jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hai Zhu | Yu Huang | Xiaoding Lou | Fan Xia
Source: View (article, 2020-08-13)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired superwetting surfaces for biosensing Abstract Inspired by nature, scientists and researchers have studied the wetting behaviors on various creatures and mimicked their structures to fabricate diverse functional superwetting materials. As one kind of emerging application, the bioinspired superwettable surfaces used for biosensing have been aroused wide interests. In this review, we summarized the recent developments of bioinspired superwettable surfaces in the field of biosensing. In the first part, superwettable creatures in nature, namely, superhydrophobic self‐cleaning lotus leaf, high‐adhesion superhydrophobic rose petal, amphiphobic springtail, patterned wetting desert beetles, slippery pitcher plant, were introduced. In sequence, we successively described the special wetting models of superhydrophobicity, superamphiphobicity, responsive wettability, patterned wettability, and slipperiness. Then, biosensing applications based on the respective patterned wettable, superhydrophobic, responsive wettable, and slippery substrates that were combined with the common detection approaches (colorimetry, fluorescence, surface‐enhanced Raman scattering (SERS), electrochemistry) were shown in detailed. At last, an insight of remaining challenges and future development for bioinspired superwetting materials applied in biosensing was provided. Creatures ; Biosensor ; Nanotechnology ; Wetting ; Materials science ; Biology Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Advanced Sensor and Energy Harvesting Materials
Authors: Riccardo Zaffaroni | Eduard O. Bobylev | Raoul Plessius | Jarl Ivar van der Vlugt | Joost N. H. Reek
Source: Journal of the American Chemical Society (article, 2020-04-17)
Field: Chemistry — Organic Chemistry

How to Control the Rate of Heterogeneous Electron Transfer across the Rim of M <sub>6</sub> L <sub>12</sub> and M <sub>12</sub> L <sub>24</sub> Nanospheres Catalysis in confined spaces, such as those provided by supramolecular cages, is quickly gaining momentum. It allows for second coordination sphere strategies to control the selectivity and activity of transition metal catalysts, beyond the classical methods like fine-tuning the steric and electronic properties of the coordinating ligands. Only a few electrocatalytic reactions within cages have been reported, and there is no information regarding the electron transfer kinetics and thermodynamics of redox-active species encapsulated into supramolecular assemblies. This contribution revolves around the preparation of M<sub>6</sub>L<sub>12</sub> and larger M<sub>12</sub>L<sub>24</sub> (M = Pd or Pt) nanospheres functionalized with different numbers of redox-active probes encapsulated within their cavity, either in a covalent fashion via different types of linkers (flexible, rigid and conjugated or rigid and nonconjugated) or by supramolecular hydrogen bonding interactions. The redox probes can be addressed by electrochemical electron transfer across the rim of nanospheres, and the thermodynamics and kinetics of this process are described. Our study identifies that the linker type and the number of redox probes within the cage are useful handles to fine-tune the electron transfer rates, paving the way for the encapsulation of electroactive catalysts and electrocatalytic applications of such supramolecular assemblies. Supramolecular chemistry ; Chemistry ; Electron transfer ; Redox ; Catalysis ; Electrochemistry ; Steric effects ; Covalent bond ; Kinetics ; Electrocatalyst ; Nanotechnology ; Combinatorial chemistry ; Chemical physics ; Photochemistry ; Crystallography ; Physical chemistry ; Electrode ; Stereochemistry ; Inorganic chemistry ; Organic chemistry ; Crystal structure Supramolecular Chemistry and Complexes ; Porphyrin and Phthalocyanine Chemistry ; Molecular Junctions and Nanostructures
Authors: Mans Broekgaarden | Anne‐Laure Bulin | Estelle Porret | Benjamin Musnier | Benoît Chovelon | Corinne Ravelet | Lucie Sancey | Hélène Elleaume | Pierre Hainaut | Jean‐Luc Coll | Xavier Le Guével
Source: Nanoscale (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Surface functionalization of gold nanoclusters with arginine: a trade-off between microtumor uptake and radiotherapy enhancement Ultra-small gold nanoclusters (AuNCs) are increasingly investigated for cancer imaging and radiotherapy enhancement. While fine-tuning the AuNC surface chemistry can optimize their pharmacokinetics, its effects on radiotherapy enhancement remain largely unexplored. This study demonstrates that optimizing the surface chemistry of AuNCs for increased tumor uptake can significantly affect its potential to augment radiotherapy outcomes. Nanoclusters ; Surface modification ; Cancer therapy ; Radiation therapy ; Nanotechnology ; Materials science ; Colloidal gold ; Chemistry ; Nanoparticle ; Cancer ; Medicine ; Internal medicine ; Physical chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Nanoplatforms for cancer theranostics
Authors: Zhenghua Li | Liang Zhang | Masayoshi Nishiura | Gen Luo | Yi Luo | Zhaomin Hou
Source: ACS Catalysis (article, 2020-09-11)
Field: Chemistry — Organic Chemistry

Enantioselective Cyanoborylation of Allenes by <i>N</i>-Heterocyclic Carbene-Copper Catalysts The simultaneous incorporation of both a cyano group and a boryl unit into the C═C double bonds of allenes in a regio- and stereoselective fashion is of much interest and importance but remains a significant challenge. We report herein a copper-catalyzed chemo-, regio-, and enantioselective cyanoborylation of allenes, which afforded a family of valuable enantiopure β-boryl allyl nitriles. The high enantioselectivity was achieved by installing of appropriate substituents at the C2 and C6 positions of the naphthyl groups in our newly synthesized N-heterocyclic carbene (NHC) ligands. The reaction mechanism has been clarified by some stoichiometric reactions and computational studies. This work provides an inspiring example of the development of selective catalytic reactions for the synthesis of functional molecules through fine-tuning the ligands in catalysis. Enantioselective synthesis ; Enantiopure drug ; Carbene ; Chemistry ; Catalysis ; Combinatorial chemistry ; Stereoselectivity ; Organic chemistry Organoboron and organosilicon chemistry ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Anna Barwinska‐Sendra | Yuritzi M. Garcia | Kacper Sendra | Arnaud Baslé | Eilidh S. Mackenzie | Emma Tarrant | Patrick Card | Leandro C. Tabares | Cédric Bicep | Sun Un | Thomas E. Kehl‐Fie | Kevin J. Waldron
Source: Nature Communications (article, 2020-06-01)
Field: Chemistry — Inorganic Chemistry

An evolutionary path to altered cofactor specificity in a metalloenzyme Almost half of all enzymes utilize a metal cofactor. However, the features that dictate the metal utilized by metalloenzymes are poorly understood, limiting our ability to manipulate these enzymes for industrial and health-associated applications. The ubiquitous iron/manganese superoxide dismutase (SOD) family exemplifies this deficit, as the specific metal used by any family member cannot be predicted. Biochemical, structural and paramagnetic analysis of two evolutionarily related SODs with different metal specificity produced by the pathogenic bacterium Staphylococcus aureus identifies two positions that control metal specificity. These residues make no direct contacts with the metal-coordinating ligands but control the metal's redox properties, demonstrating that subtle architectural changes can dramatically alter metal utilization. Introducing these mutations into S. aureus alters the ability of the bacterium to resist superoxide stress when metal starved by the host, revealing that small changes in metal-dependent activity can drive the evolution of metalloenzymes with new cofactor specificity. Cofactor ; Enzyme ; Superoxide dismutase ; Biochemistry ; Chemistry ; Metal ; Metalloprotein ; Bacteria ; Biology ; Genetics Metal-Catalyzed Oxygenation Mechanisms ; Microbial Fuel Cells and Bioremediation ; Metalloenzymes and iron-sulfur proteins
Authors: Matthew Zajac | Kasturi Chakraborty | Sonali Saha | Vivek Mahadevan | Daniel T. Infield | Alessio Accardi | Zhaozhu Qiu | Yamuna Krishnan
Source: Journal of Cell Science (review, 2020-01-15)
Field: Chemistry — Spectroscopy

What biologists want from their chloride reporters – a conversation between chemists and biologists Impaired chloride transport affects diverse processes ranging from neuron excitability to water secretion, which underlie epilepsy and cystic fibrosis, respectively. The ability to image chloride fluxes with fluorescent probes has been essential for the investigation of the roles of chloride channels and transporters in health and disease. Therefore, developing effective fluorescent chloride reporters is critical to characterizing chloride transporters and discovering new ones. However, each chloride channel or transporter has a unique functional context that demands a suite of chloride probes with appropriate sensing characteristics. This Review seeks to juxtapose the biology of chloride transport with the chemistries underlying chloride sensors by exploring the various biological roles of chloride and highlighting the insights delivered by studies using chloride reporters. We then delineate the evolution of small-molecule sensors and genetically encoded chloride reporters. Finally, we analyze discussions with chloride biologists to identify the advantages and limitations of sensors in each biological context, as well as to recognize the key design challenges that must be overcome for developing the next generation of chloride sensors. Chloride ; Context (archaeology) ; Biology ; Chloride channel ; Conversation ; Transporter ; Computational biology ; Biophysics ; Biochemistry ; Chemistry ; Communication ; Gene ; Organic chemistry Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques ; Neuroscience and Neuropharmacology Research
Authors: Bettina Szerencsés | Nóra Igaz | Ákos Tóbiás | Zsombor Prucsi | Andrea Rónavári | Péter Bélteky | Dániel Madarász | Csaba Papp | Ildikó Makra | Csaba Vágvölgyi | Zoltán Kónya | Ilona Pfeiffer | Mónika Kiricsi
Source: BMC Microbiology (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Size-dependent activity of silver nanoparticles on the morphological switch and biofilm formation of opportunistic pathogenic yeasts Our results confirm that AgNPs are capable to hinder yeast-to-hypha morphological conversion and biofilm formation of opportunistic pathogens and this biological effect of AgNPs is size-dependent. Biofilm ; Hypha ; Microbiology ; Silver nanoparticle ; Biology ; Virulence ; Yeast ; Bacteria ; Nanoparticle ; Materials science ; Nanotechnology ; Biochemistry Nanoparticles: synthesis and applications ; Antifungal resistance and susceptibility ; Bacterial biofilms and quorum sensing
Authors: W.F. Sanjuan-Szklarz | M. Woińska | S. Domagała | P.M. Dominiak | Simon Grabowsky | Dylan Jayatilaka | M. Gutmann | Krzysztof Woźniak
Source: IUCrJ (article, 2020-08-24)
Field: Materials Science — Materials Chemistry

On the accuracy and precision of X-ray and neutron diffraction results as a function of resolution and the electron density model X-ray diffraction is the main source of three-dimensional structural information. In total, more than 1.5 million crystal structures have been refined and deposited in structural databanks (PDB, CSD and ICSD) to date. Almost 99.7% of them were obtained by approximating atoms as spheres within the independent atom model (IAM) introduced over a century ago. In this study, X-ray datasets for single crystals of hydrated α-oxalic acid were refined using several alternative electron density models that abandon the crude spherical approximation: the multipole model (MM), the transferable aspherical atom model (TAAM) and the Hirshfeld atom refinement (HAR) model as a function of the resolution of X-ray data. The aspherical models (MM, TAAM, HAR) give far more accurate and precise single-crystal X-ray results than IAM, sometimes identical to results obtained from neutron diffraction and at low resolution. Hence, aspherical approaches open new routes for improving existing structural information collected over the last century. Neutron diffraction ; Resolution (logic) ; Diffraction ; Crystallography ; Electron density ; Multipole expansion ; Atom (system on chip) ; Computational physics ; Neutron ; Function (biology) ; Physics ; Electron ; Chemistry ; Computer science ; Optics ; Quantum mechanics ; Artificial intelligence X-ray Diffraction in Crystallography ; Crystallography and molecular interactions ; Enzyme Structure and Function
Authors: Zijun Zhao | Wenbo Wang | Jie Xiao | Yunjiao Chen | Yong Cao
Source: Nanomaterials (article, 2020-05-30)
Field: Materials Science — Materials Chemistry

Interfacial Engineering of Pickering Emulsion Co-Stabilized by Zein Nanoparticles and Tween 20: Effects of the Particle Size on the Interfacial Concentration of Gallic Acid and the Oxidative Stability Lipid oxidation is still one of the major food-safety issues associated with the emulsion-based food systems. Engineering the interfacial region is an effective way to improve the oxidative stability of emulsion. Herein, a novel Pickering emulsion with strong oxidative stability was prepared by using zein nanoparticles and Tween 20 as stabilizers (ZPE). The modulation effects of the particle size on the distribution of gallic acid (GA) and the oxidative stability of ZPE were investigated. In the absence of GA, Pickering emulsions stabilized with different sizes of zein nanoparticles showed similar oxidative stability, and the physical barrier effect took the dominant role in retarding lipid oxidation. Moreover, in the presence of GA, ZPE stabilized by zein nanoparticles with the averaged particle size of 130 nm performed stronger oxidation than those stabilized by zein nanoparticles of 70 and 220 nm. Our study revealed that the interfacial concentration of GA (GA<sub>I</sub>) was tuned by zein nanoparticles due to the interaction between them, but the difference in the binding affinity between GA and zein nanoparticles was not the dominant factor regulating the (GA<sub>I</sub>). It was the interfacial content of zein nanoparticles (Γ), which was affected by the particle size, modulated the (GA<sub>I</sub>) and further dominated the oxidative stability of ZPEs. The present study suggested that the potential of thickening the interfacial layer to prevent lipid oxidation was limited, increasing the interfacial concentration of antioxidant by interfacial engineering offered a more efficient alternative. Pickering emulsion ; Nanoparticle ; Emulsion ; Chemical engineering ; Particle size ; Particle (ecology) ; Gallic acid ; Materials science ; Oxidative phosphorylation ; Chemistry ; Antioxidant ; Nanotechnology ; Organic chemistry ; Biochemistry Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Microencapsulation and Drying Processes
Authors: Ahmed A. M. El‐Reedy | N. K. Soliman
Source: Scientific Reports (article, 2020-04-09)
Field: Chemistry — Organic Chemistry

Synthesis, biological activity and molecular modeling study of novel 1,2,4-triazolo[4,3-b][1,2,4,5]tetrazines and 1,2,4-triazolo[4,3-b][1,2,4]triazines Different novel 1,2,4-triazolo[4,3-b][1,2,4,5]tetrazines and 1,2,4-triazolo[4,3-b][1,2,4]triazines have been obtained from heterocyclization of 3-substituted-4-amino-5-substituted-amino-1,2,4-triazoles (3a-d) and 3-substituted-4-amino-5-hydrazino-1,2,4-triazoles (9a,b) with (α and β) bifunctional compounds like chloromethyl biphenyl-phosphanoxide, pyruvic acid, phenacyl bromide, diethyl oxalate, triethyl orthoformate, triethyl phosphite, fluorinated benzaldehydes, carbon disulfide and ethyl chloroformate under different experimental settings. Fourier transformer infrared analysis (FTIR), Proton nuclear magnetic resonance (<sup>1</sup>H NMR) and <sup>13</sup>C nuclear magnetic resonance (<sup>13</sup>C NMR), as well as that of the mass spectral data, were used as the appropriate characterization techniques for the chemical structures of all newly synthesized compounds. The newly prepared compounds were examined as an anti-inflammatory, antibacterial agents (against E. coli (Escherichia coli) and P. aeruginosa (Pseudomonas aeruginosa) as examples for Gram-negative bacteria and S. aureus (Staphylococcus aureus) as examples for Gram-positive bacteria), as well as antifungal (against C. albicans (Candida albicans)) agents. The newly prepared compound showed high antibacterial, antifungal, and anti-inflammatory activities in comparing with the commercial antibiotics Indomethacin, Nalidixic acid, Imipenem, and Nystatin. Docking of the most active compounds was performed depending on the results of antibacterial screening and the anti-inflammatory assay. Chemistry ; Triethyl orthoformate ; Stereochemistry ; Organic chemistry Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity ; Click Chemistry and Applications
Authors: Hyunwook Jung | Sina Stocker | Christian Künkel | Harald Oberhofer | Byungchan Han | Karsten Reuter | Johannes T. Margraf
Source: ChemSystemsChem (article, 2020-02-04)
Field: Materials Science — Materials Chemistry

Size‐Extensive Molecular Machine Learning with Global Representations Abstract Machine learning (ML) models are increasingly used in combination with electronic structure calculations to predict molecular properties at a much lower computational cost in high‐throughput settings. Such ML models require representations that encode the molecular structure, which are generally designed to respect the symmetries and invariances of the target property. However, size‐extensivity is usually not guaranteed for so‐called global representations. In this contribution, we show how extensivity can be built into global ML models using, e. g ., the Many‐Body Tensor Representation. Properties of extensive and non‐extensive models for the atomization energy are systematically explored by training on small molecules and testing on small, medium and large molecules. Our results show that non‐extensive models are only useful in the size‐range of their training set, whereas extensive models provide reasonable predictions across large size differences. Remaining sources of error for extensive models are discussed. Computer science ; Representation (politics) ; Range (aeronautics) ; Set (abstract data type) ; Property (philosophy) ; Tensor (intrinsic definition) ; Theoretical computer science ; Machine learning ; Artificial intelligence ; Statistical physics ; Mathematics ; Physics ; Materials science ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Zhongliang Guo | Stephen Wu | Mitsuru Ohno | Ryo Yoshida
Source: Journal of Chemical Information and Modeling (article, 2020-09-25)
Field: Materials Science — Materials Chemistry

Bayesian Algorithm for Retrosynthesis The identification of synthetic routes that end with the desired product is considered an inherently time-consuming process that is largely dependent on expert knowledge regarding a limited proportion of the entire reaction space. At present, emerging machine learning technologies are reformulating the process of retrosynthetic planning. This study aimed to discover synthetic routes backwardly from a given desired molecule to commercially available compounds. The problem is reduced to a combinatorial optimization task with the solution space subject to the combinatorial complexity of all possible pairs of purchasable reactants. We address this issue within the framework of Bayesian inference and computation. The workflow consists of the training of a deep neural network, which is used to forwardly predict a product of the given reactants with a high level of accuracy, followed by inversion of the forward model into the backward one via Bayes' law of conditional probability. Using the backward model, a diverse set of highly probable reaction sequences ending with a given synthetic target is exhaustively explored using a Monte Carlo search algorithm. With a forward model prediction accuracy of approximately 87%, the Bayesian retrosynthesis algorithm successfully rediscovered 81.8 and 33.3% of known synthetic routes of one-step and two-step reactions, respectively, with top-10 accuracy. Remarkably, the Monte Carlo algorithm, which was specifically designed for the presence of multiple diverse routes, often revealed a ranked list of hundreds of reaction routes to the same synthetic target. We also investigated the potential applicability of such diverse candidates based on expert knowledge of synthetic organic chemistry. Retrosynthetic analysis ; Computer science ; Algorithm ; Monte Carlo method ; Bayesian network ; Artificial intelligence ; Bayesian inference ; Machine learning ; Bayesian probability ; Process (computing) ; Bayes' theorem ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Tomáš Bučko | Monika Gešvandtnerová | Dario Rocca
Source: Journal of Chemical Theory and Computation (article, 2020-07-27)
Field: Materials Science — Materials Chemistry

Ab Initio Calculations of Free Energy of Activation at Multiple Electronic Structure Levels Made Affordable: An Effective Combination of Perturbation Theory and Machine Learning While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the training of the machine learning model requires only a small amount of data and does not need to be performed again when the temperature is decreased. The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal approximation of density functional theory, free energies based on significantly more expensive nonlocal van der Waals and hybrid functionals are obtained with only a few tens of additional single point calculations. In this way this work paves the route to quick free energy calculations using different levels of theory or approximations that would be too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations. Ab initio ; Perturbation theory (quantum mechanics) ; Density functional theory ; van der Waals force ; Ab initio quantum chemistry methods ; Statistical physics ; Computer science ; Physics ; Computational chemistry ; Quantum mechanics ; Chemistry ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular Junctions and Nanostructures