jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaosheng Peng | Fan Yang | Ganjun Wang | Yijiang Wu | Lee Li | Zhaohui Li | Ashfaque Ahmed Bhatti | Chengke Zhou | D.M. Hepburn | Alistair Reid | M.D. Judd | Wah Hoon Siew
Source: IEEE Transactions on Power Delivery (article, 2019-03-19)
Field: Materials Science — Materials Chemistry

A Convolutional Neural Network-Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High-Voltage Cables It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications. Convolutional neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Support vector machine ; Partial discharge ; Deep learning ; Artificial neural network ; Feature extraction ; Voltage ; Engineering High voltage insulation and dielectric phenomena ; Image and Signal Denoising Methods ; Image Enhancement Techniques
Authors: Zahid Hassan | Eduard Spuling | Daniel M. Knoll | Stefan Bräse
Source: Angewandte Chemie International Edition (review, 2019-07-08)
Field: Chemistry — Organic Chemistry

Regioselective Functionalization of [2.2]Paracyclophanes: Recent Synthetic Progress and Perspectives [2.2]Paracyclophane (PCP) is a prevalent scaffold that is widely utilized in asymmetric synthesis, π-stacked polymers, energy materials, and functional parylene coatings that finds broad applications in bio- and materials science. In the last few years, [2.2]paracyclophane chemistry has progressed tremendously, enabling the fine-tuning of its structural and functional properties. This Minireview highlights the most important recent synthetic developments in the selective functionalization of PCP that govern distinct features of planar chirality as well as chiroptical and optoelectronic properties. Special focus is given to the function-inspired design of [2.2]paracyclophane-based π-stacked conjugated materials by transition-metal-catalyzed cross-coupling reactions. Current synthetic challenges, limitations, as well as future research directions and new avenues for advancing cyclophane chemistry are also summarized. Surface modification ; Nanotechnology ; Regioselectivity ; Materials science ; Cyclophane ; Chemistry ; Combinatorial chemistry ; Catalysis ; Organic chemistry ; Molecule Synthesis and Properties of Aromatic Compounds ; Organoboron and organosilicon chemistry ; Supramolecular Self-Assembly in Materials
Authors: Yuchao Chai | Weixiang Shang | Weijie Li | Guangjun Wu | Weili Dai | Naijia Guan | Landong Li
Source: Advanced Science (review, 2019-07-18)
Field: Materials Science — Materials Chemistry

Noble Metal Particles Confined in Zeolites: Synthesis, Characterization, and Applications Noble metal nanoparticles or subnanometric particles confined in zeolites, that is, metal@zeolite, represent an important type of functional materials with typical core-shell structure. This type of material is known for decades and recently became a research hotspot due to their emerging applications in various fields. Remarkable achievements are made dealing with the synthesis, characterization, and applications of noble metal particles confined in zeolites. Here, the most representative research progress in metal@zeolites is briefly reviewed, aiming to boost further research on this topic. For the synthesis of metal@zeolites, various strategies, such as direct synthesis from inorganic or ligand-assisted noble metal precursors, multistep postsynthesis encapsulation and ion-exchange followed by reduction, are introduced and compared. For the characterization of metal@zeolites, several most useful techniques, such as electron microscopy, X-ray based spectroscopy, infrared and fluorescence emission spectroscopy, are recommended to check the successful confinement of noble metal particles in zeolite matrix and their unique physiochemical properties. For the applications of metal@zeolites, catalysis and optics are involved with an emphasis on catalytic applications including the size-dependent catalytic properties, the sintering-resistance properties, the substrate shape-selective catalysis, and catalysis modulation by zeolite microenvironment. Noble metal ; Zeolite ; Catalysis ; Materials science ; Nanoparticle ; Characterization (materials science) ; Nanotechnology ; Metal ; Chemical engineering ; Heterogeneous catalysis ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Nanocluster Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Xiaobing Yan | Kaiyang Wang | Jianhui Zhao | Zhenyu Zhou | Hong Wang | Jingjuan Wang | Lei Zhang | Xiaoyan Li | Zuoao Xiao | Qianlong Zhao | Yifei Pei | Gong Wang | Cuiya Qin | Hui Li | Jianzhong Lou | Qi Liu | Peng Zhou
Source: Small (article, 2019-05-08)
Field: Materials Science — Materials Chemistry

A New Memristor with 2D Ti<sub>3</sub>C<sub>2</sub>T<i><sub>x</sub></i> MXene Flakes as an Artificial Bio‐Synapse Two-dimensional (2D) materials have attracted extensive research interest in academia due to their excellent electrochemical properties and broad application prospects. Among them, 2D transition metal carbides (Ti<sub>3</sub> C<sub>2</sub> T<sub>x</sub> ) show semiconductor characteristics and are studied widely. However, there are few academic reports on the use of 2D MXene materials as memristors. In this work, reported is a memristor based on MXene Ti<sub>3</sub> C<sub>2</sub> T<sub>x</sub> flakes. After electroforming, Al/Ti<sub>3</sub> C<sub>2</sub> T<sub>x</sub> /Pt devices exhibit repeatable resistive switching (RS) behavior. More interestingly, the resistance of this device can be continuously modulated under the pulse sequence with 10 ns pulse width, and the pulse width of 10 ns is much lower than that in other reported work. Moreover, on the nanosecond scale, the transition from short-term plasticity to long-term plasticity is achieved. These two properties indicate that this device is favorable for ultrafast biological synapse applications and high-efficiency training of neural networks. Through the exploration of the microstructure, Ti vacancies and partial oxidation are proposed as the origins of the physical mechanism of RS behavior. This work reveals that 2D MXene Ti<sub>3</sub> C<sub>2</sub> T<sub>x</sub> flakes have excellent potential for use in memristor devices, which may open the door for more functions and applications. Memristor ; Materials science ; Electroforming ; Nanotechnology ; Optoelectronics ; Microstructure ; Electronic engineering ; Composite material ; Layer (electronics) MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Julia Westermayr | Michael Gastegger | Maximilian F. S. J. Menger | Sebastian Mai | Leticia González | Philipp Marquetand
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Machine learning enables long time scale molecular photodynamics simulations Photo-induced processes are fundamental in nature but accurate simulations of their dynamics are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynamics on nanosecond time scales, which are otherwise out of reach with contemporary approaches. Instead of expensive quantum chemistry during molecular dynamics simulations, we use deep neural networks to learn the relationship between a molecular geometry and its high-dimensional electronic properties. As an example, the time evolution of the methylenimmonium cation for one nanosecond is used to demonstrate that machine learning algorithms can outperform standard excited-state molecular dynamics approaches in their computational efficiency while delivering the same accuracy. Bottleneck ; Nanosecond ; Scale (ratio) ; Molecular dynamics ; Quantum ; Artificial neural network ; Deep learning ; Molecular machine Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Strong Light-Matter Interactions
Authors: Chuanfu Wang | Lei Zhang | Xin Huang | Yufei Zhu | Gang Kevin Li | Qinfen Gu | Jingyun Chen | Linge Ma | Xiujie Li | Qihua He | Junbo Xu | Qi Sun | Chuqiao Song | Mi Peng | Junliang Sun | Ding Ma
Source: Nature Communications (article, 2019-09-25)
Field: Chemistry — Inorganic Chemistry

Maximizing sinusoidal channels of HZSM-5 for high shape-selectivity to p-xylene The shape-selective catalysis enabled by zeolite micropore's molecular-sized sieving is an efficient way to reduce the cost of chemical separation in the chemical industry. Although well studied since its discovery, HZSM-5's shape-selective capability has never been fully exploited due to the co-existence of its different-sized straight channels and sinusoidal channels, which makes the shape-selective p-xylene production from toluene alkylation with the least m-xylene and o-xylene continue to be one of the few industrial challenges in the chemical industry. Rather than modifications which promote zeolite shape-selectivity at the cost of stability and reactivity loss, here inverse Al zoned HZSM-5 with sinusoidal channels predominantly opened to their external surfaces is constructed to maximize the shape-selectivity of HZSM-5 sinusoidal channels and reach > 99 % p-xylene selectivity, while keeping a very high activity and good stability ( > 220 h) in toluene methylation reactions. The strategy shows good prospects for shape-selective control of molecules with tiny differences in size. Selectivity ; Zeolite ; Xylene ; Microporous material ; p-Xylene ; Toluene ; Chemistry ; Molecular sieve ; Catalysis ; Molecule ; Chemical engineering ; Nanotechnology ; Materials science ; Organic chemistry Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Jong Yeon Lee | Eslam Khalaf | Shang Liu | Xiaomeng Liu | Zeyu Hao | Philip Kim | Ashvin Vishwanath
Source: Nature Communications (article, 2019-11-25)
Field: Materials Science — Materials Chemistry

Theory of correlated insulating behaviour and spin-triplet superconductivity in twisted double bilayer graphene Two graphene monolayers twisted by a small magic angle exhibit nearly flat bands, leading to correlated electronic states. Here we study a related but different system with reduced symmetry - twisted double bilayer graphene (TDBG), consisting of two Bernal stacked bilayer graphenes, twisted with respect to one another. Unlike the monolayer case, we show that isolated flat bands only appear on application of a vertical displacement field. We construct a phase diagram as a function of twist angle and displacement field, incorporating interactions via a Hartree-Fock approximation. At half-filling, ferromagnetic insulators are stabilized with valley Chern number [Formula: see text]. Upon doping, ferromagnetic fluctuations are argued to lead to spin-triplet superconductivity from pairing between opposite valleys. We highlight a novel orbital effect arising from in-plane fields plays an important role in interpreting experiments. Combined with recent experimental findings, our results establish TDBG as a tunable platform to realize rare phases in conventional solids. Bilayer graphene ; Superconductivity ; Phase diagram ; Pairing ; Twist ; Bilayer ; Monolayer ; Ferromagnetism Graphene research and applications ; Topological Materials and Phenomena ; 2D Materials and Applications
Authors: Xiaqiong Fan | Ming Wen | Huitao Zeng | Zhimin Zhang | Hongmei Lü
Source: The Analyst (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Deep learning-based component identification for the Raman spectra of mixtures Raman spectroscopy is widely used as a fingerprint technique for molecular identification. However, Raman spectra contain molecular information from multiple components and interferences from noise and instrumentation. Thus, component identification using Raman spectra is still challenging, especially for mixtures. In this study, a novel approach entitled deep learning-based component identification (DeepCID) was proposed to solve this problem. Convolution neural network (CNN) models were established to predict the presence of components in mixtures. Comparative studies showed that DeepCID could learn spectral features and identify components in both simulated and real Raman spectral datasets of mixtures with higher accuracy and significantly lower false positive rates. In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets. In conclusion, DeepCID is a promising method for solving the component identification problem in the Raman spectra of mixtures. Component (thermodynamics) ; Raman spectroscopy ; Identification (biology) ; Spectral line ; Artificial intelligence ; Materials science ; Pattern recognition (psychology) ; Computer science ; Physics ; Optics ; Astronomy ; Thermodynamics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Yadan Guo | Zhiheng Gong | Chenxi Li | Bai Gao | Peng Li | Xuegang Wang | Bingcong Zhang | Xiaomeng Li
Source: Chemical Engineering Journal (article, 2019-12-02)
Field: Chemistry — Inorganic Chemistry

Efficient removal of uranium (VI) by 3D hierarchical Mg/Fe-LDH supported nanoscale hydroxyapatite: A synthetic experimental and mechanism studies Adsorption ; Nuclear chemistry ; X-ray photoelectron spectroscopy ; Specific surface area ; Scanning electron microscope ; BET theory ; Apatite ; Phosphate ; Dissolution ; Ion exchange ; Precipitation ; Langmuir adsorption model ; Extraction (chemistry) ; Fourier transform infrared spectroscopy ; Chemical engineering ; Materials science ; Chemistry ; Ion ; Mineralogy ; Catalysis ; Chromatography ; Composite material ; Organic chemistry Radioactive element chemistry and processing ; Nuclear materials and radiation effects ; Chemical Synthesis and Characterization
Authors: Johannes Hoja | Hsin-Yu Ko | Marcus A. Neumann | Roberto Car | Robert A. DiStasio | Alexandre Tkatchenko
Source: Science Advances (article, 2019-01-04)
Field: Chemistry — Physical and Theoretical Chemistry

Reliable and practical computational description of molecular crystal polymorphs Reliable prediction of the polymorphic energy landscape of a molecular crystal would yield profound insight into drug development in terms of the existence and likelihood of late-appearing polymorphs. However, the computational prediction of molecular crystal polymorphs is highly challenging due to the high dimensionality of conformational and crystallographic space accompanied by the need for relative free energies to within 1 kJ/mol per molecule. In this study, we combine the most successful crystal structure sampling strategy with the most successful first-principles energy ranking strategy of the latest blind test of organic crystal structure prediction methods. Specifically, we present a hierarchical energy ranking approach intended for the refinement of relative stabilities in the final stage of a crystal structure prediction procedure. Such a combined approach provides excellent stability rankings for all studied systems and can be applied to molecular crystals of pharmaceutical importance. Crystal structure prediction ; Crystal (programming language) ; Ranking (information retrieval) ; Curse of dimensionality ; Crystal structure ; Stability (learning theory) ; Molecular dynamics ; Yield (engineering) Crystallography and molecular interactions ; Energetic Materials and Combustion ; Crystal structures of chemical compounds
Authors: Hany S. El‐Mesery | Hanping Mao | Abd El‐Fatah Abomohra
Source: Sensors (review, 2019-02-18)
Field: Chemistry — Analytical Chemistry

Applications of Non-destructive Technologies for Agricultural and Food Products Quality Inspection The quality and safety of food is an increasing concern for worldwide business. Non-destructive methods (NDM), as a means of assessment and instrumentation have created an esteemed value in sciences, especially in food industries. Currently, NDM are useful because they allow the simultaneous measurement of chemical and physical data from food without destruction of the substance. Additionally, NDM can obtain both quantitative and qualitative data at the same time without separate analyses. Recently, many studies on non-destructive detection measurements of agro-food products and final quality assessment of foods were reported. As a general statement, the future of using NDM for assessing the quality of food and agricultural products is bright; and it is possible to come up with interesting findings through development of more efficient and precise imaging systems like the machine vision technique. The present review aims to discuss the application of different non-destructive methods (NDM) for food quality and safety evaluation. Quality (philosophy) ; Food safety ; Agriculture ; Food quality ; Risk analysis (engineering) ; Instrumentation (computer programming) ; Food products ; Computer science ; Biotechnology ; Engineering ; Agricultural engineering ; Business ; Food science ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Yekta Arya Ölçer | Marcos Tascón | Ahmet E. Eroğlu | Ezel Boyacı
Source: TrAC Trends in Analytical Chemistry (article, 2019-02-05)
Field: Chemistry — Analytical Chemistry

Thin film microextraction: Towards faster and more sensitive microextraction Solid-phase microextraction ; Computer science ; Process engineering ; Extraction (chemistry) ; Nanotechnology ; Sample preparation ; Materials science ; Biochemical engineering ; Chromatography ; Chemistry ; Gas chromatography–mass spectrometry ; Engineering ; Mass spectrometry Analytical chemistry methods development ; Ion-surface interactions and analysis ; Radioactive element chemistry and processing
Authors: Jingqu Chen | Jianhua Li | Jiajing Zhou | Zhixing Lin | Francesca Cavalieri | Ewa Czuba‐Wojnilowicz | Yingjie Hu | Agata Glab | Yi Ju | Joseph J. Richardson | Frank Caruso
Source: ACS Nano (article, 2019-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metal–Phenolic Coatings as a Platform to Trigger Endosomal Escape of Nanoparticles The intracellular delivery of functional nanoparticles (NPs) and the release of therapeutic payloads at a target site are central issues for biomedical applications. However, the endosomal entrapment of NPs typically results in the degradation of active cargo, leading to poor therapeutic outcomes. Current advances to promote the endosomal escape of NPs largely involve the use of polycationic polymers and cell-penetrating peptides (CPPs), which both can suffer from potential toxicity and convoluted synthesis/conjugation processes. Herein, we report the use of metal-phenolic networks (MPNs) as versatile and nontoxic coatings to facilitate the escape of NPs from endo/lysosomal compartments. The MPNs, which were engineered from the polyphenol tannic acid and Fe<sup>III</sup> or Al<sup>III</sup>, enabled the endosomal escape of both inorganic (mesoporous silica) and organic (polystyrene and melamine resin) NPs owing to the "proton-sponge effect" arising from the buffering capacity of MPNs. Postfunctionalization of the MPN-coated NPs with low-fouling polymers did not impair the endosomal escape, indicating the modular and generalizable nature of this approach. We envisage that the ease of fabrication, versatility, low cytotoxicity, and promising endosomal escape performance displayed by the MPN coatings offer opportunities for such coatings to be used for the efficient delivery of cytoplasm-targeted therapeutics using NPs. Nanoparticle ; Endosome ; Nanotechnology ; Materials science ; Metal ; Chemistry ; Metallurgy Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Lipid Membrane Structure and Behavior
Authors: Kiarashinejad, Yashar | Abdollahramezani, Sajjad | Adibi, Ali
Source: INRIA a CCSD electronic archive server (article, 2019-08-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures In this paper, we demonstrate a computationally ecient new approach based on deep learning (DL) techniques for analysis, design, and optimization of electromagnetic (EM) nanostructures. We use the strong correlation among features of a generic EM problem to considerably reduce the dimensionality of the problem and thus, the computational complexity, without imposing considerable errors. By employing the dimensionality reduction concept using the more recently demonstrated autoencoder technique, we redene the conventional many-to-one design problem in EM nanostruc-tures into a one-to-one problem plus a much simpler many-to-one problem, which can be simply solved using an analytic formulation. This approach reduces the computational complexity in solving both the forward problem (i.e., analysis) and the inverse problem (i.e., design) by orders of magnitude compared to conventional approaches. In addition, it provides analytic formulations that, despite their complexity, can be used to obtain intuitive understanding of the physics and dynamics of EM wave interaction with nanostructures with minimal computation requirements. As a proof-of-concept, 1 we applied such an ecacious method to design a new class of on-demand recongurable optical metasurfaces based on phase-change materials (PCM). The experimental results of the fabricated devices are in good agreement with those predicted by the proposed approach. We envision that the integration of such a DL-based technique with full-wave commercial software packages oers a powerful toolkit to facilitate the analysis, design, and optimization of the EM nanostructures as well as explaining, understanding , and predicting the observed responses in such structures. It will thus enable to solve complex design problems that could not be solved with existing techniques. Curse of dimensionality ; Dimensionality reduction ; Autoencoder ; Computer science ; Reduction (mathematics) ; Computational complexity theory ; Computation ; Deep learning ; Nanostructure ; Inverse ; Computer engineering ; Artificial intelligence ; Algorithm ; Mathematics ; Nanotechnology ; Materials science Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications