jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zeinab Heidari | Mohammad Amin Sobati
Source: Journal of Molecular Liquids (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Evaluation of the flammability characteristics of alkyl esters: New QSPR models Quantitative structure–activity relationship ; Flammability ; Alkyl ; Molecular descriptor ; Flammability limit ; Linear regression ; Chemistry ; Biological system ; Predictive modelling ; Biochemical engineering ; Thermodynamics ; Organic chemistry ; Computer science ; Machine learning ; Engineering ; Stereochemistry ; Combustion ; Physics Thermal and Kinetic Analysis ; Computational Drug Discovery Methods ; Energetic Materials and Combustion
Authors: Shaofeng Xu | Xiaoxia Zhong | Ying Guo | Jianjun Shi
Source: Applied Surface Science (article, 2023-02-08)
Field: Chemistry — Electrochemistry

Effects of gas-liquid interface on the theoretical X-ray absorption spectroscopy of phenylenediamines Aqueous solution ; Absorption spectroscopy ; Molecular dynamics ; Absorption (acoustics) ; Chemistry ; Spectroscopy ; Spectral line ; Analytical Chemistry (journal) ; Polar ; Chemical physics ; Physical chemistry ; Computational chemistry ; Materials science ; Organic chemistry ; Optics ; Physics Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Songhao Li | Huilin Wu | Jing Zhao | Yu Liu | Yunpeng Li | Houcheng Liu | Yiting Zhang | Yubin Lan | Xinglong Zhang | Yutao Liu | Yongbing Long
Source: Journal of Near Infrared Spectroscopy (article, 2023-01-26)
Field: Chemistry — Analytical Chemistry

Leaf-based species classification of hybrid cherry tomato plants by using hyperspectral imaging Approaches based on near infrared hyperspectral imaging (NIR-HSI) technology combined with machine learning have been developed to classify the leaves of hybrid cherry tomatoes and then identify the species of hybrid cherry tomato plants. The near infrared (NIR) hyperspectral images of 400 cherry tomato leaves (100 per species) were collected in the wavelength range of 900–1700 nm. Machine learning algorithms such as linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM) were employed to construct leaf classification models with the hyperspectral data preprocessed by Savitzky-Golay (SG) smoothing filter, first derivative (first Der) and standard normal variate (SNV). Principle of Component Analysis (PCA) was also used to reduce the data dimension and extract spectral features. It is revealed that the LDA model reaches the highest classification accuracy among the three machine learning algorithms and SNV can lead to higher improvement in model accuracy than other preprocessing methods of SG smoothing and first Der. Analysis based on PCA spectral feature extraction demonstrates that differences occur in internal material content in the leaves of cherry tomato plants with different species, which renders the models being able to distinguish between the species. Another important work was performed to reveal the different effects of the mesophyll and vein regions (VR) on the accuracy of the leaf classification model. It is demonstrated that the classification accuracy is improved by a value of 0.033 or 0.042 when mesophyll substitutes vein or whole leaf as regions of interest (ROI) to extract reflectance spectra for modeling. As a result, the accuracy of the training and test set respectively reached a high value of 0.998 and 0.973 for the LDA classification model combined with the SNV preprocessing method. The results propose that the use of mesophyll region (MR) as ROI can improve the performance of the leaf classification model, which provides a new strategy for efficient and non-destructive classification of different hybrid cherry tomato plants. Hyperspectral imaging ; Artificial intelligence ; Smoothing ; Linear discriminant analysis ; Support vector machine ; Pattern recognition (psychology) ; Principal component analysis ; Mathematics ; Preprocessor ; Random forest ; Computer science ; Biological system ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Junbao Li | Weibing Li | Xiaoming Wang
Source: Defence Technology (article, 2023-10-11)
Field: Materials Science — Materials Chemistry

Al/Hf ratio-dependent mechanisms of microstructure and mechanical property of nearly fully dense Al–Hf reactive material This study proposed three types of Al–Hf reactive materials with particle size ratios (α), which were almost completely dense (porosity of <5.40 %) owing to their preparation using hot-pressing technology. Microstructure characteristics and phase composition were analyzed, and the influence of particle size ratios on dynamic mechanical behavior and damage mechanism were investigated. The prepared sample with α = 0.1 exhibited continuous wrapping of the Hf phase by the Al phase. Hf–Hf contact (continuous Hf phase) within the sample gradually increased with increasing α, and a small amount of fine Hf appeared for the sample with α = 1. The reactive materials exhibited clear strain-rate sensitivity, with flow stress σ0.05 and failure strain εf increasing approximately linearly with increasing strain rate ε˙. It is found that the plastic deformation of the material increased with increasing strain rate. As α increased from 0.1 to 1, the flow stress gradually increased. Impact failure of the material was dominated by ductile fracture with a large Al phase plastic deformation band for lower α, while brittle fracture with crushed Hf particles occurred at higher α. Finally, a constitutive model based on BP neural network was proposed to describe the stress-strain relationships of the materials, with an average relative error of 2.22 %. Materials science ; Microstructure ; Brittleness ; Composite material ; Flow stress ; Plasticity ; Strain rate ; Deformation (meteorology) ; Phase (matter) ; Fracture (geology) ; Stress (linguistics) ; Reactive material ; Particle (ecology) ; Particle size ; Strain (injury) ; Metallurgy ; Chemical engineering ; Chemistry High-Velocity Impact and Material Behavior ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Lukáš Pečínka | Monika Vlachová | Lukáš Moráň | Jana Gregorová | Volodymyr Porokh | Petra Kovačovicová | Martina Almáši | Luděk Pour | Martin Štork | Josef Havel | Sabina Ševčı́ková | Petr Vaňhara
Source: Journal of the American Society for Mass Spectrometry (article, 2023-11-23)
Field: Chemistry — Spectroscopy

Improved Screening of Monoclonal Gammopathy Patients by MALDI-TOF Mass Spectrometry Monoclonal gammopathies are a group of blood diseases characterized by presence of abnormal immunoglobulins in peripheral blood and/or urine of patients. Multiple myeloma and plasma cell leukemia are monoclonal gammopathies with unclear etiology, caused by malignant transformation of bone marrow plasma cells. Mass spectrometry with matrix-assisted laser desorption/ionization and time-of-flight detection is commonly used for investigation of the peptidome and small proteome of blood plasma with high accuracy, robustness, and cost-effectivity. In addition, mass spectrometry coupled with advanced statistics can be used for molecular profiling, classification, and diagnosis of liquid biopsies and tissue specimens in various malignancies. Despite the fact there have been fully optimized protocols for mass spectrometry of normal blood plasma available for decades, in monoclonal gammopathy patients, the massive alterations of biophysical and biochemical parameters of peripheral blood plasma often limit the mass spectrometry measurements. In this paper, we present a new two-step extraction protocol and demonstrated the enhanced resolution and intensity (>50×) of mass spectra obtained from extracts of peripheral blood plasma from monoclonal gammopathy patients. When coupled with advanced statistics and machine learning, the mass spectra profiles enabled the direct identification, classification, and discrimination of multiple myeloma and plasma cell leukemia patients with high accuracy and precision. A model based on PLS-DA achieved the best performance with 71.5% accuracy (95% confidence interval, CI = 57.1-83.3%) when the 10× repeated 5-fold CV was performed. In summary, the two-step extraction protocol improved the analysis of monoclonal gammopathy peripheral blood plasma samples by mass spectrometry and provided a tool for addressing the complex molecular etiology of monoclonal gammopathies. Multiple myeloma ; Chemistry ; Mass spectrometry ; Monoclonal ; Plasma cell leukemia ; Monoclonal gammopathy of undetermined significance ; Monoclonal antibody ; Chromatography ; Antibody ; Medicine ; Internal medicine ; Immunology Mass Spectrometry Techniques and Applications ; Multiple Myeloma Research and Treatments ; Glycosylation and Glycoproteins Research
Authors: Ryan Jacobs | Philip E. Goins | Dane Morgan
Source: Machine Learning Science and Technology (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap Abstract Materials discovery and design typically proceeds through iterative evaluation (both experimental and computational) to obtain data, generally targeting improvement of one or more properties under one or more constraints (e.g. time or budget). However, there can be great variation in the quality and cost of different data, and when they are mixed together in what we here call multifidelity data, the optimal approaches to their utilization are not established. It is therefore important to develop strategies to acquire and use multifidelity data to realize the most efficient iterative materials exploration. In this work, we assess the impact of using multifidelity data through mock demonstration of designing solar cell materials, using the electronic bandgap as the target property. We propose a new approach of using multifidelity data through leveraging machine learning models of both low- and high-fidelity data, where using predicted low-fidelity data as an input feature in the high-fidelity model can improve the impact of a multifidelity data approach. We show how tradeoffs of low- versus high-fidelity measurement cost and acquisition can impact the materials discovery process. We find that the use of multifidelity data has maximal impact on the materials discovery campaign when approximately five low-fidelity measurements per high-fidelity measurement are performed, and when the cost of low-fidelity measurements is approximately 5% or less than that of high-fidelity measurements. This work provides practical guidance and useful qualitative measures for improving materials discovery campaigns that involve multifidelity data. Fidelity ; Computer science ; High fidelity ; Feature (linguistics) ; Process (computing) ; Machine learning ; Data mining ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Software Engineering Research
Authors: Lekan Taofeek Popoola | Oluwafemi Fadayini
Source: Heliyon (article, 2023-04-01)
Field: Chemistry — Analytical Chemistry

Optimization of Azare low-grade barite beneficiation: comparative study of response surface methodology and artificial neural network approach This study examined the efficacy of response surface methodology (RSM) and artificial neural network (ANN) optimization approaches on barite composition optimization from low-grade Azare barite beneficiation. The Box-Behnken Design (BBD) and Central Composite Design (CCD) approaches were used as RSM methods. The best predictive optimization tool was determined via a comparative study between these methods and ANN. Barite mass (60-100 g), reaction time (15-45 min) and particle size (150-450 μm) at three levels were considered as the process parameters. The ANN architecture is a 3-16-1 feed-forward type. Sigmoid transfer function was adopted and mean square error (MSE) technique was used for network training. Experimental data were divided into training, validation and testing. Batch experimental result revealed maximum barite composition of 98.07% and 95.43% at barite mass, reaction time and particle size of 100 g, 30 min and 150 μm; and 80 g, 30 min and 300 μm for BBD and CCD respectively. The predicted and experimental barite compositions of 98.71% and 96.98%; and 94.59% and 91.05% were recorded at optimum predicted point for BBD and CCD respectively. The analysis of variance revealed high significance of developed model and process parameters. The correlation of determination recorded by ANN for training, validation and testing were 0.9905, 0.9419 and 0.9997; and 0.9851, 0.9381 and 0.9911 for BBD and CCD. The best validation performance was 48.5437 and 5.1777 at epoch 5 and 1 for BBD and CCD respectively. In conclusion, the overall mean squared error of 14.972, 43.560 and 0.255; R<sup>2</sup> value of 0.942, 0.9272 and 0.9711; and absolute average deviation of 3.610, 4.217 and 0.370 recorded for BBD, CCD and ANN respectively proved ANN to be the best. Response surface methodology ; Central composite design ; Beneficiation ; Artificial neural network ; Design of experiments ; Box–Behnken design ; Mathematics ; Materials science ; Mean squared error ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Computer science ; Artificial intelligence ; Statistics ; Metallurgy Spectroscopy and Chemometric Analyses ; Grey System Theory Applications ; Industrial Vision Systems and Defect Detection
Authors: Zhongxin Chen | Yiran Shen | Binbin Chen | Jun Zhou | Panling Huang | Hengchang Zang | Yongxia Guan
Source: Engineering Applications of Artificial Intelligence (article, 2023-07-20)
Field: Chemistry — Analytical Chemistry

VasLine: Realize online detection and augmented NIR using deep learning Computer science ; Artificial intelligence ; Generative model ; Deep learning ; Process (computing) ; Machine learning ; Quality (philosophy) ; Generative grammar ; Data mining ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Advanced Chemical Sensor Technologies
Authors: M. J. Hartnett | William Watson | Jake A. Janssen | Jenna Hua | Jarod Grossman | Qingchu Peng | Prativa Hartnett | Kristin Favela
Source: Journal of the American Society for Mass Spectrometry (article, 2023-07-06)
Field: Chemistry — Spectroscopy

Rapid Screening of Consumer Products by GCxGC-HRT and Machine Learning Assisted Data Processing This work demonstrates high-throughput screening of personal care products to provide an overview of potential exposure. Sixty-seven products from five categories (body/fragrance oil, cleaning product, hair care, hand/body wash, lotion, sunscreen) were rapidly extracted and then analyzed using suspect screening by two-dimensional gas chromatography (GCxGC) high-resolution mass spectrometry (GCxGC-HRT). Initial peak finding and integration were performed using commercial software, followed by batch processing using the machine learning program Highlight. Highlight automatically performs background subtraction, chromatographic alignment, signal quality review, multidilution aggregation, peak grouping, and iterative integration. This data set resulted in 2,195 compound groups and 43,713 individual detections. Compounds of concern (101) were downselected and classified as mild irritants (29%), environmental toxicants/severe irritants (51%) and endocrine disrupting chemicals/carcinogens (20%). High risk compounds such as phthalates, parabens, and avobenzone were detected in 46 out of the 67 products (69%), and only 5 out of the 67 products (7%) listed these compounds on their ingredient labels. The Highlight results for the compounds of concern were compared to commercial software results (ChromaTOF) and 5.3% of the individual detections were discerned only by Highlight, demonstrating the strength of the iterative algorithm to effectively discover low-level signatures. Highlight provides a significant labor advantage, requiring only 2.6% of the time estimated for a largely manual workflow using commercial software. In order to address significant time needed for postprocessing assignment of identification confidence, a new machine-learning-based algorithm was developed to assess the quality of assigned library matches, and a balanced accuracy of 79% was achieved. Workflow ; Software ; Chemistry ; Cosmetics ; Chromatography ; Machine learning ; Artificial intelligence ; Computer science ; Database Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Xuanzhi Liu | Jixin Liang | Yuping Ye | Zhan Song | Juan Zhao
Source:  (article, 2023-07-07)
Field: Chemistry — Analytical Chemistry

A Food Package Recognition and Sorting System Based on Structured Light and Deep Learning Vision algorithm-based robotic arm grasping system is one of the robotic arm systems that can be applied to a wide range of scenarios. It uses algorithms to automatically identify the location of the target and guide the robotic arm to grasp it, which has more flexible features than the teachable robotic arm grasping system. However, for some food packages, their transparent packages or reflective materials bring challenges to the recognition of vision algorithms, and traditional vision algorithms cannot achieve high accuracy for these packages. In addition, in the process of robotic arm grasping, the positioning on the z-axis height still requires manual setting of parameters, which may cause errors. Based on the above two problems, we designed a sorting system for food packaging using deep learning algorithms and structured light 3D reconstruction technology. Using a pre-trained MASK R-CNN model to recognize the class of the object in the image and get its 2D coordinates, then using structured light 3D reconstruction technique to calculate its 3D coordinates, and finally after the coordinate system conversion to guide the robotic arm for grasping. After testing, it is shown that the method can fully automate the recognition and grasping of different kinds of food packages with high accuracy. Using this method, it can help food manufacturers to reduce production costs and improve production efficiency. Computer science ; Sorting ; Artificial intelligence ; Deep learning ; Programming language Spectroscopy and Chemometric Analyses ; Currency Recognition and Detection ; Advanced Chemical Sensor Technologies
Authors: Prabal Datta Barua | Şengül Doğan | Gürkan Kavuran | Türker Tuncer | Ru‐San Tan | U. Rajendra Acharya
Source: Neural Computing and Applications (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

NFSDense201: microstructure image classification based on non-fixed size patch division with pre-trained DenseNet201 layers Abstract In the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which incorporates several key components. Firstly, we propose a unique nested patch division approach that divides each input image into four patches of varying dimensions. Secondly, we utilize DenseNet201, a deep neural network pretrained on ImageNet1k, to extract 2920 deep features from the last fully connected and global average pooling layers. Thirdly, we introduce an iterative neighborhood component analysis function to select the most discriminative features from the merged feature vector, which is formed by concatenating the four feature vectors extracted per input image. This process results in a final feature vector of optimal length 698. Lastly, we employ a standard shallow support vector machine classifier to perform the actual classification. To evaluate the performance of NFSDense201, we conducted experiments using a large public SEM image dataset. The dataset consists of 972, 162, 326, 4590, 3820, 3925, 4755, 181, 917, and 1624.jpeg images belonging to the following microstructural categories: “biological,” “fibers,” “film-coated surfaces,” “MEMS devices and electrodes,” “nanowires,” “particles,” “pattern surfaces,” “porous sponge,” “powder,” and “tips,” respectively. For both four-class and ten-class classification tasks, we evaluated NFSDense201 using subsets of the dataset containing 5080 and 21,272 images, respectively. The results demonstrate the superior performance of NFSDense201, achieving a four-class classification accuracy rate of 99.53% and a ten-class classification accuracy rate of 97.09%. These accuracy rates compare favorably against previously published SEM image classification models. Additionally, we report the performance of NFSDense201 for each class in the dataset. Pooling ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Contextual image classification ; Feature vector ; Artificial neural network ; Feature (linguistics) ; Discriminative model ; Support vector machine ; Image (mathematics) Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Electron and X-Ray Spectroscopy Techniques
Authors: Lewis Y. Geer | Joel Lapin | Douglas J. Slotta | Tytus D. Mak | Stephen E. Stein
Source: Journal of Proteome Research (article, 2023-05-26)
Field: Chemistry — Spectroscopy

AIomics: Exploring More of the Proteome Using Mass Spectral Libraries Extended by Artificial Intelligence The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides. False positive paradox ; Computer science ; Proteome ; Spectral line ; Computational biology ; Sequence (biology) ; Database search engine ; Matching (statistics) ; Pattern recognition (psychology) ; Artificial intelligence ; Chemistry ; Bioinformatics ; Information retrieval ; Biology ; Physics ; Search engine ; Mathematics ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics