jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kais Dhbaibi | Paola Matozzo | Laura Abella | Marion Jean | Nicolas Vanthuyne | Jochen Autschbach | Ludovic Favereau | Jeanne Crassous
Source: Chemical Communications (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Exciton coupling chirality in helicene-porphyrin conjugates Enantiopure helicene-porphyrin conjugates were prepared. They show strong changes in their circular dichroic response as compared to classical helicene derivatives, with highly intense bisignate Exciton Coupling (EC) signal and Δ<i>ε</i> values up to 680 M<sup>-1</sup> cm<sup>-1</sup> for the Soret band. They also display circularly polarized fluorescence in the (far-)red region, with dissymmetry factors up to 7 × 10<sup>-4</sup>. Helicene ; Enantiopure drug ; Chirality (physics) ; Porphyrin ; Exciton ; Fluorescence ; Circular dichroism ; Dichroic glass ; Conjugate ; Coupling (piping) ; Chemistry ; Photochemistry ; Crystallography ; Materials science ; Nanotechnology ; Enantioselective synthesis ; Optics ; Molecule ; Condensed matter physics ; Organic chemistry ; Physics ; Chiral symmetry Porphyrin and Phthalocyanine Chemistry ; Synthesis and Properties of Aromatic Compounds ; Photoreceptor and optogenetics research
Authors: Kilian Colas | Susanne Doloczki | Aikaterina Kesidou | Lourdes Sainero‐Alcolado | Aida Rodriguez‐Garcia | Marie Arsenian‐Henriksson | Christine Dyrager
Source: ChemPhotoChem (article, 2021-02-23)
Field: Materials Science — Materials Chemistry

Photophysical Characteristics of Polarity‐Sensitive and Lipid Droplet‐Specific Phenylbenzothiadiazoles Abstract In this study, we present a series of solvatochromic phenylbenzothiadiazoles that display dual emission from the locally excited (LE) and intramolecular charge transfer (ICT) excited states. The donor‐acceptor derivatives are highly sensitive to polarity changes, which can be monitored by differences in emission efficiency, spectroscopic shifts and variations of the LE/ICT ratio. One of the compounds in the series, containing a thiomethyl substituent, emerged as an excellent blue emitting stain for intracellular lipid droplets, a biomarker for various types of cancer. In addition, a non‐emissive nitro derivative becomes fluorescent upon bioreduction in hypoxic cancer cells and accumulates in lipid droplets with a high signal‐to‐background ratio. Solvatochromism ; Excited state ; Polarity (international relations) ; Substituent ; Fluorescence ; Photochemistry ; Chemistry ; Lipid droplet ; Stain ; Intramolecular force ; Luminescence ; Materials science ; Molecule ; Optoelectronics ; Staining ; Stereochemistry ; Organic chemistry ; Optics ; Biochemistry Luminescence and Fluorescent Materials ; Photochemistry and Electron Transfer Studies ; Photochromic and Fluorescence Chemistry
Authors: Iulia Cojocariu | Silvia Carlotto | Giovanni Zamborlini | Matteo Jugovac | Luca Schio | Luca Floreano | Maurizio Casarin | Vitaliy Feyer | Claus M. Schneider
Source: Journal of Materials Chemistry C (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Reversible redox reactions in metal-supported porphyrin: the role of spin and oxidation state The reduced Co( i ) metal ion in the molecular array facilitates the formation of the cobalt–ligand chemical bond already at RT. We demonstrate that molecular reactivity goes beyond the sole presence of unpaired electrons in the valence shell. Unpaired electron ; Redox ; Porphyrin ; Reactivity (psychology) ; Metal ; Valence (chemistry) ; Oxidation state ; Cobalt ; Materials science ; Photochemistry ; Spin states ; Ligand (biochemistry) ; Valence electron ; Ion ; Electron ; Metal ions in aqueous solution ; Valence bond theory ; Inorganic chemistry ; Molecule ; Chemistry ; Molecular orbital ; Organic chemistry Porphyrin and Phthalocyanine Chemistry ; Surface Chemistry and Catalysis ; Molecular Junctions and Nanostructures
Authors: Qizhen Hong | Massimiliano Bartolomei | Cecilia Coletti | Andrea Lombardi | Quanhua Sun | Fernando Pirani
Source: Molecules (article, 2021-11-25)
Field: Chemistry — Spectroscopy

