jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Michelle Hechenbichler | André Laschewsky | Michael Gradzielski
Source: Colloid & Polymer Science (article, 2020-08-10)
Field: Chemistry — Organic Chemistry

Poly(N,N-bis(2-methoxyethyl)acrylamide), a thermoresponsive non-ionic polymer combining the amide and the ethyleneglycolether motifs Abstract Poly( N , N -bis(2-methoxyethyl)acrylamide) (PbMOEAm) featuring two classical chemical motifs from non-ionic water-soluble polymers, namely, the amide and ethyleneglycolether moieties, was synthesized by reversible addition fragmentation transfer (RAFT) polymerization. This tertiary polyacrylamide is thermoresponsive exhibiting a lower critical solution temperature (LCST)–type phase transition. A series of homo- and block copolymers with varying molar masses but low dispersities and different end groups were prepared. Their thermoresponsive behavior in aqueous solution was analyzed via turbidimetry and dynamic light scattering (DLS). The cloud points (CP) increased with increasing molar masses, converging to 46 °C for 1 wt% solutions. This rise is attributed to the polymers’ hydrophobic end groups incorporated via the RAFT agents. When a surfactant-like strongly hydrophobic end group was attached using a functional RAFT agent, CP was lowered to 42 °C, i.e., closer to human body temperature. Also, the effect of added salts, in particular, the role of the Hofmeister series, on the phase transition of PbMOEAm was investigated, exemplified for the kosmotropic fluoride, intermediate chloride, and chaotropic thiocyanate anions. A pronounced shift of the cloud point of about 10 °C to lower or higher temperatures was observed for 0.2 M fluoride and thiocyanate, respectively. When PbMOEAm was attached to a long hydrophilic block of poly( N , N -dimethylacrylamide) (PDMAm), the cloud points of these block copolymers were strongly shifted towards higher temperatures. While no phase transition was observed for PDMAm-b-pbMOEAm with short thermoresponsive blocks, block copolymers with about equally sized PbMOEAm and PDMAm blocks underwent the coil-to-globule transition around 60 °C. Lower critical solution temperature ; Polymer chemistry ; Chemistry ; Copolymer ; Cloud point ; Thiocyanate ; Raft ; Polymer ; Chaotropic agent ; Hofmeister series ; Polymerization ; Dynamic light scattering ; Upper critical solution temperature ; Aqueous solution ; Chemical engineering ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Surfactants and Colloidal Systems ; Hydrogels: synthesis, properties, applications
Authors: Hakeem K. Henry | Brandon Johnston | Darrin Liau | Emily Sahadeo | Sang Bok Lee
Source: Journal of The Electrochemical Society (article, 2020-01-08)
Field: Materials Science — Polymers and Plastics

Dual Effect of Structure and Hydration on Magnesium-Ion Insertion into Electrodeposited V <sub>2</sub> O <sub>5</sub> Thin Films In recent years there has been some discrepancy about whether crystalline or amorphous V 2 O 5 is the superior cathode material for magnesium-ion batteries, with many publications suggesting that amorphous V 2 O 5 more readily stores magnesium ions. In this work, we report the systematic investigation of magnesium ion storage in crystalline and amorphous V 2 O 5 electrodeposited thin films. Our results indicate that the electrochemical performance of V 2 O 5 thin films is primarily impacted by the presence of adsorbed water. This study finds that the adsorbed water left over from aqueous electrodeposition is mostly responsible for the observed improved performance of amorphous V 2 O 5 thin films, thereby indicating that the drying conditions, rather than the crystal structure, play a direct role in enhancing the electrochemical performance. We propose an explanation for this observation in that the amorphous thin film has much larger water content, leading to increased interlayer spacing within the disordered structure and possible charge shielding for magnesium-ion storage. Ultimately, this study demonstrates the importance of considering the effect of adsorbed water, especially when comparing the electrochemical performance of amorphous and crystalline V 2 O 5 synthesized from wet electrodeposition techniques. Amorphous solid ; Materials science ; Electrochemistry ; Thin film ; Magnesium ; Chemical engineering ; Aqueous solution ; Ion ; Adsorption ; Inorganic chemistry ; Nanotechnology ; Metallurgy ; Crystallography ; Chemistry ; Organic chemistry ; Physical chemistry ; Electrode Transition Metal Oxide Nanomaterials ; Advancements in Battery Materials ; Supercapacitor Materials and Fabrication
Authors: Alexander Mohmeyer | Malte Schäfer | Andreas Schaate | S. Locmelis | Andreas Schneider | Peter Behrens
Source: Chemistry - A European Journal (article, 2020-02-04)
Field: Chemistry — Inorganic Chemistry

Inside/Outside: Post‐Synthetic Modification of the Zr‐Benzophenonedicarboxylate Metal–Organic Framework The Zr-based metal-organic framework, Zr-bzpdc-MOF, contains the photoreactive linker molecule benzophenone-4,4'-dicarboxylate (bzpdc) which imparts the possibility for photochemical post-synthetic modification. Upon irradiation with UV light, the keto group of the benzophenone moiety will react with nearly every C-H bond-containing molecule. Within this paper, we further explore the photochemical reactivity of the Zr-bzpdc-MOF, especially with regard to which restrictions govern internal versus external reactions. We show that apart from reactions with C-H bond-containing molecules, the MOF reacts also with water. By studying the reactivity versus linear alcohols we find a clear delineation in that shorter alcohol molecules (up to butanol as a borderline case) react with photoexcited keto groups throughout the whole crystals whereas longer ones react only with surface-standing keto groups. In addition, we show that with the alkanes n-butane to n-octane, the reaction is restricted to the outer surface. We hypothesize that the reactivity of the Zr-bzpdc-MOF versus different reagents depends on the accessibility of the pore system which in turn depends mainly on the size of the reagents and on their polarity. The possibility to direct the post-synthetic modification of the Zr-bzpdc-MOF (selective modification of the whole pore system versus surface modification) gives additional degrees of freedom in the design of this metal-organic framework for shaping and for applications. Benzophenone ; Reactivity (psychology) ; Surface modification ; Moiety ; Chemistry ; Reagent ; Molecule ; Photochemistry ; Linker ; Octane ; Metal-organic framework ; Combinatorial chemistry ; Organic chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes
Authors: Corina Stoian | Marian Olaru | Teodor A. Cucuiet | Krisztina T. Kegyes | Alexandru Sava | Alexey Y. Timoshkin | Ciprian I. Raţ | Jens Beckmann
Source: Chemistry - A European Journal (article, 2020-12-25)
Field: Chemistry — Organic Chemistry

Bulky Polyfluorinated Terphenyldiphenylboranes: Water Tolerant Lewis Acids Protocols for the synthesis of the bulky polyfluorinated triarylboranes 2,6-(C<sub>6</sub> F<sub>5</sub> )<sub>2</sub> C<sub>6</sub> F<sub>3</sub> B(C<sub>6</sub> F<sub>5</sub> )<sub>2</sub> (1), 2,6-(C<sub>6</sub> F<sub>5</sub> )<sub>2</sub> C<sub>6</sub> F<sub>3</sub> B[3,5-(CF<sub>3</sub> )<sub>2</sub> C<sub>6</sub> H<sub>3</sub> ] (2), 2,4,6-(C<sub>6</sub> F<sub>5</sub> )<sub>3</sub> C<sub>6</sub> H<sub>2</sub> B(C<sub>6</sub> F<sub>5</sub> )<sub>2</sub> (3), 2,4,6-(C<sub>6</sub> F<sub>5</sub> )<sub>3</sub> C<sub>6</sub> H<sub>2</sub> B[3,5-(CF<sub>3</sub> )<sub>2</sub> C<sub>6</sub> H<sub>3</sub> ] (4) were developed. All boranes are water tolerant and according to the Gutmann-Beckett method, 1-3 display Lewis acidities larger than that of the prominent B(C<sub>6</sub> F<sub>5</sub> )<sub>3</sub> . Lewis acids and bases ; Boranes ; Chemistry ; Stereochemistry ; Medicinal chemistry ; Catalysis ; Boron ; Organic chemistry Organoboron and organosilicon chemistry ; Crystallography and molecular interactions ; Fluorine in Organic Chemistry
Authors: Valentin Rougier | Julien Cellier | Moussa Gomina | Joël Bréard
Source: Journal of Colloid and Interface Science (article, 2020-09-15)
Field: Materials Science — Surfaces, Coatings and Films

Slip transition in dynamic wetting for a generalized Navier boundary condition Wetting ; Slip (aerodynamics) ; Contact angle ; Mechanics ; Wetting transition ; Materials science ; Boundary value problem ; Capillary action ; Thermodynamics ; Physics ; Composite material ; Mathematics ; Mathematical analysis Surface Modification and Superhydrophobicity ; Fluid Dynamics and Thin Films ; Block Copolymer Self-Assembly
Authors: Reza Mohammadzaheri | Mehdi Ansari | Maryam Kazemipour | Kambiz Soltaninejad
Source: Archives of Industrial Hygiene and Toxicology (article, 2020-03-01)
Field: Chemistry — Analytical Chemistry

Experimental central composite design-based dispersive liquid-liquid microextraction for HPLC-DAD determination of diazinon in human urine samples: method development and validation Diazinon poisoning is an important issue in occupational, clinical, and forensic toxicology. While sensitive and specific enough to analyse diazinon in biological samples, current methods are time-consuming and too expensive for routine analysis. The aim of this study was therefore to design and validate a simple dispersive liquid-liquid microextraction (DLLME) for the preparation of urine samples to be analysed for diazinon with high performance liquid chromatography with diode-array detector (HPLC-DAD) to establish diazinon exposure and poisoning. To do that, we first identified critical parameters (type and volume of extraction and disperser solvents, pH, surfactant, and salt concentrations) in preliminary experiments and then used central composite design to determine the best experimental conditions for DLLME-HPLC-DAD. For DLLME they were 800 µL of methanol (disperser solvent) and 310 µL of toluene (extraction solvent) injected to the urine sample rapidly via a syringe. The sample was injected into a HPLC-DAD (C18 column, 250×4.6 mm, 5 μm), and the mobile phase was a mixture of acetonitrile and buffer (63:37 v/v, pH 3.2; flow rate: 1 mL/ min). Standard calibration curves for diazinon were linear with the concentration range of 0.5-4 µg/mL, yielding a regression equation Y=0.254X+0.006 with a correlation coefficient of 0.993. The limit of detection and limit of quantification for diazinon were 0.15 µg/mL and 0.45 µg/mL, respectively. The proposed method was accurate, precise, sensitive, and linear over a wide range of diazinon concentrations in urine samples. This method can be employed for diazinon analysis in routine clinical and forensic toxicology settings. Diazinon ; Chromatography ; Disperser ; Detection limit ; High-performance liquid chromatography ; Extraction (chemistry) ; Central composite design ; Chemistry ; Calibration curve ; Solvent ; Response surface methodology ; Materials science ; Pesticide Analytical chemistry methods development ; Pesticide Exposure and Toxicity ; Pesticide Residue Analysis and Safety
Authors: Edward L. Pang | Peter Mahler Larsen | Christopher A. Schuh
Source: Journal of Applied Crystallography (article, 2020-07-24)
Field: Materials Science — Materials Chemistry

Resolving pseudosymmetry in tetragonal ZrO<sub>2</sub> using electron backscatter diffraction with a modified dictionary indexing approach Resolving pseudosymmetry has long presented a challenge for electron backscatter diffraction and has been notoriously challenging in the case of tetragonal ZrO 2 in particular. In this work, a method is proposed to resolve pseudosymmetry by building upon the dictionary indexing method and augmenting it with the application of global optimization to fit accurate pattern centers, clustering of the Hough-indexed orientations to focus the dictionary in orientation space and interpolation to improve the accuracy of the indexed solution. The proposed method is demonstrated to resolve pseudosymmetry with 100% accuracy in simulated patterns of tetragonal ZrO 2 , even with high degrees of binning and noise. The method is then used to index an experimental data set, which confirms its ability to efficiently and accurately resolve pseudosymmetry in these materials. The present method can be applied to resolve pseudosymmetry in a wide range of materials, possibly even some more challenging than tetragonal ZrO 2 . Source code for this implementation is available online. Tetragonal crystal system ; Electron backscatter diffraction ; Search engine indexing ; Diffraction ; Interpolation (computer graphics) ; Electron diffraction ; Computer science ; Crystallography ; Materials science ; Physics ; Optics ; Crystal structure ; Chemistry ; Artificial intelligence ; Image (mathematics) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: Chin-Tan Lee | Shih‐Cheng Horng
Source: Energies (article, 2020-05-17)
Field: Materials Science — Materials Chemistry

Abnormality Detection of Cast-Resin Transformers Using the Fuzzy Logic Clustering Decision Tree Failures of cast-resin transformers not only reduce the reliability of power systems, but also have great effects on power quality. Partial discharges (PD) occurring in epoxy resin insulators of high-voltage electrical equipment will result in harmful effects on insulation and can cause power system blackouts. Pattern recognition of PD is a useful tool for improving the reliability of high-voltage electrical equipment. In this work, a fuzzy logic clustering decision tree (FLCDT) is proposed to diagnose the PD concerning the abnormal defects of cast-resin transformers. The FLCDT integrates a hierarchical clustering scheme with the decision tree. The hierarchical clustering scheme uses splitting attributes to divide the data set into suspended clusters according to separation matrices. The hierarchical clustering scheme is regarded as a preprocessing stage for classification using a decision tree. The whole data set is divided by the hierarchical clustering scheme into some suspended clusters, and the patterns in each suspended cluster are classified by the decision tree. The FLCDT was successfully adopted to classify the aberrant PD of cast-resin transformers. Classification results of FLCDT were compared with two software packages, See5 and CART. The FLCDT performed much better than the CART and See5 in terms of classification precisions. Cluster analysis ; Data mining ; Hierarchical clustering ; Decision tree ; Computer science ; Partial discharge ; Fuzzy clustering ; Fuzzy logic ; Artificial intelligence ; Pattern recognition (psychology) ; Reliability engineering ; Voltage ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Hatice Yaprak Aydin | Suat Altun
Source: Bartın Orman Fakültesi Dergisi (article, 2020-09-04)
Field: Materials Science — Polymers and Plastics

Odun Kökenli Malzemelerin Takviyesi ile Oluşturulan Polilaktik Asit Matrisli Kompozitler Bu derleme çalışmasında, son 10 yılda odun kökenli doğal liflerin polilaktik asit matrisine takviyesi ile oluşturulan kompozit malzemeler üzerine yapılan çalışmalar incelenmiştir. Odun kökenli doğal lifler, güçlü, hafif ve düşük ağırlıkta, yüksek özgül mukavemete sahip, ucuz, çevre dostu ve doğada biyolojik olarak parçalanabilir özelliklerde olduğundan polimer matrisli kompozitlerde kullanımı yaygındır. Genel olarak lifler, odun, sisal, kenevir, keten, kenaf ve bambu gibi bitki kaynaklı doğal malzemelerden elde edilmektedir. Odun unu, odun lifi, selüloz lifi, mikrokristalin selüloz ve selüloz nano parçacıklar gibi elde edilen bu malzemeler, polilaktik asit polimer matrisine takviye edilerek, mekanik özelliklerinin geliştirilmesi sağlanmaktadır. Bu çalışmada, polilaktik asit polimer matrisine odun kökenli malzemelerin takviyesi ile üretilen kompozit malzemelerin mekanik özellikleri, üretim teknikleri, takviye elamanlarının polilaktik asit matrisi üzerine etkileri, ilave edilen takviye oranları, ara yüz malzemelerin etkileri üzerine yapılmış çalışmalar incelenerek, elde edilen tüm bulgular ve sonuçlar özetlenmiştir. Nuclear chemistry ; Chemistry ; Physics Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Advanced Cellulose Research Studies
Authors: Punyaslok Pattnaik | Shampa Raghunathan | Tarun Kalluri | Prabhakar Bhimalapuram | C. V. Jawahar | U. Deva Priyakumar
Source: ChemRxiv (preprint, 2020-05-08)
Field: Materials Science — Materials Chemistry

