jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hongchen Chu | Haoyang Zhang | Yang Zhang | Ru‐Wen Peng | Mu Wang | Yang Hao | Yun Lai
Source: Nature Communications (article, 2021-07-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Invisible surfaces enabled by the coalescence of anti-reflection and wavefront controllability in ultrathin metasurfaces Reflection inherently occurs on the interfaces between different media. In order to perfectly manipulate waves on the interfaces, integration of antireflection function in metasurfaces is highly desired. In this work, we demonstrate an approach to realize exceptional metasurfaces that combine the two vital functionalities of antireflection and arbitrary phase manipulation in the deep subwavelength scale. Such ultrathin devices confer reflection-less transmission through impedance-mismatched interfaces with arbitrary wavefront shapes. Theoretically and experimentally, we demonstrate a three-layer antireflection metasurface that achieves an intriguing phenomenon: the simultaneous elimination of the reflection and refraction effects on a dielectric surface. Incident waves transmit straightly through the dielectric surface as if the surface turns invisible. We further demonstrate a wide variety of applications such as invisible curved surfaces, "cloaking" of dielectric objects, reflection-less negative refraction and flat axicons on dielectric-air interfaces, etc. The coalescence of antireflection and wavefront controllability in the deep subwavelength scale brings new opportunities for advanced interface optics with high efficiency and great flexibility. Wavefront ; Optics ; Dielectric ; Reflection (computer programming) ; Refraction ; Controllability ; Cloaking ; Metamaterial ; Coalescence (physics) ; Optoelectronics ; Materials science ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Fahad Khan | Nabisab Mujawar Mubarak | Mohammad Khalid | Mohammad Mansoob Khan | Yie Hua Tan | Rashmi Walvekar | E.C. Abdullah | Rama Rao Karri | Muhammad Ekhlasur Rahman
Source: Critical reviews in solid state and materials sciences/CRC critical reviews in solid state and materials sciences (article, 2021-07-07)
Field: Materials Science — Materials Chemistry

Comprehensive review on carbon nanotubes embedded in different metal and polymer matrix: fabrications and applications Carbonaceous material, especially carbon nanotubes (CNTs), have incredible properties, such as high thermal and mechanical stabilities, good catalytic and adsorption capabilities. In recent years, hybrid nanocomposites have attained considerable attention, due to the combination of unique organic and inorganic elements in a single material. Hence, these nanocomposites have been employed for various applications, such as drug delivery, sensors, corrosion protection materials, flame retardant additives, and pollutant adsorbent. These nanocomposites can be fabricated through various approaches that include powder metallurgy, solution processing, reaction processing, melt processing, electrochemical, and many more. This present review mainly summarizes the various techniques for the fabrication, separation, and purification of CNTs and their nanocomposites, especially CNTs-based polymer and CNTs-based metals/metal oxides nanocomposites. Besides, effects of CNTs embedded with polymers (such as polypyrrole, poly-aniline, and poly-thiophene, etc.) and metals/metal oxides (such as manganese oxide (MnO2), copper (Cu), gold (Au), platinum (Pt), etc.) and how they can be employed toward innovative devices with fascinating properties for a broad range of applications are thoroughly discussed. Further, , industrial applications of CNTs-based polymer/ metal/metal oxides nanocomposites have been reviewed and discussed. Materials science ; Nanocomposite ; Carbon nanotube ; Polymer ; Nanotechnology ; Conductive polymer ; Polymer nanocomposite ; Corrosion ; Composite material Carbon Nanotubes in Composites ; Supercapacitor Materials and Fabrication ; Conducting polymers and applications
Authors: Bhavish Dinakar | Alexander C. Forse | Henry Z. H. Jiang | Ziting Zhu | Jung‐Hoon Lee | Eugene J. Kim | Surya T. Parker | Connor J. Pollak | Rebecca L. Siegelman | Phillip J. Milner | Jeffrey A. Reimer | Jeffrey R. Long
Source: Journal of the American Chemical Society (article, 2021-09-07)
Field: Chemistry — Inorganic Chemistry

Overcoming Metastable CO<sub>2</sub> Adsorption in a Bulky Diamine-Appended Metal–Organic Framework Carbon capture at fossil fuel-fired power plants is a critical strategy to mitigate anthropogenic contributions to global warming, but widespread deployment of this technology is hindered by a lack of energy-efficient materials that can be optimized for CO<sub>2</sub> capture from a specific flue gas. As a result of their tunable, step-shaped CO<sub>2</sub> adsorption profiles, diamine-functionalized metal-organic frameworks (MOFs) of the form diamine-Mg<sub>2</sub>(dobpdc) (dobpdc<sup>4-</sup> = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) are among the most promising materials for carbon capture applications. Here, we present a detailed investigation of dmen-Mg<sub>2</sub>(dobpdc) (dmen = 1,2-diamino-2-methylpropane), one of only two MOFs with an adsorption step near the optimal pressure for CO<sub>2</sub> capture from coal flue gas. While prior characterization suggested that this material only adsorbs CO<sub>2</sub> to half capacity (0.5 CO<sub>2</sub> per diamine) at 1 bar, we show that the half-capacity state is actually a metastable intermediate. Under appropriate conditions, the MOF adsorbs CO<sub>2</sub> to full capacity, but conversion from the half-capacity structure happens on a very slow time scale, rendering it inaccessible in traditional adsorption measurements. Data from solid-state magic angle spinning nuclear magnetic resonance spectroscopy, coupled with van der Waals-corrected density functional theory, indicate that ammonium carbamate chains formed at half capacity and full capacity adopt opposing configurations, and the need to convert between these states likely dictates the sluggish post-half-capacity uptake. By use of the more symmetric parent framework Mg<sub>2</sub>(pc-dobpdc) (pc-dobpdc<sup>4-</sup> = 3,3'-dioxidobiphenyl-4,4'-dicarboxylate), the metastable trap can be avoided and the full CO<sub>2</sub> capacity of dmen-Mg<sub>2</sub>(pc-dobpdc) accessed under conditions relevant for carbon capture from coal-fired power plants. Chemistry ; Diamine ; Metal-organic framework ; Metastability ; Adsorption ; Metal ; Inorganic chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Daniel Seidel | Subhradeep Dutta | Bowen Li | Dillon R. L. Rickertsen | Daniel A. Valles
Source: SynOpen (article, 2021-07-01)
Field: Chemistry — Organic Chemistry

C–H Bond Functionalization of Amines: A Graphical Overview of Diverse Methods This Graphical Review provides a concise overview of the manifold and mechanistically diverse methods that enable the functionalization of sp<sup>3</sup> C-H bonds in amines and their derivatives. Surface modification ; Chemistry ; Bond ; Computer science ; Combinatorial chemistry ; Business Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Synthesis and Catalytic Reactions
Authors: Daniel Schwalbe-Koda | Aik Rui Tan | Rafael Gómez-Bombarelli
Source: Nature Communications (article, 2021-08-24)
Field: Materials Science — Materials Chemistry

Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks Neural network (NN) interatomic potentials provide fast prediction of potential energy surfaces, closely matching the accuracy of the electronic structure methods used to produce the training data. However, NN predictions are only reliable within well-learned training domains, and show volatile behavior when extrapolating. Uncertainty quantification methods can flag atomic configurations for which prediction confidence is low, but arriving at such uncertain regions requires expensive sampling of the NN phase space, often using atomistic simulations. Here, we exploit automatic differentiation to drive atomistic systems towards high-likelihood, high-uncertainty configurations without the need for molecular dynamics simulations. By performing adversarial attacks on an uncertainty metric, informative geometries that expand the training domain of NNs are sampled. When combined with an active learning loop, this approach bootstraps and improves NN potentials while decreasing the number of calls to the ground truth method. This efficiency is demonstrated on sampling of kinetic barriers, collective variables in molecules, and supramolecular chemistry in zeolite-molecule interactions, and can be extended to any NN potential architecture and materials system. Exploit ; Sampling (signal processing) ; Differentiable function ; Artificial neural network ; Domain (mathematical analysis) ; Matching (statistics) ; Adversarial system ; Uncertainty quantification ; Energy (signal processing) Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Graph Neural Networks
Authors: Dario Caramelli | Jarosław M. Granda | S. Hessam M. Mehr | Dario Cambié | Alon Henson | Leroy Cronin
Source: ACS Central Science (article, 2021-11-11)
Field: Materials Science — Materials Chemistry

Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (<i>p</i>-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of 6 equiv of TosMIC in a "multistep, single-substrate" cascade reaction yielding a trimeric product in high yield (47% unoptimized) with the formation of five new C-C bonds involving sp-sp<sup>2</sup> and sp-sp<sup>3</sup> carbon centers. An analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of a reactivity-first robotic discovery of unknown reaction methodologies without requiring human input. Reactivity (psychology) ; Chemical space ; Reagent ; Computer science ; Chemistry ; Combinatorial chemistry ; Biochemical engineering ; Chemical reaction ; Artificial neural network ; Substrate (aquarium) ; Biological system ; Artificial intelligence ; Drug discovery ; Organic chemistry ; Biology Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Qingye Meng | Ying Sun | Hailin Cong | Hao Hu | Fu-Jian Xu
Source: Drug Delivery and Translational Research (review, 2021-01-26)
Field: Materials Science — Biomaterials

An overview of chitosan and its application in infectious diseases Infectious disease (medical specialty) ; Biosafety ; Disease ; Ebola virus ; Chitosan ; Drug ; Medicine ; Intensive care medicine ; Virus ; Biotechnology ; Virology ; Biology ; Pharmacology Nanocomposite Films for Food Packaging ; Advanced Drug Delivery Systems ; Antimicrobial agents and applications
Authors: Yanjie Li | Mahmoud Al-Sarayreh | Kenji Irie | D.L. Hackell | Graeme W. Bourdôt | Marlon M. Reis | Kioumars Ghamkhar
Source: Frontiers in Plant Science (article, 2021-01-25)
Field: Chemistry — Analytical Chemistry

Identification of Weeds Based on Hyperspectral Imaging and Machine Learning Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (<i>Setaria pumila</i> [yellow bristle grass] and <i>Stipa arundinacea</i> [wind grass]) and two broad leaf weed species (<i>Ranunculus acris</i> [giant buttercup] and <i>Cirsium arvense</i> [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70-100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures. Cirsium arvense ; Weed ; Hyperspectral imaging ; Stipa ; Weed control ; Ranunculus ; Linear discriminant analysis ; Artificial intelligence ; Thistle ; Agronomy ; Identification (biology) ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Biology ; Computer science ; Botany Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Advanced Chemical Sensor Technologies
Authors: Shree Sowndarya S. V. | Peter C. St. John | Robert S. Paton
Source: Chemical Science (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

A quantitative metric for organic radical stability and persistence using thermodynamic and kinetic features Long-lived organic radicals are promising candidates for the development of high-performance energy solutions such as organic redox batteries, transistors, and light-emitting diodes. However, "stable" organic radicals that remain unreactive for an extended time and that can be stored and handled under ambient conditions are rare. A necessary but not sufficient condition for organic radical stability is the presence of thermodynamic stabilization, such as conjugation with an adjacent π-bond or lone-pair, or hyperconjugation with a σ-bond. However, thermodynamic factors alone do not result in radicals with extended lifetimes: many resonance-stabilized radicals are transient species that exist for less than a millisecond. Kinetic stabilization is also necessary for persistence, such as steric effects that inhibit radical dimerization or reaction with solvent molecules. We describe a quantitative approach to map organic radical stability, using molecular descriptors intended to capture thermodynamic and kinetic considerations. The comparison of an extensive dataset of quantum chemical calculations of organic radicals with experimentally-known stable radical species reveals a region of this feature space where long-lived radicals are located. These descriptors, based upon maximum spin density and buried volume, are combined into a single metric, the radical stability score, that outperforms thermodynamic scales based on bond dissociation enthalpies in identifying remarkably long-lived radicals. This provides an objective and accessible metric for use in future molecular design and optimization campaigns. We demonstrate this approach in identifying Pareto-optimal candidates for stable organic radicals. Persistence (discontinuity) ; Metric (unit) ; Kinetic energy ; Stability (learning theory) ; Thermodynamics ; Biological system ; Chemistry ; Statistical physics ; Computer science ; Physics ; Biology ; Engineering ; Classical mechanics ; Machine learning ; Geotechnical engineering Free Radicals and Antioxidants ; Photochemistry and Electron Transfer Studies ; Advanced Chemical Physics Studies
Authors: Z. M. Saigl
Source: Indonesian Journal of Chemistry (review, 2021-06-23)
Field: Chemistry — Analytical Chemistry

Various Adsorbents for Removal of Rhodamine B Dye: A Review Lately, there has been an increase in dye manufacturing, resulting in increased environmental pollution. Recent studies show a wide availability of usage adsorbents, including banana peels, potatoes, algae, etc. Food and Drug Administration prohibited the use of Rhodamine B (RhB) for its toxicity and harmful effects. Therefore, this study presents a wide range of non-conventional low-cost alternative adsorbents to remove RhB dye from wastewater. It has been observed that the mechanism of the dye adsorption is focused on kinetics, isotherm, and thermodynamics models, which depend on the chemical nature of the materials and various physicochemical experimental conditions such as solution pH, initial dye concentration, adsorbent dosage, and temperature of the system. The kinetic data of adsorption of RhB dye usually follow the pseudo-first-order and pseudo-second-order kinetic models. Several studies revealed that Langmuir and Freundlich adsorption isotherm models are frequently used to evaluate the adsorption capacity of the adsorbents. Furthermore, thermodynamic examination showed that RhB adsorption was endothermic and unconstrained in nature. Thus, both photocatalytic degradation and adsorption methods offer good potential to remove RhB dye from industrial effluents. The work is in progress to evaluate the possibility of using other modified waste biomass for industrial pollution control. Adsorption ; Chemistry ; Freundlich equation ; Rhodamine B ; Endothermic process ; Langmuir ; Effluent ; Wastewater ; Langmuir adsorption model ; Chemical engineering ; Pulp and paper industry ; Environmental chemistry ; Organic chemistry ; Photocatalysis ; Environmental engineering ; Environmental science ; Catalysis Dye analysis and toxicity
Authors: Lei Feng | Baohua Wu | Yong He | Chu Zhang
Source: Frontiers in Plant Science (article, 2021-09-29)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection Various rice diseases threaten the growth of rice. It is of great importance to achieve the rapid and accurate detection of rice diseases for precise disease prevention and control. Hyperspectral imaging (HSI) was performed to detect rice leaf diseases in four different varieties of rice. Considering that it costs much time and energy to develop a classifier for each variety of rice, deep transfer learning was firstly introduced to rice disease detection across different rice varieties. Three deep transfer learning methods were adapted for 12 transfer tasks, namely, fine-tuning, deep CORrelation ALignment (CORAL), and deep domain confusion (DDC). A self-designed convolutional neural network (CNN) was set as the basic network of the deep transfer learning methods. Fine-tuning achieved the best transferable performance with an accuracy of over 88% for the test set of the target domain in the majority of transfer tasks. Deep CORAL obtained an accuracy of over 80% in four of all the transfer tasks, which was superior to that of DDC. A multi-task transfer strategy has been explored with good results, indicating the potential of both pair-wise, and multi-task transfers. A saliency map was used for the visualization of the key wavelength range captured by CNN with and without transfer learning. The results indicated that the wavelength range with and without transfer learning was overlapped to some extent. Overall, the results suggested that deep transfer learning methods could perform rice disease detection across different rice varieties. Hyperspectral imaging, in combination with the deep transfer learning method, is a promising possibility for the efficient and cost-saving field detection of rice diseases among different rice varieties. Transfer of learning ; Deep learning ; Hyperspectral imaging ; Artificial intelligence ; Convolutional neural network ; Computer science ; Pattern recognition (psychology) ; Test set ; Classifier (UML) ; Rice plant ; Machine learning ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Murad S. Abuaisha | Joël Billiotte
Source: Journal of Energy Storage (article, 2021-04-23)
Field: Materials Science — Materials Chemistry

A discussion on hydrogen migration in rock salt for tight underground storage with an insight into a laboratory setup Ambiguity ; Salt (chemistry) ; Process (computing) ; Environmental science ; Mining engineering ; Petroleum engineering ; Computer science ; Geology ; Chemistry Hydrogen Storage and Materials ; Methane Hydrates and Related Phenomena ; CO2 Sequestration and Geologic Interactions
Authors: Hongjuan Liu | Yuanbing Mao
Source: ES Materials & Manufacturing (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Graphene Oxide-based Nanomaterials for Uranium Adsorptive Uptake The growth of nuclear power generation and the necessity to acquire uranium reserves for energy security and pollution regulation for environmental protection put much emphasis on the removal and recovery of uranium from aqueous solutions. Adsorption has been proved to be a promising method for this purpose method because of its high adsorption efficiency, easy operation, low cost, reusability and availability of massive adsorbents. Among a wide variety of adsorbents, graphene oxide (GO) has demonstrated excellent adsorption potential for uranium uptake and recovery due to its unique 2D structure, high specific surface area and abundant oxygen-containing functional groups. Regarding the functional groups, it can make GO with high dispersion and hydrophilicity and participate in the complexation of uranium, leading to high adsorption efficiency for uranium. In this review, the research status and progress of GO-based nanomaterials for uranium adsorption are summarized. Their adsorption capacities, influencing factors, kinetics, isotherms and thermodynamics are compared and discussed. The microscopic mechanisms of uranium adsorption onto these GO-based nanomaterials are elaborated at molecular level by spectral analysis, surface complexation models, and theoretical calculations. Meanwhile, the challenges and research trends in the study of uranium adsorption by GO-based nanomaterials are pointed out. We believe that our focused review provides not only a summarizing reference on the current status of uranium removal and recovery by GObased nanomaterials, but also future directions for related follow-up research and practical applications. Adsorption ; Uranium ; Nanomaterials ; Uranium oxide ; Graphene ; Oxide ; Materials science ; Nanotechnology ; Depleted uranium ; Chemical engineering ; Chemistry ; Organic chemistry ; Metallurgy Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Extraction and Separation Processes