jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexandra N. Edelbrock | Zaida Álvarez | Dina Simkin | Timmy Fyrner | Stacey M. Chin | Kohei Sato | Evangelos Kiskinis | Samuel I. Stupp
Source: Nano Letters (article, 2018-09-13)
Field: Materials Science — Biomaterials

Supramolecular Nanostructure Activates TrkB Receptor Signaling of Neuronal Cells by Mimicking Brain-Derived Neurotrophic Factor Brain-derived neurotrophic factor (BDNF), a neurotrophin that binds specifically to the tyrosine kinase B (TrkB) receptor, has been shown to promote neuronal differentiation, maturation, and synaptic plasticity in the central nervous system (CNS) during development or after injury and onset of disease. Unfortunately, native BDNF protein-based therapies have had little clinical success due to their suboptimal pharmacological properties. In the past 20 years, BDNF mimetic peptides have been designed with the purpose of activating certain cell pathways that mimic the functional activity of native BDNF, but the interaction of mimetic peptides with cells can be limited due to the conformational specificity required for receptor activation. We report here on the incorporation of a BDNF mimetic sequence into a supramolecular peptide amphiphile filamentous nanostructure capable of activating the BDNF receptor TrkB and downstream signaling in primary cortical neurons in vitro. Interestingly, we found that this BDNF mimetic peptide is only active when displayed on a peptide amphiphile supramolecular nanostructure. We confirmed that increased neuronal maturation is linked to TrkB signaling pathways by analyzing the phosphorylation of downstream signaling effectors and tracking electrical activity over time. Furthermore, three-dimensional gels containing the BDNF peptide amphiphile (PA) nanostructures encourage cell infiltration while increasing functional maturation. Our findings suggest that the BDNF mimetic PA nanostructure creates a highly bioactive matrix that could serve as a biomaterial therapy in injured regions of the CNS. This new strategy has the potential to induce endogenous cell infiltration and promote functional neuronal maturation through the presentation of the BDNF mimetic signal. Tropomyosin receptor kinase B ; Neurotrophin ; Cell biology ; Neurotrophic factors ; Brain-derived neurotrophic factor ; Signal transduction ; Neuroscience ; Receptor ; Biology ; Chemistry ; Biochemistry Supramolecular Self-Assembly in Materials ; Nerve injury and regeneration ; Adenosine and Purinergic Signaling
Authors: Maria V. Grabchenko | N. N. Mikheeva | G. V. Mamontov | M.A. Salaev | Leonarda Francesca Liotta | O. V. Vodyankina
Source: Catalysts (article, 2018-07-16)
Field: Materials Science — Materials Chemistry

Ag/CeO2 Composites for Catalytic Abatement of CO, Soot and VOCs Nowadays catalytic technologies are widely used to purify indoor and outdoor air from harmful compounds. Recently, Ag–CeO2 composites have found various applications in catalysis due to distinctive physical-chemical properties and relatively low costs as compared to those based on other noble metals. Currently, metal–support interaction is considered the key factor that determines high catalytic performance of silver–ceria composites. Despite thorough investigations, several questions remain debating. Among such issues, there are (1) morphology and size effects of both Ag and CeO2 particles, including their defective structure, (2) chemical and charge state of silver, (3) charge transfer between silver and ceria, (4) role of oxygen vacancies, (5) reducibility of support and the catalyst on the basis thereof. In this review, we consider recent advances and trends on the role of silver–ceria interactions in catalytic performance of Ag/CeO2 composites in low-temperature CO oxidation, soot oxidation, and volatile organic compounds (VOCs) abatement. Promising photo- and electrocatalytic applications of Ag/CeO2 composites are also discussed. Catalysis ; Materials science ; Soot ; Noble metal ; Metal ; Chemical engineering ; Nanotechnology ; Composite material ; Chemistry ; Metallurgy ; Organic chemistry ; Combustion Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Catalysis and Oxidation Reactions
Authors: Parteek Prasher | Manjeet Singh | Harish Mudila
Source: 3 Biotech (review, 2018-09-14)
Field: Materials Science — Materials Chemistry

Silver nanoparticles as antimicrobial therapeutics: current perspectives and future challenges Antimicrobial ; Silver nanoparticle ; Antibiotics ; Antimicrobial drug ; Nanotechnology ; Intensive care medicine ; Engineering ethics ; Medicine ; Biotechnology ; Risk analysis (engineering) ; Biology ; Microbiology ; Engineering ; Materials science ; Nanoparticle Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Mohammed Mostafa Adnan | Erlend Grytli Tveten | Julia Glaum | Marit‐Helen Ese | Sverre Hvidsten | Wilhelm R. Glomm | Mari‐Ann Einarsrud
Source: Advanced Electronic Materials (review, 2018-11-14)
Field: Materials Science — Materials Chemistry

Epoxy‐Based Nanocomposites for High‐Voltage Insulation: A Review Abstract Epoxy nanocomposites, with inorganic oxide nanoparticles as filler, can exhibit novel property combinations, such as enhanced mechanical strength, higher thermal conductivity, increased dielectric breakdown strength, and reduced complex permittivity. Therefore, they have interesting applications in nanodielectrics, such as high‐voltage insulation materials or in microelectromechanical systems. The primary challenge in the processing of nanocomposites is achieving a homogeneous dispersion of the nanoparticles. The dispersion quality affects the interfaces between the organic and the inorganic components, which can determine the final properties of the nanocomposite. Here, the processing methods and the resulting dielectric, mechanical, and thermal properties of epoxy nanocomposites with inorganic oxide fillers are presented. Functionalization of the nanoparticle generally improves the dispersion of the particles in the polymer matrix. Different oxide fillers are observed to have similar effects on the properties of the nanocomposites. Epoxy‐based nanocomposites exhibit improved dielectric breakdown strength and lower complex permittivity with inorganic oxide nanoparticles at low filler contents, compared to conventional composites with micrometer‐sized particles. While there are some inconsistencies in the findings, which may be attributed to differences in the dispersion quality, an improved understanding of the nanoparticle–epoxy interfaces in nanocomposites will enable tailoring of the desired properties, opening new avenues for application. Materials science ; Nanocomposite ; Epoxy ; Composite material ; Nanoparticle ; Dielectric ; Dielectric strength ; Dispersion (optics) ; Oxide ; Polymer nanocomposite ; Permittivity ; Polymer ; Surface modification ; Filler (materials) ; Nanotechnology ; Chemical engineering ; Optoelectronics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electromagnetic wave absorption materials
Authors: Frederik Lermyte | Dirk Valkenborg | Joseph A. Loo | Frank Sobott
Source: Mass Spectrometry Reviews (review, 2018-02-09)
Field: Chemistry — Spectroscopy

Radical solutions: Principles and application of electron‐based dissociation in mass spectrometry‐based analysis of protein structure In recent years, electron capture (ECD) and electron transfer dissociation (ETD) have emerged as two of the most useful methods in mass spectrometry-based protein analysis, evidenced by a considerable and growing body of literature. In large part, the interest in these methods is due to their ability to induce backbone fragmentation with very little disruption of noncovalent interactions which allows inference of information regarding higher order structure from the observed fragmentation behavior. Here, we review the evolution of electron-based dissociation methods, and pay particular attention to their application in "native" mass spectrometry, their mechanism, determinants of fragmentation behavior, and recent developments in available instrumentation. Although we focus on the two most widely used methods-ECD and ETD-we also discuss the use of other ion/electron, ion/ion, and ion/neutral fragmentation methods, useful for interrogation of a range of classes of biomolecules in positive- and negative-ion mode, and speculate about how this exciting field might evolve in the coming years. Chemistry ; Mass spectrometry ; Electron-capture dissociation ; Dissociation (chemistry) ; Computational chemistry ; Chromatography ; Collision-induced dissociation ; Analytical Chemistry (journal) ; Tandem mass spectrometry ; Physical chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Takayuki Kojima | Satoshi Kameoka | Shinpei Fujii | Shigenori Ueda | A.‐P. Tsai
Source: Science Advances (article, 2018-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Catalysis-tunable Heusler alloys in selective hydrogenation of alkynes: A new potential for old materials Heusler alloys (<i>X</i> <sub>2</sub> <i>YZ</i>) are well-established intermetallic compound materials in various fields because their function can be precisely adjusted by elemental substitution (e.g., <i>X</i> <sub>2</sub> <i>YZ</i> <sub>1-<i>x</i></sub> <i>Z</i>' <sub><i>x</i></sub> ). Although intermetallic compound catalysts started attracting attention recently, catalysis researchers are not familiar with Heusler alloys. We report their potential as novel catalysts focusing on the selective hydrogenation of alkynes. We found that Co<sub>2</sub>MnGe and Co<sub>2</sub>FeGe alloys have great alkene selectivity. Mutual substitution of Mn and Fe (Co<sub>2</sub>Mn <sub><i>x</i></sub> Fe<sub>1-<i>x</i></sub> Ge) enhanced the reaction rate without changing selectivity. The substitution of Ga for Ge decreased the selectivity but increased the reaction rate monotonically with Ga composition. Elucidation of these mechanisms revealed that the fine tuning of catalytic properties is possible in Heusler alloys by separately using ligand and ensemble effects of elemental substitution. Intermetallic ; Selectivity ; Catalysis ; Substitution (logic) ; Materials science ; Alkene ; Chemical engineering ; Inorganic chemistry ; Chemistry ; Metallurgy ; Alloy ; Organic chemistry Heusler alloys: electronic and magnetic properties ; Hydrogen Storage and Materials ; MXene and MAX Phase Materials
Authors: Louis Longley | Sean M. Collins | Chao Zhou | Glen J. Smales | Sarah E. Norman | Nick J. Brownbill | Christopher W. Ashling | Philip A. Chater | Robert Tovey | Carola‐Bibiane Schönlieb | Thomas F. Headen | Nicholas J. Terrill | Yuanzheng Yue | Andrew J. Smith | Frédéric Blanc | David A. Keen | Paul A. Midgley | Thomas D. Bennett
Source: Nature Communications (article, 2018-05-24)
Field: Chemistry — Inorganic Chemistry

Liquid phase blending of metal-organic frameworks The liquid and glass states of metal-organic frameworks (MOFs) have recently become of interest due to the potential for liquid-phase separations and ion transport, alongside the fundamental nature of the latter as a new, fourth category of melt-quenched glass. Here we show that the MOF liquid state can be blended with another MOF component, resulting in a domain structured MOF glass with a single, tailorable glass transition. Intra-domain connectivity and short range order is confirmed by nuclear magnetic resonance spectroscopy and pair distribution function measurements. The interfacial binding between MOF domains in the glass state is evidenced by electron tomography, and the relationship between domain size and T<sub>g</sub> investigated. Nanoindentation experiments are also performed to place this new class of MOF materials into context with organic blends and inorganic alloys. Metal-organic framework ; Phase (matter) ; Metal ; Materials science ; Nanotechnology ; Chemistry ; Metallurgy ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Jing Jiang | Xinhao Wang | Shuang Li | Fei Ding | Nantao Li | Shaoyu Meng | Ruifan Li | Jia Qi | Qingjun Liu | Gang Logan Liu
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic nano‐arrays for ultrasensitive bio‐sensing Abstract Surface plasmon resonance (SPR) and localized SPR (LSPR) effects have been shown as the principles of some highlysensitive sensors in recent decades. Due to the advances in nano‐fabrication technology, the plasmon nano‐array sensors based on SPR and LSPR phenomena have been widely used in chemical and bioloical analysis. Sensing with surface‐enhanced field and sensing for refractive index changes are able to identify the analytes quantitatively and qualitatively. With the newly developed ultrasensitive plasmonic biosensors, platforms with excellent performance have been built for various biomedical applications, including point‐of‐care diagnosis and personalized medicine. In addition, flexible integration of plasmonics nano‐arrays and combining them with electrochemical sensing have significantly enlarged the application scenarios of the plasmonic nano‐array sensors, as well as improved the sensing accuracy. Plasmon ; Nanomaterials ; Nano- ; Nanotechnology ; Materials science ; Optical sensing ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Baowei Zhang | Tingting Zhu | Mingyang Ou | N. L. Rowell | Hongsong Fan | Jiantao Han | Lei Tan | Martin T. Dove | Yang Ren | Xiaobing Zuo | Shuo Han | Jianrong Zeng | Kui Yu
Source: Nature Communications (article, 2018-06-21)
Field: Materials Science — Materials Chemistry

Thermally-induced reversible structural isomerization in colloidal semiconductor CdS magic-size clusters Structural isomerism of colloidal semiconductor nanocrystals has been largely unexplored. Here, we report one pair of structural isomers identified for colloidal nanocrystals which exhibit thermally-induced reversible transformations behaving like molecular isomerization. The two isomers are CdS magic-size clusters with sharp absorption peaks at 311 and 322 nm. They have identical cluster masses, but slightly different structures. Furthermore, their interconversions follow first-order unimolecular reaction kinetics. We anticipate that such isomeric kinetics are applicable to a variety of small-size functional nanomaterials, and that the methodology developed for our kinetic study will be helpful to investigate and exploit solid-solid transformations in other semiconductor nanocrystals. The findings on structural isomerism should stimulate attention toward advanced design and synthesis of functional nanomaterials enabled by structural transformations. Isomerization ; Semiconductor ; Materials science ; Colloid ; Nanotechnology ; Chemical physics ; Chemistry ; Optoelectronics ; Physical chemistry ; Catalysis Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Asif Chowdhury | Wenqiang Yang | Eric A. Walker | Osman Mamun | Andreas Heyden | Gabriel Terejanu
Source: The Journal of Physical Chemistry C (article, 2018-11-09)
Field: Materials Science — Materials Chemistry

Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis. Adsorption ; Catalysis ; Chemical engineering ; Metal ; Materials science ; Chemistry ; Physical chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Yanbo Pan | Xiaochen Shen | Libo Yao | Abdulaziz Bentalib | Zhenmeng Peng
Source: Catalysts (article, 2018-10-20)
Field: Materials Science — Materials Chemistry

Active Sites in Heterogeneous Catalytic Reaction on Metal and Metal Oxide: Theory and Practice Active sites play an essential role in heterogeneous catalysis and largely determine the reaction properties. Yet identification and study of the active sites remain challenging owing to their dynamic behaviors during catalysis process and issues with current characterization techniques. This article provides a short review of research progresses in active sites of metal and metal oxide catalysts, which covers the past achievements, current research status, and perspectives in this research field. In particular, the concepts and theories of active sites are introduced. Major experimental and computational approaches that are used in active site study are summarized, with their applications and limitations being discussed. An outlook of future research direction in both experimental and computational catalysis research is provided. Biochemical engineering ; Catalysis ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Valentin Stanev | Velimir V. Vesselinov | A. Gilad Kusne | Graham Antoszewski | Ichiro Takeuchi | Boian S. Alexandrov
Source: npj Computational Materials (article, 2018-07-31)
Field: Materials Science — Materials Chemistry

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering Abstract Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capable of robust determination of the number of basis patterns present in the data which, in turn, enables straightforward identification of any possible peak-shifted patterns. Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries. Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns, which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions. The process can be utilized to determine accurately the compositional phase diagram of a system under study. The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities. Cluster analysis ; Basis (linear algebra) ; Pattern recognition (psychology) ; Non-negative matrix factorization ; Identification (biology) ; Matrix decomposition ; Basis function ; Process (computing) ; Matrix (chemical analysis) X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Minh-Tuan Nguyen | Việt Hùng Nguyễn | Suk-Jun Yun | Yong‐Hwa Kim
Source: Energies (article, 2018-05-09)
Field: Materials Science — Materials Chemistry

Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS. Switchgear ; Partial discharge ; Support vector machine ; Feature extraction ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Focus (optics) ; Artificial neural network ; Representation (politics) ; Feature (linguistics) ; Recurrent neural network ; Data mining ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection