jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoman Zhang | Pengfei Tian | Weifeng Tu | Zhenzhou Zhang | Jing Xu | Yi‐Fan Han
Source: ACS Catalysis (article, 2018-04-30)
Field: Materials Science — Materials Chemistry

Tuning the Dynamic Interfacial Structure of Copper–Ceria Catalysts by Indium Oxide during CO Oxidation The substitution of base-metal oxides for noble metals is a great challenge for catalysts, sensors, and other functional materials. In this work, the dynamic structure at the interface of binary metal oxides, as a popular natural phenomenon in material science and catalysis, was studied in detail in the case of a binary copper–ceria species (CuOx-CeO2). The catalytic activity of CuOx-CeO2 could be largely improved by doping indium oxide (In2O3). The reaction rate of 1.26 × 10–5 molCO gcat.–1 s–1 for a 1.25In5Cu/CeO2 catalyst toward CO oxidation is 12 times higher than that from commercial Pd catalysts. In addition, the indium-doped catalyst shows strong resistance to CO2 and H2O poisoning. We determined the dynamic interfacial structure of CuOx/CeO2 catalysts induced by In2O3 during CO oxidation using in situ techniques, intrinsic kinetics, and density functional theory calculations (DFT). Indeed, the surface of CuOx particles could be reconstructed through the interaction with In2O3. Such an interaction not only helps to generate more active sites at interfaces between CuOx and CeO2 but also lowers the CO adsorption strength and reduces the accumulation of surface carbonates. Meanwhile, In2O3 could also modify the electronic structure to improve the reducibility of CuOx, thus shifting the redox equilibrium of Cu2+ + Ce3+ ↔ Cu+ + Ce4+ to create Cu+ or Cu0 species at the interfacial sites. This study not only reveals the dynamic interfacial structure of metal oxide catalysts but also demonstrates a feasible way to fine-tune the interfacial structure of binary metal oxides. Catalysis ; Oxide ; Indium ; Redox ; Metal ; Materials science ; Copper ; Inorganic chemistry ; Adsorption ; Chemical engineering ; Density functional theory ; Chemistry ; Physical chemistry ; Computational chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Nanomaterials for catalytic reactions
Authors: Xiaoling Wu | Changlong Hao | Jatish Kumar | Hua Kuang | Nicholas A. Kotov | Luis M. Liz‐Marzán | Chuanlai Xu
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Environmentally responsive plasmonic nanoassemblies for biosensing Assemblies of plasmonic nanoparticles enable new modalities for biosensing. Engineered superstructures from metal nanoparticles can enhance the plasmon resonances and chiroptical activity of nanoscale dispersions. Such phenomena are keys to the fabrication of highly sensitive, selective and fast-responding detection platforms, making them promising candidates for clinical applications. This tutorial review summarizes and discusses recent advances in this area. The topics covered in the review include the basic strategies adopted for assembly and engineering of plasmonic nanoparticles, optical properties of the assembled nanostructures and their applications to both in vitro and in vivo detection of biological compounds. We also offer our vision of the future prospects of this field of research. Among emerging applications in this area are novel nanosensors and platforms, for food safety, environmental monitoring, health safeguarding, as well as biodefense. Nanotechnology ; Nanosensor ; Plasmon ; Biosensor ; Biodefense ; Materials science ; Plasmonic nanoparticles ; Computer science ; Nanoparticle ; Medicine Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: David A. Hopper | Henry J. Shulevitz | Lee C. Bassett
Source: Micromachines (review, 2018-08-30)
Field: Materials Science — Materials Chemistry

Spin Readout Techniques of the Nitrogen-Vacancy Center in Diamond The diamond nitrogen-vacancy (NV) center is a leading platform for quantum information science due to its optical addressability and room-temperature spin coherence. However, measurements of the NV center's spin state typically require averaging over many cycles to overcome noise. Here, we review several approaches to improve the readout performance and highlight future avenues of research that could enable single-shot electron-spin readout at room temperature. Diamond ; Nitrogen-vacancy center ; Spin (aerodynamics) ; Coherence (philosophical gambling strategy) ; Vacancy defect ; Coherence time ; Quantum sensor ; Center (category theory) ; Physics ; Materials science ; Optoelectronics ; Quantum information ; Quantum ; Condensed matter physics ; Quantum network ; Chemistry ; Quantum mechanics Diamond and Carbon-based Materials Research ; Semiconductor materials and devices ; Electronic and Structural Properties of Oxides
Authors: Rong Ye | Aleksandr V. Zhukhovitskiy | Roman V. Kazantsev | Sirine C. Fakra | Brent B. Wickemeyer | F. Dean Toste | Gábor A. Somorjai
Source: Journal of the American Chemical Society (article, 2018-03-05)
Field: Chemistry — Organic Chemistry

Supported Au Nanoparticles with <i>N</i>-Heterocyclic Carbene Ligands as Active and Stable Heterogeneous Catalysts for Lactonization Attachment of N-heterocyclic carbenes (NHCs) on the surface of metal nanoparticle (NP) catalysts permits fine-tuning of catalytic activity and product selectivity. Yet, NHC-coated Au NPs have been seldom used in catalysis beyond hydrogenation chemistry. One challenge in this field has been to develop a platform that permits arbitrary ligand modification without having to compromise NP stability toward aggregation or leaching. Herein, we exploit the strategy of supported dendrimer-encapsulated metal clusters (DEMCs) to achieve aggregation-stable yet active heterogeneous Au NP catalysts with NHC ligands. Dendrimers function as aggregation-inhibitors during the NP synthesis, and NHCs, well-known for their strong attachment to the gold surface, provide a handle to modify the stereochemistry, stereoelectronics, and chemical functionality of the NP surface. Indeed, compared to "ligandless" Au NPs which are virtually inactive below 80 °C, the NHC-ligated Au NP catalysts enable a model lactonization reaction to proceed at 20 °C on the same time scale (hours). Based on Eyring analysis, proto-deauration is the turnover-limiting step accelerated by the NHC ligands. Furthermore, the use of chiral NHCs led to asymmetric induction (up to 16% enantiomeric excess) in the lactonization transformations, which demonstrates the potential of supported DEMCs with ancillary ligands in enantioselective catalysis. Chemistry ; Catalysis ; Carbene ; Combinatorial chemistry ; Dendrimer ; Enantioselective synthesis ; Nanoparticle ; Ligand (biochemistry) ; Selectivity ; Metal ; Heterogeneous catalysis ; Organic chemistry ; Nanotechnology N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Rongxin Xia | Sabre Kais
Source: Nature Communications (article, 2018-10-04)
Field: Materials Science — Materials Chemistry

Quantum machine learning for electronic structure calculations Considering recent advancements and successes in the development of efficient quantum algorithms for electronic structure calculations-alongside impressive results using machine learning techniques for computation-hybridizing quantum computing with machine learning for the intent of performing electronic structure calculations is a natural progression. Here we report a hybrid quantum algorithm employing a restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. By exploiting a quantum algorithm to help optimize the underlying objective function, we obtained an efficient procedure for the calculation of the electronic ground state energy for a small molecule system. Our approach achieves high accuracy for the ground state energy for H<sub>2</sub>, LiH, H<sub>2</sub>O at a specific location on its potential energy surface with a finite basis set. With the future availability of larger-scale quantum computers, quantum machine learning techniques are set to become powerful tools to obtain accurate values for electronic structures. Electronic structure ; Quantum machine learning ; Quantum ; Restricted Boltzmann machine ; Ground state ; Energy (signal processing) ; Quantum computer ; Quantum algorithm Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Physical and Chemical Molecular Interactions
Authors: Qian Ma | Qiao Hong | Xin Gao | Hong Bo Jing | Che Liu | Guo Dong Bai | Qiang Cheng | Tie Jun Cui
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Smart sensing metasurface with self‐defined functions in dual polarizations Abstract For the intelligence of metamaterials, the ‐sensing mechanism and programmable reaction units are two important components for self‐recognition and ‐determination. However, their realization still face great challenges. Here, we propose a smart sensing metasurface to achieve self‐defined functions in the framework of digital coding metamaterials. A sensing unit that can simultaneously process the sensing channel and realize phase‐programmable capability is designed by integrating radio frequency (RF) power detector and PIN diodes. Four sensing units distributed on the metasurface aperture can detect the microwave incidences in the x ‐ and y ‐polarizations, while the other elements can modulate the reflected phase patterns under the control of a field programmable gate array (FPGA). To validate the performance, three schemes containing six coding patterns are presented and simulated, after which two of them are measured, showing good agreements with designs. We envision that this work may motivate studies on smart metamaterials with high‐level recognition and manipulation. Metamaterial ; Field-programmable gate array ; PIN diode ; Computer science ; Realization (probability) ; Coding (social sciences) ; Diode ; Microwave ; Detector ; Electronic engineering ; Computer hardware ; Physics ; Optoelectronics ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Blassan P. George | Neeraj Kumar | Heidi Abrahamse | Suprakas Sinha Ray
Source: Scientific Reports (article, 2018-09-19)
Field: Materials Science — Materials Chemistry

Apoptotic efficacy of multifaceted biosynthesized silver nanoparticles on human adenocarcinoma cells Metallic nanoparticles (NPs) especially silver (Ag) NPs have shown immense potential in medical applications due to their distinctive physio-chemical and biological properties. This article reports the conjugation of Ag NPs with Rubus fairholmianus extract. The modification of Ag NPs was confirmed using various physico-chemical characterization techniques. The cytotoxic effect of Rubus-conjugated Ag NPs (RAgNPs) was studied by LDH assay and proliferation by ATP assay. The apoptotic inducing ability of the NPs were investigated by Annexin V/PI staining, caspase 3/7 analysis, cytochrome c release, intracellular ROS analysis, Hoechst staining and mitochondrial membrane potential analysis using flow cytometry. The expression of apoptotic proteins caspase 3, Bax and P53 were analyzed using ELISA and caspase 3, Bax using western blotting. Cells treated with 10 µg/mL RAgNPs showed an increased number of cell death by microscopic analysis compared to untreated control cells. The RAgNPs induced a statistically significant dose-dependent decrease in proliferation (p < 0.001 for 5 and 10 µg/mL) and increased cytotoxicity in MCF-7 cells. A 1.83 fold increase in cytotoxicity was observed in cells treated with 10 µg/mL (p < 0.05) compared to the untreated cells. Nuclear damage and intracellular ROS production were observed upon treatment with all tested concentrations of RAgNPs and the highest concentrations (5 and 10 µg/mL) showed significant (p < 0.05, p < 0.01) expression of caspase 3, Bax and P53 proteins. The data strongly suggest that RAgNPs induces cell death in MCF-7 cells through the mitochondrial-mediated intrinsic apoptosis pathway. The present investigation supports the potential of RAgNPs in anticancer drug development. Apoptosis ; Cytotoxicity ; Annexin ; Flow cytometry ; Molecular biology ; Intracellular ; Cytochrome c ; Programmed cell death ; Chemistry ; Caspase ; Caspase 3 ; Biology ; Biochemistry ; In vitro Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Cell death mechanisms and regulation
Authors: Michael J. Willatt | Félix Musil | Michele Ceriotti
Source: Physical Chemistry Chemical Physics (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes it possible to improve the accuracy of the model for a given amount of reference data. When using a description of the structures that is transparent and well-principled, optimizing the representation might reveal insights into the chemistry of the data set. Here we show how one can generalize the SOAP kernel to introduce a distance-dependent weight that accounts for the multi-scale nature of the interactions, and a description of correlations between chemical species. We show that this improves substantially the performance of ML models of molecular and materials stability, while making it easier to work with complex, multi-component systems and to extend SOAP to coarse-grained intermolecular potentials. The element correlations that give the best performing model show striking similarities with the conventional periodic table of the elements, providing an inspiring example of how machine learning can rediscover, and generalize, intuitive concepts that constitute the foundations of chemistry. Representation (politics) ; Kernel (algebra) ; Table (database) ; Feature (linguistics) ; Periodic table ; Intermolecular force ; Element (criminal law) Machine Learning in Materials Science ; History and advancements in chemistry ; Graph theory and applications
Authors: Zhenbin Wang | Jungmin Ha | Yoon Hwa Kim | Won Bin Im | Joanna McKittrick | Shyue Ping Ong
Source: Joule (article, 2018-02-19)
Field: Materials Science — Materials Chemistry

Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes Phosphor ; Color rendering index ; Materials science ; Diode ; Light-emitting diode ; Optoelectronics ; Blue light ; White light ; Excited state ; Solid-state lighting ; Physics Luminescence Properties of Advanced Materials ; Advanced Photocatalysis Techniques ; Perovskite Materials and Applications
Authors: Jia Liu | Yajuan Wei | Yanli Zhao
Source: ACS Sustainable Chemistry & Engineering (article, 2018-11-30)
Field: Chemistry — Inorganic Chemistry

Trace Carbon Dioxide Capture by Metal–Organic Frameworks Climate deterioration is closely related to the CO2 concentration in the atmosphere, which is considered one of the major environmental challenges we are facing today. It is urgent to take immediate actions to prevent further climate change. In comparison with postcombustion CO2 capture technologies from flue gas, trace CO2 capture directly from air is still a challenge but very important for both CO2 control in the atmosphere and air quality control in confined space. This review highlights recent research advances in the use of metal–organic frameworks (MOFs) for trace CO2 capture, with the emphasis on employing amine-functionalized MOFs and ultramicroporous MOFs. Herein, the reported strategies to enhance CO2 adsorption capacity and selectivity by MOFs are categorized into three main directions, including the developments of MOFs with open metal sites, ultramicroporous MOFs, and amine-functionalized MOFs. The mechanisms combined with trace CO2 capture performance by these MOFs are discussed in detail, offering some promising adsorption solutions for future practical applications of MOF materials. In addition, the performance for CO2 capture under humid conditions and the regenerability of these MOF adsorbents are revealed. In order to address major issues of capacity, selectivity, and stability especially under humid conditions, precise construction and engineering of MOFs to achieve the optimized porous materials are needed. Metal-organic framework ; Flue gas ; Adsorption ; Nanotechnology ; Trace gas ; TRACE (psycholinguistics) ; Environmental science ; Materials science ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Masato Sumita | Xiufeng Yang | Shinsuke Ishihara | Ryo Tamura | Koji Tsuda
Source: ACS Central Science (article, 2018-08-20)
Field: Materials Science — Materials Chemistry

Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using a molecule generator and a DFT simulator, and attempted to generate novel photofunctional molecules whose lowest excited states lie at desired energetic levels. A 10 day run on the 12-core server discovered 86 potential photofunctional molecules around target lowest excitation levels, designated as 200, 300, 400, 500, and 600 nm. Among the molecules discovered, six were synthesized, and five were confirmed to reproduce DFT predictions in ultraviolet visible absorption measurements. This result shows the potential of AI-assisted chemistry to discover ready-to-synthesize novel molecules with modest computational resources. Molecule ; Density functional theory ; Excited state ; Generator (circuit theory) ; Excitation ; Organic molecules ; Computer science ; Computational chemistry ; Ultraviolet ; Nanotechnology ; Chemistry ; Artificial intelligence ; Materials science ; Physics ; Atomic physics ; Organic chemistry ; Quantum mechanics ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Mohamad Tarhini | Ihsane Benlyamani | Selim Hamdani | G. Agusti | Hatem Fessi | Hélène Greige‐Gerges | Abderrazzak Bentaher | Abdelhamid Elaı̈ssari
Source: Materials (article, 2018-03-07)
Field: Materials Science — Biomaterials

Protein-Based Nanoparticle Preparation via Nanoprecipitation Method Nanoparticles are nowadays largely investigated in the field of drug delivery. Among nanoparticles, protein-based particles are of paramount importance since they are natural, biodegradable, biocompatible, and nontoxic. There are several methods to prepare proteins containing nanoparticles, but only a few studies have been dedicated to the preparation of protein- based nanoparticles. Then, the aim of this work was to report on the preparation of bovine serum albumin (BSA)-based nanoparticles using a well-defined nanoprecipitation process. Special attention has been dedicated to a systematic study in order to understand separately the effect of each operating parameter of the method (such as protein concentration, solvent/non-solvent volume ratio, non-solvent injection rate, ionic strength of the buffer solution, pH, and cross-linking) on the colloidal properties of the obtained nanoparticles. In addition, the mixing processes (batch or drop-wise) were also investigated. Using a well-defined formulation, submicron protein-based nanoparticles have been obtained. All prepared particles have been characterized in terms of size, size distribution, morphology, and electrokinetic properties. In addition, the stability of nanoparticles was investigated using Ultraviolet (UV) scan and electrophoresis, and the optimal conditions for preparing BSA nanoparticles by the nanoprecipitation method were concluded. Nanoparticle ; Ionic strength ; Materials science ; Bovine serum albumin ; Solvent ; Nanotechnology ; Chemical engineering ; Chromatography ; Chemistry ; Organic chemistry ; Aqueous solution Nanoparticle-Based Drug Delivery ; Protein Interaction Studies and Fluorescence Analysis ; Microfluidic and Capillary Electrophoresis Applications
Authors: Christel M. Marian | Adrian Heil | Martin Kleinschmidt
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2018-10-23)
Field: Chemistry — Physical and Theoretical Chemistry

The DFT/MRCI method In the past two decades, the combined density functional theory and multireference configuration interaction (DFT/MRCI) method has developed from a powerful approach for computing spectral properties of singlet and triplet excited states of large molecules into a more general multireference method applicable to states of all spin multiplicities. In its original formulation, it shows great efficiency in the evaluation of singlet and triplet excited states which mainly originate from local one‐electron transitions. Moreover, DFT/MRCI is one of the few methods applicable to large systems that yields the correct ordering of states in extended π ‐systems where double excitations play a significant role. A recently redesigned DFT/MRCI Hamiltonian extends the application range of the method to bi‐chromophores such as hydrogen‐bonded or π ‐stacked dimers and loosely coupled donor–acceptor systems. In conjunction with a restricted‐open shell Kohn–Sham optimization of the molecular orbitals, even electronically excited doublet and quartet states can be addressed. After a short outline of the general ideas behind this semi‐empirical method and a brief review of alternative approaches combining density functional and multireference wavefunction theory, formulae for the DFT/MRCI Hamiltonian matrix elements are presented and the adjustments of the two‐electron contributions are discussed. The performance of the DFT/MRCI variants on excitation energies of organic molecules and transition metal compounds against experimental or ab initio reference data is analyzed and case studies are presented which show the strengths and limitations of the method. Finally, an overview over the properties available from DFT/MRCI wavefunctions and further developments is given. This article is categorized under: Electronic Structure Theory &gt; Density Functional Theory Electronic Structure Theory &gt; Semiempirical Electronic Structure Methods Software &gt; Quantum Chemistry Multireference configuration interaction ; Wave function ; Excited state ; Density functional theory ; Singlet state ; Hamiltonian (control theory) ; Configuration interaction ; Chemistry ; Atomic orbital ; Ab initio ; Atomic physics ; Computational chemistry ; Quantum mechanics ; Physics ; Electron ; Mathematics Photochemistry and Electron Transfer Studies ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies