jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vali Rasooli Sharabiani | Sajad Sabzi | Razieh Pourdarbani | Edgardo Solis-Carmona | Mario Hernández-Hernández | José Luis Hernández-Hernández
Source: Plants (article, 2020-12-06)
Field: Chemistry — Analytical Chemistry

Non-Destructive Prediction of Titratable Acidity and Taste Index Properties of Gala Apple Using Combination of Different Hybrids ANN and PLSR-Model Based Spectral Data Non-destructive estimation of the internal properties of fruits and vegetables is very important, because better management can be provided for subsequent operations. Researchers and scientists around the world are focusing on non-destructive methods because if they are developed and commercialized, there will be an impressive change in the food industry. In this regard, this paper aims to present a non-destructive method based on Vis-NIR spectral data. The different stages of the proposed algorithm are: (1) Collection of samples of Gala apples, (2) Spectral data extraction by spectroscopy, (3) Pre-processing of spectral data, (4) Measurement of chemical properties of titratable acidity (TA) and taste index, (5) Selection of key wavelengths using hybrid artificial neural network-firefly algorithm (ANN-FA), (6) Non-destructive estimation of the properties using two methods of hybrid ANN- Particle swarm optimization algorithm and partial least squares regression. For considering the reliability of methods for estimating the chemical properties, the prediction operation was executed in 300 iterations. The results represented that the mean and standard deviation of the correlation coefficient and the root mean square error of hybrid ANN-PSO and PLSR for TA were 0.9095 ± 0.0175, 0.0598 ± 0.0064, 0.834 ± 0.0313 and 0.0761 ± 0.0061 respectively. These values for taste index were 0.918 ± 0.02, 3.2 ± 0.39, 0.836 ± 0.033 and 4.09 ± 0.403, respectively. Therefore, it can be concluded that the hybrid ANN-PSO has a better performance for non-destructive prediction of the two mentioned chemical properties than the PLSR method. In general, the proposed method can predict the chemical properties of TA and taste index non-destructively, which is very useful for mechanized harvesting and management of post-harvest operation. Titratable acid ; Partial least squares regression ; Particle swarm optimization ; Mean squared error ; Mathematics ; Artificial neural network ; Coefficient of determination ; Biological system ; Statistics ; Computer science ; Algorithm ; Artificial intelligence ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Satyam Srivastava | Bukke Vani | Shashikant Sadistap
Source: Journal of Food Measurement & Characterization (article, 2020-10-14)
Field: Chemistry — Analytical Chemistry

Handheld, smartphone based spectrometer for rapid and nondestructive testing of citrus cultivars Ripeness ; Spectrometer ; Detector ; Microcontroller ; Computer science ; Process engineering ; Computer hardware ; Chemistry ; Engineering ; Optics ; Telecommunications ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Alauddin Ahmed | Donald S. Siegel
Source: ChemRxiv (preprint, 2020-12-09)
Field: Chemistry — Inorganic Chemistry

Predicting Hydrogen Storage in MOFs via Machine Learning &lt;div&gt;&lt;p&gt;The H&lt;sub&gt;2&lt;/sub&gt; storage capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials under physisorptive conditions. The identified MOFs are predominantly hypothetical compounds having low densities (&lt;0.31 g cm&lt;sup&gt;-3&lt;/sup&gt;) in combination with high surface areas (&gt; 5,300 m&lt;sup&gt;2&lt;/sup&gt; g&lt;sup&gt;-1&lt;/sup&gt;), void fractions (~0.90), and pore volumes (&gt;3.3 cm&lt;sup&gt;3&lt;/sup&gt; g&lt;sup&gt;-1&lt;/sup&gt;). In addition, the relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The single most important features for predicting H&lt;sub&gt;2&lt;/sub&gt; uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available for use via the web, allowing for rapid and accurate predictions of usable hydrogen capacities for MOFs with only minimal structural data as input; for the simplest models only a single input feature is required.&lt;/p&gt;&lt;/div&gt; Chemistry ; Gravimetric analysis ; Analytical Chemistry (journal) ; Crystallography ; Algorithm ; Mathematics ; Chromatography Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Andrew Rohskopf | Spencer Wyant | Kiarash Gordiz | Hamid Reza Seyf | Murali Gopal Muraleedharan | Asegun Henry
Source: Computational Materials Science (article, 2020-06-26)
Field: Materials Science — Materials Chemistry

Fast &amp; accurate interatomic potentials for describing thermal vibrations Molecular dynamics (MD) is a powerful technique that can be used to study thermal vibrations/phonons and properly account for their role in different phenomena that are important in mechanical engineering, chemistry, physics and materials science. However, despite the widespread usage of MD to study various phenomena, direct comparisons between experiments and simulations are often associated with low fidelity, due to the inaccuracy of the interatomic potentials (IAPs) employed. This issue has become the main barrier to utilizing MD for studying phenomena that depend on or involve atomic vibrations, and subsequently deriving physically meaningful insights. Towards solving this problem, we present a new approach to making IAPs that are specifically optimized to accurately describe thermal vibrations/phonons. The approach enables nearly exact reproduction of ab initio phonon dispersion relations (i.e., < 1%) error), accurate forces and thermal conductivity (i.e., <5% and <10% error respectively), and low computational expense like that of traditional IAPs. Phonon ; Vibration ; Thermal conductivity ; Thermal ; Statistical physics ; Molecular dynamics ; Molecular vibration ; Interatomic potential ; Ab initio ; Physics ; Materials science ; Condensed matter physics ; Thermodynamics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Thermal properties of materials ; Force Microscopy Techniques and Applications
Authors: John Bradshaw | Brooks Paige | Matt J. Kusner | Marwin Segler | José Miguel Hernández-Lobato
Source: Apollo (University of Cambridge) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Barking up the right tree: an approach to search over molecule synthesis DAGs When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic graph (DAG), describing how a large vocabulary of simple building blocks can be recursively combined through chemical reactions to create more complicated molecules of interest. In contrast, many current deep generative models for molecules ignore synthesizability. We therefore propose a deep generative model that better represents the real world process, by directly outputting molecule synthesis DAGs. We argue that this provides sensible inductive biases, ensuring that our model searches over the same chemical space that chemists would also have access to, as well as interpretability. We show that our approach is able to model chemical space well, producing a wide range of diverse molecules, and allows for unconstrained optimization of an inherently constrained problem: maximize certain chemical properties such that discovered molecules are synthesizable. Chemical space ; Directed acyclic graph ; Computer science ; Interpretability ; Theoretical computer science ; Tree (set theory) ; Simple (philosophy) ; Generative model ; Vocabulary ; Generative grammar ; Artificial intelligence ; Algorithm ; Drug discovery ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Yuliia Orlova | Alessa A. Gambardella | Rebecca E. Harmon | Ivan Kryven | Piet D. Iedema
Source: Chemical Engineering Journal (article, 2020-08-08)
Field: Chemistry — Spectroscopy

Finite representation of reaction kinetics in unbounded biopolymer structures Several systems in organic chemistry are so large and complex that formulating the rate equations for their kinetics is difficult. Examples include polymerization of bio-derived materials, such as natural oils and resins, and metabolic oxidation reactions, as in the Krebs cycle. The challenge lies in the diversity of reaction products and their growth in size, which may be unbounded due to the polymerization-like reactions. Here we demonstrate an algorithm that formulates the kinetics of chemical systems of unlimited size in terms of a fine number of fragment species, and therefore, renders such structures tractable for kinetic modelling. We study a complex system of ethyl linoleate polymerization using such an algorithm. Additionally, we show that the algorithm may assist interpreting experimental measurements from electrospray ionization mass spectrometry. Polymerization ; Kinetics ; Biopolymer ; Electrospray ionization ; Chemistry ; Mass spectrometry ; Representation (politics) ; Chemical kinetics ; Reaction rate ; Rate equation ; Biological system ; Materials science ; Computational chemistry ; Thermodynamics ; Organic chemistry ; Polymer ; Chromatography ; Physics Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Zaiku Xie | Bao‐Lian Su
Source: Frontiers of Chemical Science and Engineering (article, 2020-03-11)
Field: Chemistry — Inorganic Chemistry

Crystalline porous materials: from zeolites to metal-organic frameworks (MOFs) Metal-organic framework ; Porosity ; Materials science ; Chemical engineering ; Zeolite ; Porous medium ; Nanotechnology ; Chemistry ; Adsorption ; Composite material ; Organic chemistry ; Catalysis ; Engineering Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Hui Zhang | Tianyan Jiang | Xinghua He | Tiantian Chen | Li Fan | Mengxi Gao | Pengtao Liu
Source: BioResources (article, 2020-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation and properties of cellulose nanofibril-graphene nanosheets/polyaniline composite conductive aerogels Polyaniline (PANI) is a conductive polymer that allows cellulose aerogels to achieve high electrical conductivity. However, aerogels containing PANI alone display a low mechanical stability. Graphene nanosheets (GNS) display high conductivity and mechanical strength but are prone to agglomeration, hindering their electroactive sites. To avoid shortcomings of the individual components, a composite aerogel was prepared via addition of graphene nanosheets (GNS) and PANI to a suspension of cellulose nanofibril (CNF). Transmission electron microscopy and scanning electron microscopy were used to analyze the structural morphology of the CNF/GNS/PANI aerogel. The electrochemical properties were analyzed using a four-probe conductivity meter, cyclic voltammetry, galvanostatic charge-discharge tests, and electrochemical impedance spectroscopy. A 2:2:1 ratio of CNF/GNS/PANI provided optimal structural and electrochemical results. Incorporation of PANI through in-situ polymerization for 6 h resulted in uniform mixing of the three components. The CNF/GNS/PANI composite aerogel displayed a high electrical conductivity with a specific capacitance of 375 Fg-1 at a current density of 0.2 Ag-1. As a base binder and dispersant, CNF made use of PANI as a conductive medium and of GNS as a conductive reinforcing medium to form a flexible nanocellulose composite conductive material with increased stability and improved performance. Materials science ; Aerogel ; Polyaniline ; Graphene ; Composite number ; Composite material ; Nanocellulose ; Conductive polymer ; Dielectric spectroscopy ; Cyclic voltammetry ; Conductivity ; Chemical engineering ; Cellulose ; Electrochemistry ; Polymerization ; Polymer ; Nanotechnology ; Electrode Supercapacitor Materials and Fabrication ; Advanced Cellulose Research Studies ; Advanced Sensor and Energy Harvesting Materials
Authors: Richard Y. Liu | Joseph M. Dennis | Stephen L. Buchwald
Source: ChemRxiv (preprint, 2020-01-17)
Field: Chemistry — Organic Chemistry

The Quest for the Ideal Base: Rational Design of a Nickel Precatalyst Enables Mild, Homogeneous C–N Cross-Coupling Palladium-catalyzed amination reactions using soluble organic bases have provided a solution to the many issues associated with heterogeneous reaction conditions. Still, homogeneous C–N cross-coupling approaches cannot yet employ bases as weak and economical as trialkylamines. Furthermore, organic base-mediated methods have not been developed for Ni(0/II) catalysis, despite some advantages of such systems over analogous Pd-based catalysts. We designed a new air-stable and easily prepared Ni(II) precatalyst bearing an electron-deficient bidentate phosphine ligand that enables the cross-coupling of aryl triflates with aryl amines using triethylamine (TEA) as base. The method is tolerant of sterically-congested coupling partners, as well as those bearing base- and nucleophile-sensitive functional groups. With the aid of density functional theory (DFT) calculations, we determined that the electron-deficient auxiliary ligands decrease both the pKa of the Ni-bound amine and the barrier to reductive elimination from the resultant Ni(II)–amido complex. Moreover, we determined that precluding Lewis acid-base complexation between the Ni catalyst and the base, due to steric factors, is important for avoiding catalyst inhibition. Steric effects ; Chemistry ; Aryl ; Triethylamine ; Catalysis ; Amination ; Phosphine ; Nucleophile ; Combinatorial chemistry ; Denticity ; Base (topology) ; Palladium ; Ligand (biochemistry) ; Homogeneous catalysis ; Amine gas treating ; Density functional theory ; Lewis acids and bases ; Catalytic cycle ; Organic chemistry ; Computational chemistry Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Mārtiņš Ruciņš | Rufus Smits | Anda Sipola | Brigita Vı̄gante | Илона Домрачева | Baiba Turovska | Ruslan Muhamadejev | Kārlis Pajuste | Māra Plotniece | Arkadij Sobolev | Г. Дубурс | Aiva Plotniece
Source: Oxidative Medicine and Cellular Longevity (article, 2020-12-31)
Field: Chemistry — Organic Chemistry

Pleiotropic Properties of Amphiphilic Dihydropyridines, Dihydropyridones, and Aminovinylcarbonyl Compounds Three groups of synthetic lipids are chosen for studies: (1) 1,4-dihydropyridines (1,4-DHPs) containing two cationic moieties and their analogues; (2) 3,4-dihydro-2(1<i>H</i>)-pyridones containing a cationic moiety; and (3) acyclic, open-chain analogues, i.e., 2-amino-3-alkoxycarbonylalkylammonium derivatives. 1,4-DHPs possessing dodecyl alkyl chains in the ester groups in positions 3 and 5 and cationic nitrogen-containing groups in positions 2 and 6 have high cytotoxicity in cancer cells HT-1080 (human lung fibrosarcoma) and MH-22A (mouse hepatoma), but low cytotoxicity in the noncancerous NIH3T3 cells (mouse embryonic fibroblast). On the contrary, similar compounds having short (methyl, ethyl, or propoxyethyl) chains in the ester groups in positions 3 and 5 lack cytotoxicity in the cancer cells HT-1080 and MH-22A even at high doses. Inclusion of fluorine atoms in the alkyl chains in positions 3 and 5 of the DHP cycle decreases the cytotoxicity of the mentioned compounds. Structurally related dihydropyridones with a polar head group are substantially more toxic to normal and cancerous cells than the DHP analogues. Open-chain analogues of DHP lipids comprise the same conjugated aminovinylcarbonyl moiety and possess anticancer activity, but they also have high basal cytotoxicity. Electrochemical oxidation data demonstrate that oxidation potentials of selected compounds are in the range of 1.6-1.7 V for cationic 1,4-DHP, 2.0-2.4 V for cationic 3,4-dihydropyridones, and 1.2-1.5 V for 2-amino-3-alkoxycarbonylalkylammonium derivatives. Furthermore, the tested cationic 1,4-DHP amphiphiles possess antiradical activity. Molecular topological polar surface area values for the tested compounds were defined in accordance with the main fragments of compound structures. The determined log<i>P</i> values were highest for dodecyl ester groups in positions 3 and 5 of the 1,4-DHP and lowest for short alkyl chain-containing amphiphiles. Cationic polymerization ; Cytotoxicity ; Moiety ; Chemistry ; Stereochemistry ; Amphiphile ; Alkyl ; Phosphatidylcholine ; DHPS ; Structure–activity relationship ; Phospholipid ; In vitro ; Biochemistry ; Organic chemistry ; Membrane ; Biology Multicomponent Synthesis of Heterocycles ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity
Authors: Bandar R. Alsehli
Source: Processes (article, 2020-03-29)
Field: Chemistry — Analytical Chemistry

A Simple Approach for Determining the Maximum Sorption Capacity of Chlorpropham from Aqueous Solution onto Granular Activated Charcoal UV-Vis spectrophotometer was used to determine chlorpropham (CIPC) concentration in aqueous solution. The method was validated in term of linearity, precision and limit of detection and limit of quantitation. The correlation coefficient of standards calibration curve of (1.0–10.0 µg/mL CIPC) was R2 = 1 with a precision (RSD%, n=10) ranged from (0.87–0.53%). The limit of detection (LOD) and limit of quantitation (LOQ) based on the regression statistics of the calibration curve data of (1.0–10.0 µg/mL CIPC) were 0.04 µg/mL and 0.11 µg/mL respectively. The activated carbon adsorbent was found to be effective for the removal approximately 80% of CIPC from aqueous solution. Several isotherm models (Langmuir, Freundlich, Tempkin and Dubinin–Radushkevich) were evaluated. The maximum monolayer sorption capacity (Qm) from the Langmuir isotherm model was determined to be (44316.92 µg/g). The separation factor (RL) is 0.11 which indicates a favorable equilibrium sorption with the R2 value of 0.99, indicating that the Langmuir isotherm model fit the experimental sorption data well. Detection limit ; Freundlich equation ; Sorption ; Langmuir ; Adsorption ; Aqueous solution ; Chemistry ; Calibration curve ; Langmuir adsorption model ; Chromatography ; Monolayer ; Activated carbon ; Activated charcoal ; Analytical Chemistry (journal) ; Organic chemistry Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Xiaoyuan Zhang | Guanghua Zhang | Gang Wei | Zhiqiang Su
Source: Biosensors (article, 2020-09-23)
Field: Materials Science — Materials Chemistry

One-Pot, In-Situ Synthesis of 8-Armed Poly(Ethylene Glycol)-Coated Ag Nanoclusters as a Fluorescent Sensor for Selective Detection of Cu2+ Fluorescent nanomaterials, such as quantum dots, have developed rapidly in recent years and have been significantly developed. Herein, we demonstrate a facile, one-pot, and in-situ synthesis strategy to obtain fluorescent silver nanoclusters (AgNCs) coated with eight-armed poly (ethylene glycol) polymers (8PEG-AgNCs) via a direct gel-mediated process. During the synthesis, ammonium (NH<sub>3</sub>) served as the crosslinker for the gel formation via a amine-type Michael addition reaction. This hydrogel can be used as a template to synthesize AgNCs using its volume-limiting effect. The in-situ generation of AgNCs takes place inside the nanocages of the formed gels, which guarantees the homogenous distribution of AgNCs in the gel matrix, as well as the efficient coating of PEG on the nanoclusters. After the degradation of gels, the released 8PEG-AgNCs nanohybrids showed strong blue fluorescence and exhibited long-term stability in aqueous solution for nearly one year. Results showed that the fabricated sensor revealed excellent fluorescent sensitivity for the selective detection of Cu<sup>2+</sup> with a detection limit of 50 nM and a wide linear detection range of 5-100 μM. It is proposed that the greater cross-linking density leads to smaller gel pores and allows the synthesis of AgNCs with fluorescent properties. These results indicate that this novel hydrogel with certain biodegradation has the potential to be applied as a fluorescent sensor for catalytic synthesis, fluorescence tracing in cells, and fluorescence detection fields. Meanwhile, the novel design principle has a certain versatility to accelerate the development and application of other kinds of metal nanoclusters and quantum dots. Nanoclusters ; Fluorescence ; Ethylene glycol ; Materials science ; Detection limit ; Aqueous solution ; Chemical engineering ; Linear range ; Nanotechnology ; Chemistry ; Organic chemistry ; Chromatography Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis