jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Claiton Zanini Brusamarello | Michele Di Domenico | Camila da Silva | Fernanda de Castilhos
Source: Revista Mexicana de Ingeniería Química (article, 2019-08-01)
Field: Chemistry — Analytical Chemistry

A COMPARATIVE STUDY BETWEEN MULTIVARIATE CALIBRATION AND ARTIFICIAL NEURAL NETWORK IN QUANTIFICATION OF SOYBEAN BIODIESEL Biodiesel is an alternative fuel and can be obtained by the transesterification of vegetable oils. Spectrophotometric methods can be used for the quantification of mixtures, associated with chemometric tools, enabling the analysis of overlapping signals. The aim of this work is to apply the multivariate calibration with PLS (partial least squares) and artificial neural network (ANN), to estimate the concentration of esters in the transesterification of soybean oil using molecular absorption spectrophotometry as analytical technique. Absorbance measurements were performed in a spectrophotometer UV/VIS. Synthetic solutions were prepared with standards of the five major compounds of soybean biodiesel and real samples were obtained by the reaction of transesterification of soybean oil with NaOH and enzymatic method was used Lipozyme® IM (Novozymes), as catalysts. Results shows that all components of this reaction medium absorb in the wavelength range of 190-280 nm. Reactions of basic catalysis reached conversions close to 100%, while enzymatic reactions reached lower conversions. For both methods, calibration and validation groups were composed, respectively, by synthetic and real samples. Results showed that the concentrations of esters estimated by ANN model in the real samples are more accurate (R2-0.93), showing the good ability to estimation of ANN. Transesterification ; Biodiesel ; Soybean oil ; Absorbance ; Chemistry ; Calibration ; Chemometrics ; Catalysis ; Partial least squares regression ; Chromatography ; Organic chemistry ; Mathematics ; Machine learning ; Computer science ; Food science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Hanjong You | Hyungjik Kim | Dong-Kyun Joo | Seung Min Lee | Jeongung Kim | Sunwoong Choi
Source:  (article, 2019-02-01)
Field: Chemistry — Analytical Chemistry

Classification of Food Powders with Open Set using Portable VIS-NIR Spectrometer Near Infrared (NIR) spectroscopy is fast and non-destructive methods for analyzing materials without pretreatment. Especially as portable NIR spectrometers have been developed, the research of spectral analysis has applied to various open environment and field. In this paper, we classify visually indistinguishable eight food powders using portable VIS-NIR spectrometer with a wavelength range of 450 to 1000 nm with CNN (Convolutional Neural Network), one of the machine learnings. Further we consider open set recognition where unknown classes should be rejected at test time. The proposed CNN model achieved an accuracy of 100% for eight food powders, and 91.2% with open set. Our experimental results demonstrate the potential of material analysis using a portable VIS-NIR spectrometer with machine learning. Spectrometer ; Set (abstract data type) ; Computer science ; Materials science ; Optics ; Physics ; Programming language Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Ren Togo | Naoki Saito | Takahiro Ogawa | Miki Haseyama
Source: IEEE Access (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Estimating Regions of Deterioration in Electron Microscope Images of Rubber Materials via a Transfer Learning-Based Anomaly Detection Model A method for estimating regions of deterioration in electron microscope images of rubber materials is presented in this paper. Deterioration of rubber materials is caused by molecular cleavage, external force, and heat. An understanding of these characteristics is essential in the field of material science for the development of durable rubber materials. Rubber material deterioration can be observed by using on electron microscope but it requires much effort and specialized knowledge to find regions of deterioration. In this paper, we propose an automated deterioration region estimation method based on deep learning and anomaly detection techniques to support such material development. Our anomaly detection model, called Transfer Learning-based Deep Autoencoding Gaussian Mixture Model (TL-DAGMM), uses only normal regions for training since obtaining training data for regions of deterioration is difficult. TL-DAGMM makes use of extracted high representation features from a pre-trained deep learning model and can automatically learn the characteristics of normal rubber material regions. Regions of deterioration are estimated at the pixel level by calculated anomaly scores. Experiments on real rubber material electron microscope images demonstrated the effectiveness of our model. Natural rubber ; Anomaly (physics) ; Materials science ; Anomaly detection ; Pixel ; Electron microscope ; Microscope ; Artificial intelligence ; Scanning electron microscope ; Computer science ; Electron ; Composite material ; Biological system ; Pattern recognition (psychology) ; Optics ; Physics Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Infrastructure Maintenance and Monitoring
Authors: Piero Gasparotto | Davide Bochicchio | Michele Ceriotti | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2019-11-29)
Field: Materials Science — Biomaterials

Identifying and Tracking Defects in Dynamic Supramolecular Polymers A central paradigm of self-assembly is to create ordered structures starting from molecularmonomers that spontaneously recognize and interact with each other via noncovalent interactions.In the recent years, great efforts have been directed toward reaching the perfection in thedesign of a variety of supramolecular polymers and materials with different architectures. Theresulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at thelevel of statistical ensembles to assess their average properties. However, molecular simulationsrecently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamicbehavior and properties. The study of these defects poses considerable challenges, as theflexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutesa defect, and to characterize its stability and evolution. Here, we demonstrate the powerof unsupervised machine learning techniques to systematically identify and compare defects insupramolecular polymer variants in different conditions, using as a benchmark 5°A-resolutioncoarse-grained molecular simulations of a family of supramolecular polymers. We shot that thisapproach allows a complete data-driven characterization of the internal structure and dynamicsof these complex assemblies and of the dynamic pathways for defects formation and resorption.This provides a useful, generally applicable approach to unambiguously identify defects inthese dynamic self-assembled materials and to classify them based on their structure, stabilityand dynamics. Supramolecular chemistry ; Supramolecular polymers ; Nanotechnology ; Computer science ; Materials science ; Polymer ; Biological system ; Chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Polymer composites and self-healing
Authors: Chaoli Chen | Qikai Li | Qing Zhang | Yanlan Li | Yong Wei | Shifa Wang
Source: 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) (article, 2019-12-01)
Field: Chemistry — Inorganic Chemistry

Artificial neural network algorithm for predict the photocatalytic activity of the Mn co-doped MgAl<sub>2</sub>O<sub>4</sub>: Ce composite photocatalyst A gamma-ray irradiation assisted polyacrylamide gel method have been used to synthesize the Mn co-doped MgAl <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> : Ce composite photocatalyst. The effects of catalyst load, dye concentration, pH value and irradiation time on the photocatalytic activity of the Mn co-doped MgAl <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> : Ce composite photocatalyst were investigated by the degradation of the rhodamine B dye under simulated sunlight irradiation. An artificial neural network model was well-established by using variables including catalyst load, dye concentration, pH value and irradiation time as input layer and degradation rate as output layer. The appropriate neural network model was established to achieve better prediction for the degradation rate and then the response input parameters were used to screen the output data for optimizing the experimental parameters of photocatalytic reaction. The application of presented artificial neural network algorithm can be used as an important tool for predict the photocatalytic activity of the Mn co-doped MgAl <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> : Ce composite photocatalyst. This algorithm shows potential applications in material performance prediction, space radiation environment prediction and geological disaster prediction in three gorges reservoir area. Photocatalysis ; Doping ; Artificial neural network ; Materials science ; Rhodamine B ; Degradation (telecommunications) ; Catalysis ; Irradiation ; Nuclear chemistry ; Algorithm ; Computer science ; Analytical Chemistry (journal) ; Artificial intelligence ; Physics ; Chemistry ; Organic chemistry ; Optoelectronics ; Telecommunications Pigment Synthesis and Properties ; Advanced Photocatalysis Techniques
Authors: Hiroshi Ohno
Source: Applied Soft Computing (article, 2019-08-07)
Field: Materials Science — Materials Chemistry

Neural network-based transductive regression model Computer science ; Hyperparameter ; Regression ; Artificial neural network ; Benchmark (surveying) ; Artificial intelligence ; Machine learning ; Inference ; Generalization ; Linear regression ; Regression analysis ; Function (biology) ; Pattern recognition (psychology) ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Dmitry Nazarenko | И. А. Родин | О. А. Шпигун
Source: Inorganic Materials (article, 2019-12-01)
Field: Chemistry — Analytical Chemistry

The Use of Machine Learning in the Analytical Control of the Preparations of Medicinal Plants Despite the fact that the global market for medicinal plants amounts to hundreds of billions of dollars, there is almost no government control over the quality of such pharmaceuticals in most countries of the world. This is partly attributed to the complex composition of plant materials: traditional analytical methodology is based on the use of standard reference samples for each analyte. In this case, preparations based on medicinal plants may contain tens and hundreds of physiologically active components. Isolation of those compounds in a pure form in practice is carried out using preparative chromatography, which leads to their high cost. Moreover, variation of the chemical composition of medicinal plants depending on the geographical origin of the raw materials interferes with prescribing strict ranges of permissible contents for all physiologically active components. The combination of the above factors limits the possibilities of using traditional approaches to analysis requiring strict standardization, the list of compounds for each type of plant, levels of contents, and the availability of reference materials and standards of comparison. This led to the study of the possibility of introducing various mathematical approaches as an auxiliary methodology. Unlike traditional methodologies, machine learning approaches are based on the correct collection of the data samples. Such a sample should contain groups of samples that correspond to the states of the object which the developed algorithm must distinguish: authentic/fake, pure/containing impurities, effective/not containing a certain level of active components, etc. This review is devoted to consideration of the application of machine learning techniques to the problems of chemical analysis and production control of raw materials of medicinal plants and preparations on their basis for the last 15 years. Standardization ; Raw material ; Quality (philosophy) ; Analyte ; Computer science ; Sample (material) ; Biochemical engineering ; Medicinal plants ; Chemistry ; Traditional medicine ; Chromatography ; Engineering ; Organic chemistry ; Medicine Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Metabolomics and Mass Spectrometry Studies
Authors: Mohammad Sayem Mozumder | Anusha Mairpady | Abdel‐Hamid I. Mourad
Source: Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena (article, 2019-02-15)
Field: Materials Science — Biomaterials

HDPE/TiO&lt;sub&gt;2&lt;/sub&gt; Nanocomposite: Fabrication and Optimization of Mechanical Property by RSM and ANN Polymeric nanocomposites have proven to be excellent candidate as biomaterials. However, materials and approaches used to improve the mechanical property of the polymer are still under scrutiny. In this study, improvement of mechanical property upon addition of nanotitanium oxide (n-TiO 2 ), cellulose nanocrystal (CNC) and two different types of coupling agent was analyzed. Influence of the individual factors and its interaction with tensile strength was evaluated using analysis of variance. From the analyses of main effect and interaction effects, it could be concluded that n-TiO 2 and CNC have major influence on the improving mechanical properties. Moreover, the coupling agent and compatibilizing agent did not have considerable effect on the mechanical properties. The central composite design was used to evaluate the best combination of n-TiO 2 and CNC to be experimented. The responses were modeled and optimized using response surface methodology (RSM) and artificial neural network (ANN). The predicted data was in agreement with the experimental data. The modeling accuracy and efficiency is evaluated based on regression coefficient (R square value). Both the method had recommendable R square value. However, the R square value of the Artificial neural network (R 2 &gt;95%) was higher than Response surface methodology (R 2 &gt;70 %). Materials science ; Response surface methodology ; Ultimate tensile strength ; Nanocomposite ; Central composite design ; Artificial neural network ; Composite number ; Composite material ; Cellulose ; Nano- ; Chemical engineering ; Machine learning ; Computer science Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging
Authors: Yunyang Chi | Liping Luo | Xueyong Huang | Meng Cui | Ximo Dai | Yingbin Hao | Xiali Guo | Huolin Luo
Source: Journal of Food Quality (article, 2019-08-18)
Field: Chemistry — Analytical Chemistry

Rapid Determination of the Freshness of Lotus Seeds Using Surface Desorption Atmospheric Pressure Chemical Ionization-Mass Spectrometry with Multivariate Analyses In order to explore a new method to detect the freshness of lotus seeds, the lotus seeds stored for 0, 1, 2, and 3 years, respectively, were used as experimental materials and analyzed by DAPCI-MS (desorption atmospheric pressure chemical ionization-mass spectrometry). The obtained data were processed by principal component analysis (PCA) and backpropagation artificial neural networks (BP-ANNs). The result showed that DAPCI-MS could obtain abundant chemical material information from the slice surface of lotus seeds. The BP-ANNs model could be applied not only to distinguish fresh and aged lotus seeds with the testing set accuracies of 95.0% and 91.7%, respectively, but also to classify lotus seeds with different storage times with the testing set accuracies of 90.0%, 85.0%, 85.0%, and 90.0%, respectively. The paper developed a fast, convenient, and accurate method for the freshness detection of lotus seed and would provide reliable reference value for rapid authentication of food freshness by the rapid mass spectrometry technique. Lotus ; Mass spectrometry ; Atmospheric-pressure chemical ionization ; Desorption ; Chemistry ; Lotus effect ; Analytical Chemistry (journal) ; Principal component analysis ; Backpropagation ; Ionization ; Chromatography ; Biological system ; Chemical ionization ; Artificial neural network ; Artificial intelligence ; Computer science ; Botany ; Adsorption ; Ion Chromatography in Natural Products ; Analytical Chemistry and Chromatography ; Tea Polyphenols and Effects
Authors: Helle Hein | Ljubov Jaanuska
Source: AIP conference proceedings (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Modal-based parameter identification in vibrating nano-beams using machine learning This paper addresses the parameter identification in vibrating nano-beams as a classification or regression problem using machine learning methods. It is assumed that the beams have constant dimensions and are weakened by cracks. The vibration model is based on size-dependent nonlocal elasticity theory developed by Eringen. The cracked nano-beam is modelled by dividing the cracked element into two segments connected by a rotational spring at the crack position. The material parameters as well as the crack severities and positions are identified with the aid of machine learning methods using modal-based characteristics. Modal ; Vibration ; Beam (structure) ; Structural engineering ; Modal analysis ; Spring (device) ; Nano- ; Elasticity (physics) ; Identification (biology) ; Position (finance) ; Constant (computer programming) ; Computer science ; Materials science ; Acoustics ; Engineering ; Physics ; Composite material Nonlocal and gradient elasticity in micro/nano structures ; Numerical methods in engineering ; Composite Structure Analysis and Optimization
Authors: Mikołaj Mizera | Kornelia Lewandowska | Andrzej Miklaszewski | Judyta Cielecka‐Piontek
Source: Molecules (article, 2019-02-19)
Field: Chemistry — Spectroscopy

Machine Learning Approach for Determining the Formation of β-Lactam Antibiotic Complexes with Cyclodextrins Using Multispectral Analysis The problem of determining the formation of complexes of β-lactam antibiotics with cyclodextrins (CDs) and the interactions involved in this process were addressed by machine learning on multispectral images. Complexes of β-lactam antibiotics, including cefuroxime axetil, cefetamet pivoxil, and pivampicillin, as well as CDs, including αCD, βCD, γCD, hydroxypropyl-αCD, methyl-βCD, hydroxypropyl-βCD, and hydroxypropyl-γCD, were prepared in all combinations. Thermograms confirming the formation of cyclodextrin complexes were obtained using differential scanning calorimetry. Transmission Fourier-transform infrared (tFTIR) and complementary attenuated total reflectance FTIR (ATR) coupled with machine learning were techniques chosen as a nondestructive alternative. The machine learning algorithm was used to determine the formation of complexes in samples using solely their tFTIR and ATR spectra at the prediction stage. Parameterized method 7 (PM7) was used to support the analysis by molecular modeling of the complexes. The model developed through machine learning properly distinguished samples with formed complexes form noncomplexed samples with a cross-validation accuracy of 90.4%. Analysis of the contribution of spectral bands to the model indicated interactions of ester groups of β-lactam antibiotics with CDs, as well as some interactions of cephem ring in cefetamet pivoxil and penam moiety in pivampicillin. Molecular modeling with PM7 helped to explain experimental results and allowed to propose possible binding modes. Moiety ; Chemistry ; Multispectral image ; Cephem ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry ; Artificial intelligence ; Computer science ; Carboxylic acid Analytical Chemistry and Chromatography ; Antibiotics Pharmacokinetics and Efficacy ; Analytical Methods in Pharmaceuticals
Authors: Vajrayudha Ristiawanto | Budhi Irawan | Casi Setianingsih
Source: 2019 International Conference on Information and Communications Technology (ICOIACT) (article, 2019-07-01)
Field: Chemistry — Organic Chemistry

Wood Classification with Transfer Learning Method and Bottleneck Features Wood is one of the raw materials that is important for helping human needs such as home appliance like chair, table, and the other things made from wood. Wood industries require wood for produce their product that made from wood. There are several classes of wood must be classified before entering the process stage in wood industries. The class of wood is indicated the quality of the wood itself. So, wood industries must classify carefully for increase the productivity. Wood industries usually classify the wood with manual method such as classifying with eyesight of human however it just produces about 55% of correct classification rate. Convolutional Neural Network (CNN) is a development from Artificial Neural Network (ANN) to classify the image, image segmentation, and object recognition with high accuracy and high performance. Wood classification is one of the texture classifications because the wood can be classified depend on texture of the wood fiber itself. So, Convolutional Neural Network (CNN) comes to solve this problem. Deep Convolutional Neural Network (D-CNN) is developed to improve the accuracy and performance from many deep layers of Convolutional Neural Network (CNN) model and is designed for classify image with small dataset. This paper investigates the usage of Deep Convolutional Neural Network (D-CNN) with transfer learning method and bottleneck features for wood classification with a small dataset. There are five different classes of wood classifications in this paper such as class I, class II, class III, class IV, and class V. The best quality of this wood is class I and the worst quality in this wood is class V. Transfer learning method and bottleneck features can increase the accuracy of Deep Convolutional Neural Network (D-CNN). In this paper, the experiment achieves 95.69% of accuracy with transfer learning method and bottleneck features. Convolutional neural network ; Artificial intelligence ; Computer science ; Bottleneck ; Artificial neural network ; Pattern recognition (psychology) ; Deep learning ; Class (philosophy) ; Wood processing ; Transfer of learning ; Segmentation ; Wood industry ; Contextual image classification ; Machine learning ; Image (mathematics) ; Engineering ; Forestry Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications