jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wil Gardner | Ruqaya Maliki | Suzanne M. Cutts | Benjamin W. Muir | Davide Ballabio | David A. Winkler | Paul J. Pigram
Source: Analytical Chemistry (article, 2020-07-02)
Field: Chemistry — Analytical Chemistry

Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data We present an optimization of the toroidal self-organizing map (SOM) algorithm for the accurate visualization of hyperspectral data. This represents a significant advancement on our previous work, in which we demonstrated the use of toroidal SOMs for the visualization of time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. We have previously shown that the toroidal SOM can be used, unsupervised, to produce a multicolor similarity map of the analysis area, in which pixels with similar mass spectra are assigned a similar color. Here, we use an additional algorithm, relational perspective mapping (RPM), to produce more accurate visualizations of hyperspectral data. The SOM output is used as an input for the RPM algorithm, which is a nonlinear dimensionality reduction technique designed to produce a two-dimensional map of high-dimensional data. Using the topological information provided by the SOM, RPM provides complementary distance information. The result is a color scheme that more accurately reflects the local spectral distances between pixels in the data. We exemplify SOM-RPM using ToF-SIMS imaging data from a mouse tumor tissue section. The similarity maps produced are compared with those produced by two leading hyperspectral visualization techniques in the field of mass spectrometry imaging: t-distributed stochastic neighborhood embedding (t-SNE) and uniform manifold approximation and projection (UMAP). We evaluate the performance of each technique both qualitatively and quantitatively, investigating the correlations between distances in the models and distances in the data. SOM-RPM is demonstrably highly competitive with t-SNE and UMAP, according to our evaluations. Furthermore, the use of a neural network offers distinct advantages in data characterization, which we discuss. We also show how spectra extracted from regions of interest identified by SOM-RPM can be further analyzed using linear discriminant analysis for the validation and characterization of the surface chemistry. Hyperspectral imaging ; Visualization ; Self-organizing map ; Pixel ; Similarity (geometry) ; Artificial intelligence ; Pattern recognition (psychology) ; Mass spectrometry imaging ; Curse of dimensionality ; Perspective (graphical) ; Data visualization ; Cluster analysis ; Chemistry ; Computer science ; Mass spectrometry ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Zhun Liu | Guangren Na | Fuyu Tian | Liping Yu | Jingbo Li | Lijun Zhang
Source: InfoMat (article, 2020-02-27)
Field: Materials Science — Materials Chemistry

Computational functionality‐driven design of semiconductors for optoelectronic applications Abstract The rapid development of the semiconductor industry has motivated researchers passion for accelerating the discovery of advanced optoelectronic materials. Computational functionality‐driven design is an emerging branch of material science that has become effective at making material predictions. By combining advanced solid‐state knowledge and high‐throughput first‐principles computational approaches with intelligent algorithms plus database development, experts can now efficiently explore many novel materials by taking advantage of the power of supercomputer architectures. Here, we discuss a set of typical design strategies that can be used to accelerate inorganic optoelectronic materials discovery from computer simulations: In silico computational screening; knowledge‐based inverse design; and algorithm‐based searching. A few representative examples in optoelectronic materials design are discussed to illustrate these computational functionality‐driven modalities. Challenges and prospects for the computational functionality‐driven design of materials are further highlighted at the end of the review. image Computer science ; Supercomputer ; Computer architecture ; Computational science ; Parallel computing Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Mohammad Amin Oyarhossein | As’ad Alizadeh | Mostafa Habibi | Mahmoud Makkiabadi | Mohsen Daman | Hamed Safarpour | Dong Won Jung
Source: Scientific Reports (article, 2020-03-27)
Field: Materials Science — Materials Chemistry

Dynamic response of the nonlocal strain-stress gradient in laminated polymer composites microtubes This study presents the frequency analysis of a size-dependent laminated polymer composite microtube using a nonlocal strain-stress gradient (NSG) model. By applying energy methods (known as Hamilton's principle), the motion equations of the laminated micro tube composites are developed. The thermodynamic equations of the laminated microtube are based on first-order shear deformation theory (FSDT), and a generalized differential quadrature method (GDQM) is employed to find the model for the natural frequencies. The results show that by considering C-F boundary conditions (BCs) and every even layers' number in lower value of length scale parameter, the frequency of the structure drops by soaring this parameter. However, this matter is inverse in its higher value. Eventually, the ply angle's influences, nonlocality as well as length scale element on the vibration of the laminated composite microstructure are investigated. Materials science ; Composite number ; Composite material ; Boundary value problem ; Quadrature (astronomy) ; Equations of motion ; Vibration ; Length scale ; Mechanics ; Mathematical analysis ; Classical mechanics ; Physics ; Mathematics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Thermoelastic and Magnetoelastic Phenomena
Authors: Sai Liu | Chi Yan Tso | Hau Him Lee | Yi Zhang | K. M. Yu | Christopher Y.H. Chao
Source: Scientific Reports (article, 2020-07-09)
Field: Materials Science — Polymers and Plastics

Bio-inspired TiO2 nano-cone antireflection layer for the optical performance improvement of VO2 thermochromic smart windows Vanadium dioxide (VO<sub>2</sub>) is a promising material for thermochromic glazing. However, VO<sub>2</sub> thermochromic smart windows suffer from several problems that prevent commercialization: low luminous transmittance (T<sub>lum</sub>) and low solar modulation ability (ΔT<sub>sol</sub>). The solution to these problems can be sought from nature where the evolution of various species has enabled them to survive. Investigations into the morphology of moths eyes has shown that their unique nanostructures provide an excellent antireflection optical layer that helps moths sharply capture the light in each wavelength from a wide angle. Inspired by this mechanism, a VO<sub>2</sub> thermochromic smart window coated with a TiO<sub>2</sub> antireflection layer with a novel nano-cone structure, is presented in this study to achieve high T<sub>lum</sub> and ΔT<sub>sol</sub>. Optimization for the key structure parameters is summarized based on the FDTD numerical simulations. The optimized structure exhibits a T<sub>lum</sub> of 55.4% with ΔT<sub>sol</sub> of 11.3%, an improvement of about 39% and 72% respectively compared to the VO<sub>2</sub> window without an antireflection layer. Furthermore, wide-angle antireflection and polarization independence are also demonstrated by this nano-cone coating. This work provides an alternative method to enhance the optical performance of VO<sub>2</sub> smart windows. Thermochromism ; Materials science ; Transmittance ; Optoelectronics ; Glazing ; Optics ; Layer (electronics) ; Nanotechnology ; Composite material Transition Metal Oxide Nanomaterials ; Optical Coatings and Gratings ; Optical Wireless Communication Technologies
Authors: Stefan Schulze | Zachary Adams | Micaela Cerletti | Rosana De Castro | Sébastien Ferreira-Cerca | Christian Fufezan | María Inés Giménez | Michael Hippler | Ž Jevtić | Robert Knüppel | Georgio Legerme | Christof Lenz | Anita Marchfelder | Julie A. Maupin‐Furlow | Roberto A. Paggi | Friedhelm Pfeiffer | Ansgar Poetsch | Henning Urlaub | Mechthild Pohlschröder
Source: Nature Communications (article, 2020-06-19)
Field: Chemistry — Spectroscopy

The Archaeal Proteome Project advances knowledge about archaeal cell biology through comprehensive proteomics While many aspects of archaeal cell biology remain relatively unexplored, systems biology approaches like mass spectrometry (MS) based proteomics offer an opportunity for rapid advances. Unfortunately, the enormous amount of MS data generated often remains incompletely analyzed due to a lack of sophisticated bioinformatic tools and field-specific biological expertise for data interpretation. Here we present the initiation of the Archaeal Proteome Project (ArcPP), a community-based effort to comprehensively analyze archaeal proteomes. Starting with the model archaeon Haloferax volcanii, we reanalyze MS datasets from various strains and culture conditions. Optimized peptide spectrum matching, with strict control of false discovery rates, facilitates identifying > 72% of the reference proteome, with a median protein sequence coverage of 51%. These analyses, together with expert knowledge in diverse aspects of cell biology, provide meaningful insights into processes such as N-terminal protein maturation, N-glycosylation, and metabolism. Altogether, ArcPP serves as an invaluable blueprint for comprehensive prokaryotic proteomics. Proteome ; Proteomics ; Computational biology ; Proteogenomics ; Biology ; Systems biology ; Archaea ; Bioinformatics ; Genomics ; Genome ; Genetics ; Bacteria ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Glycosylation and Glycoproteins Research
Authors: Andrej Turk | Shengda D. Pu | David Bombač | Pedro E.J. Rivera-Díaz-del-Castillo | E.I. Galindo-Nava
Source: Acta Materialia (article, 2020-07-17)
Field: Materials Science — Metals and Alloys

