jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Danliang Zhang | Ying Liu | Mai He | Ao Zhang | Shula Chen | Qingjun Tong | Lanyu Huang | Zhiyuan Zhou | Weihao Zheng | Mingxing Chen | Kai Braun | Alfred J. Meixner | Xiao Wang | Anlian Pan
Source: Nature Communications (article, 2020-09-07)
Field: Materials Science — Materials Chemistry

Room temperature near unity spin polarization in 2D Van der Waals heterostructures The generation and manipulation of spin polarization at room temperature are essential for 2D van der Waals (vdW) materials-based spin-photonic and spintronic applications. However, most of the high degree polarization is achieved at cryogenic temperatures, where the spin-valley polarization lifetime is increased. Here, we report on room temperature high-spin polarization in 2D layers by reducing its carrier lifetime via the construction of vdW heterostructures. A near unity degree of polarization is observed in PbI<sub>2</sub> layers with the formation of type-I and type-II band aligned vdW heterostructures with monolayer WS<sub>2</sub> and WSe<sub>2</sub>. We demonstrate that the spin polarization is related to the carrier lifetime and can be manipulated by the layer thickness, temperature, and excitation wavelength. We further elucidate the carrier dynamics and measure the polarization lifetime in these heterostructures. Our work provides a promising approach to achieve room temperature high-spin polarizations, which contribute to spin-photonics applications. Spintronics ; Heterojunction ; Polarization (electrochemistry) ; Spin polarization ; van der Waals force ; Monolayer ; Condensed matter physics ; Materials science ; Optoelectronics ; Photonics ; Physics ; Nanotechnology ; Chemistry ; Ferromagnetism 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Fei Yang | Zhen Li | Qing Wang | Beibei Jiang | Biaojie Yan | Pengcheng Zhang | Wei Xu | Chuang Dong | Peter K. Liaw
Source: npj Computational Materials (article, 2020-07-21)
Field: Materials Science — Materials Chemistry

Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus Abstract The present work formulated a materials design approach, a cluster-formula-embedded machine learning (ML) model, to search for body-centered-cubic (BCC) β-Ti alloys with low Young’s modulus ( E ) in the Ti–Mo–Nb–Zr–Sn–Ta system. The characteristic parameters, including the Mo equivalence and the cluster-formula approach, are implemented into the ML to ensure the accuracy of prediction, in which the former parameter represents the BCC-β structural stability, and the latter reflects the interactions among elements expressed with a composition formula. Both auxiliary gradient-boosting regression tree and genetic algorithm methods were adopted to deal with the optimization problem in the ML model. This cluster-formula-embedded ML can not only predict alloy property in the forward design, but also design and optimize alloy compositions with desired properties in multicomponent systems efficiently and accurately. By setting different objective functions, several new β-Ti alloys with either the lowest E ( E = 48 GPa) or a specific E ( E = 55 and 60 GPa) were predicted by ML and then validated by a series of experiments, including the microstructural characterization and mechanical measurements. It could be found that the experimentally obtained E of predicted alloys by ML could reach the desired objective E , which indicates that the cluster-formula-embedded ML model can make the prediction and optimization of composition and property more accurate, effective, and controllable. Cluster (spacecraft) ; Alloy ; Materials science ; Modulus ; Equivalence (formal languages) ; Titanium alloy ; Series (stratigraphy) ; Algorithm ; Property (philosophy) ; Computer science ; Mathematics ; Composite material ; Pure mathematics Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Intermetallics and Advanced Alloy Properties
Authors: Ziyan Zhang | Aria Mansouri Tehrani | Anton O. Oliynyk | Blake Day | Jakoah Brgoch
Source: Advanced Materials (article, 2020-12-04)
Field: Materials Science — Materials Chemistry

Finding the Next Superhard Material through Ensemble Learning An ensemble machine-learning method is demonstrated to be capable of finding superhard materials by directly predicting the load-dependent Vickers hardness based only on the chemical composition. A total of 1062 experimentally measured load-dependent Vickers hardness data are extracted from the literature and used to train a supervised machine-learning algorithm utilizing boosting, achieving excellent accuracy (R<sup>2</sup> = 0.97). This new model is then tested by synthesizing and measuring the load-dependent hardness of several unreported disilicides and analyzing the predicted hardness of several classic superhard materials. The trained ensemble method is then employed to screen for superhard materials by examining more than 66 000 compounds in crystal structure databases, which show that 68 known materials have a Vickers hardness ≥40 GPa at 0.5 N (applied force) and only 10 exceed this mark at 5 N. The hardness model is then combined with the data-driven phase diagram generation tool to expand the limited number of reported high hardness compounds. Eleven ternary borocarbide phase spaces are studied, and more than ten thermodynamically favorable compositions with a hardness above 40 GPa (at 0.5 N) are identified, proving this ensemble model's ability to find previously unknown materials with outstanding mechanical properties. Vickers hardness test ; Materials science ; Boosting (machine learning) ; Ternary operation ; Phase diagram ; Phase (matter) ; Machine learning ; Composite material ; Computer science ; Microstructure Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Advanced materials and composites
Authors: Irini Furxhi | Finbarr Murphy | Martin Mullins | Athanasios Arvanitis | Craig A. Poland
Source: Nanotoxicology (review, 2020-02-26)
Field: Materials Science — Materials Chemistry

Nanotoxicology data for <i>in silico</i> tools: a literature review The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicological properties of a variety of NPs is by means of computational tools that decode how nano-specific features relate to toxicity and enable its prediction. This literature review records systematically the data used in published studies that predict nano (eco)-toxicological endpoints using machine learning models. Instead of seeking mechanistic interpretations this review maps the pathways followed, involving biological features in relation to NPs exposure, their physico-chemical characteristics and the most commonly predicted outcomes. The results, derived from published research of the last decade, are summarized visually, providing prior-based data mining paradigms to be readily used by the nanotoxicology community in computational studies. Nanotoxicology ; Computer science ; Risk assessment ; Variety (cybernetics) ; Hazard ; Risk analysis (engineering) ; Hazard analysis ; Data science ; Machine learning ; Artificial intelligence ; Nanotechnology ; Engineering ; Medicine ; Biology ; Reliability engineering ; Nanoparticle Nanoparticles: synthesis and applications
Authors: Joris Paret | Robert L. Jack | Daniele Coslovich
Source: The Journal of Chemical Physics (article, 2020-04-09)
Field: Materials Science — Materials Chemistry

Assessing the structural heterogeneity of supercooled liquids through community inference We present an information-theoretic approach inspired by distributional clustering to assess the structural heterogeneity of particulate systems. Our method identifies communities of particles that share a similar local structure by harvesting the information hidden in the spatial variation of two- or three-body static correlations. This corresponds to an unsupervised machine learning approach that infers communities solely from the particle positions and their species. We apply this method to three models of supercooled liquids and find that it detects subtle forms of local order, as demonstrated by a comparison with the statistics of Voronoi cells. Finally, we analyze the time-dependent correlation between structural communities and particle mobility and show that our method captures relevant information about glassy dynamics. Cluster analysis ; Voronoi diagram ; Inference ; Supercooling ; Community structure ; Particle (ecology) ; Variation (astronomy) ; Structural complexity Material Dynamics and Properties ; Theoretical and Computational Physics ; Block Copolymer Self-Assembly
Authors: Rebecca Varney | Daniel I. Speiser | Carmel McDougall | Bernard M. Degnan | Kevin M. Kocot
Source: Genome Biology and Evolution (article, 2020-12-14)
Field: Materials Science — Biomaterials

The Iron-Responsive Genome of the Chiton <i>Acanthopleura granulata</i> Molluscs biomineralize structures that vary in composition, form, and function, prompting questions about the genetic mechanisms responsible for their production and the evolution of these mechanisms. Chitons (Mollusca, Polyplacophora) are a promising system for studies of biomineralization because they build a range of calcified structures including shell plates and spine- or scale-like sclerites. Chitons also harden the calcified teeth of their rasp-like radula with a coat of iron (as magnetite). Here we present the genome of the West Indian fuzzy chiton Acanthopleura granulata, the first from any aculiferan mollusc. The A. granulata genome contains homologs of many genes associated with biomineralization in conchiferan molluscs. We expected chitons to lack genes previously identified from pathways conchiferans use to make biominerals like calcite and nacre because chitons do not use these materials in their shells. Surprisingly, the A. granulata genome has homologs of many of these genes, suggesting that the ancestral mollusc may have had a more diverse biomineralization toolkit than expected. The A. granulata genome has features that may be specialized for iron biomineralization, including a higher proportion of genes regulated directly by iron than other molluscs. A. granulata also produces two isoforms of soma-like ferritin: one is regulated by iron and similar in sequence to the soma-like ferritins of other molluscs, and the other is constitutively translated and is not found in other molluscs. The A. granulata genome is a resource for future studies of molluscan evolution and biomineralization. Biomineralization ; Polyplacophora ; Biology ; Genome ; Evolutionary biology ; Mollusca ; Gene ; Ecology ; Paleontology ; Genetics Calcium Carbonate Crystallization and Inhibition ; Paleontology and Stratigraphy of Fossils ; Marine Biology and Ecology Research
Authors: José Carlos Curvelo Santana | Roberta Bezerra Gardim | Poliana Fernandes de Almeida | Giovanna Bonfante Borini | Ada Patricia Barturén Quispe | Segundo Alberto Vásquez Llanos | Jorge Heredia | Stella Regina Zamunér | Félix Martin Carbajal Gamarra | T.M.B. Farias | Linda Lee Ho | Fernando Tobal Berssaneti
Source: Polymers (article, 2020-03-02)
Field: Materials Science — Biomaterials

Valorization of Chicken Feet By-Product of the Poultry Industry: High Qualities of Gelatin and Biofilm from Extraction of Collagen In this research, products with high quality were obtained from natural sources. The sensorial qualities, chemical characterization, and physical properties of gelatin extracted from chicken feet were compared with commercial gelatins. The extraction process was performed using acetic acid on a concentration ranging from 0.318% to 3.682%, processing time between 1.0 h and 8.4 h and extraction temperature between 43.3 °C and 76.8 °C. After the end of each assay, the yield was measured. Results showed that, under the best conditions, the collagen extraction yield was above 8%, and comprised 78.525 g/100 g of protein. Collagen analyzed by ICP-MS was composed of 99.44% of macro-minerals that are of great importance to human health. ATR-FTIR analysis showed that approximately 70.90% of the total protein from chicken feet is collagen, whereas, in commercial gelatin, only 30.31% is collagen. When comparing chicken gelatin with commercial gelatin, most sensory attributes were similar and chicken gelatin gained acceptance by more than 80% of the consumers. Additionally, the collagen films obtained from chicken feet and swine showed water absorption, odors, and texture characteristics similar to commercial material, such as latex and celofane. Consequently, due to its similarity to human skin, it is possible to apply it as a biocurative. Gelatin ; Extraction (chemistry) ; Food science ; Chemistry ; Yield (engineering) ; Acetic acid ; Fourier transform infrared spectroscopy ; Chromatography ; Materials science ; Biochemistry ; Composite material ; Chemical engineering Collagen: Extraction and Characterization ; Meat and Animal Product Quality ; Protein Hydrolysis and Bioactive Peptides
Authors: Roberta Ruotolo | Giuseppe De Giorgio | Ilaria Minato | Massimiliano G. Bianchi | Ovidio Bussolati | Nelson Marmiroli
Source: Nanomaterials (article, 2020-01-29)
Field: Materials Science — Materials Chemistry

Cerium Oxide Nanoparticles Rescue α-Synuclein-Induced Toxicity in a Yeast Model of Parkinson’s Disease Over the last decades, cerium oxide nanoparticles (CeO<sub>2</sub> NPs) have gained great interest due to their potential applications, mainly in the fields of agriculture and biomedicine. Promising effects of CeO<sub>2</sub> NPs are recently shown in some neurodegenerative diseases, but the mechanism of action of these NPs in Parkinson's disease (PD) remains to be investigated. This issue is addressed in the present study by using a yeast model based on the heterologous expression of the human α-synuclein (α-syn), the major component of Lewy bodies, which represent a neuropathological hallmark of PD. We observed that CeO<sub>2</sub> NPs strongly reduce α-syn-induced toxicity in a dose-dependent manner. This effect is associated with the inhibition of cytoplasmic α-syn foci accumulation, resulting in plasma membrane localization of α-syn after NP treatment. Moreover, CeO<sub>2</sub> NPs counteract the α-syn-induced mitochondrial dysfunction and decrease reactive oxygen species (ROS) production in yeast cells. In vitro binding assay using cell lysates showed that α-syn is adsorbed on the surface of CeO<sub>2</sub> NPs, suggesting that these NPs may act as a strong inhibitor of α-syn toxicity not only acting as a radical scavenger, but through a direct interaction with α-syn in vivo. Reactive oxygen species ; Toxicity ; Chemistry ; Cerium oxide ; In vivo ; Cell biology ; Parkinson's disease ; Alpha-synuclein ; Mitochondrion ; Biophysics ; Yeast ; In vitro ; Heterologous ; Biochemistry ; Pharmacology ; Biology ; Disease ; Medicine Advanced Nanomaterials in Catalysis ; Parkinson's Disease Mechanisms and Treatments ; Alzheimer's disease research and treatments
Authors: Georgy A. Ermolaev | Alexey P. Tsapenko | Valentyn S. Volkov | Anton S. Anisimov | Yuriy G. Gladush | Albert G. Nasibulin
Source: Applied Physics Letters (article, 2020-06-08)
Field: Materials Science — Materials Chemistry

Express determination of thickness and dielectric function of single-walled carbon nanotube films Single-walled carbon nanotube (SWCNT) films are promising building blocks for diversified applications in electronics, photovoltaics, and photonics. However, their electrical and optical engineering is still a challenging task owing to multiple obstacles, including the absence of fast and easy-to-use methods for the determination of SWCNT film properties. Here, we present a rapid, contactless, and universal technique for accurate estimation of both SWCNT film thicknesses and their dielectric functions. The approach combines broadband optical absorbance and highly sensitive spectroscopic ellipsometry measurements. The observed linear dependence of the film thickness on its absorbance at 550 nm provides a time-effective and contactless method of thickness assignment, which is of significant importance to the practical implementation of SWCNT films in optoelectronic devices. Additionally, our approach revealed that a simple procedure of film densification allows to controllably alter the dielectric response by at least 40% and, thus, to add extra fine-tuning capabilities during material property engineering. Therefore, this express technique as a whole offers an advanced metrological tool for current and next-generation SWCNT-based devices. Carbon nanotube ; Materials science ; Absorbance ; Photonics ; Dielectric ; Metrology ; Electronics ; Optoelectronics ; Nanotechnology ; Photovoltaics ; Ellipsometry ; Thin film ; Optics ; Photovoltaic system Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications ; Transition Metal Oxide Nanomaterials
Authors: Christian K. Adokoh
Source: RSC Advances (review, 2020-01-01)
Field: Chemistry — Organic Chemistry

Therapeutic potential of dithiocarbamate supported gold compounds Chrysotherapy or aurotherapy, the use of gold as medicine, is two thousand years old. Hitherto, numerous diverse gold stabilizing ligands for instance vitamins, pyridine, phosphines, naphthylamine and xanthanes have been developed and their 'chelating effect' in addition to their anti-proliferative properties have been extensively studied. Recent advances in the field of bioinorganic chemistry have led to the design of biologically relevant metal complexes with appropriate fine-tuned ligands such as metallic conjugates of dithiocarbamates (DTCs). DTC compounds have been recognised to possess diverse applications and have demonstrated interesting biological properties. For instance, the chemoprotective and antitumour properties of gold metal ions and DTC compounds respectively, presents an innovative and effective approach to cancer management. This review presents therefore the therapeutic potential of DTC ligand systems as a support for gold compounds. The importance of dithiocarbamate supported gold compounds as potential therapeutic agents is highlighted with emphasis on the therapeutic potential of gold(iii) and gold(i) dithiocarbamate derivatives. Dithiocarbamate ; Chemistry ; Combinatorial chemistry ; Environmental chemistry ; Nanotechnology ; Organic chemistry ; Materials science Organometallic Compounds Synthesis and Characterization ; Metal complexes synthesis and properties ; Enzyme function and inhibition
Authors: Rui Pedro Moura | Catarina Pacheco | Ana Paula Pêgo | Anne des Rieux | Bruno Sarmento
Source: Journal of Controlled Release (review, 2020-04-02)
Field: Materials Science — Biomaterials

Lipid nanocapsules to enhance drug bioavailability to the central nervous system Nanocapsules ; Drug delivery ; Central nervous system ; Nanocarriers ; Bioavailability ; Pharmacology ; Blood–brain barrier ; Drug ; Nanotechnology ; Neuroscience ; Chemistry ; Medicine ; Materials science ; Biology Nanoparticle-Based Drug Delivery ; Lipid Membrane Structure and Behavior ; RNA Interference and Gene Delivery
Authors: Anthonin Gori | Benjamin Boucherle | Aurélien Rey | Maxime Rome | Nicola Fuzzati | Marine Peuchmaur
Source: Fitoterapia (article, 2020-12-08)
Field: Chemistry — Analytical Chemistry

Development of an innovative maceration technique to optimize extraction and phase partition of natural products Maceration (sewage) ; Extraction (chemistry) ; Yield (engineering) ; Chromatography ; Acetonitrile ; Volume (thermodynamics) ; Partition coefficient ; Ethyl acetate ; Chemistry ; Process engineering ; Biological system ; Biochemical engineering ; Materials science ; Engineering Chromatography in Natural Products ; Natural product bioactivities and synthesis ; Ginseng Biological Effects and Applications
Authors: Florian Bouville
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2020-02-24)
Field: Materials Science — Biomaterials

Strong and tough nacre-like aluminas: Process–structure–performance relationships and position within the nacre-inspired composite landscape Abstract Materials science ; Composite number ; Position (finance) ; Composite material ; Process (computing) ; Polymer science ; Chemical engineering ; Engineering ; Computer science ; Business Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Paleontology and Stratigraphy of Fossils
Authors: Junbo Sun | Yimiao Huang | Farhad Aslani | Guowei Ma
Source: Construction and Building Materials (article, 2020-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Properties of a double-layer EMW-absorbing structure containing a graded nano-sized absorbent combing extruded and sprayed 3D printing Materials science ; Composite material ; Porosity ; Nano- ; Layer (electronics) ; Scanning electron microscope ; Nanotechnology Electromagnetic wave absorption materials ; Dielectric materials and actuators ; Magnesium Oxide Properties and Applications
Authors: Jared O’Leary | Kapil Sawlani | Ali Mesbah
Source: IEEE Transactions on Semiconductor Manufacturing (article, 2020-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning for Classification of the Chemical Composition of Particle Defects on Semiconductor Wafers Manual classification of particle defects on semiconductor wafers is labor-intensive, which leads to slow solutions and longer learning curves on product failures while being prone to human error. This work explores the promise of deep learning for the classification of the chemical composition of these defects to reduce analysis time and inconsistencies due to human error, which in turn can result in systematic root cause analysis for sources of semiconductor defects. We investigate a deep convolutional neural network (CNN) for defect classification based on a combination of scanning electron microscopy (SEM) images and energy-dispersive x-ray (EDX) spectroscopy data. SEM images of sections of semiconductor wafers that contain particle defects are fed into a CNN in which the defects' EDX spectroscopy data is merged directly with the CNN's fully connected layer. The proposed CNN classifies the chemical composition of semiconductor wafer particle defects with an industrially pragmatic accuracy. We also demonstrate that merging spectral data with the CNN's fully connected layer significantly improves classification performance over CNNs that only take either SEM image data or EDX spectral data as an input. The impact of training data collection and augmentation on CNN performance is explored and the promise of transfer learning for improving training speed and testing accuracy is investigated. Wafer ; Convolutional neural network ; Semiconductor ; Artificial intelligence ; Deep learning ; Pattern recognition (psychology) ; Computer science ; Materials science ; Scanning electron microscope ; Semiconductor device fabrication ; Optoelectronics ; Artificial neural network ; Biological system Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Machine Learning in Materials Science