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations &lt;p&gt;The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.&lt;/p&gt; Molecular dynamics ; Force field (fiction) ; Artificial neural network ; Representation (politics) ; Computer science ; Statistical physics ; Field (mathematics) ; Work (physics) ; Density functional theory ; Quantum ; Ab initio ; Artificial intelligence ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics
Authors: Hirofumi Hazama | Daisuke Murai | Naoyuki Nagasako | Masaki Hasegawa | Nobuhiro Ogihara
Source: Advanced Materials Technologies (article, 2020-06-26)
Field: Chemistry — Inorganic Chemistry

Optimization of Material Composition of Li‐Intercalated Metal–Organic Framework Electrodes Using a Combination of Experiments and Machine Learning of X‐Ray Diffraction Patterns Abstract Although individual Li‐intercalated metal–organic frameworks (iMOFs) exhibit properties that make them suitable for use as electrodes in Li‐ion batteries, none of these materials have the ideal combination of electrochemical characteristics. Using the high specific capacity and low polarization of 2,6‐naphthalene dicarboxylate dilithium as a basis for improvement, 26 iMOFs from different combinations of aqueous terephthalic, 2,6‐naphthalene dicarboxylic, and 4,4′‐biphenyl dicarboxylic‐based solutions with Li source are synthesized to obtain a material with the optimal electrochemical performances. For a more comprehensive search of the optimal ratio of raw material solutions, a machine learning‐based prediction model is constructed, using a combination of X‐ray diffraction (XRD) patterns and the experimentally derived characteristics of the 26 iMOFs. With this model, the optimal iMOF ratio is found to be a 2,6‐naphthalene dicarboxylic acid‐rich ternary solution of raw material. It is concluded that the improvement in the electrochemical properties originates from a change in the iMOFs crystal structure caused by synthesis from three solutions. As the model is independent of fabrication parameters such as heating temperature, it can also be used for evaluation of synthesis procedures. Hence, the XRD data‐based machine learning method introduced in this study is a powerful tool for practical material development. Materials science ; Electrochemistry ; Naphthalene ; Dilithium ; Diffraction ; Fabrication ; Raw material ; Electrode ; Polarization (electrochemistry) ; Terephthalic acid ; Chemical engineering ; Composite material ; Ion ; Organic chemistry ; Chemistry ; Optics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials ; Polyoxometalates: Synthesis and Applications
Authors: Ryan Murdock | Steven Kauwe | Anthony Wang | Taylor D. Sparks
Source: ChemRxiv (preprint, 2020-02-21)
Field: Materials Science — Materials Chemistry

Is Domain Knowledge Necessary for Machine Learning Materials Properties? &lt;div&gt;New methods for describing materials as vectors in order to predict their properties using machine learning are common in the field of material informatics. However, little is known about the comparative efficacy of these methods. This work sets out to make clear which featurization methods should be used across various circumstances. Our findings include, surprisingly, that simple one-hot encoding of elements can be as effective as traditional and new descriptors when using large amounts of data. However, in the absence of large datasets or data that is not fully representative we show that domain knowledge offers advantages in predictive ability.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt; Computer science ; Domain (mathematical analysis) ; Field (mathematics) ; Simple (philosophy) ; Encoding (memory) ; Machine learning ; Artificial intelligence ; Informatics ; Domain knowledge ; Support vector machine ; Mathematics ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Taher Hajilounezhad | Zakariya A. Oraibi | Ramakrishna Surya | Filiz Bunyak | Matthew R. Maschmann | Prasad Calyam | Kannappan Palaniappan
Source:  (preprint, 2020-01-22)
Field: Materials Science — Materials Chemistry

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties. Random forest ; Artificial intelligence ; Histogram ; Machine learning ; Carbon nanotube ; Process (computing) ; Classifier (UML) ; Computer science ; Materials science ; Algorithm ; Nanotechnology Carbon Nanotubes in Composites ; Machine Learning in Materials Science