jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miguel Lastra-Mejías | M. Izquierdo | Albertina Torreblanca-Zanca | Regina Aroca-Santos | John C. Cancilla | Julia E. Sepúlveda-Díaz | José S. Torrecilla
Source: Food Control (article, 2019-03-30)
Field: Chemistry — Organic Chemistry

Cognitive fluorescence sensing to monitor the storage conditions and locate adulterations of extra virgin olive oil Olive oil ; Bottle ; Environmental science ; Artificial neural network ; Computer science ; Pulp and paper industry ; Chemistry ; Process engineering ; Biological system ; Artificial intelligence ; Food science ; Materials science ; Engineering ; Composite material ; Biology Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Siqian Zheng | Yuekuan Zhou
Source: Advanced Theory and Simulations (article, 2019-07-19)
Field: Chemistry — Spectroscopy

Numerical Study on the Thermal and Optical Performances of an Aerogel Glazing System with the Multivariable Optimization Using an Advanced Machine Learning Algorithm Abstract The implementation of advanced materials in high‐efficient glazing system is important for green buildings. In this study, aerogel granules are implemented in the glazing system to form a translucent window with super‐insulating performance. An experimentally validated numerical modeling integrating both heat transfer model and optical model is developed to characterize the sophisticated heat transfer and solar radiation transmission mechanisms. Sensitivity analysis is presented with quantifiable contribution ratio of each parameter to the total heat gain. Instead of returning back to numerical modeling repeatedly, an advanced optimization engine implemented with a generic optimization methodology with competitive computational efficiency and accuracy is proposed by implementing the supervised machine learning and advanced optimization algorithms. The research results show that the developed artificial neural network modeling is more accurate and computational‐efficient than the traditional lsqcurvefit fitting methodology. In addition, the optimal case through the teaching‐learning‐based optimization is more robust and competitive than the optimal case through the particle swarm optimization in terms of the total heat gain. This study presents an in‐depth understanding of heat transfer and solar radiation transmission of nanoporous aerogel granules together with a robust optimal design, which is important for the promotion of green buildings with high‐energy performance. Glazing ; Particle swarm optimization ; Solar gain ; Computer science ; Heat transfer ; Aerogel ; Artificial neural network ; Algorithm ; Mathematical optimization ; Engineering ; Materials science ; Machine learning ; Solar energy ; Mathematics ; Nanotechnology Aerogels and thermal insulation ; Building Energy and Comfort Optimization ; Urban Heat Island Mitigation
Authors: Roman A. Eremin | Pavel N. Zolotarev | Andrey A. Golov | Nadezhda A. Nekrasova | Tilmann Leisegang
Source: The Journal of Physical Chemistry C (article, 2019-11-18)
Field: Materials Science — Materials Chemistry

Ionic Transport in Doped Solid Electrolytes by Means of DFT Modeling and ML Approaches: A Case Study of Ti-Doped KFeO<sub>2</sub> We present a comprehensive study on the influence of Ti doping on K+ migration in the K1–xFe1–xTixO2 solid electrolyte. A novel approach is proposed which is based on modeling of configurational spaces (CSs) and full sets of inequivalent migration pathways by means of density functional theory (DFT) calculations and machine learning (ML) techniques. A 2 × 1 × 1 supercell (32 formula units) of a low-temperature polymorph of the KFeO2 compound with space group symmetry Pbca was used. For the three lowest Ti contents (x = 0.03, 0.06, and 0.09), all symmetrically inequivalent configurations of atomic arrangements (CSs) and K+ migration pathways (total numbers: 128, 59520, and 8630400) were generated. With the DFT-derived energetics of K+ migration at the lowest doping level (x = 0.03), the ML models were trained to predict ionic transport properties by using geometrical descriptors for the pathway-dopant arrangement. The trained ML models were then used to evaluate the K+ migration properties for pathways at higher doping levels. The computational results obtained are in good agreement with the results of a previous experimental study of the title compound. This demonstrates the applicability of the proposed approach for modeling and predicting effects of doping in crystalline solids, such as solid electrolytes and intercalation cathodes. Brief recommendations are given on the application of the proposed combined approach. Density functional theory ; Doping ; Dopant ; Supercell ; Ionic bonding ; Electrolyte ; Materials science ; Intercalation (chemistry) ; Computational chemistry ; Crystallography ; Chemistry ; Thermodynamics ; Ion ; Chemical physics ; Physical chemistry ; Inorganic chemistry ; Computer science ; Physics ; Electrode Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Xiaohong Chen | Jiaqi Gao | Tao Wang | Xinrong Jiang | Jiang Chen | Xiao Liang | Jianmin Wu
Source: Analytical Chemistry (article, 2019-07-29)
Field: Chemistry — Spectroscopy

Hepatocarcinoma Discrimination by Ratiometric Lipid Profiles Using Tip-Contact Sampling/Ionization Mass Spectrometry Precise diagnosis at the molecular level is essential for the improvement of surgery and treatment. High-throughput and spatial-resolved mass spectrometric (MS) methods for in situ detection of metabolites on tissue samples can reveal the dysregulation of metabolism in abnormal tissue and help identification of tumor. We here report a nondestructive MS method named as tip-contact sampling/ionization (TCSI)-MS technology which can quickly acquire lipidomic information from liver tissue and thereby realize tumor identification. Using this technology, fatty acids and lipids at the liver tissue surface can be rapidly imprinted onto a silicon nanowire tip attached with reduced graphene oxide (rGO) and sensitively detected by on-chip MS. With proper data pretreatment and statistical analysis, the clinical primary hepatocellular carcinoma (HCC) tissues can be discriminated from the nontumor parts. In addition, we found that a panel of adjacent dual peaks' ratio can be used to build a prediction model in artificial neural networks (ANN), resulting in high accuracy (91.7-98.3%) for tumor discrimination. Ratiometric TCSI-MS imaging using a selected dual peaks' ratio can greatly enhance the spatial resolution of tumor margin. The feature ratiometric data of lipid molecules may guide the study of metabolism pathways involved in hepatocarcinoma and ultimately become new metabolic biomarkers in clinical diagnosis. The present work demonstrated that the TCSI-MS technology may pave a novel way for surgery guidance and precision diagnosis in tissue biopsy. Chemistry ; Graphene ; Mass spectrometry ; Sampling (signal processing) ; Chromatography ; Nanotechnology ; Computer science Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Simon Hunkler | Tobias Lemke | Christine Peter | Oleksandra Kukharenko
Source: The Journal of Chemical Physics (article, 2019-10-15)
Field: Materials Science — Materials Chemistry

Back-mapping based sampling: Coarse grained free energy landscapes as a guideline for atomistic exploration One ongoing topic of research in MD simulations is how to enable sampling to chemically and biologically relevant time scales. We address this question by introducing a back-mapping based sampling (BMBS) that combines multiple aspects of different sampling techniques. BMBS uses coarse grained (CG) free energy surfaces (FESs) and dimensionality reduction to initiate new atomistic simulations. These new simulations are started from atomistic conformations that were back-mapped from CG points all over the FES in order to sample the entire accessible phase space as fast as possible. In the context of BMBS, we address relevant back-mapping related questions like where to start the back-mapping from and how to judge the atomistic ensemble that results from the BMBS. The latter is done with the use of the earth mover's distance, which allows us to quantitatively compare distributions of CG and atomistic ensembles. By using this metric, we can also show that the BMBS is able to correct inaccuracies of the CG model. In this paper, BMBS is applied to a just recently introduced neural network (NN) based approach for a radical coarse graining to predict free energy surfaces for oligopeptides. The BMBS scheme back-maps these FESs to the atomistic scale, justifying and complementing the proposed NN based CG approach. The efficiency benefit of the algorithm scales with the length of the oligomer. Already for the heptamers, the algorithm is about one order of magnitude faster in sampling compared to a standard MD simulation. Sampling (signal processing) ; Context (archaeology) ; Curse of dimensionality ; Computer science ; Metric (unit) ; Granularity ; Energy (signal processing) ; Scale (ratio) ; Biological system ; Algorithm ; Statistical physics ; Artificial intelligence ; Mathematics ; Physics ; Engineering ; Statistics ; Computer vision Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: Lur Alonso‐Cotchico | Jaime Rodrı́guez-Guerra | Agustı́ Lledós | Jean‐Didier Maréchal
Source: Frontiers in Chemistry (article, 2019-04-10)
Field: Chemistry — Organic Chemistry

The Effect of Cofactor Binding on the Conformational Plasticity of the Biological Receptors in Artificial Metalloenzymes: The Case Study of LmrR The design of Artificial Metalloenzymes (ArMs), which result from the incorporation of organometallic cofactors into biological structures, has grown steadily in the last two decades and important new-to-Nature reactions have been reached. These type of exercises could greatly benefit from an understanding of the structural impact that the inclusion of organometallic moieties may have on the biological host. To date though, our understanding of this phenomenon is highly partial. This lack of knowledge is one of the elements that condition that first-generation ArMs generally display relatively poor catalytic profiles. In this work, we approach this matter by assessing the dynamics and stability of a series of ArMs resulting from the inclusion, via different anchoring strategies, of a variety of organometallic cofactors into the Lactococcal multidrug resistance regulator (LmrR) protein. To this aim, we coupled standard force field-based techniques such as Protein-Ligand Docking and Molecular Dynamics simulations with a variety of trajectory convergence analyses, capable of assessing both the stability and flexibility of the different systems under study upon the binding of cofactors. Together with the experimental evidence obtained in other studies, we provide an overview on how these changes can affect the catalytic outcomes obtained from the different ArMs. Fundamentally, our results show that the convergence analysis used in this work can assess how the inclusion of synthetic metallic cofactors in proteins can condition different structural modulations of their host. Those conformational modifications are key to the success of the desired catalytic activity and their proper identification can be wisely used to improve the quality and the rate of success of the ArMs. Cofactor ; Plasticity ; Chemistry ; Receptor ; Stereochemistry ; Biochemistry ; Enzyme ; Physics ; Thermodynamics Organometallic Complex Synthesis and Catalysis ; Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Kiran H. Kanekal | Tristan Bereau
Source: The Journal of Chemical Physics (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Resolution limit of data-driven coarse-grained models spanning chemical space Increasing the efficiency of materials design remains a significant challenge given the large size of chemical compound space (CCS). The use of a chemically transferable coarse-grained model enables different molecular fragments to map to the same bead type, significantly increasing screening efficiency. Here, we propose new criteria for the design of coarse-grained models allowing for the optimization of their chemical transferability and evaluate the Martini model within this framework. We further investigate the scope of this transferability by parameterizing three Martini-like models in which the number of bead types ranges from 5 to 16. These force fields are fully compatible with existing Martini environments because they are parameterized by interpolating the Martini interaction matrix. We then implement a Bayesian approach to determining which chemical groups are likely to be present on fragments corresponding to specific bead types for each model. We demonstrate that a level of accuracy comparable to Martini is obtained with a force field with fewer bead types, using the water/octanol partitioning free energy (ΔG<sub>W→Ol</sub>) as our metric for comparison. However, the advantage of including more bead types is a reduction of uncertainty when back-mapping these bead types to specific chemistries. Just as reducing the size of the coarse-grained particles leads to a finer mapping of conformational space, increasing the number of bead types yields a finer mapping of CCS. Finally, we note that, due to the large size of fragments mapping to a single Martini bead, a resolution limit arises when using ΔG<sub>W→Ol</sub> as the only descriptor when coarse-graining CCS. Transferability ; Metric (unit) ; Limit (mathematics) ; Bead ; Reduction (mathematics) ; Resolution (logic) ; Ranging ; Parameterized complexity ; Limiting Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Pickering emulsions and particle stabilization
Authors: Gino Groeneveld | Bob W.J. Pirok | Peter J. Schoenmakers
Source: Faraday Discussions (article, 2019-01-01)
Field: Chemistry — Spectroscopy

Perspectives on the future of multi-dimensional platforms Two-dimensional liquid chromatography (2D-LC) formats have emerged to help address separation problems that are too complex for conventional one-dimensional LC. There are a number of obstacles to the proliferation of 2D-LC that are gradually being removed. Reliable commercial instrumentation has become available and data analysis software is being improved. Detector-sensitivity and phase-system compatibility issues can largely be solved by using active-modulation strategies. The remaining challenge, developing good and fast 2D-LC methods within a reasonable time, may be solved with smart algorithms. The technology platform that has been developed for 2D-LC also creates a number of other possibilities. Between the two separation stages, all kinds of physical (e.g. dissolution) or chemical (e.g. enzymatic or light-induced degradation) processes can be made to take place, allowing a wide variety of experiments to be performed within a single, efficient and automated analysis. All these developments are discussed in this paper and a number of critical issues are identified. A practical example, the characterization of polysorbates by high-resolution comprehensive two-dimensional liquid chromatography in combination with high-resolution mass spectrometry, is described as a culmination of recent developments in 2D-LC and as an illustration of the current state of the art. Characterization (materials science) ; Resolution (logic) ; Culmination ; Mass spectrometry ; High resolution ; Chromatography ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Artificial intelligence ; Physics ; Remote sensing ; Geology Analytical Chemistry and Chromatography ; Analytical Chemistry and Sensors ; Analytical chemistry methods development
Authors: Yunfeng Chen | Yue Chen | Xuping Feng | Xufeng Yang | Jinnuo Zhang | Zhengjun Qiu | Yong He
Source: Molecules (article, 2019-07-09)
Field: Chemistry — Analytical Chemistry

Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000-550 cm<sup>-1</sup> were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties. Principal component analysis ; Computer science ; Pattern recognition (psychology) ; Autoencoder ; Encoder ; Support vector machine ; Artificial intelligence ; Linear discriminant analysis ; Fourier transform ; Set (abstract data type) ; Wavenumber ; Algorithm ; Biological system ; Mathematics ; Artificial neural network ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Yuanyuan Chen | Zhibin Wang
Source: Molecules (article, 2019-01-24)
Field: Chemistry — Analytical Chemistry

Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, these algorithms suffer from the problem of unrobustness, which means that the selected wavelengths of each optimization are different. To solve this problem, this paper proposes a novel wavelength selection method based on the binary dragonfly algorithm (BDA), which includes three typical frameworks: single-BDA, multi-BDA, ensemble learning-based BDA settings. The experimental results for the public gasoline NIR spectroscopy dataset showed that: (1) By using the multi-BDA and ensemble learning-based BDA methods, the stability of wavelength selection can improve; (2) With respect to the generalized performance of the quantitative analysis model, the model established with the wavelengths selected by using the multi-BDA and the ensemble learning-based BDA methods outperformed the single-BDA method. The results also indicated that the proposed method is not limited to the dragonfly algorithm but can also be combined with other swarm optimization algorithms. In addition, the ensemble learning idea can be applied to other feature selection areas to obtain more robust results. Genetic algorithm ; Selection (genetic algorithm) ; Preprocessor ; Computer science ; Feature selection ; Binary number ; Algorithm ; Wavelength ; Spectroscopy ; Clonal selection algorithm ; Stability (learning theory) ; Swarm behaviour ; Optimization problem ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Alvin Sarraga Alon
Source: International Journal of Advanced Trends in Computer Science and Engineering (article, 2019-12-15)
Field: Chemistry — Analytical Chemistry

An Image Processing Approach of Multiple Eggs’ Quality Inspection The current system of checking and grading egg quality in the Philippines was done manually one by one using the traditional way where graders exert great effort that resulted in graders' visual stress. To address the problem identified the researchers proposed a scientific way of checking and grading the egg quality by using image processing based non-destructive and cost-effective technique to detect various cracks, dirt, and defect in eggs. Upon testing, the system obtained a total of 91.33% as high-quality eggs and the presence of either crack or dirt while 8.66% were inspected as low quality. For the internal part of each egg, the system achieved 100% detection of the yolk. The main results achieved have been quite promising; the researchers are encouraged to continue the labor of improving the generation of internal and external egg detection. Dirt ; Grading (engineering) ; Computer science ; Artificial intelligence ; Image processing ; Quality (philosophy) ; Image quality ; Engineering drawing ; Computer vision ; Reliability engineering ; Engineering ; Image (mathematics) ; Mechanical engineering ; Civil engineering Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Chris E. Freye | Patrick R. Bowden | Margo Greenfield | Bryce C. Tappan
Source: Talanta (article, 2019-12-28)
Field: Chemistry — Spectroscopy

Non-targeted discovery-based analysis for gas chromatography with mass spectrometry: A comparison of peak table, tile, and pixel-based Fisher ratio analysis Analyte ; Chemistry ; Chromatography ; Mass spectrometry ; False positive paradox ; Analytical Chemistry (journal) ; Pixel ; Statistics ; Artificial intelligence ; Mathematics ; Computer science Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies