jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrew Hunter Davis | Elan Hofman | Kevin Chen | Zhijun Li | Alex Khammang | Hediyeh Zamani | John M. Franck | Mathew M. Maye | Robert W. Meulenberg | Weiwei Zheng
Source: Chemistry of Materials (article, 2019-03-18)
Field: Materials Science — Materials Chemistry

Exciton Energy Shifts and Tunable Dopant Emission in Manganese-Doped Two-Dimensional CdS/ZnS Core/Shell Nanoplatelets The ability to dope transition-metal ions into semiconductor nanocrystals (NCs) allows for the introduction and exploitation of new extrinsic properties in the original intrinsic material. Although the synthesis of doped zero-dimensional quantum dots and one-dimensional nanorods/nanowires has been widely reported, transition-metal ion-doped two-dimensional (2D) NCs have been less explored. In this study, we developed a one-pot synthesis of Mn2+-doped 2D CdS (i.e., Mn:CdS) nanoplatelets (NPLs). Successful Mn doping inside the CdS NPL lattice was confirmed by electron paramagnetic resonance and X-ray diffraction measurements. Surprisingly, only CdS photoluminescence (PL), without contribution from Mn PL, was observed in the Mn:CdS NPLs, regardless of Mn doping concentration. To address the issue of poor thermal stability and improve the optical properties of the 2D Mn:CdS NPLs, we synthesized ZnS shell-passivated Mn:CdS/ZnS core/shell NPLs using a single-source shelling precursor method, which allows for ZnS surface passivation of NPLs at relatively low temperatures, while being thermally adaptable to ensure minimal NPL degradation. An extremely large exciton red shift (∼420 meV), upon ZnS shell passivation, was observed because of the increased effective thickness of the CdS core NPLs. Steady-state and time-resolved emission measurements indicate that the host–dopant energy-transfer efficiency and Mn–Mn interactions within the 2D Mn:CdS/ZnS core/shell NPLs can be fine-tuned via the dopant concentration, resulting in an intense Mn PL as well as tunable dual-band emission from the host NPLs and Mn dopants. Magnetic measurements indicate intrinsic spin states in the 2D NPLs and complex magnetic interactions at high doping concentrations, including antiferromagnetic exchange between dopants and possible dopant–surface state interaction. Dopant ; Materials science ; Photoluminescence ; Passivation ; Doping ; Exciton ; Manganese ; Nanorod ; Quantum dot ; Optoelectronics ; Nanotechnology ; Condensed matter physics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Steven Kauwe | Trevor David Rhone | Taylor D. Sparks
Source: Crystals (article, 2019-01-18)
Field: Materials Science — Materials Chemistry

Data-Driven Studies of Li-Ion-Battery Materials Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials. Battery (electricity) ; Computer science ; Process (computing) ; Component (thermodynamics) ; Data science ; Systems engineering ; Engineering ; Power (physics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Battery Technologies Research
Authors: Gentoku Takasao | Toru Wada | Ashutosh Thakur | Patchanee Chammingkwan | Minoru Terano | Toshiaki Taniike
Source: ACS Catalysis (article, 2019-02-07)
Field: Materials Science — Materials Chemistry

Machine Learning-Aided Structure Determination for TiCl<sub>4</sub>–Capped MgCl<sub>2</sub> Nanoplate of Heterogeneous Ziegler–Natta Catalyst Dynamic reconstruction under a physicochemical environment is an intrinsic property of solid surfaces, in particular when associated with catalysis on nanosized or nanostructured materials. Here, we report nonempirical structure determination of TiCl4-capped MgCl2 nanoplates that is based on the combination of a genetic algorithm and density functional calculations. The methodology for the nonempirical structure determination was developed, and its application was demonstrated for 7MgCl2, 15MgCl2, and 15MgCl2/4TiCl4 in relation to the hidden identity of primary particles of the Ziegler–Natta catalyst. Bare MgCl2 nanoplates dominantly exposed {100} surfaces at their lateral cuts, but the chemisorption of TiCl4 induced reconstruction by stabilizing {110} surfaces. The most important finding of the present research is that TiCl4 exhibited distributed adsorption states as consequences of chemisorption on nonideal finite surfaces and the diversity of thermodynamically accessible structures. The assessment of the Ti distribution is essential for the distribution of primary structures of produced polymer, and in this study, we made these determinations. Natta ; Chemisorption ; Catalysis ; Adsorption ; Polymer ; Materials science ; Density functional theory ; Chemical engineering ; Ziegler–Natta catalyst ; Nanotechnology ; Chemical physics ; Chemistry ; Physical chemistry ; Computational chemistry ; Polymerization ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Catalytic Processes in Materials Science
Authors: Kei Terayama | Ryo Tamura | Yoshitaro Nose | Hidenori Hiramatsu | Hideo Hosono | Yasushi Okuno | Koji Tsuda
Source: Physical Review Materials (article, 2019-03-08)
Field: Materials Science — Materials Chemistry

Efficient construction method for phase diagrams using uncertainty sampling We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phases can be rapidly found, and smaller numbers of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science. Construct (python library) ; Sampling (signal processing) ; Phase diagram ; Phase (matter) ; Reduction (mathematics) ; Materials science ; Scratch ; Sample (material) ; Statistical physics ; Computer science ; Algorithm ; Mathematics ; Thermodynamics ; Geometry ; Programming language ; Physics ; Computer vision Machine Learning in Materials Science ; Machine Learning and Algorithms ; Force Microscopy Techniques and Applications
Authors: Olga Guselnikova | A. Trelin | Anastasia Skvortsova | Pavel Ulbrich | Павел С. Постников | Alexandra G. Pershina | David Sýkora | V. Svorcik | Oleksiy Lyutakov
Source: Biosensors and Bioelectronics (article, 2019-09-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage Artificial neural network ; Computer science ; Biological system ; Surface-enhanced Raman spectroscopy ; Spectrometer ; Raman scattering ; Raman spectroscopy ; Substrate (aquarium) ; Materials science ; Pattern recognition (psychology) ; Oligonucleotide ; Artificial intelligence ; DNA ; Chemistry ; Optics ; Physics ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced biosensing and bioanalysis techniques
Authors: Edwin Soedarmadji | Helge S. Stein | Santosh K. Suram | Dan Guevarra | John M. Gregoire
Source: npj Computational Materials (article, 2019-07-26)
Field: Materials Science — Materials Chemistry

Tracking materials science data lineage to manage millions of materials experiments and analyses Abstract In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research. Metadata ; Computer science ; Data science ; Interface (matter) ; Data management ; Software ; Raw data ; Automation ; Information retrieval ; Database ; World Wide Web ; Engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Research Data Management Practices
Authors: Faling Ling | Wei Kang | Huirong Jing | Wen Zeng | Yankun Chen | Xiaoqing Liu | Yixin Zhang | Qi Lin | Liang Fang | Miao Zhou
Source: npj Computational Materials (article, 2019-02-18)
Field: Materials Science — Materials Chemistry

Enhancing hydrogen evolution on the basal plane of transition metal dichacolgenide van der Waals heterostructures Abstract Recent years have seen a surge in the use of low-dimensional transition metal dichacolgenides, such as MoS 2 , as catalysts for the electrochemical hydrogen evolution reaction. In particular, sulfur vacancies in MoS 2 can activate the inert basal plane, but that requires an unrealistically high defect concentration (~9%) to achieve optimal activity. In this work, we demonstrate by first-principles calculations that assembling van der Waals heterostructures can enhance the catalytic activity of MoS 2 with low concentrations of sulfur vacancies. We integrate MoS 2 with various two-dimensional nanostructures, including graphene, h -BN, phosphorene, transition metal dichacolgenides, MXenes, and their derivatives, aiming to fine-tune the free energy of atomic hydrogen adsorption. Remarkably, an optimal free energy can be achieved for a low sulfur vacancy concentration of ~2.5% in the MoS 2 /MXene-OH heterostructure, as well as high porosity and tunability. These results demonstrate the potential of combining two-dimensional van der Waals assembly with defect engineering for efficient hydrogen production. van der Waals force ; MXenes ; Heterojunction ; Vacancy defect ; Chemical physics ; Transition metal ; Molybdenum disulfide ; Catalysis ; Materials science ; Phosphorene ; Nanotechnology ; Hydrogen ; Graphene ; Sulfur ; Chemistry ; Optoelectronics ; Molecule ; Crystallography MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Martin Koller
Source: The EuroBiotech Journal (article, 2019-01-01)
Field: Materials Science — Biomaterials

Switching from petro-plastics to microbial polyhydroxyalkanoates (PHA): the biotechnological escape route of choice out of the plastic predicament? Abstract The benefit of biodegradable “green plastics” over established synthetic plastics from petro-chemistry, namely their complete degradation and safe disposal, makes them attractive for use in various fields, including agriculture, food packaging, and the biomedical and pharmaceutical sector. In this context, microbial polyhydroxyalkanoates (PHA) are auspicious biodegradable plastic-like polyesters that are considered to exert less environmental burden if compared to polymers derived from fossil resources. The question of environmental and economic superiority of bio-plastics has inspired innumerable scientists during the last decades. As a matter of fact, bio-plastics like PHA have inherent economic drawbacks compared to plastics from fossil resources; they typically have higher raw material costs, and the processes are of lower productivity and are often still in the infancy of their technical development. This explains that it is no trivial task to get down the advantage of fossil-based competitors on the plastic market. Therefore, the market success of biopolymers like PHA requires R&amp;D progress at all stages of the production chain in order to compensate for this disadvantage, especially as long as fossil resources are still available at an ecologically unjustifiable price as it does today. Ecological performance is, although a logical argument for biopolymers in general, not sufficient to make industry and the society switch from established plastics to bio-alternatives. On the one hand, the review highlights that there’s indeed an urgent necessity to switch to such alternatives; on the other hand, it demonstrates the individual stages of the production chain, which need to be addressed to make PHA competitive in economic, environmental, ethical, and performance-related terms. In addition, it is demonstrated how new, smart PHA-based materials can be designed, which meet the customer’s expectations when applied, e.g., in the biomedical or food packaging sector. Polyhydroxyalkanoates ; Bioplastic ; Context (archaeology) ; Biochemical engineering ; Production (economics) ; Productivity ; Raw material ; Fossil fuel ; Biotechnology ; Natural resource economics ; Business ; Economics ; Engineering ; Waste management ; Ecology ; Biology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Additive Manufacturing and 3D Printing Technologies
Authors: Elizabeth Hampson | Jamie M. Cameron | Sharad S. Amin | Joungman Kyo | Julie A. Watts | Hiroki Oshio | Graham N. Newton
Source: Angewandte Chemie International Edition (article, 2019-10-09)
Field: Materials Science — Materials Chemistry

Asymmetric Hybrid Polyoxometalates: A Platform for Multifunctional Redox‐Active Nanomaterials Access to asymmetrically functionalized polyoxometalates is a grand challenge as it could lead to new molecular nanomaterials with multiple or modular functionality. Now, a simple one-pot synthetic approach to the isolation of an asymmetrically functionalized organic-inorganic hybrid Wells-Dawson polyoxometalate in good yield is presented. The cluster bears two organophosphonate moieties with contrasting physical properties: a chelating metal-binding group, and a long aliphatic chain that facilitates solvent-dependent self-assembly into soft nanostructures. The orthogonal properties of the modular system are effectively demonstrated by controlled assembly of POM-based redox-active nanoparticles. This simple, high-yielding synthetic method is a promising new approach to the preparation of multi-functional hybrid metal oxide clusters, supermolecular systems, and soft-nanomaterials. Polyoxometalate ; Nanomaterials ; Nanotechnology ; Hybrid material ; Nanoparticle ; Materials science ; Cluster (spacecraft) ; Redox ; Combinatorial chemistry ; Modular design ; Chemistry ; Organic chemistry ; Computer science ; Catalysis Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Rouba Ghobeira | Charlot Philips | Len Liefooghe | Marieke Verdonck | Mahtab Asadian | Pieter Cools | Heidi Declercq | Winnok H. De Vos | Nathalie De Geyter | Rino Morent
Source: Applied Surface Science (article, 2019-04-24)
Field: Materials Science — Biomaterials

Synergetic effect of electrospun PCL fiber size, orientation and plasma-modified surface chemistry on stem cell behavior Fiber ; Polycaprolactone ; Wetting ; Nanofiber ; Materials science ; Nanotechnology ; Synthetic fiber ; Chemistry ; Crystallinity ; Extracellular matrix ; Chemical engineering ; Biophysics ; Composite material ; Polymer Electrospun Nanofibers in Biomedical Applications ; Additive Manufacturing and 3D Printing Technologies ; Microplastics and Plastic Pollution
Authors: Genadi Naydenov | P. J. Hasnip | Vlado K. Lazarov | Matt Probert
Source: Journal of Physics Materials (article, 2019-04-08)
Field: Materials Science — Materials Chemistry

Huge power factor in p-type half-Heusler alloys NbFeSb and TaFeSb Abstract NbFeSb is a promising thermoelectric material which according to experimental and theoretical studies exhibits a high power factor of up to 10 mW m −1 K −2 at room temperature and ZT of 1 at 1000 K. In all previous theoretical studies, κ latt is calculated using simplified models, which ignore structural defects. In this work, we calculate κ latt by solving the Boltzmann transport equation and subsequently including the contributions of grain boundaries, point defects and electron–phonon interaction. The results for κ latt and ZT are in excellent agreement with experimental measurements. In addition, we investigate theoretically the thermoelectric properties of TaFeSb. The material has recently been synthesised experimentally, thus confirming the theoretical hypothesis for its stability. This encourages a full-scale computation of its thermoelectric performance. Our results show that TaFeSb is indeed an excellent thermoelectric material which has a very high power factor of 16 mW m −1 K −2 at room temperature and ZT of 1.5 at 1000 K. Thermoelectric effect ; Materials science ; Seebeck coefficient ; Condensed matter physics ; Thermoelectric materials ; Work (physics) ; Phonon ; Boltzmann equation ; Power factor ; Boltzmann constant ; Power (physics) ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; 2D Materials and Applications
Authors: Alicja Mikołajczyk | Natalia Sizochenko | Ewa Mulkiewicz | Anna Malankowska | Bakhtiyor Rasulev | Tomasz Puzyn
Source: Nanoscale (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

A chemoinformatics approach for the characterization of hybrid nanomaterials: safer and efficient design perspective In this study, photocatalytic properties and in vitro cytotoxicity of 29 TiO2-based multi-component nanomaterials (i.e., hybrids of more than two composition types of nanoparticles) were evaluated using a combination of the experimental testing and supervised machine learning modeling. TiO2-based multi-component nanomaterials with metal clusters of silver, and their mixtures with gold, palladium, and platinum were successfully synthesized. Two activities, photocatalytic activity and cytotoxicity, were studied. A novel cheminformatic approach was developed and applied for the computational representation of the photocatalytic activity and cytotoxicity effect. In this approach, features of investigated TiO2-based hybrid nanomaterials were reflected by a series of novel additive descriptors for hybrid and hybrid nanostructures (denoted as "hybrid nanosctructure descriptors"). These descriptors are based on quantum chemical calculations and the Smoluchowski equation. The obtained experimental data and calculated hybrid-nanostructure descriptors were used to develop novel predictive Quantitative Structure-Activity Relationship computational models (called "nano-QSARmix"). The proposed modeling approach is an initial step in the understanding of the relationships between physicochemical properties of hybrid nanoparticles, their toxicity, and photochemical activity under UV-vis irradiation. Acquired knowledge supports the safe-by-design approaches relevant to the development of efficient hybrid nanomaterials with reduced hazardous effects. SAFER ; Perspective (graphical) ; Cheminformatics ; Characterization (materials science) ; Nanomaterials ; Nanotechnology ; Risk analysis (engineering) ; Computer science ; Biochemical engineering ; Systems engineering ; Materials science ; Engineering ; Artificial intelligence ; Chemistry ; Business ; Computer security ; Computational chemistry Nanoparticles: synthesis and applications ; Machine Learning in Materials Science ; Nanotechnology research and applications
Authors: Geng Sun | Anastassia N. Alexandrova | Philippe Sautet
Source: The Journal of Chemical Physics (article, 2019-11-20)
Field: Materials Science — Materials Chemistry

Pt8 cluster on alumina under a pressure of hydrogen: Support-dependent reconstruction from first-principles global optimization Alumina supported Pt nanoclusters under a hydrogen environment play a crucial role in many heterogeneous catalysis applications. We conducted grand canonical genetic algorithm simulations for supported Pt<sub>8</sub> clusters in a hydrogen gas environment to study the intracluster, cluster-support, and cluster-adsorbate interactions. Two alumina surfaces, α-Al<sub>2</sub>O<sub>3</sub>(0001) and γ-Al<sub>2</sub>O<sub>3</sub>(100), and two conditions, T = 600 °C, p<sub>H<sub>2</sub></sub> = 0.1 bar and T = 25 °C, p<sub>H<sub>2</sub></sub> = 1.0 bar, were considered corresponding to low and high hydrogen chemical potential μ<sub>H</sub>, respectively. The low free energy ensemble of Pt<sub>8</sub> is decorated by a medium (2-12 H), respectively, high (20-30 H), number of hydrogen atoms under equilibrium at low μ<sub>H</sub>, respectively, high μ<sub>H</sub>, and undergoes different morphological transformations on the two surfaces. On α-Al<sub>2</sub>O<sub>3</sub>(0001), Pt<sub>8</sub> is mostly 3D but very fluxional in structure at low μ<sub>H</sub> and converts to open one-layer 2D structures with minimal fluxionality at high μ<sub>H</sub>, whereas on γ-Al<sub>2</sub>O<sub>3</sub>(100), the exact opposite occurs: Pt<sub>8</sub> clusters present one-layer 2D shapes at low μ<sub>H</sub> and switch to compact 3D shapes under high μ<sub>H</sub>, during which the Pt<sub>8</sub> cluster preserves moderate fluxionality. Further analysis reveals a similar Pt-Pt bond length increase when switching from low μ<sub>H</sub> to high μ<sub>H</sub> on both surfaces although morphological transformations are different. Electronic structure analysis shows the existence of bonding interactions between Pt and Lewis acidic Al<sup>3+</sup> sites along with the Pt-O interaction, which implies the necessity to include Al neighbors to discuss the electronic structure of small Pt clusters. Nanoclusters ; Cluster (spacecraft) ; Hydrogen ; Crystallography ; Electronic structure ; Bar (unit) ; Catalysis ; Chemistry ; Hydrogen bond ; Chemical physics ; Materials science ; Nanotechnology ; Molecule ; Computational chemistry ; Physics ; Computer science Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies