jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pao Li | Shangke Li | Guorong Du | Liwen Jiang | Xia Liu | Shenghua Ding | Yang Shan
Source: Food Science & Nutrition (article, 2020-04-14)
Field: Chemistry — Analytical Chemistry

A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near‐infrared spectroscopy A simple and nondestructive method for the analysis of soluble solid content in citrus was established using portable visible to near-infrared spectroscopy (Vis/NIRS) in reflectance mode in combination with appropriate chemometric methods. The spectra were obtained directly by the portable Vis/NIRS without destroying samples. Outlier detection was performed by using leave-one-out cross-validation (LOOCV) with the 3σ criterion, and the calibration models were established by partial least squares (PLS) algorithm. Besides, different data pretreatment methods were used to eliminate noise and background interference before calibration, to determine the one that will lead to better model accuracy. However, the correlation coefficients are all <0.62 and the results of all pretreatments are still unsatisfactory. Variable selection methods were discussed for improving the accuracy, and variable adaptive boosting partial least squares (VABPLS) method was used to get higher robustness models. The results show that standard normal variate (SNV) transformation is the best pretreatment method, while VABPLS can significantly simplify the calculation and improve the result even without pretreatment. The correlation coefficient of the best prediction models is 0.82, while the value is 0.48 for the raw data. The high performance shows the feasibility of portable Vis/NIRS technology combination with appropriate chemometric methods for the determination of citrus soluble solid content. Partial least squares regression ; Calibration ; Chemometrics ; Near-infrared spectroscopy ; Outlier ; Correlation coefficient ; Robustness (evolution) ; Computer science ; Biological system ; Mathematics ; Analytical Chemistry (journal) ; Artificial intelligence ; Chemistry ; Statistics ; Chromatography ; Optics ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: André M. Striegel
Source: TrAC Trends in Analytical Chemistry (article, 2020-07-29)
Field: Chemistry — Spectroscopy

Method development in interaction polymer chromatography Analyte ; Polymer ; Molar mass ; Chromatography ; Macromolecule ; Elution ; Chemistry ; Bespoke ; Copolymer ; Materials science ; Process engineering ; Computer science ; Organic chemistry ; Engineering Analytical Chemistry and Chromatography ; Chromatography in Natural Products ; Microfluidic and Capillary Electrophoresis Applications
Authors: Ashanul Haque | Rayya A. Al-Balushi | Paul R. Raithby | Muhammad S. Khan
Source: Molecules (review, 2020-06-06)
Field: Materials Science — Polymers and Plastics

Recent Advances in π-Conjugated N^C-Chelate Organoboron Materials Boron-containing π-conjugated materials are archetypical candidates for a variety of molecular scale applications. The incorporation of boron into the π-conjugated frameworks significantly modifies the nature of the parent π-conjugated systems. Several novel boron-bridged π-conjugated materials with intriguing structural, photo-physical and electrochemical properties have been reported over the last few years. In this paper, we review the properties and multi-dimensional applications of the boron-bridged fused-ring π-conjugated systems. We critically highlight the properties of π-conjugated N^C-chelate organoboron materials. This is followed by a discussion on the potential applications of the new materials in opto-electronics (O-E) and other areas. Finally, attempts will be made to predict the future direction/outlook for this class of materials. Conjugated system ; Boron ; Nanotechnology ; Chelation ; Materials science ; Organoboron compounds ; Combinatorial chemistry ; Chemistry ; Organic chemistry ; Polymer Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Luminescence and Fluorescent Materials
Authors: Mziyanda Mbaba | Taryn M. Golding | Gregory S. Smith
Source: Molecules (review, 2020-11-12)
Field: Chemistry — Organic Chemistry

Recent Advances in the Biological Investigation of Organometallic Platinum-Group Metal (Ir, Ru, Rh, Os, Pd, Pt) Complexes as Antimalarial Agents In the face of the recent pandemic and emergence of infectious diseases of viral origin, research on parasitic diseases such as malaria continues to remain critical and innovative methods are required to target the rising widespread resistance that renders conventional therapies unusable. The prolific use of auxiliary metallo-fragments has augmented the search for novel drug regimens in an attempt to combat rising resistance. The development of organometallic compounds (those containing metal-carbon bonds) as antimalarial drugs has been exemplified by the clinical development of ferroquine in the nascent field of Bioorganometallic Chemistry. With their inherent physicochemical properties, organometallic complexes can modulate the discipline of chemical biology by proffering different modes of action and targeting various enzymes. With the beneficiation of platinum group metals (PGMs) in mind, this review aims to describe recent studies on the antimalarial activity of PGM-based organometallic complexes. This review does not provide an exhaustive coverage of the literature but focusses on recent advances of bioorganometallic antimalarial drug leads, including a brief mention of recent trends comprising interactions with biomolecules such as heme and intracellular catalysis. This resource can be used in parallel with complementary reviews on metal-based complexes tested against malaria. Malaria ; Chemistry ; Organometallic chemistry ; Combinatorial chemistry ; Drug discovery ; Computational biology ; Nanotechnology ; Biology ; Catalysis ; Biochemistry ; Materials science ; Immunology Ferrocene Chemistry and Applications ; Metal complexes synthesis and properties ; Synthesis and Biological Evaluation
Authors: Yang Wang | Xudong Hou | Congyan Liu | Mohamed K. Albolkany | Yan Wang | Niannian Wu | C. Chen | Bo Liu
Source: Nature Communications (article, 2020-06-19)
Field: Chemistry — Inorganic Chemistry

Combustible ice mimicking behavior of hydrogen-bonded organic framework at ambient condition Adsorption of guest molecules by porous materials proceeds in a spontaneous exothermic way, whereas desorption usually requires external energy input as an endothermic process. Reducing such energy consumption makes great sense in practice. Here we report the reversible and automatic methanol (MeOH) adsorption/release in an ionic hydrogen-bonded organic framework (iHOF) constructed from guanidinium cation and borate anion ([B(OCH<sub>3</sub>)<sub>4</sub>]<sub>3</sub>[C(NH<sub>2</sub>)<sub>3</sub>]<sub>4</sub>Cl•4CH<sub>3</sub>OH, termed Gd-B) at ambient condition. The metastable Gd-B automatically releases all sixteen MeOH molecules (63.4 wt%) via desorption and tetra-methyl borate hydrolysis at ambient atmosphere and the structure can be recovered when re-exposed to MeOH vapor or liquid, mimicking combustible ice behavior but at ambient condition. Reversible capture/release of four guest MeOH molecules is also realized without destroying its crystal structure. The combustible Gd-B paves a way for exploring metastable iHOF materials as carrier for alternative energy source and drug delivery etc. Endothermic process ; Desorption ; Molecule ; Adsorption ; Hydrogen bond ; Methanol ; Boron ; Hydrolysis ; Chemistry ; Exothermic reaction ; Hydrogen ; Ionic bonding ; Metastability ; Chemical engineering ; Ion ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes
Authors: Mingqiu Hu | Thomas P. Russell
Source: Materials Chemistry Frontiers (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

Polymers with advanced architectures as emulsifiers for multi-functional emulsions The architecture of polymer chains can play a significant role in their behavior at interfaces. Polymer ; Materials science ; Functional polymers ; Polymer architecture ; Polymer science ; Nanotechnology ; Chemical engineering ; Copolymer ; Composite material ; Engineering Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Proteins in Food Systems
Authors: Iva Pernicová | Ivana Nováčková | Petr Sedláček | Xenie Kouřilová | Michal Kalina | Adriána Kovalcik | Martin Koller | Jana Nebesářová | Vladislav Krzyžánek | Kamila Hrubanová | Jiří Másilko | Eva Slaninová | Stanislav Obruča
Source: Polymers (article, 2020-05-29)
Field: Materials Science — Biomaterials

Introducing the Newly Isolated Bacterium Aneurinibacillus sp. H1 as an Auspicious Thermophilic Producer of Various Polyhydroxyalkanoates (PHA) Copolymers–1. Isolation and Characterization of the Bacterium Extremophilic microorganisms are considered being very promising candidates for biotechnological production of various products including polyhydroxyalkanoates (PHA). The aim of this work was to evaluate the PHA production potential of a novel PHA-producing thermophilic Gram-positive isolate <i>Aneurinibacillus</i> sp. H1. This organism was capable of efficient conversion of glycerol into poly(3-hydroxybutyrate) (P3HB), the homopolyester of 3-hydroxybutyrate (3HB). In flasks experiment, under optimal cultivation temperature of 45 °C, the P3HB content in biomass and P3HB titers reached 55.31% of cell dry mass and 2.03 g/L, respectively. Further, the isolate was capable of biosynthesis of PHA copolymers and terpolymers containing high molar fractions of 3-hydroxyvalerate (3HV) and 4-hydroxybutyrate (4HB). Especially 4HB contents in PHA were very high (up to 91 mol %) when 1,4-butanediol was used as a substrate. Based on these results, it can be stated that <i>Aneurinibacillus</i> sp. H1 is a very promising candidate for production of PHA with tailored material properties. Polyhydroxyalkanoates ; Glycerol ; Thermophile ; Bacteria ; Bioplastic ; Biomass (ecology) ; Microorganism ; Chemistry ; Fermentation ; Food science ; Isolation (microbiology) ; Biochemistry ; Biology ; Microbiology ; Enzyme biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Enzyme Catalysis and Immobilization
Authors: Amanda Parker | George Opletal | Amanda S. Barnard
Source: Journal of Applied Physics (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Classification of platinum nanoparticle catalysts using machine learning Computer simulations and machine learning provide complementary ways of identifying structure/property relationships that are typically targeting toward predicting the ideal singular structure to maximize the performance in a given application. This can be inconsistent with experimental observations that measure the collective properties of entire samples of structures that contain distributions or mixture of structures, even when synthesized and processed with care. Metallic nanoparticle catalysts are an important example. In this study, we have used a multi-stage machine learning workflow to identify the correct structure/property relationships of Pt nanoparticles relevant to oxygen reduction, hydrogen oxidation, and hydrogen evolution reactions. By including classification prior to regression, we identified two distinct classes of nanoparticles and subsequently generated the class-specific models based on experimentally relevant criteria that are consistent with observations. These multi-structure/multi-property relationships, predicting properties averaged over a large sample of structures, provide a more accessible way to transfer data-driven predictions into the lab. Nanoparticle ; Workflow ; Computer science ; Property (philosophy) ; Catalysis ; Artificial intelligence ; Machine learning ; Materials science ; Biological system ; Nanotechnology ; Chemistry ; Database Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Computational Drug Discovery Methods
Authors: Dongbo Dai | Qing Liu | Rui Hu | Xiao Wei | Guangtai Ding | Baoyu Xu | Tao Xu | Jincang Zhang | Xu Yan | Huiran Zhang
Source: Materials & Design (article, 2020-09-30)
Field: Materials Science — Materials Chemistry

Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications Data driven material research is a hot topic in the cross field of artificial intelligence and materials science. The core of new material prediction is to find the relationship between material structure and properties. In this research, machine learning will have important advantages and play an important role for materials data. In this paper,we put forward a framework combining feature engineering and linear regression to find the correlation between structure and properties from materials data. High temperature superconductor and double perovskites for solar cells were employed to test the feasibility of the method. In the former, we successfully rebuilt a descriptor (ℓζ)−1 from data mining which is consistent with the theoretical formula. In the latter, as an exploration, we obtain a new descriptor χb2rsx2ersx−1 from data mining which expresses the heat of formation (ΔHF) in the double perovskite. By our experiment, the method can obtain related expressions of structure-property relationship for material.The results show that the method is a simple yet efficient paradigm to construct the structure-property relationship and provides valuable hints to accelerate the process of materials design. Property (philosophy) ; Construct (python library) ; Field (mathematics) ; Process (computing) ; Simple (philosophy) ; Feature (linguistics) ; Computer science ; Materials science ; Artificial intelligence ; Material Design ; Data mining ; Machine learning ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Ricardo Mello Di Benedetto | Edson Cocchieri Botelho | Anderson Janotti | Antônio Carlos Ancelotti | Guilherme Ferreira Gomes
Source: Composite Structures (article, 2020-10-10)
Field: Materials Science — Polymers and Plastics

Development of an artificial neural network for predicting energy absorption capability of thermoplastic commingled composites Artificial neural network ; Crashworthiness ; Context (archaeology) ; Materials science ; Thermoplastic ; Computer science ; Machine learning ; Composite material ; Engineering ; Structural engineering ; Finite element method Natural Fiber Reinforced Composites ; Engineering and Material Science Research ; Structural Response to Dynamic Loads
Authors: M.A.N. Dewapriya | R. K. N. D. Rajapakse | W. P. S. Dias
Source: Carbon (article, 2020-03-21)
Field: Materials Science — Materials Chemistry

Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks Artificial neural network ; Convolutional neural network ; Graphene ; Extrapolation ; Fracture (geology) ; Materials science ; Artificial intelligence ; Stress (linguistics) ; Computer science ; Vacancy defect ; Durability ; Biological system ; Nanotechnology ; Mathematics ; Composite material ; Mathematical analysis ; Physics ; Condensed matter physics Graphene research and applications ; Machine Learning in Materials Science ; Non-Destructive Testing Techniques
Authors: Hanjun Dai | Chengtao Li | Connor W. Coley | Bo Dai | Le Song
Source: arXiv (Cornell University) (article, 2020-01-06)
Field: Materials Science — Materials Chemistry

Retrosynthesis Prediction with Conditional Graph Logic Network Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely on template-based models that define subgraph matching rules, but whether or not a chemical reaction can proceed is not defined by hard decision rules. In this work, we propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model built upon graph neural networks that learns when rules from reaction templates should be applied, implicitly considering whether the resulting reaction would be both chemically feasible and strategic. We also propose an efficient hierarchical sampling to alleviate the computation cost. While achieving a significant improvement of $8.1\%$ over current state-of-the-art methods on the benchmark dataset, our model also offers interpretations for the prediction. Retrosynthetic analysis ; Computer science ; Benchmark (surveying) ; Graphical model ; Artificial intelligence ; Task (project management) ; Computation ; Graph ; Theoretical computer science ; Machine learning ; Data mining ; Algorithm ; Engineering ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Matthew P. Miller | Darren C. Pagan | A.J. Beaudoin | Kelly E. Nygren | Dalton Shadle
Source: Metallurgical and Materials Transactions A (article, 2020-06-30)
Field: Materials Science — Materials Chemistry

Understanding Micromechanical Material Behavior Using Synchrotron X-rays and In Situ Loading Synchrotron ; Materials science ; Diffraction ; Context (archaeology) ; Crystallite ; Optics ; Detector ; Synchrotron radiation ; Physics ; Metallurgy ; Geology Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Nicholas Marcella | Yang Liu | Janis Timoshenko | Erjia Guan | Mathilde Luneau | Tanya Shirman | Anna M. Płonka | Jessi E. S. van der Hoeven | Joanna Aizenberg | C. M. Friend | Anatoly I. Frenkel
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy X-ray absorption spectroscopy is a common method for probing the local structure of nanocatalysts. One portion of the X-ray absorption spectrum, the X-ray absorption near edge structure (XANES) is a useful alternative to the commonly used extended X-ray absorption fine structure (EXAFS) for probing three-dimensional geometry around each type of atomic species, especially in those cases when the EXAFS data quality is limited by harsh reaction conditions and low metal loading. A methodology for quantitative determination of bimetallic architectures from their XANES spectra is currently lacking. We have developed a method, based on the artificial neural network, trained on ab initio site-specific XANES calculations, that enables accurate and rapid reconstruction of the structural descriptors (partial coordination numbers) from the experimental XANES data. We demonstrate the utility of this method on the example of a series of PdAu bimetallic nanoalloys. By validating the neural network-yielded metal-metal coordination numbers based on the XANES analysis by previous EXAFS characterization, we obtained new results for in situ restructuring of dilute (2.6 at% Pd in Au) PdAu nanoparticles, driven by their gas and temperature treatments. XANES ; Extended X-ray absorption fine structure ; Bimetallic strip ; Absorption (acoustics) ; Nanomaterial-based catalyst ; X-ray absorption spectroscopy ; Spectroscopy ; Coordination number ; Absorption spectroscopy ; X-ray absorption fine structure ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Nanoparticle ; Metal ; Nanotechnology ; Optics ; Physics ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Xin Wang | Wei Xiong
Source: Science and Technology of Advanced Materials (article, 2020-01-31)
Field: Materials Science — Materials Chemistry

Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning Stacking fault energy (SFE) is of the most critical microstructure attribute for controlling the deformation mechanism and optimizing mechanical properties of austenitic steels, while there are no accurate and straightforward computational tools for modeling it. In this work, we applied both thermodynamic modeling and machine learning to predict the stacking fault energy (SFE) for more than 300 austenitic steels. The comparison indicates a high need of improving low-temperature CALPHAD (CALculation of PHAse Diagrams) databases and interfacial energy prediction to enhance thermodynamic model reliability. The ensembled machine learning algorithms provide a more reliable prediction compared with thermodynamic and empirical models. Based on the statistical analysis of experimental results, only Ni and Fe have a moderate monotonic influence on SFE, while many other elements exhibit a complex effect that their influence on SFE may change with the alloy composition. Stacking-fault energy ; Austenite ; CALPHAD ; Energy (signal processing) ; Fault (geology) ; Stacking ; Deformation (meteorology) ; Monotonic function Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; High Temperature Alloys and Creep
Authors: Shu Wang | Zhan Ma | Wenxiao Pan
Source: Soft Matter (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system. The CG modeling is built upon the framework of the generalized Langevin equation (GLE). The key is to determine each term in the GLE by directly linking it to atomistic data. In particular, we propose a two-stage Gaussian process-based Bayesian optimization method to infer the non-Markovian memory kernel from the data of the velocity autocorrelation function (VACF). Considering that the long-time behaviors of the VACF and memory kernel for polymer solutions can exhibit hydrodynamic scaling (algebraic decay with time), we further develop an active learning method to determine the emergence of hydrodynamic scaling, which can accelerate the inference process of the memory kernel. The proposed methods do not rely on how the mean force or CG potential in the GLE is constructed. Thus, we also compare two methods for constructing the CG potential: a deep learning method and the iterative Boltzmann inversion method. With the memory kernel and CG potential determined, the GLE is mapped onto an extended Markovian process to circumvent the expensive cost of directly solving the GLE. The accuracy and computational efficiency of the proposed CG modeling are assessed in a model star-polymer solution system at three representative concentrations. By comparing with the reference atomistic simulation results, we demonstrate that the proposed CG modeling can robustly and accurately reproduce the dynamic and structural properties of polymers in solution. Kernel (algebra) ; Computer science ; Scaling ; Autocorrelation ; Statistical physics ; Gaussian process ; Inference ; Algorithm ; Biological system ; Gaussian ; Physics ; Mathematics ; Artificial intelligence ; Computational chemistry ; Chemistry Block Copolymer Self-Assembly ; Material Dynamics and Properties ; Machine Learning in Materials Science