jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jie Feng | Hongshuai Wang | Nopporn Rujisamphan | Youyong Li
Source: Frontiers in Chemistry (article, 2020-11-05)
Field: Chemistry — Organic Chemistry

Theoretical Design of Dithienopicenocarbazole-Based Molecules by Molecular Engineering of Terminal Units Toward Promising Non-fullerene Acceptors Dithienopicenocarbazole (DTPC), as the kernel module in A-D-A non-fullerene acceptors (NFA), has been reported for its ultra-narrow bandgap, high power conversion efficiency, and extremely low energy loss. To further improve the photovoltaic performance of DTPC-based acceptors, molecular engineering of end-capped groups could be an effective method according to previous research. In this article, a class of acceptors were designed via bringing terminal units with an enhanced electron-withdrawing ability to the DTPC central core. Their geometrical structures, frontier molecular orbitals, absorption spectrum, and intramolecular charge transfer and energy loss have been systematically investigated on the basis of density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations. Surprisingly, NFA 4 highlights the dominance for its increased open circuit voltages while NFA 2, 7, and 8 exhibit great potential for their enhanced charge transfer and lower energy loss, corresponding to a higher short-circuit current density. Our results also manifest that proper modifications of the terminal acceptor with extensions of π-conjugation might bring improved outcomes for overall properties. Such a measure could become a feasible strategy for the synthesis of new acceptors, thereby facilitating the advancement of organic solar cells. Fullerene ; Terminal (telecommunication) ; Molecule ; Molecular engineering ; Materials science ; Computational chemistry ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Chemical physics ; Computer science ; Organic chemistry ; Telecommunications Fullerene Chemistry and Applications ; Organic Electronics and Photovoltaics ; Organic Chemistry Cycloaddition Reactions
Authors: Nam Jung Heo | Ju Hyun Oh | Jeong Tae Lee | Qing He | Jonathan L. Sessler | Sung Kuk Kim
Source: Organic Chemistry Frontiers (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Phenanthroline-strapped calix[4]pyrroles: anion receptors displaying affinity reversal as a function of solvent polarity Calix[4]pyrroles<bold>1</bold>and<bold>2</bold>, diametrically strapped with a phenanthroline<italic>via</italic>ester and amide linkages, respectively, have been synthesized as anion receptors. Chemistry ; Ion ; Polarity (international relations) ; Amide ; Phenanthroline ; Receptor ; Stereochemistry ; Calixarene ; Crystallography ; Organic chemistry ; Molecule ; Biochemistry Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes
Authors: Monica Boffito | Rossella Laurano | Dimitra Giasafaki | Theodore Steriotis | Athanasios Papadopoulos | Chiara Tonda‐Turo | Claudio Cassino | Georgia Charalambopoulou | Gianluca Ciardelli
Source: Nanomaterials (article, 2020-10-29)
Field: Materials Science — Materials Chemistry

Embedding Ordered Mesoporous Carbons into Thermosensitive Hydrogels: A Cutting-Edge Strategy to Vehiculate a Cargo and Control Its Release Profile The high drug loading capacity, cytocompatibility and easy functionalization of ordered mesoporous carbons (OMCs) make them attractive nanocarriers to treat several pathologies. OMCs' efficiency could be further increased by embedding them into a hydrogel phase for an <i>in loco</i> prolonged drug release. In this work, OMCs were embedded into injectable thermosensitive hydrogels. In detail, rod-like (diameter ca. 250 nm, length ca. 700 nm) and spherical (diameter approximately 120 nm) OMCs were synthesized by nanocasting selected templates and loaded with ibuprofen through a melt infiltration method to achieve complete filling of their pores (100% loading yield). In parallel, an amphiphilic Poloxamer® 407-based poly(ether urethane) was synthesized (Mn¯ 72 kDa) and solubilized at 15 and 20% <i>w/v</i> concentration in saline solution to design thermosensitive hydrogels. OMC incorporation into the hydrogels (10 mg/mL concentration) did not negatively affect their gelation potential. Hybrid systems successfully released ibuprofen at a slower rate compared to control gels (gels embedding ibuprofen as such), but with no significant differences between rod-like and spherical OMC-loaded gels. OMCs can thus work as effective drug reservoirs that progressively release their payload over time and also upon encapsulation in a hydrogel phase, thus opening the way to their application to treat many different pathological states (e.g., as topical medications). Self-healing hydrogels ; Mesoporous material ; Embedding ; Materials science ; Enhanced Data Rates for GSM Evolution ; Chemical engineering ; Nanotechnology ; Polymer chemistry ; Chemistry ; Organic chemistry ; Computer science ; Engineering ; Catalysis Mesoporous Materials and Catalysis ; Aerogels and thermal insulation ; Supercapacitor Materials and Fabrication
Authors: Meghan Winterlich | Constantinos G. Efthymiou | Wassilios Papawassiliou | José P. Carvalho | Andrew J. Pell | Júlia Mayans | Albert Escuer | Michael P. Carty | Patrick McArdle | Emmanuel Tylianakis | Liam Morrison | George E. Froudakis | Constantina Papatriantafyllopoulou
Source: Materials Advances (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

A biocompatible ZnNa<sub>2</sub>-based metal–organic framework with high ibuprofen, nitric oxide and metal uptake capacity The synthesis and characterization of a mixed metal ZnNa<sub>2</sub> MOF (<bold>NUIG1</bold>) is discussed; <bold>NUIG1</bold> displays a new framework topology, and possesses an exceptionally high drug adsorption capacity and metal encapsulation potential. Metal-organic framework ; Biocompatible material ; Adsorption ; Metal ; Materials science ; Nitric acid ; Oxide ; Ibuprofen ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Biomedical engineering ; Metallurgy ; Engineering ; Medicine ; Pharmacology Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Covalent Organic Framework Applications
Authors: A.V. Nikitin | A. E. Protasevich | A.A. Rodina | M. Rey | Attila Tajti | Vladimir G. Tyuterev
Source: Journal of Quantitative Spectroscopy and Radiative Transfer (article, 2020-12-13)
Field: Chemistry — Spectroscopy

Vibrational levels of formaldehyde: Calculations from new high precision potential energy surfaces and comparison with experimental band origins Ab initio ; Potential energy ; Ab initio quantum chemistry methods ; Kinetic energy ; Potential energy surface ; Atomic physics ; Chemistry ; Physics ; Molecule ; Quantum mechanics Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Atmospheric chemistry and aerosols
Authors: Áron Péter | David J. Procter
Source: CHIMIA International Journal for Chemistry (article, 2020-02-26)
Field: Chemistry — Organic Chemistry

Cascades, Catalysis and Chiral Ligand Control with SmI2; The Rebirth of a Reagent This review focuses on recent developments from our laboratory in the field of radical reactions mediated by the archetypal reductive single electron transfer (SET) reagent, SmI2. Namely, we have expanded the scope of reducible carbonyl moieties to esters and amides and have exploited the resultant ketyl radicals in radical cascade reactions that generate unprecedented scaffolds. Moreover, we have taken the first steps to address the long-standing challenges of catalysis and chiral ligand control associated with the reagent. Ketyl ; Reagent ; Chemistry ; Combinatorial chemistry ; Radical ; Ligand (biochemistry) ; Catalysis ; Cascade ; Electron transfer ; Photoredox catalysis ; Photochemistry ; Organic chemistry ; Receptor Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts ; Oxidative Organic Chemistry Reactions
Authors: Ping Zhou | Ulrich Aschauer | Silvio Decurtins | Thomas Feurer | Robert Häner | Shi‐Xia Liu
Source: Chemical Communications (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chemical control of photoinduced charge-transfer direction in a tetrathiafulvalene-fused dipyrrolylquinoxaline difluoroborate dyad A new approach for a compact annulation of tetrathiafulvalene (TTF) and dipyrrolylquinoxaline difluoroborate (QB) is presented, leading to strong electronic interactions between the TTF and QB units. Regulation of distinct photoinduced charge flows within this dyad is achieved by external stimuli, which is also verified by TD-DFT calculations. Dyad ; Tetrathiafulvalene ; Charge (physics) ; Transfer (computing) ; Materials science ; Chemistry ; Photochemistry ; Computer science ; Physics ; Molecule ; Organic chemistry Organic and Molecular Conductors Research ; Nonlinear Optical Materials Research ; Porphyrin and Phthalocyanine Chemistry
Authors: Roger Bresolí‐Obach | Walter A. Massad | Abasi Abudulimu | Larry Lüer | Cristina Flors | Javier G. Luis | Laura I. Rosquete | Teresa A. Grillo | Ommid Anamimoghadam | Götz Bucher | Santi Nonell
Source: Dyes and Pigments (article, 2020-12-05)
Field: Materials Science — Materials Chemistry

9-Aryl-phenalenones: Bioinspired thermally reversible photochromic compounds for photoswitching applications in the pico-to milliseconds range Photochromism ; Picosecond ; Aryl ; Millisecond ; Photochemistry ; Molecule ; Ultrashort pulse ; Diarylethene ; Chemistry ; Yield (engineering) ; Materials science ; Organic chemistry ; Optics ; Laser ; Physics Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Porphyrin and Phthalocyanine Chemistry
Authors: Nicolas Godard | Patrick Grysan | Emmanuel Defaÿ | Sebastjan Glinšek
Source: Journal of Materials Chemistry C (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

Growth of {100}-oriented lead zirconate titanate thin films mediated by a safe solvent 1-Methoxy-2-propanol was found to be a promising safe alternative to carcinogenic and teratogenic 2-methoxyethanol as solvent for chemical solution deposition of {100}-oriented lead zirconate titanate thin films on platinized silicon. Lead zirconate titanate ; Materials science ; Solvent ; Zirconate ; Thin film ; Lead (geology) ; Chemical engineering ; Titanate ; Dielectric ; Nanotechnology ; Ferroelectricity ; Organic chemistry ; Composite material ; Optoelectronics ; Chemistry ; Ceramic Ferroelectric and Piezoelectric Materials ; Perovskite Materials and Applications ; Semiconductor materials and devices
Authors: A. Ceferino | Kok Wee Song | S. J. Magorrian | Viktor Zólyomi | Vladimir I. Fal’ko
Source: Physical review. B./Physical review. B (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Crossover from weakly indirect to direct excitons in atomically thin films of InSe We perform a $\mathbf{k}\ifmmode\cdot\else\textperiodcentered\fi{}\mathbf{p}$ theory analysis of the spectra of the lowest energy and excited states of the excitons in few-layer atomically thin films of InSe taking into account in plane electric polarizability of the film and the influence of the encapsulation environment. For the thinner films, the lowest-energy state of the exciton is weakly indirect in momentum space, with its dispersion showing minima at a layer-number-dependent wave number, due to an inverted edge of a relatively flat topmost valence band branch of the InSe film spectrum, and we compute the activation energy from the momentum dark exciton ground state into the bright state. For the films with more than seven ${\mathrm{In}}_{2}{\mathrm{Se}}_{2}$ layers, the exciton dispersion minimum shifts to $\mathrm{\ensuremath{\Gamma}}$ point. Exciton ; Condensed matter physics ; Excited state ; Degenerate energy levels ; Polarizability ; Thin film ; Spatial dispersion ; Valence (chemistry) ; Physics ; Materials science ; Molecular physics ; Atomic physics ; Optics ; Quantum mechanics ; Molecule 2D Materials and Applications ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Mina Zare | Seeram Ramakrishna
Source:  (article, 2020-12-01)
Field: Materials Science — Biomaterials

Current Progress of Electrospun Nanocarriers for Drug Delivery Applications Electrospinning is a powerful and cost-effective method that applies a strong electric field and polymer solution to fabricate versatile nanofiber using a wide array of raw materials for different biological applications. Electrospun nanofibers have developed as potential materials for the manufacturing of nanocarriers for therapeutic platforms due to controllable surface decoration, large surface areas, complex pore structure, good surface functionalization with high biocompatibility. The prominence of this technique is the possibility of merging low soluble drugs loaded in the nanofibers to enhance drug release and bioavailability. Incorporation of metal nanomaterials with electrocatalytic and/or plasmonic features into electrospun polymer nanofiber enhances thermodynamic and mechanical properties to obtain synergetic function and properties. This review discussed an overview on the theory of electrospinning, achievements and concerns, and challenges currently face in fabricating an efficient drug delivery system. Additionally, the drug-loaded into electrospun nanocarriers for drug delivery applications and opportunities, and insights into future prospects were explained. Nanocarriers ; Electrospinning ; Nanotechnology ; Materials science ; Nanofiber ; Drug delivery ; Surface modification ; Nanomaterials ; Biocompatibility ; Polymer ; Chemical engineering ; Composite material ; Engineering Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Conducting polymers and applications
Authors: Cheng Wang | Demin Liu | Wenbin Lin
Source: UNC Libraries (article, 2020-11-05)
Field: Chemistry — Inorganic Chemistry

Metal–Organic Frameworks as A Tunable Platform for Designing Functional Molecular Materials Metal-organic frameworks (MOFs), also known as coordination polymers, represent an interesting class of crystalline molecular materials that are synthesized by combining metal-connecting points and bridging ligands. The modular nature of and mild conditions for MOF synthesis have permitted the rational structural design of numerous MOFs and the incorporation of various functionalities via constituent building blocks. The resulting designer MOFs have shown promise for applications in a number of areas, including gas storage/separation, nonlinear optics/ferroelectricity, catalysis, energy conversion/storage, chemical sensing, biomedical imaging, and drug delivery. The structure-property relationships of MOFs can also be readily established by taking advantage of the knowledge of their detailed atomic structures, which enables fine-tuning of their functionalities for desired applications. Through the combination of molecular synthesis and crystal engineering MOFs thus present an unprecedented opportunity for the rational and precise design of functional materials. Metal-organic framework ; Nanotechnology ; Materials science ; Computer science ; Systems engineering ; Engineering ; Chemistry ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Fanghui Yin | Pingyuan Liu | Hongwei Mei | Liming Wang | Licheng Li | M. Farzaneh
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

The Shed Hole Influence on the Electrical Performance of Composite Insulator In recent years, shed holes appeared on the surface of in-service composite insulators have been observed in transmission lines in the southern and middle regions of China. Their presence can shorten the insulator creepage distance and therefore may impair the composite insulator's electrical performance. In this paper, extensive experiments were carried out to study the influence of shed hole (shed hole diameter, shed hole arrangement, shed hole percentage) on the electrical performance including pollution flashover voltage, pollution-rain flashover voltage, dry flashover voltage, wet flashover voltage, and lightning impulse voltage. The experimental results showed that the presence of shed hole had insignificant impact on the dry flashover voltage, wet flashover voltage, and lightning impulse when the shed diameter was 1.0 mm. However, the shed hole influence on the pollution flashover and pollution-rain flashover performance was appreciable. When the shed hole diameter is 1.0 mm, both the pollution flashover voltage and the pollution-rain flashover voltage are higher with alternating arrangement than that with straight-line arrangement. With the same shed hole diameter and pollution degree, the electrical performance of pollution-rain flashover is better than the pollution flashover. It is also found that the diameter of shed hole is increased by 3% to 60% after the pollution flashover test. Arc flash ; Insulator (electricity) ; Materials science ; Voltage ; Pollution ; Impulse (physics) ; Composite material ; Environmental science ; High voltage ; Electric power transmission ; Electrical engineering ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Aneesh Pappu | Brooks Paige
Source: arXiv (Cornell University) (preprint, 2020-11-24)
Field: Materials Science — Materials Chemistry

Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning Graph neural networks have become very popular for machine learning on molecules due to the expressive power of their learnt representations. However, molecular machine learning is a classically low-data regime and it isn't clear that graph neural networks can avoid overfitting in low-resource settings. In contrast, fingerprint methods are the traditional standard for low-data environments due to their reduced number of parameters and manually engineered features. In this work, we investigate whether graph neural networks are competitive in small data settings compared to the parametrically 'cheaper' alternative of fingerprint methods. When we find that they are not, we explore pretraining and the meta-learning method MAML (and variants FO-MAML and ANIL) for improving graph neural network performance by transfer learning from related tasks. We find that MAML and FO-MAML do enable the graph neural network to outperform models based on fingerprints, providing a path to using graph neural networks even in settings with severely restricted data availability. In contrast to previous work, we find ANIL performs worse that other meta-learning approaches in this molecule setting. Our results suggest two reasons: molecular machine learning tasks may require significant task-specific adaptation, and distribution shifts in test tasks relative to train tasks may contribute to worse ANIL performance. Overfitting ; Computer science ; Artificial intelligence ; Artificial neural network ; Machine learning ; Graph ; Deep neural networks ; Deep learning ; Theoretical computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yashaswi Pathak | Karandeep Singh Juneja | Girish Varma | Masahiro Ehara | U. Deva Priyakumar
Source: ChemRxiv (preprint, 2020-05-18)
Field: Materials Science — Materials Chemistry

Deep Learning Enabled Inorganic Material Generator &lt;div&gt;Recent years have witnessed utilization of modern machine learning approaches for predicting properties of material using available datasets. However, to identify potential candidates for material discovery, one has to systematically scan through a large chemical space and subsequently calculate the properties of all such samples. On the other hand, generative methods are capable of efficiently sampling the chemical space and can generate molecules/materials with desired properties. In this study, we report a deep learning based inorganic material generator (DING) framework consisting of a generator module and a predictor module. The generator module is developed based upon conditional variational autoencoders (CVAE) and the predictor module consists of three deep neural networks trained for predicting enthalpy of formation, volume per atom and energy per atom chosen to demonstrate the proposed method. The predictor and generator modules have been developed using a one hot key representation of the material composition. A series of tests were done to examine the robustness of the predictor models, to demonstrate the continuity of the latent material space, and its ability to generate materials exhibiting target property values. The DING architecture proposed in this paper can be extended to other properties based on which the chemical space can be efficiently explored for interesting materials/molecules.&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt; Chemical space ; Robustness (evolution) ; Generator (circuit theory) ; Deep learning ; Computer science ; Artificial intelligence ; Biological system ; Artificial neural network ; Representation (politics) ; Algorithm ; Chemistry ; Physics ; Thermodynamics ; Power (physics) Machine Learning in Materials Science
Authors: Serim Ryou | Michael Maser | Alexander Cui | Travis J. DeLano | Yisong Yue | Sarah E. Reisman
Source: arXiv (Cornell University) (preprint, 2020-07-08)
Field: Materials Science — Materials Chemistry

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning. Computer science ; Graph ; Artificial neural network ; Identification (biology) ; Artificial intelligence ; Reagent ; Machine learning ; Chemistry ; Theoretical computer science ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks