jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Karol Dąbrowski | Jan Barbacki | Szymon Kuczyński | Rafał Smulski | Stanisław Nagy | M Al-Marhoun | S Ali | A Abdulraheem | S Nizamuddin | A Muhammadain | D Al-Sirri | R Gharbi | K Brzi | S Fraser-Miller | K Gordon | O Beyssac | J Blitz | D Klarup | M Briones | G Rojas | L Bryndzia | N Braunsdorf | R Hofmann | Q Morgan | P Christian | A Toews | M Buldakov | B Korolev | I Matrosov | D Petrov | A Tikhomirov | R Byrd | P Lu | J Nocedal | C Zhu | K Dbrowski | S Kuczyski | J Barbacki | T Wodek | R Smulski | S Nagy | T De Beer | A Burggraeve | M Fonteyne | L Saerens | J Remon | C Vervaet | S Eichmann | J Kiefer | J Benz | T Kempf | A Leipertz | T Seeger | A Elsharkawy | T Frosch | T Meyer | M Schmitt | J Popp | R Goodacre | M Neal | D Kell | S Hansen | R Berg | E Stenby | P Hegeman | C Dong | N Varotsis | V Gaganis | M Hippler | Rekha Gautam | R Keiner | T Massad | S Trumbore | K Obert | A Bsmann | P Wasserscheid | S Steuer | S Schorsch | M Weikl
Source: Acta Montanistica Slovaca (article, 2022-05-31)
Field: Chemistry — Organic Chemistry

Estimation of Uncertainties in Determination of Natural Gas Thermodynamic Properties Based Upon In-situ Raman Analyses of Gas Mixtures Raman spectroscopy is used in a real-time determination of the thermodynamic properties of natural gas. Two methods of Raman signal analysis are discussed. The first method is an analytical algorithm based on linear regression, which utilizes peak area. The second is an Artificial Neural Network algorithm that can utilize both peak area and its position. A detailed discussion of presented approaches is shown on reference data obtained using specially designed setup Raman with 105 m long fiber optic probe and a high-pressure cell. The proposed analysis allowed the calculation of composition for any arbitrarily chosen pressure. The feasibility of this approach is shown by the determination of composition for a gas flow in a separator installation on a gas production well. Results are compared with gas chromatography which allows the uncertainty determination of the method. Natural gas ; In situ ; Raman spectroscopy ; Thermodynamics ; Environmental science ; Materials science ; Chemistry ; Physics ; Optics ; Organic chemistry Chemical Thermodynamics and Molecular Structure ; Phase Equilibria and Thermodynamics ; Spectroscopy and Laser Applications
Authors: Mengmeng Li
Source:  (preprint, 2022-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep-learning-based Metasurface Design Method Considering Near-field Couplings &lt;p&gt; Planar metasurfaces have been applied in several fields. Near-field coupling is typically neglected in traditional metasurface designs. A numerical modeling method for macrocells that considers near-field couplings between meta-atoms is proposed. A deep neural network (DNN) is constructed to accurately predict the electromagnetic response from different macrocells. Transfer learning is employed to reduce the size of the training datasets. The designed neural network is embedded in the optimization algorithm as an effective surrogate model. Both the deflector and high numerical aperture (NA) metalens are simulated and optimized with our design framework, approximately 30% improvements of efficiencies are achieved. &lt;/p&gt; Coupling (piping) ; Planar ; Artificial neural network ; Field (mathematics) ; Computer science ; Aperture (computer memory) ; Near and far field ; Deep learning ; Electromagnetic field ; Numerical aperture ; Transfer of learning ; Electronic engineering ; Algorithm ; Artificial intelligence ; Materials science ; Physics ; Optics ; Engineering ; Mathematics ; Acoustics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Travis Kessler | Amina SubLaban | J. Hunter Mack
Source:  (article, 2022-10-16)
Field: Chemistry — Organic Chemistry

Predicting the Cetane Number, Sooting Tendency, and Energy Density of Terpene Fuel Additives Abstract Discovering renewable fuels and fuel additives is paramount in reducing carbon emissions from internal combustion engines. Terpenes, a group of compounds that can be synthesized from plant matter and microorganisms, have gained significant interest in recent years as promising candidates for fuels/additives. Terpenes are a diverse class of compounds that contain rings and methyl branches, resulting in high energy densities and optimal cold weather behavior. Their variation in bond order, carbon chains, and functional groups lead to varying degrees of soot formation and performance in existing engines. The present work leverages predictive models, namely artificial neural networks, to predict the cetane number (CN), sooting tendency (quantified with yield sooting index, YSI), and energy density (quantified with lower heating value, LHV) of terpenes and hydrogenated terpenes whose sooting propensities were previously determined through experimental means. Predicted sooting propensities of these terpenes are compared with experimental values, and predicted cetane numbers and energy densities are used to comment on the compounds’ ability to act as fuels/additives. Expected prediction errors for CN, YSI, and LHV, defined by blind test set median absolute error, are within 5.56 cetane units, 3.63 yield sooting index units, and 0.77 MJ/kg respectively. Additionally, the present work investigates a variety of correlation/dependence metrics for property-property relationships, furthering our understanding of how combustion-relevant properties are related. Cetane number ; Terpene ; Combustion ; Heat of combustion ; Materials science ; Work (physics) ; Organic chemistry ; Carbon fibers ; Soot ; Yield (engineering) ; Chemistry ; Chemical engineering ; Biodiesel ; Thermodynamics ; Composite material ; Composite number ; Catalysis ; Engineering ; Physics Chemical Thermodynamics and Molecular Structure ; Free Radicals and Antioxidants ; Advanced Combustion Engine Technologies
Authors: Kristaps Saršu̅ns | Agris Be̅rziņš
Source: ChemRxiv (preprint, 2022-11-02)
Field: Materials Science — Materials Chemistry

Experimental and computational investigation of benperidol and droperidol solid solutions in different crystal structures We explored solid solution formation between structurally highly similar active pharmaceutical ingredients droperidol and benperidol in different crystal phases formed by these compounds. We crystallized samples having different compound molar ratio to evaluate the solid solution formation capabilities. Different crystallization conditions were used to evaluate solid solution formation in different crystal phases, including nonsolvates, dihydrates, and several solvates. We also explored the phase obtained in the desolvation of the obtained solvated products. Our results clearly demonstrate that the formation of solid solutions strongly depends on the crystal structure, as in part of the structures almost complete replacement of benperidol with droperidol was achieved but in other structures the replacement was possible only up to a limited molar ratio. Moreover, only limited replacement of droperidol with benperidol was achieved and only in part of the structures. We investigated change of intramolecular and intermolecular energy introduced by the molecule replacement using computational calculations. We show that experimentally we obtained only structures where molecule replacement allows formation of efficient intermolecular interactions, and energetic requirements of intermolecular interaction changes to obtain solid solutions in nonsolvated phase are less strict than that for solvates. Intermolecular force ; Intramolecular force ; Crystallization ; Molecule ; Droperidol ; Crystal (programming language) ; Solid solution ; Crystal structure ; Phase (matter) ; Chemistry ; Crystallography ; Materials science ; Physical chemistry ; Computational chemistry ; Chemical physics ; Stereochemistry ; Organic chemistry Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Fitya Syarifa Mozar
Source: Journal of Applied Pharmaceutical Science (article, 2022-01-01)
Field: Chemistry — Spectroscopy

Nanoparticle protein corona and its role in disease diagnostic In the past decades, nanoparticles (NP) have shown tremendous potential for biomedical applications, such as targeted therapeutics, medical imaging, and biosensors. After administration, NP will directly interact with various biological components in the body, forming a protein corona (PC) on their surface. The PC composition affects the NP identity and behavior, including its stability, targeting ability, cellular uptake, toxicity, biodistribution, and elimination. Thus, a more profound understanding of the nano-bio-interface is crucial to improving the NP design for theranostic applications. The personalized PC (PPC) concept allows specific PC characteristics identification for early disease diagnosis and personalized therapeutics. However, accurate PC characterization is challenging due to its dynamic and complex nature. Until now, most studies have been focused on the NP PC characterization in vitro yet put less emphasis on its translational aspects. In this mini-review, the author will discuss various challenges surrounding PPC research, strategies to bridge that gap, clinical relevance, and future outlook. PPC's application for biomarker discoveries and recent advances in PPC analysis methodologies such as multiomics approach, Proteograph workflow, and machine learning algorithm will also be explored. Overall, PPC technology keeps evolving, and it holds a promising future in the personalized medicine era. Corona (planetary geology) ; Nanoparticle ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Astrobiology Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: William P. Feeney | Arun S. Moorthy | Edward Sisco
Source: ChemRxiv (preprint, 2022-05-01)
Field: Chemistry — Spectroscopy

Spectral Trends in GC-EI-MS Data Obtained from the SWGDRUG Library and Literature: A Resource for the Identification of Unknown Compounds Rapid identification of new or emerging psychoactive substances remains a critical challenge in forensic drug chemistry laboratories. Current analytical protocols are well-designed for confirmation of known substances yet struggle when new compounds are encountered. Many laboratories initially attempt to classify new compounds using gas chromatography mass spectrometry (GC-MS) data. Though there is a large body of research focused on analysis of illicit substances with GC-MS, there is little high-level discussion of mass spectral trends for different classes of drugs. This manuscript compiles literature information and performs simple exploratory analyses on evaluated GC-MS data to investigate trends of electron ionization (EI) mass spectra on the most reported illicit substance classes. Additionally, this work offers other important aspects: brief discussions of how each class of drugs is used; descriptions of proposed fragmentation pathways of commonly observed ions in EI-MS data; and summaries of mass spectral trends that can help an analyst classify new illicit compounds. Fragmentation (computing) ; Mass spectrometry ; Gas chromatography–mass spectrometry ; Electron ionization ; Identification (biology) ; Chemistry ; Mass spectrum ; Resource (disambiguation) ; Illicit drug ; Exploratory analysis ; Chromatography ; Analytical Chemistry (journal) ; Computer science ; Data science ; Ionization ; Drug ; Ion ; Psychology ; Organic chemistry ; Psychiatry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Forensic Toxicology and Drug Analysis
Authors: João Victor de Souza | Victor H. R. Nogueira | Alessandro S. Nascimento
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-08-03)
Field: Materials Science — Materials Chemistry

Ligand Binding Free Energy Evaluation by Monte Carlo Recursion Abstract The correct evaluation of ligand binding free energies by computational methods is still a very challenging active area of research. The most employed methods for these calculations can be roughly classified into four groups: ( i ) the fastest and less accurate methods, such as molecular docking, designed to sample a large number of molecules and rapidly rank them according to the potential binding energy; ( ii ) the second class of methods use a thermodynamic ensemble, typically generated by molecular dynamics, to analyze the endpoints of the thermodynamic cycle for binding and extract differences, in the so-called ‘end-point’ methods; ( iii ) the third class of methods is based on the Zwanzig relationship and computes the free energy difference after a chemical change of the system (alchemical methods); and ( iv ) methods based on biased simulations, such as metadynamics, for example. These methods require increased computational power and as expected, result in increased accuracy for the determination of the strength of binding. Here, we describe an intermediate approach, based on the Monte Carlo Recursion (MCR) method first developed by Harold Scheraga. In this method, the system is sampled at increasing effective temperatures, and the free energy of the system is assessed from a series of terms W ( b, T ), computed from Monte Carlo (MC) averages at each iteration. We show the application of the MCR for ligand binding with datasets of guest-hosts systems (N=75) and we observed that a good correlation is obtained between experimental data and the binding energies computed with MCR. We also compared the experimental data with an end-point calculation from equilibrium Monte Carlo calculations that allowed us to conclude that the lower-energy (lower-temperature) terms in the calculation are the most relevant to the estimation of the binding energies, resulting in similar correlations between MCR and MC data and the experimental values. On the other hand, the MCR method provides a reasonable view of the binding energy funnel, with possible connections with the ligand binding kinetics, as well. The codes developed for this analysis are publicly available on GitHub as a part of the LiBELa / MCLiBELa project ( https://github.com/alessandronascimento/LiBELa ). Metadynamics ; Monte Carlo method ; Thermodynamic integration ; Statistical physics ; Molecular dynamics ; Recursion (computer science) ; Chemistry ; Binding energy ; Computational chemistry ; Computer science ; Physics ; Mathematics ; Algorithm ; Quantum mechanics ; Statistics Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; Protein Structure and Dynamics
Authors: Marina A. Zhilinskaya | Е. А. Данилова
Source: IZVESTIYA VYSSHIKH UCHEBNYKH ZAVEDENIY KHIMIYA KHIMICHESKAYA TEKHNOLOGIYA (article, 2022-02-01)
Field: Chemistry — Organic Chemistry

SYNTHESIS OF NON-CYCLIC COMPOUNDS WITH A FRAGMENT OF 1,3-THIAZOLE В работе обсуждаются синтез и свойства нециклических двух- и трехзвенных продуктов, полученных взаимодействием соответствующего фталонитрила, который под действием метилата нартия в метаноле переводили в соответствующие 1,1-диметокси-3-иминоизоиндолины, и вводили в реакцию с 2,4-диамино-1,3-тиазол гидрохлоридом. Для получения последнего нами был усовершенствован предлагаемый метод. Известно, что большинство замещенных тиазолов получают взаимодействием соответствующих производных тиомочевины с хлорацетальдегидом в присутствии триэтиламина. Нами была апробирована данная методика, которая показала приемлемость этого метода для синтеза 2,4-диамино-1,3-тиазол гидрохлорида, который ранее получали из тиомочевины и хлорацетонитрила при нагревании в спирте. Использование 2,4-диамино-1,3-тиазол гидрохлорида как исходного продукта в синтезе нециклических двух- и трехзвенных соединений оправдано, т.к. 2,4-диамино-1,3-тиазол является нестабильным соединением и легко окисляется. Установлено, что присутствие кислорода воздуха способствует образованию дикетонов (2,4-бис(изоиндол-1-илиденамино-3-он)-1,3-тиазол). Для направленного синтеза 2,4-бис(3-иминоизоиндолин-1-илиденамино)-1,3-тиазола мы использовали аргонную «подушку». В результате было получено целевое соединение, которое выделяли из реакционной массы путем отгонки растворителя, промывали органическими растворителями. Переход на замещенный фталонитрил показал совершенно иные результаты. Была получена смесь двух продуктов, наличие которых установлено на основании данных масс-спектрометрии и электронных спектров поглощения. В отличие от предыдущих трехзвенных продуктов реакция шла в направлении образования трехзвенного продукта, который содержит два тиазольных и один трет-бутилзамещенный изоиндольный фрагменты. Провести хроматографическое разделение продуктов оказалось затруднительным из-за нестабильности полученных соединений. Строение синтезированных веществ было установлено на основании современных методов исследования: электронной, ИК, 1Н ЯМР спектроскопии, масс-спектрометрии. Методом TDDFT/B3LYP/6-31G нами выполнен расчет теоретического спектра поглощения оптимизированной молекулы 2,4-бис(3-иминоизоиндолин-1-илиденамино)-1,3-тиазола. Fragment (logic) ; Thiazole ; Time-dependent density functional theory ; Chemistry ; Stereochemistry ; Computational chemistry ; Mathematics ; Density functional theory ; Algorithm Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity
Authors: Viviana Andrea Morales Sánchez | Joohan Andrés Zuluaga García | Juan David Aguirre Salazar | Diomer Aristizábal Buitrago | Ana Marcela Ruiz Montilla
Source: Dialnet (Universidad de la Rioja) (article, 2022-01-01)
Field: Materials Science — Biomaterials

Biopolímeros y pectina:: Una revisión con Tree of Science Most of the materials used for food packaging are derived from petroleum, an alternative proposed in this review is the use of biodegradable materials that meet the growing demands of society for sustainability and environmental safety, and thus mitigate consumption. For this reason, proposals for different biopolymers have recently emerged to decrease the environmental impact of single-use plastics. This study presents a review of pectin as a biopolymer option for the solution of these environmental problems. A Scopus search was performed and the results were uploaded to the Tree of Science platform to identify articles, root, trunk and branches (subareas). The results show that, although the use of biopolymers has been limited due to poor mechanical and barrier properties, these properties can be improved by coating the outside with nanocomposites, such as nanoclays, which present a fine-grained crystalline material that is used as an additive for the manufacture of nanocomposites and significantly improve the properties of polymeric materials. Sustainability ; Biopolymer ; Coating ; Pectin ; Tree (set theory) ; Environmental impact assessment Nanocomposite Films for Food Packaging ; Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies
Authors: Peiye Li
Source: Frontiers in Computing and Intelligent Systems (article, 2022-10-25)
Field: Chemistry — Analytical Chemistry

Classification of black tea leaf water content based on hyperspectral imaging Withering is an important process step in black tea production, and the degree of withering directly determines the quality and flavor of black tea. Presently, the judgment of the degree of the withering of black tea at home and abroad mainly depends on the experience of tea masters, which is highly subjective and quickly leads to the inconsistent quality of finished tea. Based on these reasons, this thesis takes Ying Hong No. 9 black tea as the research object, extracts the spectral information of 50 tea leaves in four time periods using hyperspectral imaging technology and an image processing algorithm, and classifies them using a support vector machine, K-value proximity, random forest algorithm, and BP neural network, and the experimental results show that the spectral information of tea leaves in different periods has some variability. The BP neural network in The classification accuracy on the test set reached 73.3%, which was significantly better than the other three algorithms. Therefore, it is feasible to use hyperspectral imaging technology and related classification algorithms to nondestructively identify and classify the water content of black tea leaves during the tail-withering process. Hyperspectral imaging ; Black tea ; Artificial intelligence ; Chinese tea ; Artificial neural network ; Mathematics ; Pattern recognition (psychology) ; Computer science ; Geography ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Jia-hong LI | Qing-li ZHANG | Guihua Sun | Jin-yun GAO | Xiaofei Wang | Ren-qin DOU | Shou-jun DING | D.F. Zhang | Wen-peng LIU | Jian-qiao LUO | Yu Sun
Source: Chinese Journal of Luminescence (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Spectral Parameter Computation of Yb∶GdScO&lt;sub&gt;3&lt;/sub&gt; Crystal éç¨æææ³çé¿äºè´¨éä¼è¯çYbâ¶GdScO3æ¶ä½ï¼å¯¹æ ·åçXå°çº¿ç²æ«è¡å°è¿è¡äºRietveldç²¾ä¿®ï¼ç»åºäºæ¶ä½è®¡ç®å¯åº¦åæºææµåº¦ãYb3+çææååç³»æ°è®¡ç®ä¸º1.04ãå®¤æ¸©ä¸æµéäºå ¶å¸æ¶å è°±ãåå°å è°±åè§å å¯¿å½ï¼é¦æ¬¡è®¡ç®äºGdScO3åºè´¨ä¸­Yb3+çå¸æ¶ãåå°æ¯å­å¼ºåº¦ãè°±çº¿å¼ºåº¦ãè·è¿æ¦çãè½çº§å¯¿å½åç§¯ååå°æªé¢ç­éè¦å è°±åæ°ï¼å¯¹å ¶æ¿å æ§è½è¿è¡äºåæ­¥è¯ä¼°ãç»æè¡¨æï¼Ybâ¶GdScO3æäºè¾åº1 030 nmå1 060 nmæ³¢æ®µéè¿çæ¿å ï¼èå¯¹äº1 000 nméè¿çæ¿å è¾åºååªæå¨é«ç²å­æ°åè½¬æ åµä¸æå¯è½å®ç°ã Materials science ; Computation ; Crystal (programming language) ; Algorithm ; Computer science Solid-state spectroscopy and crystallography
Authors: Michelangelo Domina | Urvesh Patil | Matteo Cobelli | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2022-08-22)
Field: Materials Science — Materials Chemistry

Cluster expansion constructed over Jacobi-Legendre polynomials for accurate force fields We introduce a compact cluster expansion method, constructed over Jacobi and Legendre polynomials, to generate highly accurate and flexible machine-learning force fields. The constituent many-body contributions are separated, interpretable and adaptable to replicate the physical knowledge of the system. In fact, the flexibility introduced by the use of the Jacobi polynomials allows us to impose, in a natural way, constrains and symmetries to the cluster expansion. This has the effect of reducing the number of parameters needed for the fit and of enforcing desired behaviours of the potential. For instance, we show that our Jacobi-Legendre cluster expansion can be designed to generate potentials with a repulsive tail at short inter-atomic distances, without the need of imposing any external function. Our method is here continuously compared with available machine-learning potential schemes, such as the atomic cluster expansion and potentials built over the bispectrum. As an example we construct a Jacobi-Legendre potential for carbon, by training a slim and accurate model capable of describing crystalline graphite and diamond, as well as liquid and amorphous elemental carbon. Legendre polynomials ; Jacobi polynomials ; Cluster (spacecraft) ; Homogeneous space ; Cluster expansion ; Flexibility (engineering) ; Legendre wavelet ; Mathematics ; Applied mathematics ; Orthogonal polynomials ; Computer science ; Mathematical analysis ; Physics ; Geometry ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Quantum, superfluid, helium dynamics