Quantification of hydrogen trapping in multiphase steels: Part II – Effect of austenite morphology We tackle the role of austenite in multiphase steels on hydrogen diffusion systematically for the first time, considering a range of factors such as morphology, interface kinetics and the additional effect of point traps using both experiments and modelling. This follows the findings from part I where we showed that austenite cannot be parametrised and modelled as point traps under the assumption of local equilibrium, unlike grain boundaries and dislocations. To solve this, we introduce a 2D hydrogen diffusion model accounting for the difference in diffusivities and solubilities between the phases. We first revisit the as-quenched martensite permeation results from part I and show that the extremely low H diffusivity there can be partly explained with the new description of austenite but is partly likely due to quench vacancies. We then also look at the H absorption and desorption rates in a duplex steel as a case study using a combination of simulations and experiments. The rates are shown to depend heavily on austenite morphology and the kinetics of H transition from ferrite to austenite and that an energy barrier is likely associated to this transition. We show that H diffusion through the ferrite matrix and austenite islands proceeds at similar rates and the assumption of negligible concentration gradients in ferrite occasionally applied in the literature is a poor approximation. This approach is also applicable to other austenite-containing steels as well as other multiphase alloys. Austenite ; Materials science ; Ferrite (magnet) ; Martensite ; Thermal diffusivity ; Hydrogen ; Thermodynamics ; Diffusion ; Kinetics ; Metallurgy ; Microstructure ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Corrosion Behavior and Inhibition
Authors: Nicolas Bondon | Laurence Raehm | Clarence Charnay | Rabah Boukherroub | Jean‐Olivier Durand
Source: Journal of Materials Chemistry B (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Nanodiamonds for bioapplications, recent developments The world of biomedical research is in constant evolution, requiring more and more conditions and norms through pre-clinic and clinic studies. Nanodiamonds (NDs) with exceptional optical, thermal and mechanical properties emerged on the global scientific scene and recently gained more attention in biomedicine and bioanalysis fields. Many problematics have been deliberated to better understand their in vitro and in vivo efficiency and compatibility. Light was shed on their synthesis, modification and purification steps, as well as particle size and surface properties in order to find the most suitable operating conditions. In this review, we present the latest advances of NDs use in bioapplications. A large variety of subjects including anticancer and antimicrobial systems, wound healing and tissue engineering management tools, but also bioimaging and labeling probes are tackled. The key information resulting from these recent works were evidenced to make an overview of the potential features of NDs, with a special look on emerging therapeutic and diagnosis combinations. Nanotechnology ; Materials science Diamond and Carbon-based Materials Research ; Luminescence and Fluorescent Materials ; Analytical chemistry methods development
Authors: Wells Utembe | Harvey J. Clewell | Natasha Sanabria | Philip Doganis | Mary Gulumian
Source: Nanomaterials (review, 2020-06-29)
Field: Materials Science — Biomaterials

Current Approaches and Techniques in Physiologically Based Pharmacokinetic (PBPK) Modelling of Nanomaterials There have been efforts to develop physiologically based pharmacokinetic (PBPK) models for nanomaterials (NMs). Since NMs have quite different kinetic behaviors, the applicability of the approaches and techniques that are utilized in current PBPK models for NMs is warranted. Most PBPK models simulate a size-independent endocytosis from tissues or blood. In the lungs, dosimetry and the air-liquid interface (ALI) models have sometimes been used to estimate NM deposition and translocation into the circulatory system. In the gastrointestinal (GI) tract, kinetics data are needed for mechanistic understanding of NM behavior as well as their absorption through GI mucus and their subsequent hepatobiliary excretion into feces. Following absorption, permeability (<i>P<sub>t</sub></i>) and partition coefficients (<i>PCs</i>) are needed to simulate partitioning from the circulatory system into various organs. Furthermore, mechanistic modelling of organ- and species-specific NM corona formation is in its infancy. More recently, some PBPK models have included the mononuclear phagocyte system (MPS). Most notably, dissolution, a key elimination process for NMs, is only empirically added in some PBPK models. Nevertheless, despite the many challenges still present, there have been great advances in the development and application of PBPK models for hazard assessment and risk assessment of NMs. Physiologically based pharmacokinetic modelling ; Pharmacokinetics ; Chemistry ; Biochemical engineering ; Pharmacology ; Medicine ; Engineering Nanoparticle-Based Drug Delivery ; Inhalation and Respiratory Drug Delivery ; Advanced Drug Delivery Systems
Authors: Magnus Nord | Robert W. H. Webster | Kirsty A. Paton | S. McVitie | D. McGrouther | Ian MacLaren | Gary W. Paterson
Source: Microscopy and Microanalysis (article, 2020-07-06)
Field: Materials Science — Surfaces, Coatings and Films

Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing, and Storage The use of fast pixelated detectors and direct electron detection technology is revolutionizing many aspects of scanning transmission electron microscopy (STEM). The widespread adoption of these new technologies is impeded by the technical challenges associated with them. These include issues related to hardware control, and the acquisition, real-time processing and visualization, and storage of data from such detectors. We discuss these problems and present software solutions for them, with a view to making the benefits of new detectors in the context of STEM more accessible. Throughout, we provide examples of the application of the technologies presented, using data from a Medipix3 direct electron detector. Most of our software are available under an open source licence, permitting transparency of the implemented algorithms, and allowing the community to freely use and further improve upon them. Detector ; Software ; Transparency (behavior) ; Data acquisition ; Context (archaeology) ; Computer science ; Scanning transmission electron microscopy ; Transmission (telecommunications) ; Visualization ; Computer hardware ; Instrument control ; Computer data storage ; Optics ; Transmission electron microscopy ; Physics ; Artificial intelligence ; Telecommunications ; Computer security ; Operating system Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Particle Detector Development and Performance
Authors: Kaycee Low | Rika Kobayashi | Ekaterina I. Izgorodina
Source: The Journal of Chemical Physics (article, 2020-09-08)
Field: Materials Science — Materials Chemistry

The effect of descriptor choice in machine learning models for ionic liquid melting point prediction The characterization of an ionic liquid's properties based on structural information is a longstanding goal of computational chemistry, which has received much focus from ab initio and molecular dynamics calculations. This work examines kernel ridge regression models built from an experimental dataset of 2212 ionic liquid melting points consisting of diverse ion types. Structural descriptors, which have been shown to predict quantum mechanical properties of small neutral molecules within chemical accuracy, benefit from the addition of first-principles data related to the target property (molecular orbital energy, charge density profile, and interaction energy based on the geometry of a single ion pair) when predicting the melting point of ionic liquids. Out of the two chosen structural descriptors, ECFP4 circular fingerprints and the Coulomb matrix, the addition of molecular orbital energies and all quantum mechanical data to each descriptor, respectively, increases the accuracy of surrogate models for melting point prediction compared to using the structural descriptors alone. The best model, based on ECFP4 and molecular orbital energies, predicts ionic liquid melting points with an average mean absolute error of 29 K and, unlike group contribution methods, which have achieved similar results, is applicable to any type of ionic liquid. Ionic liquid ; Melting point ; Molecular dynamics ; Ion ; Quantum chemistry ; Ionic bonding ; Coulomb ; Molecular descriptor ; Chemical physics ; Ab initio ; Chemistry ; Molecular orbital ; Statistical physics ; Thermodynamics ; Work (physics) ; Materials science ; Computational chemistry ; Molecule ; Physics ; Quantitative structure–activity relationship ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Ionic liquids properties and applications ; Computational Drug Discovery Methods
Authors: Lei Mei | Shu‐wen An | Kong‐Qiu Hu | Lin Wang | Jipan Yu | Zhi‐wei Huang | Xiang‐he Kong | Chuanqin Xia | Zhifang Chai | Wei‐Qun Shi
Source: Angewandte Chemie International Edition (article, 2020-05-26)
Field: Chemistry — Inorganic Chemistry

Molecular Spring‐like Triple‐Helix Coordination Polymers as Dual‐Stress and Thermally Responsive Crystalline Metal–Organic Materials Elastic metal-organic materials (MOMs) capable of multiple stimuli-responsiveness based on dual-stress and thermally responsive triple-helix coordination polymers are presented. The strong metal-coordination linkage and the flexibility of organic linkers in these MOMs, rather than the 4 Å stacking interactions observed in organic crystals, causes the helical chain to act like a molecular spring and thus accounts for their macroscopic elasticity. The thermosalient effect of elastic MOMs is reported for the first time. Crystal structure analyses at different temperatures reveal that this thermoresponsiveness is achieved by adaptive regulation of the triple-helix chains by fine-tuning the opening angle of flexible V-shaped organic linkers and rotation of its lateral conjugated groups to resist possible expansion, thus demonstrating the vital role of adaptive reorganization of triple-helix metal-organic chains as a molecular spring-like motif in crystal jumping. Stacking ; Triple helix ; Materials science ; Polymer ; Helix (gastropod) ; Metal ; Conjugated system ; Coordination polymer ; Crystallography ; Crystal structure ; Spring (device) ; Nanotechnology ; Composite material ; Chemistry ; Stereochemistry ; Organic chemistry ; Structural engineering ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Magnetism in coordination complexes
Authors: Tim P. Seifert | Vanitha R. Naina | Thomas J. Feuerstein | Nicolai D. Knöfel | Peter W. Roesky
Source: Nanoscale (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Molecular gold strings: aurophilicity, luminescence and structure–property correlations This review covers the compound class of one-dimensional gold strings. These compounds feature a formally infinite repetition of gold complexes as monomers/repeating units that are held together by aurophilic interactions, i.e. direct gold-gold contacts. Their molecular structures are primarily determined in the solid state using single crystal X-ray diffraction. The chemical composition of the employed gold complexes is diverse and furthermore plays a key role in terms of structure characteristics and the resulting properties. One of the most common features of gold strings is their photoluminescence upon UV excitation. The emission energy is often dependent on the distance of adjacent gold ions and the electronic structure of the whole string. In terms of gold strings, these parameters can be fine-tuned by external stimuli such as solvent, pH value, pressure or mechanical stress. This leads to direct structure-property correlations, not only with regard to the photophysical properties, but also electric conductivity for potential application in nanoelectronics. Concerning these correlations, gold strings, consisting of self-assembled individual complexes as building blocks, are the ideal compound class to look at, as perturbations by an inhomogeneity in the ligand sphere (such as the end of a molecule) can be neglected. Therefore, the aim of this review is to shed light on the past achievements and current developments in this area. Luminescence ; Materials science ; Chemical physics ; Property (philosophy) ; Nanotechnology ; Chemistry ; Optoelectronics Nanocluster Synthesis and Applications ; Molecular Sensors and Ion Detection ; Metal complexes synthesis and properties
Authors: Shuanma Yan | Haichang Fu | Ling Zhang | Yujie Dong | Weijun Li | Mi Ouyang | Cheng Zhang
Source: Chemical Engineering Journal (article, 2020-08-31)
Field: Materials Science — Polymers and Plastics

Conjugated polymer multilayer by in situ electrochemical polymerization for black-to-transmissive eletrochromism Electrochromism ; Materials science ; Polymer ; Optoelectronics ; Electrochromic devices ; Polymerization ; Electrochemistry ; Contrast ratio ; Electrode ; Nanotechnology ; Composite material ; Chemistry Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Advanced Sensor and Energy Harvesting Materials