Vibrational Energy Transfer in CO+N2 Collisions: A Database for V–V and V–T/R Quantum-Classical Rate Coefficients Knowledge of energy exchange rate constants in inelastic collisions is critically required for accurate characterization and simulation of several processes in gaseous environments, including planetary atmospheres, plasma, combustion, etc. Determination of these rate constants requires accurate potential energy surfaces (PESs) that describe in detail the full interaction region space and the use of collision dynamics methods capable of including the most relevant quantum effects. In this work, we produce an extensive collection of vibration-to-vibration (V-V) and vibration-to-translation/rotation (V-T/R) energy transfer rate coefficients for collisions between CO and N2 molecules using a mixed quantum-classical method and a recently introduced (A. Lombardi, F. Pirani, M. Bartolomei, C. Coletti, and A. Laganà, Frontiers in chemistry, 7, 309 (2019)) analytical PES, critically revised to improve its performance against ab initio and experimental data of different sources. The present database gives a good agreement with available experimental values of V-V rate coefficients and covers an unprecedented number of transitions and a wide range of temperatures. Furthermore, this is the first database of V-T/R rate coefficients for the title collisions. These processes are shown to often be the most probable ones at high temperatures and/or for highly excited molecules, such conditions being relevant in the modeling of hypersonic flows, plasma, and aerospace applications. Excited state ; Ab initio ; Reaction rate constant ; Quantum ; Chemistry ; Range (aeronautics) ; Atomic physics ; Potential energy surface ; Potential energy ; Physics ; Work (physics) ; Thermodynamics ; Physical chemistry ; Computational chemistry ; Materials science ; Quantum mechanics ; Kinetics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies
Authors: James E. Tyrrell | Konstantinos Petkos | Emmanuel M. Drakakis | Martyn G. Boutelle | Alasdair J. Campbell
Source: Advanced Functional Materials (article, 2021-06-12)
Field: Materials Science — Polymers and Plastics

Organic Electrochemical Transistor Common‐Source Amplifier for Electrophysiological Measurements Abstract The portability of physiological monitoring has necessitated the biocompatibility of components used in circuitry local to biological environments. A key component in processing circuitry is the linear amplifier. Amplifier circuit topologies utilize transistors, and recent advances in bioelectronics have focused on organic electrochemical transistors (OECTs). OECTs have shown the capability to transduce physiological signals at high signal‐to‐noise ratios. In this study high‐performance interdigitated electrode OECTs are implemented in a common source linear amplifier topology. Under the constraints of OECT operation, stable circuit component parameters are found, and OECT geometries are varied to determine the best amplifier performance. An equation is formulated which approximates transistor behavior in the linear, nonlinear, and saturation regimes. This equation is used to simulate the amplifier response of the circuits with the best performing OECT geometries. The amplifier figures of merit, including distortion characterizations, are then calculated using physical and simulation measurements. Based on the figures of merit, prerecorded electrophysiological signals from spreading depolarizations, electrocorticography, and electromyography fasciculations are inputted into an OECT linear amplifier. Using frequency filtering, the primary features of events in the bioelectric signals are resolved and amplified, demonstrating the capability of OECT amplifiers in bioelectronics. Bioelectronics ; Amplifier ; Transistor ; Transistor array ; Materials science ; Direct-coupled amplifier ; Electronic circuit ; Electronic engineering ; Current-feedback operational amplifier ; Differential amplifier ; Optoelectronics ; Electrical engineering ; Operational amplifier ; Nanotechnology ; Engineering ; CMOS ; Biosensor ; Voltage Conducting polymers and applications ; Neuroscience and Neural Engineering ; Electrochemical Analysis and Applications
Authors: Yuanfeng Gao | Ye Liu | Yuan Fu | Qianhui Wang | Zheng Liu | Roumu Hu | Xinchun Yang | Mulei Chen
Source: BMC Cardiovascular Disorders (article, 2021-01-09)
Field: Materials Science — Metals and Alloys

The potential regulatory role of hsa_circ_0004104 in the persistency of atrial fibrillation by promoting cardiac fibrosis via TGF-β pathway CircRNAs dysregulation plays vital roles in AF persistency. hsa_circ_0004104 could be a potential regulator and biomarker in AF persistency by promoting cardiac fibrosis via targeting MAPK and TGF-beta pathways. Atrial fibrillation ; Medicine ; Angiology ; Cardiac fibrosis ; microRNA ; Internal medicine ; Fibrosis ; Microarray ; In silico ; Cardiology ; Bioinformatics ; Cancer research ; Biology ; Gene expression ; Gene ; Genetics Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Circular RNAs in diseases
Authors: Antonino Proto | Jaroslav Vondrak | Martin Schmidt | Jan Kubíček | Ojan Majidzadeh Gorjani | Jan Havlík | Marek Penhaker
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

A Flexible Thermoelectric Generator Worn on the Leg to Harvest Body Heat Energy and to Recognize Motor Activities: A Preliminary Study Wearable devices are commonly used to monitor human movement since motor activity is a fundamental element in all phases of a person's life. Patients with motor disorders need to be monitored for a prolonged period and the battery life can be a limit for such a goal. Here the technique of harvesting energy from body heat to supply energy to wearable devices is investigated. A commercial flexible thermoelectric generator, equipped with an accelerometer, is placed on the lower leg above the ankle. The accelerometer serves to detect diverse motor activities carried out by ten students of VSB-Technical University of Ostrava involved in the execution of two tasks. To summarize, the motor activities analyzed in the proposed work are: “Sit”, “Walk”, “Rest”, “Go biking”, “Rest after biking”, and “Go down and up the stairs”. The maximum measured value of power density was 20.3 μW cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for the “Walk” activity, corresponding to a gradient of temperature between the hot and cold side of the thermocouples constituting the flexible thermoelectric generator of 1.5 °C, while the minimum measured value of power density was 8.3 μW cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> for the “Sit” activity, corresponding to a gradient of temperature of 1.1 °C. Moreover, a mathematical model was developed for the recognition of motor activities carried out during the execution of the experiments. As a preliminary result, it is possible to state that semi-stationary parts of the signal generated by the thermoelectric generator can be traced back to the performance of an activity. Thermoelectric generator ; Automotive engineering ; Generator (circuit theory) ; Thermoelectric effect ; Electricity ; Electrical engineering ; Computer science ; Environmental science ; Mechanical engineering ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Innovative Energy Harvesting Technologies
Authors: Daniil A. Boiko | Evgeniy O. Pentsak | Vera A. Cherepanova | Evgeniy G. Gordeev | Valentine P. Ananikov
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials Smoothness/defectiveness of the carbon material surface is a key issue for many applications, spanning from electronics to reinforced materials, adsorbents and catalysis. Several surface defects cannot be observed with conventional analytic techniques, thus requiring the development of a new imaging approach. Here, we evaluate a convenient method for mapping such "hidden" defects on the surface of carbon materials using 1-5 nm metal nanoparticles as markers. A direct relationship between the presence of defects and the ordering of nanoparticles was studied experimentally and modeled using quantum chemistry calculations and Monte Carlo simulations. An automated pipeline for analyzing microscopic images is described: the degree of smoothness of experimental images was determined by a classification neural network, and then the images were searched for specific types of defects using a segmentation neural network. An informative set of features was generated from both networks: high-dimensional embeddings of image patches and statics of defect distribution. Artificial neural network ; Materials science ; Nanoparticle ; Carbon fibers ; Smoothness ; Monte Carlo method ; Computer science ; Surface (topology) ; Segmentation ; Pipeline (software) ; Image segmentation ; Nanotechnology ; Artificial intelligence ; Mathematics ; Composite material ; Geometry Machine Learning in Materials Science ; Graphene research and applications ; Carbon Nanotubes in Composites
Authors: Akinori Sato | Tomoyuki Miyao | Kimito Funatsu
Source: Molecular Informatics (article, 2021-09-29)
Field: Materials Science — Materials Chemistry

Prediction of Reaction Yield for Buchwald‐Hartwig Cross‐coupling Reactions Using Deep Learning Chemical reaction yield is one of the most important factors for determining reaction conditions. Recently, several machine learning-based prediction models using high-throughput experiment (HTE) data sets were reported for the prediction of reaction yield. However, none of them were at a practical level in terms of predictive ability. In this study, we propose a message passing neural network (MPNN) model for chemical yield prediction, focusing on the Buchwald-Hartwig cross-coupling HTE data set. As an initial atom embedding in MPNN model, we propose to use the Mol2Vec feature vectors pre-trained using a large compound database. Predictive ability of the proposed model was higher than that of previously reported five models for the three out of five data sets. Moreover, visualization of important atoms based on self-attention mechanism was in favor of Mol2Vec as an atom embedding rather than other embeddings including previously employed simple representations. Yield (engineering) ; Embedding ; Computer science ; Data set ; Artificial neural network ; Set (abstract data type) ; Coupling reaction ; Deep learning ; Artificial intelligence ; Cheminformatics ; Atom (system on chip) ; Throughput ; Visualization ; Coupling (piping) ; Machine learning ; Chemistry ; Biological system ; Computational chemistry ; Materials science ; Parallel computing ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Takuma Wakiya | Yoshinobu Kamakura | Hiroki Shibahara | Kazuyoshi Ogasawara | Akinori Saeki | Ryosuke Nishikubo | Akihiro Inokuchi | Hirofumi Yoshikawa | Daisuke Tanaka
Source: Angewandte Chemie International Edition (article, 2021-08-25)
Field: Chemistry — Inorganic Chemistry

Machine‐Learning‐Assisted Selective Synthesis of a Semiconductive Silver Thiolate Coordination Polymer with Segregated Paths for Holes and Electrons Coordination polymers (CPs) with infinite metal-sulfur bond networks have unique electrical conductivities and optical properties. However, the development of new (-M-S-)<sub>n</sub> -structured CPs is hindered by difficulties with their crystallization. Herein, we describe the use of machine learning to optimize the synthesis of trithiocyanuric acid (H<sub>3</sub> ttc)-based semiconductive CPs with infinite Ag-S bond networks, report three CP crystal structures, and reveal that isomer selectivity is mainly determined by proton concentration in the reaction medium. One of the CPs, [Ag<sub>2</sub> Httc]<sub>n</sub> , features a 3D-extended infinite Ag-S bond network with 1D columns of stacked triazine rings, which, according to first-principle calculations, provide separate paths for holes and electrons. Time-resolved microwave conductivity experiments show that [Ag<sub>2</sub> Httc]<sub>n</sub> is highly photoconductive (φΣμ<sub>max</sub> =1.6×10<sup>-4</sup> cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> ). Thus, our method promotes the discovery of novel CPs with selective topologies that are difficult to crystallize. Crystallization ; Materials science ; Electron ; Proton ; Topology (electrical circuits) ; Selectivity ; Metal ; Polymer ; Crystallography ; Chemistry ; Composite material ; Physics ; Organic chemistry ; Electrical engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Organic and Molecular Conductors Research
Authors: Mahmudul Hasan | Pınar Acar
Source: Computational Materials Science (article, 2021-11-11)
Field: Materials Science — Materials Chemistry

Machine learning reinforced microstructure-sensitive prediction of material property closures Linear regression ; Regularization (linguistics) ; Materials science ; Bayesian multivariate linear regression ; Material properties ; Linear elasticity ; Linear model ; Artificial neural network ; Computer science ; Algorithm ; Composite material ; Artificial intelligence ; Machine learning ; Finite element method ; Physics ; Thermodynamics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Mingzhe Chi | Rihab Gargouri | Tim Schrader | Kamel Damak | Ramzi Maâlej | Marek Sierka
Source: Polymers (article, 2021-12-22)
Field: Materials Science — Materials Chemistry

Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization ΔHvap of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting ΔHvap of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of ΔHvap of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database. Polymer ; Solubility ; Multilinear map ; Molecule ; Molecular descriptor ; Quantitative structure–activity relationship ; Thermodynamics ; Hildebrand solubility parameter ; Materials science ; Chemistry ; Physical chemistry ; Mathematics ; Organic chemistry ; Physics ; Stereochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Thermal and Kinetic Analysis
Authors: Yoshihiro Hayashi | Yuri Nakano | Yuki Marumo | Shungo Kumada | Kotaro Okada | Yoshinori Onuki
Source: International Journal of Pharmaceutics (article, 2021-10-07)
Field: Chemistry — Analytical Chemistry

Application of machine learning to a material library for modeling of relationships between material properties and tablet properties Material properties ; Compression (physics) ; Regression analysis ; Ultimate tensile strength ; Partial least squares regression ; Absorption of water ; Percentile ; Mathematics ; Materials science ; Artificial intelligence ; Computer science ; Biological system ; Machine learning ; Statistics ; Composite material Spectroscopy and Chemometric Analyses ; Drug Solubulity and Delivery Systems ; Computational Drug Discovery Methods
Authors: Yoshiki Takagiwa | Zhufeng Hou | Koji Tsuda | Teruyuki Ikeda | Hiroyasu KOJIMA
Source: ACS Applied Materials & Interfaces (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Fe–Al–Si Thermoelectric (FAST) Materials and Modules: Diffusion Couple and Machine-Learning-Assisted Materials Development To lower the introduction and maintenance costs of autonomous power supplies for driving Internet-of-things (IoT) devices, we have developed low-cost Fe-Al-Si-based thermoelectric (FAST) materials and power generation modules. Our development approach combines computational science, experiments, mapping measurements, and machine learning (ML). FAST materials have a good balance of mechanical properties and excellent chemical stability, superior to that of conventional Bi-Te-based materials. However, it remains challenging to enhance the power factor (PF) and lower the thermal conductivity of FAST materials to develop reliable power generation devices. This forum paper describes the current status of materials development based on experiments and ML with limited data, together with power generation module fabrication related to FAST materials with a view to commercialization. Combining bulk combinatorial methods with diffusion couple and mapping measurements could accelerate the search to enhance PF for FAST materials. We report that ML prediction is a powerful tool for finding unexpected off-stoichiometric compositions of the Fe-Al-Si system and dopant concentrations of a fourth element to enhance the PF, i.e., Co substitution for Fe atoms in FAST materials. Materials science ; Dopant ; Thermoelectric materials ; Commercialization ; Fabrication ; Diffusion ; Thermal conductivity ; Reliability (semiconductor) ; Engineering physics ; Nanotechnology ; Power (physics) ; Computer science ; Optoelectronics ; Composite material ; Doping ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: M. Wang | Hulei Yu | Y. Chen | M.X. Huang
Source: Computational Materials Science (article, 2021-04-29)
Field: Materials Science — Biomaterials

Machine learning assisted screening of non-rare-earth elements for Mg alloys with low stacking fault energy Ductility (Earth science) ; Density functional theory ; Ternary operation ; Materials science ; Stacking fault ; Stacking ; Stacking-fault energy ; Dislocation ; Work (physics) ; Alloy ; Bulk modulus ; Thermodynamics ; Computational chemistry ; Computer science ; Chemistry ; Metallurgy ; Composite material ; Physics Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties