jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emayaruba G. Barathi Dassan | A. Anjang | Mohamad Jafre Zainol Abidin | Hazizan Md Akil
Source: Nanotechnology Reviews (review, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Carbon nanotube–reinforced polymer composite for electromagnetic interference application: A review Abstract The growth of the application of electronic devices has created a new form of pollution known as noise or radio frequency interference, electromagnetic radiation, or electromagnetic interference (EMI), which results in the malfunction of equipment. A new carbon-based polymer composite has been unlocked through the discovery of polymer composites. Carbon nanotubes (CNTs) have shown potential as reinforcement fillers in polymer to enhance an EMI shielding material owing to their large specific surface area, well-defined 3D networking structure, and unique electronic structure. The main focus of this review is the role of CNT as fillers in intrinsic conducting polymer and conducting polymer composite. The factors that influence EMI shielding performance are also included in this review. The roles of the size; shape; and electronic, mechanical, and chemical properties of nanomaterials in tuning the EMI shielding effectiveness of polymer hybrid are emphasized. The structural design of CNT polymer composite has been reviewed as well. Future research direction has been proposed to overcome the current technological limitations and realize the most advanced EMI shielding materials for future use. The composites have a potential to replace traditional shielding materials owing to their advantageous properties. EMI ; Electromagnetic interference ; Electromagnetic shielding ; Materials science ; Carbon nanotube ; Composite number ; Polymer ; Composite material ; Nanotechnology ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Luchun Yan | Yupeng Diao | Zhaoyang Lang | Kewei Gao
Source: Science and Technology of Advanced Materials (article, 2020-01-31)
Field: Materials Science — Metals and Alloys

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate (<i>R<sup>2</sup></i> values, 0.94 and 0.73 to the training set and testing set) to different low-alloy steel samples in several typical marine atmospheric environments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors. Corrosion ; Random forest ; Alloy ; Regression analysis ; Set (abstract data type) ; Predictive modelling ; Metallurgy ; Atmosphere (unit) ; Materials science ; Empirical modelling ; Machine learning ; Computer science ; Simulation ; Meteorology Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Sarfaraz Ahmad | Aftab Aslam Parwaz Khan | Ahmad Husain | Anish Khan | Abdullah M. Asiri
Source: Polymers (article, 2020-12-18)
Field: Materials Science — Polymers and Plastics

Electrical Conductivity Based Ammonia Sensing Properties of Polypyrrole/MoS2 Nanocomposite Polypyrrole (PPy) and Polypyrrole/MoS<sub>2</sub> (PPy/MoS<sub>2</sub>) nanocomposites were successfully prepared, characterized and studied for ammonia sensing properties. The as-prepared PPy and PPy/MoS<sub>2</sub> nanocomposites were confirmed by FTIR (Fourier transform infrared spectroscopy), XRD (X-ray diffraction), SEM (scanning electron microscopy) and TEM (transmission electron microscopy) techniques. The ammonia sensing properties of PPy and PPy/MoS<sub>2</sub> nanocomposites were studied in terms of change in DC electrical conductivity on exposure to ammonia vapors followed by ambient air at room temperature. It was observed that the incorporation of MoS<sub>2</sub> in PPy showed high sensitivity, significant stability and excellent reversibility. The enhanced sensing properties of PPy/MoS<sub>2</sub> nanocomposites could be attributed to comparatively high surface area, appropriate sensing channels and efficiently available active sites. The sensing mechanism is explained on the basis of simple acid-base chemistry of polypyrrole. Polypyrrole ; Nanocomposite ; Materials science ; Fourier transform infrared spectroscopy ; Scanning electron microscope ; Transmission electron microscopy ; Chemical engineering ; Ammonia ; Conductivity ; Conductive polymer ; Polymer ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry ; Polymerization ; Physical chemistry Conducting polymers and applications ; Gas Sensing Nanomaterials and Sensors ; Analytical Chemistry and Sensors
Authors: Mahtab Asadian | Ke Vin Chan | Mohammad Norouzi | Silvia Grande | Pieter Cools | Rino Morent | Nathalie De Geyter
Source: Nanomaterials (review, 2020-01-08)
Field: Materials Science — Biomaterials

Fabrication and Plasma Modification of Nanofibrous Tissue Engineering Scaffolds This paper provides a comprehensive overview of nanofibrous structures for tissue engineering purposes and the role of non-thermal plasma technology (NTP) within this field. Special attention is first given to nanofiber fabrication strategies, including thermally-induced phase separation, molecular self-assembly, and electrospinning, highlighting their strengths, weaknesses, and potentials. The review then continues to discuss the biodegradable polyesters typically employed for nanofiber fabrication, while the primary focus lies on their applicability and limitations. From thereon, the reader is introduced to the concept of NTP and its application in plasma-assisted surface modification of nanofibrous scaffolds. The final part of the review discusses the available literature on NTP-modified nanofibers looking at the impact of plasma activation and polymerization treatments on nanofiber wettability, surface chemistry, cell adhesion/proliferation and protein grafting. As such, this review provides a complete introduction into NTP-modified nanofibers, while aiming to address the current unexplored potentials left within the field. Nanofiber ; Electrospinning ; Nanotechnology ; Tissue engineering ; Surface modification ; Materials science ; Fabrication ; Polyester ; Grafting ; Polymer ; Chemical engineering ; Biomedical engineering ; Composite material ; Engineering Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation ; Graphene and Nanomaterials Applications
Authors: Jidon Jang | Geun Ho Gu | Juhwan Noh | Juhwan Kim | Yousung Jung
Source: Journal of the American Chemical Society (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

Structure-Based Synthesizability Prediction of Crystals Using Partially Supervised Learning Predicting the synthesizability of inorganic materials is one of the major challenges in accelerated material discovery. A widely employed approximate approach is to consider the thermodynamic decomposition stability due to its simplicity of computing, but it is notorious for either producing too many candidates or missing important metastable materials. These results, however, are not unexcepted since the synthesizability is a complex phenomenon, and the thermodynamic stability is just one contributor. Here, we suggest a machine-learning model to quantify the probability of synthesis based on the partially supervised learning of materials database. We adapted the positive and unlabeled machine learning (PU learning) by implementing the graph convolutional neural network as a classifier in which the model outputs crystal-likeness scores (CLscore). The model shows 87.4% true positive (CLscore > 0.5) prediction accuracy for the test set of experimentally reported cases (9356 materials) in the Materials Project. We further validated the model by predicting the synthesizability of newly reported experimental materials in the last 5 years (2015-2019) with an 86.2% true positive rate using the model trained with the database as of the end of year 2014. Our analysis shows that our model captures the structural motif for synthesizability beyond what is possible by <i>E</i><sub>hull</sub>. We find that 71 materials among the top 100 high-scoring virtual materials have indeed been previously synthesized in the literature. With the proposed data-driven metric of the crystal-likeness score, high-throughput virtual screenings and generative models can benefit significantly by effectively reducing the chemical space that needs to be explored experimentally in the future toward more rational materials design. Chemistry ; Crystal structure prediction ; Artificial intelligence ; Crystallography ; Crystal structure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Chiheb Ben Mahmoud | Andrea Anelli | Gábor Cśanyi | Michele Ceriotti
Source: Physical review. B./Physical review. B (article, 2020-12-14)
Field: Materials Science — Materials Chemistry

Learning the electronic density of states in condensed matter The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation of experimentally observable material properties such as optical absorption and electrical conductivity. We discuss the challenges inherent in the construction of a machine-learning (ML) framework aimed at predicting the DOS as a combination of local contributions that depend in turn on the geometric configuration of neighbors around each atom, using quasiparticle energy levels from density functional theory as training data. We present a challenging case study that includes configurations of silicon spanning a broad set of thermodynamic conditions, ranging from bulk structures to clusters and from semiconducting to metallic behavior. We compare different approaches to represent the DOS, and the accuracy of predicting quantities such as the Fermi level, the electron density at the Fermi level, or the band energy, either directly or as a side product of the evaluation of the DOS. We find that the performance of the model depends crucially on the resolution chosen to smooth the DOS and that there is a tradeoff to be made between the systematic error associated with the smoothing and the error in the ML model for a specific structure. We find however that the errors are not strongly correlated among similar structures, and so the average DOS over an ensemble of configurations is in very good agreement with the reference electronic structure calculations, despite the large nominal error on individual configurations. We demonstrate the usefulness of this approach by computing the density of states of a large amorphous silicon sample, for which it would be prohibitively expensive to compute the DOS by direct electronic structure calculations and show how the atom-centered decomposition of the DOS that is obtained through our model can be used to extract physical insights into the connections between structural and electronic features. State of matter ; Condensed matter physics ; Physics ; Materials science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electronic and Structural Properties of Oxides
Authors: Ke Tian | Qingju Wang | Jin Shen | Jingyi Zhou | Zongbi Bao | Qiwei Yang | Qilong Ren
Source: Angewandte Chemie International Edition (article, 2020-04-24)
Field: Chemistry — Inorganic Chemistry

Molecular Sieving of C<sub>2</sub>‐C<sub>3</sub> Alkene from Alkyne with Tuned Threshold Pressure in Robust Layered Metal–Organic Frameworks C<sub>2</sub> -C<sub>3</sub> alkyne/alkene separation is of great importance; however, designing materials for an efficient molecular sieving of alkenes from alkynes remains challenging. Now, two hydrolytically stable layered MOFs, [Cu(dps)<sub>2</sub> (GeF<sub>6</sub> )] (GeFSIX-dps-Cu, dps=4,4'-dipyridylsulfide) and [Zn(dps)<sub>2</sub> (GeF<sub>6</sub> )] (GeFSIX-dps-Zn), can achieve almost complete exclusion of both C<sub>3</sub> H<sub>6</sub> and C<sub>2</sub> H<sub>4</sub> from their alkyne analogues. GeFSIX-dps-Cu displays a notable advanced threshold pressure for alkynes adsorption and thus substantial uptakes at lower pressures, providing record C<sub>3</sub> H<sub>4</sub> /C<sub>3</sub> H<sub>6</sub> uptake ratios and capacity-enhanced C<sub>2</sub> H<sub>2</sub> /C<sub>2</sub> H<sub>4</sub> sieving for a wide composition range. Metal substitution (Zn to Cu) affords fine tuning of linker rotation and layer stacking, creating slightly expanded pore aperture and interlayer space coupled with multiple hydrogen-bonding sites, allowing easier entrance of alkyne while excluding alkene. Breakthrough experiments confirmed tunable sieving by these MOFs for C<sub>3</sub> H<sub>4</sub> /C<sub>3</sub> H<sub>6</sub> and C<sub>2</sub> H<sub>2</sub> /C<sub>2</sub> H<sub>4</sub> mixtures. Alkyne ; Alkene ; Metal-organic framework ; Stacking ; Linker ; Materials science ; Metal ; Adsorption ; Chemistry ; Chemical engineering ; Organic chemistry ; Catalysis ; Computer science ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Graphene research and applications
Authors: Chenyu Shi | Qi Zhang | He Tian | Da‐Hui Qu
Source: SmartMat (article, 2020-11-25)
Field: Chemistry — Organic Chemistry

Supramolecular adhesive materials from small‐molecule self‐assembly Abstract Developing high‐performance adhesive materials not only aims at industrial and social requirements but also bears the fundamental importance of understanding the chemical factors of biological adhesion to develop biomimetic adhesive materials. Owing to the wide development of supramolecular chemistry, numerous supramolecular tools are exploited and proved to be reliable in the replacement of traditional covalent materials by reversible noncovalent or dynamic covalent materials. Taking advantage of these ready‐to‐use supramolecular toolboxes, supramolecular adhesive materials are rising and promising toward “smart” adhesives, that is, enabling responsiveness, reversibility, and recyclability. Compared with polymeric adhesive materials, low‐molecular‐weight adhesives feature chemically precise structure, easier engineering by molecular design, and hence higher reproducibility. However, it remains highly challenging to make high‐performance adhesive materials by low‐molecular‐weight feedstocks. This review will focus on the recent advancement in the construction of supramolecular adhesive materials by small‐molecule self‐assembly. The design guidelines and consideration on the molecular scale will be discussed and summarized on how to enhance the strength of adhesives. Meanwhile, owing to the dynamic nature of supramolecular self‐assembly, several “smart” functions of such materials will be presented, such as stimuli–responsiveness and adaptiveness. Finally, current challenges and future perspectives of this emerging field will be proposed. Supramolecular chemistry ; Adhesive ; Nanotechnology ; Materials science ; Covalent bond ; Molecule ; Chemistry ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Supramolecular Self-Assembly in Materials ; Polymer Surface Interaction Studies
Authors: Tobias A. Schaub | Ephraim Prantl | Julia Kohn | Markus Bursch | Checkers R. Marshall | Erik J. Leonhardt | Terri C. Lovell | Lev N. Zakharov | Carl K. Brozek | Siegfried R. Waldvogel | Stefan Grimme | Ramesh Jasti
Source: Journal of the American Chemical Society (article, 2020-04-11)
Field: Materials Science — Materials Chemistry

Exploration of the Solid-State Sorption Properties of Shape-Persistent Macrocyclic Nanocarbons as Bulk Materials and Small Aggregates Porous molecular materials combine benefits such as convenient processability and the possibility for atom-precise structural fine-tuning which makes them remarkable candidates for specialty applications in the areas of gas separation, catalysis, and sensing. In order to realize the full potential of these materials and guide future molecular design, knowledge of the transition from molecular properties into materials behavior is essential. In this work, the class of compounds termed cycloparaphenylenes (CPPs)-shape-persistent macrocycles with built-in cavities and radially oriented π-systems-was selected as a conceptually simple class of intrinsically porous nanocarbons to serve as a platform for studying the transition from analyte sorption properties of small aggregates to those of bulk materials. In our detailed investigation, two series of CPPs were probed: previously reported hoop-shaped [<i>n</i>]CPPs and a novel family of all-phenylene figure-8 shaped (lemniscal) bismacrocycles, termed spiro[<i>n</i>,<i>n</i>]CPPs. A series of nanocarbons with different macrocycle sizes and heteroatom content have been prepared by atom-precise organic synthetic methods, and their structural, photophysical, and electronic attributes were disclosed. Detailed experimental studies (X-ray crystallography, gas sorption, and quartz-crystal microbalance measurements) and quantum chemical calculations provided ample evidence for the importance of the solid-state arrangement on the porosity and analyte uptake ability of intrinsically porous molecular nanocarbons. We demonstrate that this molecular design principle, i.e., incorporation of sterically demanding spiro junctions into the backbone of nanohoops, enables the manipulation of solid-state morphology without significantly changing the nature and size of the macrocyclic cavities. As a result, the novel spiro[<i>n</i>,<i>n</i>]CPPs showed a remarkable performance as high affinity material for vapor analyte sensing. Chemistry ; Sorption ; Porosity ; Heteroatom ; Nanotechnology ; Quartz crystal microbalance ; Chemical engineering ; Organic chemistry ; Adsorption ; Materials science Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Surface Chemistry and Catalysis
Authors: Dinorath Olvera | Mina Sohrabi Molina | Gillian M. Hendy | Michael G. Monaghan
Source: Advanced Functional Materials (article, 2020-02-12)
Field: Materials Science — Biomaterials

Electroconductive Melt Electrowritten Patches Matching the Mechanical Anisotropy of Human Myocardium Abstract Cardiac patches represent a promising strategy for the treatment of myocardial infarction (MI). Here, an electroconductive cardiac patch that conforms to the mechanics of human myocardium is fabricated. By melt electrospinning writing (MEW), it is possible to fabricate an auxetic patch that can overcome the limited range of elasticity seen in conventional square patch designs. The auxetic patches can accommodate the strains and stresses exhibited by the human myocardium during diastole and systole. It is shown that the geometry of the auxetic patches can be fine‐tuned to reflect anisotropic mechanical properties. The anisotropic ratio of effective stiffness ( E 1 / E 2 ) of the auxetic patches agrees with the directionally‐dependent mechanics of the heart. Furthermore, in situ polymerization of doped‐polypyrrole (PPy) on the auxetic patches confers electroconductive properties close to those reported of human myocardium. This approach demonstrates the potential use of a rational design of PPy‐coated patches for their use as cardiac patches. Materials science ; Auxetics ; Anisotropy ; Composite material ; Stiffness ; Elasticity (physics) ; Electrospinning ; Polymer ; Optics Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Tissue Engineering and Regenerative Medicine
Authors: Yun Zhang | Xiaojie Xu
Source: AIP Advances (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

Machine learning modeling of lattice constants for half-Heusler alloys The Gaussian process regression model is developed as a machine learning tool to find statistical correlations among lattice constants, a0, of half-Heusler compounds, ionic radii, and Pauling electronegativity of their alloying elements. Nearly 140 half-Heusler samples, containing alloying elements of Cr, Mn, Fe, Co, Ni, Rh, Ti, V, Al, Ga, In, Si, Ge, Sn, P, As, and Sb, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants of half-Heusler compounds. Electronegativity ; Lattice constant ; Ionic radius ; Materials science ; Atomic radius ; Lattice (music) ; Alloy ; Condensed matter physics ; Thermodynamics ; Metallurgy ; Ion ; Physics ; Diffraction ; Quantum mechanics Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; X-ray Diffraction in Crystallography
Authors: Shahryar Shakeri | Milad Ashrafizadeh | Ali Zarrabi | Rasoul Roghanian | Elham Ghasemipour Afshar | Abbas Pardakhty | Reza Mohammadinejad | Anuj Kumar | Vijay Kumar Thakur
Source: Biomedicines (review, 2020-01-13)
Field: Materials Science — Biomaterials

Multifunctional Polymeric Nanoplatforms for Brain Diseases Diagnosis, Therapy and Theranostics The blood-brain barrier (BBB) acts as a barrier to prevent the central nervous system (CNS) from damage by substances that originate from the blood circulation. The BBB limits drug penetration into the brain and is one of the major clinical obstacles to the treatment of CNS diseases. Nanotechnology-based delivery systems have been tested for overcoming this barrier and releasing related drugs into the brain matrix. In this review, nanoparticles (NPs) from simple to developed delivery systems are discussed for the delivery of a drug to the brain. This review particularly focuses on polymeric nanomaterials that have been used for CNS treatment. Polymeric NPs such as polylactide (PLA), poly (D, L-lactide-co-glycolide) (PLGA), poly (ε-caprolactone) (PCL), poly (alkyl cyanoacrylate) (PACA), human serum albumin (HSA), gelatin, and chitosan are discussed in detail. Blood–brain barrier ; Drug delivery ; PLGA ; Chitosan ; Gelatin ; Systemic circulation ; Nanotechnology ; Drug carrier ; Materials science ; Central nervous system ; Medicine ; Nanoparticle ; Chemistry ; Internal medicine Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications