jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiwei Guo | Yang Long | Haitao Jiang | Jie Ren | Hong Chen
Source: Advanced Photonics (article, 2021-06-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Anomalous unidirectional excitation of high-k hyperbolic modes using all-electric metasources The unidirectional excitation of near-field optical modes is a fundamental prerequisite for many photonic applications, such as wireless power transfer and information communications. We experimentally construct all-electric Huygens and spin metasources and demonstrate anomalous unidirectional excitation of high-k hyperbolic modes in two types of hyperbolic metasurfaces. We use a Huygens metasource to study the unidirectional excitation of hyperbolic bulk modes in a planar hyperbolic metamaterial (HMM). Specifically, unidirectional excitation is the same as that in free space in the vertical direction, but opposite to that in free space in the horizontal direction. This anomalous unidirectional excitation is determined by the anisotropic HMM dispersion. In addition, we use a spin metasource to observe the anomalous photonic spin Hall effect in a planar hyperbolic waveguide. For a near-field source with a specific spin, the guide mode with a fixed directional wave vector is excited due to spin-momentum locking. Because the directions of momentum and energy flows in the HMM waveguide are opposite, the unidirectional excitation of hyperbolic guided modes is reversed. Our results not only uncover the sophisticated electromagnetic functionalities of metasources in the near-field but may also provide novel opportunities for the development of integrated optical devices. Excitation ; Physics ; Metamaterial ; Waveguide ; Planar ; Optics ; Computational physics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Shobhit K. Patel | Juveriya Parmar | Vishal Sorathiya | Truong Khang Nguyen | Vigneswaran Dhasarathan
Source: Scientific Reports (article, 2021-03-29)
Field: Materials Science — Materials Chemistry

Tunable infrared metamaterial-based biosensor for detection of hemoglobin and urine using phase change material This paper reports about the outcomes from an investigation carried out on tunable biosensor for detection using infrared in the range of 1.5 µm and 1.65 µm. The biosensor is made of phase change material formed by different alloy combinations, Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST). The nature of GST allows for the material to change phase with changes in temperature, giving the tunable sensing property for biosensing application. Sensor built with amorphous GST (aGST) and crystalline GST (cGST) in different design structures were tested on different concentrations of biomolecules: hemoglobin (10 g/l, 20 g/l, 30 g/l and 40 g/l); and urine (0-1.5 mg/dL, 2.5 mg/dL, 5 mg/dL and 10 mg/dL). The tunable response observed from the tests demonstrates the potential application of the materials in the design of switching and sensing systems. Biosensor ; Infrared ; Materials science ; Biomolecule ; Phase change ; Amorphous solid ; Urine ; Phase (matter) ; Nanotechnology ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Optics ; Crystallography ; Physics ; Organic chemistry Phase-change materials and chalcogenides ; Advanced Thermoelectric Materials and Devices
Authors: Crispin Lichtenberg
Source: Chemical Communications (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Molecular bismuth(<scp>iii</scp>) monocations: structure, bonding, reactivity, and catalysis Cationic bismuth(iii) species [BiR2]+ with weakly coordinating counteranions feature two monoanionic ligands R (such as aryls, amides, alcoholates or halides), a vacant bismuth-centred p-orbital, and an occupied bismuth-centred s-orbital. The vacant orbital is available for intra- and intermolecular σ- and π-type bonding interactions and plays a crucial role in redox chemistry. The occupied s-orbital may also show minor contributions to dative bonding and is essential when addressing reversible Bi(iii)/Bi(v) redox shuttling. Variation of the anionic ligands R, the weakly coordinating counter anions, and potential neutral ligands L allows precise fine-tuning of the coordination chemistry, Lewis acidity, redox-properties, and reactivity towards nucleophiles. This contribution summarises the fundamental properties of well-defined molecular cationic bismuth compounds and highlights recent advancements in the understanding of their Lewis acidity, in their utilisation for challenging stoichometric reactions (such as CH activation and small molecule activation), and in catalytic applications (such as Lewis acid catalysis, radical polymerisation, and Bi(iii)/Bi(v) redox catalysis). Bismuth ; Chemistry ; Reactivity (psychology) ; Catalysis ; Redox ; Lewis acids and bases ; Combinatorial chemistry ; Inorganic chemistry ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Synthesis and characterization of novel inorganic/organometallic compounds ; Organoboron and organosilicon chemistry
Authors: Siwei Li | Ruochen Cao | Mingquan Xu | Yuchen Deng | Lili Lin | Siyu Yao | Xuan Liang | Mi Peng | Zirui Gao | Yuzhen Ge | Jin‐Xun Liu | Wei‐Xue Li | Wu Zhou | Ding Ma
Source: National Science Review (article, 2021-02-08)
Field: Chemistry — Organic Chemistry

Atomically dispersed Ir/α-MoC catalyst with high metal loading and thermal stability for water-promoted hydrogenation reaction Synthesis of atomically dispersed catalysts with high metal loading and thermal stability is challenging but particularly valuable for industrial application in heterogeneous catalysis. Here, we report a facile synthesis of a thermally stable atomically dispersed Ir/α-MoC catalyst with metal loading as high as 4 wt%, an unusually high value for carbide supported metal catalysts. The strong interaction between Ir and the α-MoC substrate enables high dispersion of Ir on the α-MoC surface, and modulates the electronic structure of the supported Ir species. Using quinoline hydrogenation as a model reaction, we demonstrate that this atomically dispersed Ir/α-MoC catalyst exhibits remarkable reactivity, selectivity and stability, for which the presence of high-density isolated Ir atoms is the key to achieving high metal-normalized activity and mass-specific activity. We also show that the water-promoted quinoline hydrogenation mechanism is preferred over the Ir/α-MoC, and contributes to high selectivity towards 1,2,3,4-tetrahydroquinoline. The present work demonstrates a new strategy in constructing a high-loading atomically dispersed catalyst for the hydrogenation reaction. Catalysis ; Selectivity ; Dispersion (optics) ; Quinoline ; Thermal stability ; Materials science ; Metal ; Carbide ; Chemical engineering ; Substrate (aquarium) ; Reactivity (psychology) ; Chemistry ; Organic chemistry ; Composite material ; Metallurgy Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies ; Asymmetric Hydrogenation and Catalysis
Authors: Siyang Wang | Qianhui Liu | Lei Li | Marek W. Urban
Source: Macromolecular Rapid Communications (review, 2021-03-22)
Field: Materials Science — Polymers and Plastics

Recent Advances in Stimuli‐Responsive Commodity Polymers Known for their adaptability to surroundings, capability of transport control of molecules, or the ability of converting one type of energy to another as a result of external or internal stimuli, responsive polymers play a significant role in advancing scientific discoveries that may lead to an array of diverge applications. This review outlines recent advances in the developments of selected commodity polymers equipped with stimuli-responsiveness to temperature, pH, ionic strength, enzyme or glucose levels, carbon dioxide, water, redox agents, electromagnetic radiation, or electric and magnetic fields. Utilized diverse applications ranging from drug delivery to biosensing, dynamic structural components to color-changing coatings, this review focuses on commodity acrylics, epoxies, esters, carbonates, urethanes, and siloxane-based polymers containing responsive elements built into their architecture. In the context of stimuli-responsive chemistries, current technological advances as well as a critical outline of future opportunities and applications are also tackled. Context (archaeology) ; Nanotechnology ; Siloxane ; Commodity ; Polymer ; Adaptability ; Materials science ; Biochemical engineering ; Computer science ; Engineering ; Business ; Biology ; Composite material ; Ecology Polymer composites and self-healing ; Photochromic and Fluorescence Chemistry ; Polydiacetylene-based materials and applications
Authors: Luke Gundry | Si‐Xuan Guo | Gareth F. Kennedy | Jonathan M. Keith | Martin Robinson | David J. Gavaghan | Alan M. Bond | Jie Zhang
Source: Chemical Communications (article, 2021-01-01)
Field: Chemistry — Electrochemistry

Recent advances and future perspectives for automated parameterisation, Bayesian inference and machine learning in voltammetry Advanced data analysis tools such as mathematical optimisation, Bayesian inference and machine learning have the capability to revolutionise the field of quantitative voltammetry. Nowadays such approaches can be implemented routinely with widely available, user-friendly modern computing languages, algorithms and high speed computing to provide accurate and robust methods for quantitative comparison of experimental data with extensive simulated data sets derived from models proposed to describe complex electrochemical reactions. While the methodology is generic to all forms of dynamic electrochemistry, including the widely used direct current cyclic voltammetry, this review highlights advances achievable in the parameterisation of large amplitude alternating current voltammetry. One significant advantage this technique offers in terms of data analysis is that Fourier transformation provides access to the higher order harmonics that are almost devoid of background current. Perspectives on the technical advances needed to develop intelligent data analysis strategies and make them generally available to users of voltammetry are provided. Inference ; Bayesian inference ; Computer science ; Field (mathematics) ; Bayesian probability ; Machine learning ; Artificial intelligence ; Voltammetry ; Chemistry ; Mathematics ; Electrochemistry Electrochemical Analysis and Applications ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Garvit Agarwal | Hieu A. Doan | Lily A. Robertson | Lu Zhang | Rajeev S. Assary
Source: Chemistry of Materials (article, 2021-10-14)
Field: Materials Science — Materials Chemistry

Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried in flowable redox-active materials (redoxmers) which are stored externally and pumped to the cell during operation. Further improvements in the energy density of RFBs necessitates redoxmer designs with wider redox potential windows and higher solubility. Additionally, designing redoxmers with a fluorescence-enabled self-reporting functionality allows monitoring of the state of health of RFBs. To accelerate the discovery of redoxmers with desired properties, state-of-the-art machine learning (ML) methods, such as multiobjective Bayesian optimization (MBO), are useful. Here, we first employed density functional theory calculations to generate a database of reduction potentials, solvation free energies, and absorption wavelengths for 1400 redoxmer molecules based on a 2,1,3-benzothiadiazole (BzNSN) core structure. From the computed properties, we identified 22 Pareto-optimal molecules that represent best trade-off among all of the desired properties. We further utilized these data to develop and benchmark an MBO approach to identify candidates quickly and efficiently with multiple targeted properties. With MBO, optimal candidates from the 1400-molecule data set can be identified at least 15 times more efficiently compared to the brute force or random selection approach. Importantly, we utilized this approach for discovering promising redoxmers from an unseen database of 1 million BzNSN-based molecules, where we discovered 16 new Pareto-optimal molecules with significant improvements in properties over the initial 1400 molecules. We anticipate that this active learning technique is general and can be utilized for the discovery of any class of functional materials that satisfies multiple desired property criteria. Computer science ; Scalability ; Benchmark (surveying) ; Density functional theory ; Bayesian optimization ; Solvation ; Reduction (mathematics) ; Multi-objective optimization ; Materials science ; Chemistry ; Molecule ; Machine learning ; Database ; Computational chemistry ; Mathematics Machine Learning in Materials Science ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Zheng Fan | Panpan Ji | Jie Zhang | Doris Segets | Da‐Ren Chen | Sheng-Chieh Chen
Source: Journal of Membrane Science (article, 2021-06-08)
Field: Chemistry — Physical and Theoretical Chemistry

Wavelet neural network modeling for the retention efficiency of sub-15 nm nanoparticles in ultraﬁltration under small particle to pore diameter ratio Nanoparticle ; Materials science ; Particle (ecology) ; Wavelet ; Artificial neural network ; Particle size ; Chemical engineering ; Analytical Chemistry (journal) ; Nanotechnology ; Chemistry ; Chromatography ; Artificial intelligence ; Computer science ; Engineering ; Geology Electrostatics and Colloid Interactions ; Enhanced Oil Recovery Techniques ; Groundwater flow and contamination studies
Authors: Eunhwan Kim | Juyeon Han | Seokgyu Ryu | Youngkyu Choi | Jeeyoung Yoo
Source: Materials (review, 2021-07-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ionic Liquid Electrolytes for Electrochemical Energy Storage Devices For decades, improvements in electrolytes and electrodes have driven the development of electrochemical energy storage devices. Generally, electrodes and electrolytes should not be developed separately due to the importance of the interaction at their interface. The energy storage ability and safety of energy storage devices are in fact determined by the arrangement of ions and electrons between the electrode and the electrolyte. In this paper, the physicochemical and electrochemical properties of lithium-ion batteries and supercapacitors using ionic liquids (ILs) as an electrolyte are reviewed. Additionally, the energy storage device ILs developed over the last decade are introduced. Electrolyte ; Supercapacitor ; Ionic liquid ; Energy storage ; Electrochemistry ; Materials science ; Electrode ; Lithium (medication) ; Ion ; Electrochemical energy storage ; Ionic bonding ; Electrochemical energy conversion ; Inorganic chemistry ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering ; Catalysis Supercapacitor Materials and Fabrication ; Advanced Battery Materials and Technologies ; Conducting polymers and applications
Authors: Stéfano Frizzo Stefenon | Marcelo Picolotto Corso | Ademir Nied | Fábio Perez | Kin‐Choong Yow | Gabriel Villarrubia González | Valderi Reis Quietinho Leithardt
Source: IET Generation Transmission & Distribution (article, 2021-12-08)
Field: Materials Science — Materials Chemistry

Classification of insulators using neural network based on computer vision Abstract Insulators of the electrical power grid are usually installed outdoors, so they suffer from environmental stresses, such as the presence of contamination. Contamination can increase surface conductivity, which can lead to system failures, reducing the reliability of the network. The identification of insulators that have their properties compromised is important so that there are no discharges through its insulating body. To perform the classification of contaminated insulators, this paper presents computer vision techniques for the extraction of contamination characteristics, and a neural network (NN) model for the classification of this condition. Specifically, the Sobel edge detector, Canny edge detection, binarization with threshold, adaptive binarization with threshold, threshold with Otsu and Riddler–Calvard techniques will be evaluated. The results show that it is possible to have an accuracy of up to 97.50% for the classification of contaminated insulators from the extraction of characteristics with computer vision using the NN for the classification. The proposed model is more accurate than well‐established models such as support‐vector machine (SVM), k‐nearest neighbor (k‐NN), and ensemble learning methods. This showed that optimizing the model's parameters can make it superior to solve the problem in question. Artificial neural network ; Computer science ; Artificial intelligence ; Computer vision ; Pattern recognition (psychology) ; Machine learning High voltage insulation and dielectric phenomena ; Image Enhancement Techniques ; Industrial Vision Systems and Defect Detection
Authors: Lijie Xu | Xiaoping Jiang | Kang Liang | Meng Gao | Biao Kong
Source: Aggregate (article, 2021-09-10)
Field: Materials Science — Materials Chemistry

Frontier luminous strategy of functional silica nanohybrids in sensing and bioimaging: From ACQ to AIE Abstract Fluorescent silica organic–inorganic nanohybrids which combine designable luminescence performance of organic fluorescent dyes and various outstanding advantages of silica nanomaterials have attracted increasing research interests in these fascinating areas. Optical transparency and facile functional modification properties of silica material provide great opportunities to integrate desired fluorescent molecules for various frontier luminous applications. However, conventional organic dyes are typically subject to aggregation‐caused quenching due to their aggregation in silica matrix, which could be detrimental for their performance in sensing and biomedical applications. The appearance of aggregation‐induced emission luminogens (AIEgens) paves a new way for developing highly efficient fluorescent silica nanohybrids (FSNs). FSNs with intensive luminescence could be obtained due to the formation of aggregates and the restricted intramolecular motion of AIEgens in silica inorganic matrix. In this review, the reported fabrication methodologies of various FSNs based on colloidal silica nanoparticles (SNs) and mesoporous SNs including physical entrapment and covalent strategies are summarized. Especially, the AIEgens‐functionalized silica hybrid nanomaterials are introduced in detail. Furthermore, chemical sensing, biosensing, and bioimaging applications of resultant FSNs are also discussed. Nanotechnology ; Mesoporous silica ; Nanomaterials ; Materials science ; Fluorescence ; Luminescence ; Aggregation-induced emission ; Mesoporous material ; Chemistry ; Optoelectronics ; Organic chemistry ; Catalysis Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Nanoplatforms for cancer theranostics
Authors: Mahdieh Darroudi | Mehrdad Gholami | Majid Rezayi | Majid Khazaei
Source: Journal of Nanobiotechnology (review, 2021-11-29)
Field: Materials Science — Biomaterials

An overview and bibliometric analysis on the colorectal cancer therapy by magnetic functionalized nanoparticles for the responsive and targeted drug delivery With the growing demands for personalized medicine and medical devices, nanomedicine is a modern scientific field, and research continues to apply nanomaterials for therapeutic and damaged tissue diagnosis. In this regard, substantial progress has been made in synthesizing magnetic nanoparticles with desired sizes, chemical composition, morphologies, and surface chemistry. Among these materials, nanomagnetic iron oxides have demonstrated promise as unique drug delivery carriers due to cancer treatment. This carrier could lead to responsive properties to a specific trigger, including heat, pH, alternative magnetic field, or even enzymes, through functionalization and coating of magnetic nanoparticles, along with biocompatibility, good chemical stability, easy functionalization, simple processing, and ability to localize to the tumor site with the assistance of external magnetic field. Current studies have focused on magnetic nanoparticles' utilities in cancer therapy, especially for colorectal cancer. Additionally, a bibliometric investigation was performed on the public trends in the field of the magnetic nanoparticle to drug delivery and anticancer, which represented progressing applications of these carriers in the multidisciplinary zones with a general view on future research and identified potential opportunities and challenges. Furthermore, we outline the current challenges and forthcoming research perspective for high performance and fostering advanced MNPs in colorectal cancer treatment. Drug delivery ; Targeted drug delivery ; Colorectal cancer ; Cancer therapy ; Nanotechnology ; Magnetic nanoparticles ; Drug ; Medicine ; Pharmacology ; Nanoparticle ; Chemistry ; Cancer ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Cancer Research and Treatments
Authors: Jie Yang | Wenzhao Fu | Chaoqiu Chen | Wenyao Chen | Wugen Huang | Ruoou Yang | Qingqiang Kong | Baiyan Zhang | Ji‐Xiao Zhao | Chengmeng Chen | Jun Luo | Fan Yang | Xuezhi Duan | Zheng Jiang | Yong Qin
Source: ACS Catalysis (article, 2021-03-19)
Field: Materials Science — Materials Chemistry

Atomic Design and Fine-Tuning of Subnanometric Pt Catalysts to Tame Hydrogen Generation The rational synthesis of subnanocatalysts with controllable electronic and atomic structures remains a challenge to break the limits of traditional catalysts. Here, we report the atomic-level precise synthesis of Pt/graphene subnanocatalysts (from single atom and dimer to cluster) by atomic layer deposition, achieved by a high-temperature pulsed ozone strategy to controllably pre-create abundant in-plane epoxy groups on graphene as anchoring sites. The specific in-plane epoxy structure endows the deposited Pt species with uniformity, controllability, and stability. Their size-dependent electronic and geometric effects have been observed for ammonia borane hydrolysis, revealing a volcano-type dependence of intrinsic activity on their sizes. Their active site structures have been identified on the basis of extensive characterizations, dynamic compensation effect, kinetic isotope experiments, and density functional theory simulation. The Pt dimers show the highest catalytic activity and better durability than Pt single atoms and nanoparticles, ascribed to the C–Pt–Pt–O (C5Pt2O, metal–metal bond dimer) active site structure. Our work provides insights into the precise tailoring and catalytic mechanism at the subnanometer level. Catalysis ; Ammonia borane ; Graphene ; Materials science ; Chemical engineering ; Density functional theory ; Nanotechnology ; Chemical physics ; Nanoparticle ; Chemistry ; Computational chemistry ; Hydrogen production ; Organic chemistry Hydrogen Storage and Materials ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques