and 18.35 cm<sup>-1</sup> for Finasteride, 15.95 cm<sup>-1</sup> and 26.46 cm<sup>-1</sup> for Lamivudine, and 12.10 cm<sup>-1</sup> and 25.82 cm<sup>-1</sup> for Repaglinide. Additionally, this work provides comprehensive vibrational frequency calculations and assignments for Finasteride, Lamivudine, and Repaglinide, which have never been thoroughly investigated in previous research. Density functional theory ; Repaglinide ; Chemistry ; Mean squared error ; Molecular vibration ; Molecule ; Computational chemistry ; Mathematics ; Organic chemistry ; Statistics Analytical Chemistry and Chromatography ; Nonlinear Optical Materials Research ; Spectroscopy and Quantum Chemical Studies
Authors: Sally Howells | Mike Andrews | Michelle Canning | Emily Cuffin-Munday | Eva Balentova | Susannah Davies | Debora Giovanelli | Helen Lunn | Samuel Oldknow | Kate Tustain | Daphne Houston | Huw Hedges | Jeanne Andres | Jean-Franois Grard | Annie Castonguay | Alexander Cobb | Vera Constantino | Catharine Esterhuysen | Yannick Guari | Suman Jain | Peter Junk | Dai-Wen Pang | Karine Philippot | Akhila Sahoo | Jianji Wang | Gregory Welch | Kazunari Yoshizawa | Odile Eisenstein | M Jesni | Pralok Jacob | Mahesh Samanta | Kumar Ravva ; Babul | Rei Kalita | Mukul Star | Md Gupta | Harunar Rashid ; Hanglin | Zhicheng ; Jichao Li | Dandan Shi | Lin Wu | Runping ; Chaozhong Lin | Xue Li | Yidan Chen | Haocheng Liu | Rongrong Wu | Liyi Jia | Dongqi Shi | M Lu | Martins | Afsun Yusif Abdullayev | Sujayev | T Kamran | Armando Mahmudov | Hongwei Pombeiro ; Lijuan Cao | Yadong Zhu | Chen Li | Xilong Yang | Han-Pu Wang | ; Zhongfa Liang | Wenjun Li | ; Zhang | Mariappan Mariappan | Ravi Kanth Kamlekar | Mehboobali Pannipara | Abdullah Al-Sehemi | Savarimuthu Philip | Anthony
Source: New Journal of Chemistry (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Contents list Permissions Request permissions Contents list New J. Chem., 2023, 47, 10467 DOI: 10.1039/D3NJ90082G This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Chemistry ; Acknowledgement ; Chromatin structure remodeling (RSC) complex ; Attribution ; Library science ; World Wide Web ; Computer security ; Computer science ; Psychology Radioactive element chemistry and processing
Authors: Аюр Алексеевич Бунаев | Igor Dolganov | Irena Dolganova | Егор Михайлович Юрьев
Source: Bulletin of the Tomsk Polytechnic University Geo Assets Engineering (article, 2023-03-02)
Field: Chemistry — Analytical Chemistry

НЕСТАЦИОНАРНОЕ МОДЕЛИРОВАНИЕ ПИРОЛИЗА БЕНЗИНОВОЙ ФРАКЦИИ Ссылка для цитирования: Нестационарное моделирование пиролиза бензиновой фракции / А.А. Бунаев, И.М. Долганов, И.О. Долганова, Е.М. Юрьев // Известия Томского политехнического университета. Инжиниринг георесурсов. – 2023. – Т. 334. – № 3. – С. 73-88.&#x0D; Актуальность исследования обусловлена необходимостью увеличения производства алкенов, а также углубления понимания процесса коксообразования. Направлением развития нефтехимического комплекса и повышения эффективности процесса пиролиза является ресурсосбережение, минимизация эксплуатационных затрат и вероятности возникновения аварий. Цель: разработка математической модели пиролиза бензиновой фракции, которая будет учитывать коксообразование и его влияние на кинетическую, гидродинамическую и термодинамическую составляющие процесса. Объект: процесс пиролиза бензиновой фракции; динамика состава углеводородного потока и побочные процессы коксообразования. Методы. Методологической основой исследования является системной анализ и метод математического моделирования. Помимо этого, применяются квантово-химические методы расчета термодинамических и кинетических параметров целевых и побочных химических реакций, протекающих при переработке углеводородного сырья, и электронно-структурные методы, основанные на теории функционала плотности (DFT – Density Functional Theory); методы вычислительной гидродинамики для исследования режимов движения потока и отложения частиц кокса на стенках змеевика; разработанные экспериментальные методики определения оптимального расхода углеводородного сырья и водяного пара, прогнозирования длительности межренегерацинного цикла печи пиролиза. Результаты. Разработана нестационарная модель процесса пиролиза бензиновой фракции, описывающая побочный процесс коксообразования по длине пиролизного змеевика и с течением времени. С помощью модели рассчитана скорость образования слоя кокса с учетом технологических параметров и состава сырья. Расчетная скорость составляет 3,12∙10–7 мм в секунду. Результаты также позволили получить распределение толщины слоя кокса в радиантном змеевике, что свидетельствует об ускорении побочных процессов к концу процесса. Это связано с образованием значительного количества непредельных соединений. Установлено, что изменение основных параметров имеет два противоположных эффекта. Так, с повышением температуры и давления увеличивались как выходы целевых продуктов, так и скорость роста коксового слоя. Увеличение расхода сырья приводит к снижению обоих этих параметров. Density functional theory ; Chemistry ; Computational chemistry Petroleum Processing and Analysis ; Coal and Coke Industries Research ; Phase Equilibria and Thermodynamics
Authors: Michaela Mhlberg | Heather Montgomery | Jonathon Watson | Daniella Ferluccio | Edward Gardner | Matthew Blow | Chris Dias | Hemna Fathima | Juan Gonzalez | Eleanor Griffiths | Rob Hinde | Sam Howell | Clara Humann | Ash Hyde | Francesca Jacklin | Shruti Karnik | Sophie Koh | Tamara Kosikova | Evie Karkera | Brian Li | Sam Mansell | Carole Martin | Kirsty Mcroberts | Tiffany Rogers | Cat Schofield | Charu Storr- Vijay | Manman Wang | Tom Williams | Ella White | Elizabeth So | Lee Colwill | Jie Gao | Yu Zhang | Sam Keltie | Chunli Bai | Cinzia Casiraghi | Gianaurelio Cuniberti | Qing Dai | Christian Klinke | Quan Li | Xing Ling | Xiaogang Liu | Janet Macdonald | Teresa Pellegrino | Elena Shevchenko | Jonathan Veinot | Umesh Waghmare | Jin Zou | Zhenan Bao | Amanda Barnard | Stephanie Brock | Chunying Chen | Jingyi Chen | Wenlong Chen | Xiaodong Chen | Serena Cussen | Kristen Fichthorn | Christy Haynes | Guohua Jia | Xingyu Jiang | Song Jin | Jesse Jokerst | Kourosh Kalantar-Zadeh | Yamuna Krishnan | Katharina Landfester | Graham Leggett | Changming Li | Jie Liu | Laura Liu | Anna Fontcuberta | Morral | Catherine Murphy | T Pradeep | Lakshmi Polavarapu | Narayan Pradhan | Zhigang Shuai | Dmitri Talapin | Zhiyong Tang | Mauricio Terrones | Sarah Tolbert | Miriam Vitiello | Jianfang Wang | Xiaojun Wu | Yujie Xiong | Hongxing Xu | Lin Xu | Xiao Zeng | Hua Zhang | Seyeong Yang | Taegyun Kim | Sunghun Kim | Daewon Chung | Tae-Hyeon Kim | Jung Kyu Lee | Sungjoon Kim | Muhammad Ismail | Chandreswar Mahata | Sungjun | Sho Nagai | Shingo Urata
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Contents list Permissions Request permissions Contents list Nanoscale, 2023, 15, 13139 DOI: 10.1039/D3NR90162A This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content. Acknowledgement ; Attribution ; Chromatin structure remodeling (RSC) complex ; Computer science ; Library science ; World Wide Web ; Computer security ; Chemistry ; Psychology ; Social psychology Quantum Dots Synthesis And Properties ; Advanced Nanomaterials in Catalysis ; Legal and Policy Issues
Authors: Kwang Jun Lee | Alexander C. Trowbridge | Graham D. Bruce | George Dwapanyin | Kylie R. Dunning | Kishan Dholakia | Erik P. Schartner
Source: arXiv (Cornell University) (preprint, 2023-09-22)
Field: Chemistry — Analytical Chemistry

Learning algorithms for identification of whisky using portable Raman spectroscopy Reliable identification of high-value products such as whisky is an increasingly important area, as issues such as brand substitution (i.e. fraudulent products) and quality control are critical to the industry. We have examined a range of machine learning algorithms and interfaced them directly with a portable Raman spectroscopy device to both identify and characterize the ethanol/methanol concentrations of commercial whisky samples. We demonstrate that machine learning models can achieve over 99% accuracy in brand identification across twenty-eight commercial samples. To demonstrate the flexibility of this approach we utilised the same samples and algorithms to quantify ethanol concentrations, as well as measuring methanol levels in spiked whisky samples. Our machine learning techniques are then combined with a through-the-bottle method to perform spectral analysis and identification without requiring the sample to be decanted from the original container, showing the practical potential of this approach to the detection of counterfeit or adulterated spirits and other high value liquid samples. Bottle ; Counterfeit ; Identification (biology) ; Raman spectroscopy ; Computer science ; Algorithm ; Process engineering ; Machine learning ; Materials science ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Chi Ho Wong | Yan Ming Yeung | Xin Zhao | Wing‐Cheung Law | Chak Yin Tang | Chee Leung Mak | Chi Wah Leung | Rolf Lortz
Source: Research Square (preprint, 2023-01-19)
Field: Chemistry — Organic Chemistry

Routines for the synthesis of carbyne with more than 6000 atoms Carbyne ; Chemistry ; Catalysis ; Organic chemistry Fullerene Chemistry and Applications ; Advanced Chemical Physics Studies ; Cyclization and Aryne Chemistry
Authors: Deeki Doma Sherpa | Amit Kumar Sahu | Tarang Jadav | Niraj Rajput | Gargi Nikhil Vaidya | Dinesh Kumar | Pinaki Sengupta
Source: Biomedical Chromatography (article, 2023-12-12)
Field: Chemistry — Organic Chemistry

Ultra‐high‐performance liquid chromatography–quadrupole time of flight tandem mass spectrometry based <i>in vitro</i> metabolite profiling of DK‐GV‐04P, a novel anticancer molecule under drug discovery DK-GV-04P, chemically identified as 3-cinnamyl-2-(4-methoxyphenyl) quinazolin-4(3H)-one, is an investigational molecule synthesized at the Chemical Biology Laboratory of the National Institute of Pharmaceutical Education and Research-Ahmedabad. The compound has shown potential anticancer activity against squamous CAL27 cell lines. Metabolite identification and characterization are critical in drug discovery, providing key insights into a compound's pharmacokinetics, pharmacodynamics safety, and metabolic fate. The primary aim of the study was to identify and characterize the in vitro metabolites of DK-GV-04P. In silico identification of the site of metabolism was also carried out using xenosite online software. The molecule was incubated with human liver microsomes and human S9 liver fraction to generate in vitro metabolites, which were further identified and characterized using ultra-high-performance liquid chromatography-quadrupole time of flight tandem mass spectrometry. A total of nine metabolites (four phase I and five phase II) were identified and characterized through tandem mass spectrometry. The major biotransformation pathways involved in metabolism of DK-GV-04P were hydroxylation, O-demethylation and glucuronidation. In addition to this, a detailed biotransformation pathway of DK-GV-04P has been established in this study. Chemistry ; Metabolite ; Tandem mass spectrometry ; Glucuronidation ; Chromatography ; Mass spectrometry ; Drug metabolism ; Demethylation ; Hydroxylation ; Metabolic pathway ; Biotransformation ; Liquid chromatography–mass spectrometry ; Drug discovery ; Microsome ; In vitro ; Biochemistry ; Metabolism ; Enzyme Quinazolinone synthesis and applications ; Biochemical and Molecular Research ; Cancer therapeutics and mechanisms