jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kenzo Yahata | Shin Yoshioka | Shuhei Hori | Shu Sakurai | Yuki Kaneko | Kai Hasegawa | Shuji Akai
Source: Chemical and Pharmaceutical Bulletin (article, 2020-02-18)
Field: Chemistry — Organic Chemistry

One-Pot Formal Dehydrogenative Ketone Synthesis from Aldehydes and Non-activated Hydrocarbons Ketones are a fundamental functionality found throughout a range of natural and synthetic compounds, making their synthesis essential throughout the chemical disciplines. Herein, we describe a one-pot synthesis of ketones via decatungstate-mediated formal dehydrogenative coupling between aldehydes and non-activated hydrocarbons. A variety of substituted benzaldehydes and cycloalkanes could be used in the optimized reaction to produce the desired ketones in moderate yields. The decatungstate photocatalyst functions in two reactions in this synthesis, catalyzing both the coupling and oxidation steps of the process. Notably, the reaction displays both high atom economy and sustainability, as it uses light and oxygen as key energy sources. Chemistry ; Ketone ; Atom economy ; Formal synthesis ; Aldehyde ; Organic chemistry ; Chemical synthesis ; Photocatalysis ; Combinatorial chemistry ; Catalysis Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques
Authors: Іryna Drapak | Liliya Logoyda | Nataliya Shtoyko | Marta Sulyma | Taras Chaban | V. S. Matiychuk
Source: Biopolymers and Cell (article, 2020-12-31)
Field: Chemistry — Organic Chemistry

Synthesis and antimicrobial activity of 5-R-benzyl-2-(arylidenehydrazono)thiazolidin-4-ones Aim. Study of the synthesis and antimicrobial activity of a series of 5-R-benzyl-2-(arylidenehydrazono)thiazolidin-4-ones. Methods. Organic synthesis, analytical and spectral methods, assay of antimicrobial activity. Results. 5-R-benzyl-2-(arylidenehydrazono)thiazolidin-4-ones 6a-k and 7a,b have been synthesized in good yields via reaction of 2-bromo-3-arylpropanoates 3a-j with thiosemicarbazones aromatic aldehydes and furfuraldehyde. Their structures of synthesized compounds were confirmed by 1 H NMR spectroscopy and elemental analysis. The synthesized compounds have been evaluated for antimicrobial activity against five bacterial strains (Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Staphylococcus aureus) and two fungal strains (Candida albicans and Cryptococcus neoformans). According to the antimicrobial activity results, the tested compounds, 6b, 6h-k and 7a were found to be active against Staphylococcus aureus whereas 6a, 6d, 6f, 6g, 7b -towards Cryptococcus neoformans. The relationship between the nature of substituents in aromatic moieties and [the] activity level was established. Conclusions. A series of new 5-R-benzyl-2-(arylidenehydrazono)thiazolidin-4-ones were synthesized. The compounds with antimicrobal activity have been found. K e y w o r d s: organic synthesis, arylation, 5-R-benzyl-2-(arylidenehydrazono)thiazolidin-4-ones, antimicrobal activity. Antimicrobial ; Chemistry ; Combinatorial chemistry ; Medicinal chemistry ; Stereochemistry ; Organic chemistry Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactivity of Sulfur-Containing Compounds
Authors: Matthew Witman | Sanliang Ling | Vitalie Stavila | Pavithra Wijeratne | Hiroyasu Furukawa | Mark D. Allendorf
Source: ChemRxiv (preprint, 2020-08-27)
Field: Chemistry — Inorganic Chemistry

Design Principles for the Ultimate Gas Deliverable Capacity Material: Nonporous to Porous Deformations Without Volume Change Understanding the fundamental limits of gas deliverable capacity in porous materials is of critical importance as it informs whether technical targets (e.g., for on-board vehicular storage) are feasible. High-throughput screening studies of rigid materials, for example, have shown they are not able to achieve the original ARPA-E methane storage targets, yet an interesting question remains: what is the upper limit of deliverable capacity in flexible materials? In this work we develop a statistical adsorption model that specifically probes the limit of deliverable capacity in intrinsically flexible materials. The resulting adsorption thermodynamics indicate that a perfectly engineered, intrinsically flexible nanoporous material could achieve higher methane deliverable capacity than the best benchmark systems known to date with little to no total volume change. Density functional theory and grand canonical Monte Carlo simulations identify a known metal-organic framework (MOF) that validates key features of the model. Therefore, this work (1) motivates a continued, extensive effort to rationally design a porous material analogous to the adsorption model and (2) calls for continued discovery of additional high deliverable capacity materials that remain hidden from rigid structure screening studies due to nominal non-porosity. Deliverable ; Nanoporous ; Porosity ; Porous medium ; Benchmark (surveying) ; Volume (thermodynamics) ; Work (physics) ; Computer science ; Materials science ; Nanotechnology ; Engineering ; Mechanical engineering ; Physics ; Systems engineering ; Thermodynamics ; Geology Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Catalytic Processes in Materials Science
Authors: Igor Karlovits
Source:  (article, 2020-11-12)
Field: Materials Science — Biomaterials

Lignocellulosic bio-refinery downstream products in future packaging applications The concept of efficient utilisation of renewable bio-based materials (biomass feedstock) is the driving force in the green transformation to a more sustainable and circular society. Biorefineries or biochemical platforms convert and utilise different sources of biomass into fuels and other beneficial derivates like fibres and other bio-based chemicals. These can be used as building blocks for many potentially useful applications. In this review, we shall describe the current state of the art and trends in the conversion of lignocellulosic feedstock into materials which can be primarily used in packaging applications. The three main constituents (cellulose, hemicellulose and lignin) are being re-engineered into new products with higher added value. The main goal of all these downstream products is that they do not compete with animal feed and food applications. The main downstream products of different kind of transformations are different natural fibres which can be further processed into micro or nano fibrillated state and used for a broad application of fields from ink, adhesive and packaging materials. Also, fibres and its derivates can be bonded successfully into bio-composites or fibre-based foams applications for the protective packaging applications. Hemicellulose, as a second most abundant component, has been researched for applications in adhesives and paper and paperboard coatings. Lignin which is currently utilised as an energy source for the paper industry, has been recently actively researched. Lignin-based biopolymers have a potential to be used in many different applications from additives in the barrier coatings on the packaging to active packaging and even as lignin-based foams. All these applications are currently in the development stages and cover niche market segments, but are expected to grow and to be used in future markets. Hemicellulose ; Lignin ; Raw material ; Paperboard ; Materials science ; Biomass (ecology) ; Cellulose ; Renewable energy ; Lignocellulosic biomass ; Process engineering ; Pulp and paper industry ; Waste management ; Composite material ; Engineering ; Chemistry Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Biofuel production and bioconversion
Authors: Eric Schwenker | Weixin Jiang | Trevor Spreadbury | S. O’Brien | Nicola Ferrier | Oliver Cossairt | Maria K. Y. Chan
Source: Microscopy and Microanalysis (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Constructing Self-Labeled Materials Imaging Datasets from Open Access Scientific Journals with EXSCLAIM! Due to recent improvements in image resolution and acquisition speeds, materials microscopy is experiencing an explosion in imaging data. Yet, despite the volume of images generated, the overall accessibility landscape is highly fragmented, as researchers who do release images to the public, often only do so as snapshots of their larger private dataset in context of scientific journal publications. The effort to automatically consolidate images and descriptive information from web-based platforms has garnered broad attention from the computer vision, language technologies, and chemistry/materials informatics communities [1]-[4]. However, these methods are problematic for scientific figures because over 30% of figures are compound in nature [5], and it is the individual images themselves, paired with relevant context, that are necessary for construction a proper labeled dataset. To this end, we outline the design of a software pipeline for the automatic EXtraction, Separation, and Caption-based natural Language Annotation of IMages from scientific figures (EXSCLAIM!). Successful consolidation of materials imaging across literature sources will enhance navigation and searchability of materials microscopy images for both novice and experienced researchers, as well as establish the framework necessary for users to search by images, text, or some combination of both. Computer science ; Data science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; Radiomics and Machine Learning in Medical Imaging ; Research Data Management Practices
Authors: Khor Cheng Seong | Nur Nelly Sofia Nurazrin | Fatimah Mohd Hanafi | Sarat C. Dass | Shahrul Azman Zainal Abidin | Farah Syamim Anuar
Source: ASM Science Journal (article, 2020-06-24)
Field: Chemistry — Analytical Chemistry

Correlation Model Development for Saybolt Colour of Condensates and Light Crude Oils Saybolt colour or number is a measured physical property of petroleum condensates and light crude oils which can be used as a quality indicator. As an alternative approach to the laboratory-based colour measurement method, this work aims to determine the influential physical properties in predicting Saybolt colour by applying a regression modelling approach. Data available on Saybolt colour and several physical properties are obtained from assay reports for condensates and light crude oils of Malaysian oil and gas fields. Other unavailable but potentially influential properties are estimated using a commercial process simulation software, iCON. The properties identified as explanatory variables in this study are refractive index, kinematic viscosity at 40C, and characterization factor. This machine learning problem gives rise to applying multiple linear regression techniques based on a backward elimination approach in developing a correlation to predict Saybolt colour using the identified key properties of characterization factor, kinematic viscosity at 40C, and refractive index. Physical property ; Viscosity ; Linear regression ; Biological system ; Kinematics ; Regression analysis ; Light crude oil ; Refractive index ; Crude oil ; Characterization (materials science) ; Process engineering ; Mathematics ; Computer science ; Petroleum engineering ; Statistics ; Materials science ; Chemistry ; Optics ; Engineering ; Physics ; Nanotechnology ; Composite material ; Organic chemistry Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Cultural Heritage Materials Analysis
Authors: Anton L fgren | Joaquín Gomis‐Fons | Niklas Andersson | Bernt Nilsson | Lotta Berghard | Chistine Lagerquist H gglund
Source:  (preprint, 2020-09-16)
Field: Chemistry — Analytical Chemistry

An integrated continuous downstream process with real-time control: A case study with periodic countercurrent chromatography and continuous virus inactivation Integrated continuous downstream processes with process analytical technology offer a promising opportunity to reduce production costs and increase process flexibility and adaptability. In this case study, an integrated continuous process was used to purify a recombinant protein on laboratory scale in a two-system setup that can be used as a general downstream setup offering multi-product and multi-purpose manufacturing capabilities. The process consisted of continuous solvent/detergent virus inactivation followed by periodic countercurrent chromatography in the capture step, and a final chromatographic polishing step. A real-time controller was implemented to ensure stable operation by adapting the downstream process to external changes. A concentration disturbance was introduced to test the controller. After the disturbance was applied, the product output recovered within 6 hours, showing the effectiveness of the controller. In a comparison of the process with and without the controller, the product output per cycle increased by 27%, the resin utilization increased from 71.4% to 87.9%, and the specific buffer consumption was decreased by 21% with the controller, while maintaining a similar yield and purity as in the process without the disturbance. In addition, the integrated continuous process outperformed the batch process, increasing the productivity by 95% and the yield by 28%. Countercurrent exchange ; Controller (irrigation) ; Process (computing) ; Downstream processing ; Process engineering ; Process control ; Downstream (manufacturing) ; Continuous production ; Flexibility (engineering) ; Batch processing ; Computer science ; Chromatography ; Control theory (sociology) ; Chemistry ; Engineering ; Mathematics ; Control (management) ; Chemical engineering ; Operations management ; Physics Chromatography in Natural Products ; Pharmacological Effects of Natural Compounds ; Food Quality and Safety Studies
Authors: E. A. Efimenko | Mukhtar Bekkiev | D. R. Mayilyan | Антон Чепурненко
Source: Herald of Dagestan State Technical University Technical Sciences (article, 2020-04-21)
Field: Materials Science — Materials Chemistry

DETERMINATION OF THE OPTIMAL DISTRIBUTION OF SUPPORTS IN THE FLOOR SLABS OF IN-DUSTRIAL BUILDINGS USING STOCHASTIC METHODS Abstract. Aim. The purpose of the study is to determine the optimal location of supports used in the floor slab of an industrial building. Method. In order to determine the optimal arrangement of the columns, a Monte Carlo algorithm was used in combination with the finite element method. The calculation was carried out on the basis of the theory of elastic thin plates. Results. The article presents a solution to the problem of determining the optimal location of a given number of point-supports of a floor slab n from the condition of minimum objective function. For the objective function, the maximum deflection of the slab, the potential energy of deformation and the flow rate of reinforcement were selected as variables. The selection of reinforcement was carried out in accordance with current generally-accepted standards for the design of reinforced concrete structures. The calcu-lations were performed using a program developed by the authors in the MATLAB computing environment. The results are given for n = 3,4,5. The algorithm, which has been modified for a large num-ber of supports n, is presented alongside a comparison of the basic and modified algorithm with n = 25. The possibility of a significant reduction in plate deformations with an irregular arrangement of supports compared to a regular distribution is shown. Conclusion. A method is proposed for finding the rational locations of point supports for a floor slab for a given quantity from the condition of min-imum deflection, potential strain energy and consumption of reinforcement materials based on the Monte Carlo method. This technique is suitable for arbitrary slab configurations and arbitrary loads. A modification of the algorithm is presented that is suitable for a large number of supports. The test example shows that the maximum deflection can be reduced by 42% when using an irregular support configuration compared to regular column spacing. In the considered examples, the position of all the supports was previously considered unknown, but the developed algorithm easily allows for stationary supports, whose position does not change. Slab ; Deflection (physics) ; MATLAB ; Monte Carlo method ; Structural engineering ; Computer science ; Reinforcement ; Energy consumption ; Finite element method ; Mathematical optimization ; Algorithm ; Mathematics ; Engineering ; Physics ; Statistics Structural mechanics and materials ; Geodetic Measurements and Engineering Structures ; Construction Management and Sustainability
Authors: Facultatea de Inginerie Electrică, Universitatea POLITEHNICA din București | Ana-Maria Marilena DUMITRU | Cristina Mihaela Gheorghe | Leonard Melcescu
Source: ACTUALITĂŢI ŞI PERSPECTIVE ÎN DOMENIUL MAŞINILOR ELECTRICE (ELECTRIC MACHINES MATERIALS AND DRIVES - PRESENT AND TRENDS) (article, 2020-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

CONSTRUCTION OPTIMIZATION OF AN ELECTRICAL POWER TRANSFORMER USING THE RESPONSE SURFACE METHOD Transformatorul electric de putere reprezint una dintre cele mai importante i scumpe componente ale sistemului energetic. Din cauza fluctuaiilor preului materiilor prime, profitul productorilor acestor echipamente depinde de identificarea variantei constructive a transformatorului pentru care raportul dintre masa miezului feromagnetic i masa materialului conductor minimizeaz costul de producie, cu luarea n considerare a condiiilor de gabarit i a variaiei pe pia a preului celor dou categorii de materiale, tabl electrotehnic i material conductor. n acest context, lucrarea propune o metodologie de analiz constructiv a transformatoarelor electrice de putere n vederea identificrii unei variante constructive optime. Metoda suprafeei de rspuns (Response Surface Methodology, RSM) este aplicat pentru determinarea ansamblului de soluii (variante constructive) sub forma unei suprafee a variaiei raportului MFe/Mcond n funcie de dou dintre dimensiunile de gabarit ale transformatorului, nlime (Hm) respectiv lungime (Lm), unde MFe este masa miezului feromagnetic iar Mcond masa nfurrilor. Exemplificarea aplicrii metodologiei propuse este realizat pentru un transformator electric trifazat de putere, cu nfurri realizate din aluminiu i rcire n ulei, cu puterea aparent nominal Sn=630kVA. Humanities ; Physics ; Art Magnetic Properties and Applications ; Induction Heating and Inverter Technology ; Engineering Applied Research
Authors: James Hook | Calum Hand | Emma Whitfield
Source: arXiv (Cornell University) (preprint, 2020-09-11)
Field: Materials Science — Materials Chemistry

Bayesian Screening: Multi-test Bayesian Optimization Applied to in silico Material Screening We present new multi-test Bayesian optimization models and algorithms for use in large scale material screening applications. Our screening problems are designed around two tests, one expensive and one cheap. This paper differs from other recent work on multi-test Bayesian optimization through use of a flexible model that allows for complex, non-linear relationships between the cheap and expensive test scores. This additional modeling flexibility is essential in the material screening applications which we describe. We demonstrate the power of our new algorithms on a family of synthetic toy problems as well as on real data from two large scale screening studies. Bayesian optimization ; Flexibility (engineering) ; Computer science ; Bayesian probability ; Scale (ratio) ; Machine learning ; Data mining ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Machine Learning and Data Classification
Authors: Anh Tran | John Mitchell | Laura Swiler | Timothy Wildey
Source: Acta Materialia (preprint, 2020-05-18)
Field: Materials Science — Materials Chemistry

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics Context (archaeology) ; Microstructure ; Linkage (software) ; Computer science ; Bayesian optimization ; Process (computing) ; Materials informatics ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advancements in Photolithography Techniques
Authors: Ignacio Fdez. Galván | Gerardo Raggi | Roland Lindh
Source: ChemRxiv (preprint, 2020-11-05)
Field: Materials Science — Materials Chemistry

Restricted-Variance Constrained, Reaction Path, and Transition State Molecular Optimizations Using Gradient-Enhanced Kriging Gaussian process regression has recently been explored as an alternative to standard surrogate models in molecular equilibrium geometry optimization. In particular, the gradient-enhanced Kriging approach in association with internal coordinates, restricted-variance optimization, and an efficient and fast estimate of hyperparameters has demonstrated performance on par or better than standard methods. In this report, we extend the approach to constrained optimizations and transition states and benchmark it for a set of reactions. We compare the performance of the new developed method with the standard techniques in the location of transition states and in constrained optimizations, both isolated and in the context of reaction path computation. The results show that the method outperforms the current standard in efficiency as well as in robustness.&lt;br&gt; Kriging ; Robustness (evolution) ; Computation ; Mathematical optimization ; Gaussian ; Benchmark (surveying) ; Computer science ; Path (computing) ; Variance (accounting) ; Gaussian process ; Context (archaeology) ; Algorithm ; Mathematics ; Chemistry ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; Chemistry and Chemical Engineering
Authors: Martin F. Luna | Ernesto Martínez
Source: Latin American Applied Research - An international journal (article, 2020-02-21)
Field: Materials Science — Materials Chemistry

BAYESIAN OPTIMIZATION OF CRYSTALLIZATION PROCESSES TO GUARANTEE END-USE PRODUCT PROPERTIES For pharmaceutical solid products, the issue of reproducibly obtaining their desired end-use properties depending on crystal size and form is the main problem to be addressed and solved in process development. Lacking a reliable first-principles model of a crystallization process, a Bayesian optimization algorithm is proposed. On this basis, a short sequence of experimental runs for pinpointing operating conditions that maximize the probability of successfully complying with end-use product properties is defined. Bayesian optimization can take advantage of the full information provided by the sequence of experiments made using a probabilistic model of the probability of success based on a one-class classification method. The proposed algorithm’s performance is tested in silico using the crystallization and formulation of an API product where success is about fulfilling a dissolution profile as required by the FDA. Results obtained demonstrate that the sequence of generated experiments allows pinpointing operating conditions for reproducible quality. Sequence (biology) ; Probabilistic logic ; Bayesian probability ; Computer science ; Process (computing) ; Product (mathematics) ; Bayesian optimization ; Crystallization ; Basis (linear algebra) ; Mathematical optimization ; Algorithm ; Mathematics ; Machine learning ; Engineering ; Artificial intelligence Crystallization and Solubility Studies ; Process Optimization and Integration ; Computational Drug Discovery Methods