jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Federica Zanca | Lawson T. Glasby | Sanggyu Chong | Siyu Chen | Jihan Kim | David Fairen‐Jiménez | Bartomeu Monserrat | Peyman Z. Moghadam
Source: Journal of Materials Chemistry C (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches Computational approaches accelerate design and discovery of conductive metal–organic frameworks (MOFs). Electrical conductor ; Materials science ; Metal-organic framework ; Nanotechnology ; Quantum ; Key (lock) ; Electrically conductive ; Computer science ; Optoelectronics ; Composite material ; Physics ; Quantum mechanics ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yeseul Kim | Hongyoon Kim | Younghwan Yang | Trevon Badloe | Nara Jeon | Junsuk Rho
Source: Nanoscale (review, 2021-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Three-dimensional artificial chirality towards low-cost and ultra-sensitive enantioselective sensing Artificial chiral structures have potential applications in the field of enantioselective signal sensing. Advanced nanofabrication methods enable a large diversity in geometric structures and broad selectivity of materials, which can be exploited to manufacture artificial three-dimensional chiral structures. Various chiroptical phenomena exploiting spin and orbital angular momentum at the nanoscale have been continuously exploited as a way to effectively detect enantiomers. This review introduces precisely controlled bottom-up and large-area top-down metamaterial fabrication methods to solve the limitations of high manufacturing cost and low production speed. Particle synthesis, self-assembly, glanced angled vapor deposition, and three-dimensional plasmonic nanostructure printing are introduced. Furthermore, emerging sensitive chiral sensing methods such as cavity-enhanced chirality, photothermal circular dichroism, and helical dichroism of single particles are discussed. The continuous progress of nanofabrication technology presents the strong potential for developing artificial chiral structures for applications in biomedical, pharmaceutical, nanophotonic systems. Enantioselective synthesis ; Chirality (physics) ; Metamaterial ; Nanotechnology ; Materials science ; Computer science ; Physics ; Chemistry ; Catalysis ; Optoelectronics ; Organic chemistry Metamaterials and Metasurfaces Applications ; Liquid Crystal Research Advancements ; Photonic Crystals and Applications
Authors: C. Jin | Yuanmu Yang
Source: Nanophotonics (article, 2021-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transmissive nonlocal multilayer thin film optical filter for image differentiation Abstract It is well‐known that a Fourier optical system can be used to perform specific computing tasks, such as image differentiation, with a superior speed and power consumption in comparison with digital computers, despite bulky optical components that are often required. Recently, there has been a surge in the interest to design much more compact nanophotonic structures, such as dielectric and metallic thin films, photonic crystals, and metasurfaces with a tailored angle‐dependent (nonlocal) optical response, to directly perform image differentiation without additional lenses for Fourier transformation. Here, we present a straightforward platform, a multilayer dielectric thin film optical filter, fabricated using mature wafer‐scale thin film deposition technique, with an optimized nonlocal optical response, for isotropic image differentiation in transmission mode for arbitrary input polarization. The proposed thin film filter may be conveniently coated at various transparent surfaces and inserted in machine vision or microscopy systems for enhanced, real‐time image processing. Materials science ; Optics ; Thin film ; Wafer ; Dielectric ; Optoelectronics ; Nanophotonics ; Optical filter ; Photonics ; Transmittance ; Filter (signal processing) ; Fourier transform ; Computer science ; Nanotechnology ; Physics ; Computer vision Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Photonic Crystals and Applications
Authors: Supakorn Harnsoongnoen | Nuananong Jaroensuk
Source: Scientific Reports (article, 2021-08-17)
Field: Chemistry — Analytical Chemistry

The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor The water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real-time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R<sup>2</sup>) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry. Grading (engineering) ; Optical density ; Volume (thermodynamics) ; Mathematics ; Computer science ; Environmental science ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Animal Nutrition and Physiology ; Smart Agriculture and AI
Authors: Kanishka Jha | Y.K. Tyagi | Rajeev Kumar | Shubham Sharma | Muhammad Huzaifah Mohd Roslim | Changhe Li | R.A. Ilyas | Shashi Prakash Dwivedi | Ambuj Saxena | Alokesh Pramanik
Source: Polymers (article, 2021-09-24)
Field: Materials Science — Polymers and Plastics

Assessment of Dimensional Stability, Biodegradability, and Fracture Energy of Bio-Composites Reinforced with Novel Pine Cone In this investigation, biodegradable composites were fabricated with polycaprolactone (PCL) matrix reinforced with pine cone powder (15%, 30%, and 45% by weight) and compatibilized with graphite powder (0%, 5%, 10%, and 15% by weight) in polycaprolactone matrix by compression molding technique. The samples were prepared as per ASTM standard and tested for dimensional stability, biodegradability, and fracture energy with scanning electron micrographs. Water-absorption and thickness-swelling were performed to examine the dimensional stability and tests were performed at 23 °C and 50% humidity. Results revealed that the composites with 15 wt % of pine cone powder (PCP) have shown higher dimensional stability as compared to other composites. Bio-composites containing 15-45 wt % of PCP with low graphite content have shown higher disintegration rate than neat PCL. Fracture energy for crack initiation in bio-composites was increased by 68% with 30% PCP. Scanning electron microscopy (SEM) of the composites have shown evenly-distributed PCP particles throughout PCL-matrix at significantly high-degrees or quantities of reinforcing. Materials science ; Composite material ; Polycaprolactone ; Scanning electron microscope ; Compression molding ; Absorption of water ; Swelling ; Graphite ; Molding (decorative) ; Biodegradation ; Polymer Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Xing Wei | Marcela A. Johnson | David B. Langston | Hillary L. Mehl | Li Song
Source: Remote Sensing (article, 2021-07-19)
Field: Chemistry — Analytical Chemistry

Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning Hyperspectral sensors combined with machine learning are increasingly utilized in agricultural crop systems for diverse applications, including plant disease detection. This study was designed to identify the most important wavelengths to discriminate between healthy and diseased peanut (Arachis hypogaea L.) plants infected with Athelia rolfsii, the causal agent of peanut stem rot, using in-situ spectroscopy and machine learning. In greenhouse experiments, daily measurements were conducted to inspect disease symptoms visually and to collect spectral reflectance of peanut leaves on lateral stems of plants mock-inoculated and inoculated with A. rolfsii. Spectrum files were categorized into five classes based on foliar wilting symptoms. Five feature selection methods were compared to select the top 10 ranked wavelengths with and without a custom minimum distance of 20 nm. Recursive feature elimination methods outperformed the chi-square and SelectFromModel methods. Adding the minimum distance of 20 nm into the top selected wavelengths improved classification performance. Wavelengths of 501–505, 690–694, 763 and 884 nm were repeatedly selected by two or more feature selection methods. These selected wavelengths can be applied in designing optical sensors for automated stem rot detection in peanut fields. The machine-learning-based methodology can be adapted to identify spectral signatures of disease in other plant-pathogen systems. Hyperspectral imaging ; Arachis hypogaea ; Artificial intelligence ; Computer science ; Remote sensing ; Feature selection ; Feature (linguistics) ; Wavelength ; Plant disease ; Pattern recognition (psychology) ; Mathematics ; Horticulture ; Biology ; Optics ; Biotechnology ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Gogulan Karunanithy | Harold W. Mackenzie | D. Flemming Hansen
Source: Journal of the American Chemical Society (article, 2021-10-11)
Field: Chemistry — Spectroscopy

Virtual Homonuclear Decoupling in Direct Detection Nuclear Magnetic Resonance Experiments Using Deep Neural Networks Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular <sup>13</sup>C-detected NMR methods, one-bond <sup>13</sup>C-<sup>13</sup>C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to perform virtual decoupling by recording multiple spectra and taking linear combinations. Here, we propose an alternative method of virtual decoupling using deep neural networks, which only requires a single spectrum and gives a significant boost in resolution while reducing the minimum effective phase cycles of the experiments by at least a factor of 2. We successfully apply this methodology to virtually decouple in-phase CON (<sup>13</sup>CO-<sup>15</sup>N) protein NMR spectra, <sup>13</sup>C-<sup>13</sup>C correlation spectra of protein side chains, and <sup>13</sup>C<sub>α</sub>-detected protein <sup>13</sup>C<sub>α</sub>-<sup>13</sup>CO spectra where two large homonuclear couplings are present. The deep neural network approach effectively decouples spectra with a high degree of flexibility, including in cases where existing methods fail, and facilitates the use of simpler pulse sequences. Homonuclear molecule ; Chemistry ; Decoupling (probability) ; Artificial neural network ; Deep neural networks ; Nuclear magnetic resonance ; Magnetic resonance imaging ; Artificial intelligence ; Molecule ; Physics Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; NMR spectroscopy and applications
Authors: John A. Ramirez-Figueroa | Carlos Martín-Barreiro | Ana Belén Nieto Librero | Víctor Leiva | Purificación Galindo‐Villardón
Source: Stochastic Environmental Research and Risk Assessment (article, 2021-01-02)
Field: Chemistry — Analytical Chemistry

A new principal component analysis by particle swarm optimization with an environmental application for data science Principal component analysis ; Disjoint sets ; Dimensionality reduction ; Curse of dimensionality ; Linear subspace ; Computer science ; Particle swarm optimization ; Computational intelligence ; Mathematical optimization ; Multivariate statistics ; Principal (computer security) ; Component (thermodynamics) ; Linear discriminant analysis ; Data mining ; Mathematics ; Algorithm ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Statistical Methods and Models
Authors: Shuai Zhao | Wanfen Pu | Lei Su | Ce Shang | Yang Song | Wei Li | Hui-Zhuo He | Yigang Liu | Zhezhi Liu
Source: Petroleum Science (article, 2021-08-17)
Field: Chemistry — Analytical Chemistry

Properties, combustion behavior, and kinetic triplets of coke produced by low-temperature oxidation and pyrolysis: Implications for heavy oil in-situ combustion This work aimed at investigating the crucial factor in building and maintaining the combustion front during in-situ combustion (ISC), oxidized coke and pyrolyzed coke. The surface morphologies, elemental contents, and non-isothermal mass losses of the oxidized and pyrolyzed cokes were thoroughly examined. The results indicated that the oxidized coke could be combusted at a lower temperature compared to the pyrolyzed coke due primarily to their differences in the molecular polarity and microstructure. Kinetic triplets of coke combustion were determined using iso-conversional models and one advanced integral master plots method. The activation energy values of the oxidized and pyrolyzed cokes varied in the range of 130–153 kJ/mol and 95–120 kJ/mol, respectively. The most appropriate reaction model of combustion of the oxidized and pyrolyzed cokes followed three-dimensional diffusion model (D3) and random nucleation and subsequent growth model (F1), respectively. These observations assisted in building the numerical model of these two types of cokes to simulate the ISC process. Pyrolysis ; Coke ; Combustion ; Isothermal process ; Nucleation ; Chemical engineering ; Activation energy ; Kinetic energy ; Chemistry ; Materials science ; Thermodynamics ; Metallurgy ; Organic chemistry Petroleum Processing and Analysis ; Thermochemical Biomass Conversion Processes ; Thermal and Kinetic Analysis
Authors: Jae Kap Jung | In Gyoo Kim | Ki Soo Chung | Un Bong Baek
Source: Scientific Reports (article, 2021-03-01)
Field: Materials Science — Metals and Alloys

Gas chromatography techniques to evaluate the hydrogen permeation characteristics in rubber: ethylene propylene diene monomer We established an ex-situ technique for evaluating hydrogen gas permeability by thermal desorption analysis (TDA) gas chromatography (GC) and by self-developed diffusion analysis software. Absorbed hydrogen mass in rubber, related to the GC-peak area, is recorded as a function of elapsed time after decompressing the hydrogen under high-pressure. From the charging amount (C<sub>H0</sub>) and diffusivity (D) obtained by the developed diffusion analysis program, the solubility(S) and permeability(P) is evaluated via Henry's law and P = SD, respectively. The techniques were applied to ethylene propylene diene monomer (EPDM) rubber, sealing material candidates in hydrogen infrastructures. EPDM sample mixed with carbon black fillers showed dual hydrogen diffusion behaviors, whereas EPDM sample without carbon black showed a single hydrogen diffusion behavior. There was no appreciable pressure or size dependence on D, S and P. P are consistent with that measured by different researcher within the expanded uncertainty. Natural rubber ; Hydrogen ; Carbon black ; Permeation ; Thermal diffusivity ; Materials science ; Monomer ; Gas chromatography ; Diffusion ; Thermal desorption ; Ethylene propylene rubber ; Chemical engineering ; Diene ; EPDM rubber ; Desorption ; Polymer chemistry ; Analytical Chemistry (journal) ; Polymer ; Chemistry ; Chromatography ; Composite material ; Organic chemistry ; Thermodynamics ; Copolymer ; Membrane ; Adsorption Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Material Properties and Failure Mechanisms
Authors: Qianxiang Ai | Vinayak Bhat | Sean M. Ryno | Karol Jarolimek | Parker Sornberger | Andrew Smith | Michael M. Haley | John E. Anthony | Chad Risko
Source: The Journal of Chemical Physics (article, 2021-05-04)
Field: Materials Science — Materials Chemistry

OCELOT: An infrastructure for data-driven research to discover and design crystalline organic semiconductors Materials design and discovery are often hampered by the slow pace and materials and human costs associated with Edisonian trial-and-error screening approaches. Recent advances in computational power, theoretical methods, and data science techniques, however, are being manifest in a convergence of these tools to enable in silico materials discovery. Here, we present the development and deployment of computational materials data and data analytic approaches for crystalline organic semiconductors. The OCELOT (Organic Crystals in Electronic and Light-Oriented Technologies) infrastructure, consisting of a Python-based OCELOT application programming interface and OCELOT database, is designed to enable rapid materials exploration. The database contains a descriptor-based schema for high-throughput calculations that have been implemented on more than 56 000 experimental crystal structures derived from 47 000 distinct molecular structures. OCELOT is open-access and accessible via a web-user interface at https://oscar.as.uky.edu. Computer science ; Python (programming language) ; Application programming interface ; Software deployment ; Software engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Moaazameh Akbari | Mehdi Jafari Shahbazzadeh | Luigi La Spada | Alimorad Khajehzadeh
Source: Applied Sciences (article, 2021-08-31)
Field: Materials Science — Materials Chemistry

The Graphene Field Effect Transistor Modeling Based on an Optimized Ambipolar Virtual Source Model for DNA Detection The graphene-based Field Effect Transistors (GFETs), due to their multi-parameter characteristics, are growing rapidly as an important detection component for the apt detection of disease biomarkers, such as DNA, in clinical diagnostics and biomedical research laboratories. In this paper, the non-equilibrium Green function (NEGF) is used to create a compact model of GFET in the ballistic regime as an important building block for DNA detection sensors. In the proposed method, the self-consistent solutions of two-dimensional Poisson’s equation and NEGF, using the nearest neighbor tight-binding approach on honeycomb lattice structure of graphene, are modeled as an efficient numerical method. Then, the eight parameters of the phenomenological ambipolar virtual source (AVS) circuit model are calibrated by a least-square curve-fitting routine optimization algorithm with NEGF transfer function data. At last, some parameters of AVS that are affected by induced charge and potential of DNA biomolecules are optimized by an experimental dataset. The new compact model response, with an acceptable computational complexity, shows a good agreement with experimental data in reaction with DNA and can effectively be used in the plan and investigation of GFET biosensors. Graphene ; Ambipolar diffusion ; Transistor ; Physics ; Field-effect transistor ; Computational physics ; Biosensor ; Computation ; MOSFET ; Algorithm ; Nanotechnology ; Computer science ; Materials science ; Quantum mechanics ; Electron ; Voltage Graphene research and applications ; 2D Materials and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Olivia A. Attallah | Marija Mojićević | Eduardo Lanzagorta Garcia | Muhammad Azeem | Yuan Yuan Chen | Shumayl Asmawi | Margaret Brennan Fournet
Source: Polymers (review, 2021-06-29)
Field: Materials Science — Biomaterials

Macro and Micro Routes to High Performance Bioplastics: Bioplastic Biodegradability and Mechanical and Barrier Properties On a score sheet for plastics, bioplastics have a medium score for combined mechanical performance and a high score for biodegradability with respect to counterpart petroleum-based plastics. Analysis quickly confirms that endeavours to increase the mechanical performance score for bioplastics would be far more achievable than delivering adequate biodegradability for the recalcitrant plastics, while preserving their impressive mechanical performances. Key architectural features of both bioplastics and petroleum-based plastics, namely, molecular weight (<i>M<sub>w</sub></i>) and crystallinity, which underpin mechanical performance, typically have an inversely dependent relationship with biodegradability. In the case of bioplastics, both macro and micro strategies with dual positive correlation on mechanical and biodegradability performance, are available to address this dilemma. Regarding the macro approach, processing using selected fillers, plasticisers and compatibilisers have been shown to enhance both targeted mechanical properties and biodegradability within bioplastics. Whereas, regarding the micro approach, a whole host of bio and chemical synthetic routes are uniquely available, to produce improved bioplastics. In this review, the main characteristics of bioplastics in terms of mechanical and barrier performances, as well as biodegradability, have been assessed-identifying both macro and micro routes promoting favourable bioplastics' production, processability and performance. Bioplastic ; Biodegradation ; Materials science ; Biochemical engineering ; Compounding ; Waste management ; Engineering ; Composite material ; Chemistry ; Organic chemistry biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Additive Manufacturing and 3D Printing Technologies
Authors: Torvid Feiler | Biswajit Bhattacharya | Adam A. L. Michalchuk∞ | Seon‐Young Rhim | Vincent Schröder | Emil List | Franziska Emmerling
Source: CrystEngComm (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Tuning the mechanical flexibility of organic molecular crystals by polymorphism for flexible optical waveguides The ability to selectively tune the optical and the mechanical properties of organic molecular crystals offers a promising approach towards developing flexible optical devices. Flexibility (engineering) ; Materials science ; Polymorphism (computer science) ; Optoelectronics ; Nanotechnology ; Chemistry Luminescence and Fluorescent Materials ; Crystallography and molecular interactions ; Nonlinear Optical Materials Research