jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wan Sieng Yeo | Agus Saptoro
Source: International Journal of Computing and Digital Systems (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Introduction of LSSVR for the Prediction of the YellownessIndex Data-driven models including principal component regression (PCR), partial least square regression (PLSR), and least square support vector regression (LSSVR) have been widely applied as predictive models in various applications.However, studies employing regression models to estimate the yellowness index (YI) are scarce in the literature.This study, therefore, focuses on developing non-destructive YI measurements using regression models.The collected RGB calculated XYZ and obtained CIE LAB values were set as the input variables.Meanwhile, the YI value was denoted as the output variable.Results indicated that the LSSVR model outperforms PCR and PLSR models in predicting YI in which the root means square errors of LSSVR for the training and testing datasets were found to be 261,406% to 294,218% and 725% to 772% lower than PLSR and PCR, respectively.LSSVR is also attributed to higher coefficients of determination (R 2 ) that are superior to PLSR and PCR, whose R 2 values are very close to 1. Nonetheless, the computational times of training and testing datasets for LSSVR are much longer than those of PLSR and PCR.Consequently, a highly accurate LSSVR model-based YI sensor shows promising applications particularly if the computational load can be further minimized. Index (typography) ; Computer science ; Mathematics ; World Wide Web Dye analysis and toxicity ; Color perception and design ; Color Science and Applications
Authors: Thomas Schüler | Maylise Nastar | Kangming Li | Chu‐Chun Fu
Source: Acta Materialia (article, 2024-06-10)
Field: Materials Science — Materials Chemistry

Towards accurate thermodynamics from random energy sampling Special Quasi-random Structures (SQSs) are often used to model disordered alloys in small simulation cells. Yet, SQS-based sampling yields chemical potentials (and other thermodynamic properties) that do not match the equilibrium values at some finite temperature, which can for instance be measured in atomic Monte Carlo simulations. This is due to the lack of chemical short-range order in random samples. In this paper, we present a probabilistic analysis of chemical potential calculations based on the distribution of substitution energies and the Widom technique. Performing the analysis by sampling either equilibrium configurations or SQSs, we show that they both yield different results, but that it is possible to correct the results from the random sampling in order to get a result which is much closer to the equilibrium values. The correction is very simple to apply and does not require additional total energy calculations. Materials science ; Thermodynamics ; Statistical physics ; Sampling (signal processing) ; Physics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Thermal properties of materials
Authors: Qian Jin | Yuqi Jiang | Xudong Lu | Yumeng Liu | Yining Chen | Dawei Gao | Qi Sun | Cheng Zhuo
Source:  (article, 2024-10-27)
Field: Materials Science — Surfaces, Coatings and Films

SEM-CLIP: Precise Few-Shot Learning for Nanoscale Defect Detection in Scanning Electron Microscope Image In the field of integrated circuit manufacturing, the detection and classification of nanoscale wafer defects are critical for subsequent root cause analysis and yield enhancement. The complex background patterns observed in scanning electron microscope (SEM) images and the diverse textures of the defects pose significant challenges. Traditional methods usually suffer from insufficient data, labels, and poor transferability. In this paper, we propose a novel few-shot learning approach, SEM-CLIP, for accurate defect classification and segmentation. SEM-CLIP customizes the Contrastive Language-Image Pretraining (CLIP) model to better focus on defect areas and minimize background distractions, thereby enhancing segmentation accuracy. We employ text prompts enriched with domain knowledge as prior information to assist in precise analysis. Additionally, our approach incorporates feature engineering with textual guidance to categorize defects more effectively. SEM-CLIP requires little annotated data, substantially reducing labor demands in the semiconductor industry. Extensive experimental validation demonstrates that our model achieves impressive classification and segmentation results under few-shot learning scenarios. Scanning electron microscope ; Shot (pellet) ; Nanoscopic scale ; Artificial intelligence ; Computer vision ; Computer science ; Single shot ; Microscope ; Materials science ; Optics ; Nanotechnology ; Physics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Jianan Zhang | Aditya Koneru | Subramanian K. R. S. Sankaranarayanan | Carmen M. Lilley
Source: The Journal of Physical Chemistry C (article, 2024-04-01)
Field: Materials Science — Materials Chemistry

Evolutionary Search and Theoretical Study of Silicene Grain Boundaries’ Mechanical Properties Defects such as grain boundaries (GBs) are almost inevitable during the synthesis process of 2D materials. To take advantage of the fascinating properties of 2D materials, understanding the nature and impact of various GB structures on pristine 2D sheets is crucial. In this work, using an evolutionary algorithm search, we predict a wide variety of silicene GB structures with very different atomic structures compared with those found in graphene or hexagonal boron-nitride. Twenty-one GBs with the lowest energy were validated by density functional theory (DFT), a majority of which were previously unreported to our best knowledge. Based on the diversity of the GB predictions, we found that the formation energy and mechanical properties can be dramatically altered by adatom positions within a GB and certain types of atomic structures, such as four-atom rings. To study the mechanical behavior of these GBs, we apply strain to the GB structures stepwise and use DFT calculations to investigate the mechanical properties of 9 representative structures. It is observed that GB structures based on pentagon-heptagon pairs are likely to have similar or higher in-plane stiffness and strength compared to the zigzag orientation of pristine silicene. However, an adatom located at the hollow site of a heptagon ring can significantly deteriorate the mechanical strength. For all of the structures, the in-plane stiffness and strength were found to decrease with increasing formation energy. For the failure behavior of GB structures, it was found that GB structures based on pentagon-heptagon pairs have failure behavior similar to that of graphene. We also found that the GB structures with atoms positioned outside of the 2D plane tend to experience phase transitions before failure. Utilizing the evolutionary algorithm, we locate diverse silicene GBs and obtain useful information about their mechanical properties. Silicene ; Materials science ; Density functional theory ; Zigzag ; Graphene ; Grain boundary ; Stiffness ; Boron nitride ; Atom (system on chip) ; Graphyne ; Molecular dynamics ; Crystallography ; Chemical physics ; Nanotechnology ; Computational chemistry ; Geometry ; Composite material ; Microstructure ; Chemistry Graphene research and applications ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Amir Mansourialam | Mansour Rasekh | Sina Ardabili | M Dadkhah | Amir Mosavi
Source: Acta Technologica Agriculturae (article, 2024-11-28)
Field: Chemistry — Analytical Chemistry

Hyperspectral Method Integrated with Machine Learning to Predict the Acidity and Soluble Solid Content Values of Kiwi Fruit During the Storage Period Abstract Non-destructive evaluation is advancing in examining the properties of fruits. Kiwi fruit stands out as one of the popular fruits globally. Due to the influence of various environmental factors and storage conditions, diligent checking and storage of this fruit are essential. Therefore, monitoring changes in its properties during storage in cold storage facilities is crucial. One nondestructive method utilised in recent years to investigate changes in fruit texture is the hyperspectral method. This study uses the support vector machine (SVM) method to assess hyperspectral method‘s effectiveness in examining property changes in four kiwi varieties during storage in addition to predicting the properties such as acidity and soluble solid content. The evaluation of the predictive machine learning model revealed an accuracy of 95% in predicting acidity and soluble solid content (SSC) changes in kiwi fruit during storage. Further, investigations found that the support vector machine method provided relatively lower accuracy and sensitivity in identifying product variety during storage, with an average accuracy ranging from about 91% to 94%. These findings suggest that integrating machine learning methods with outputs from techniques like hyperspectral imaging enhances the non-destructive detection capability of fruits. This integration transforms obtained results into practical outcomes, serving as an interface between software and hardware. Hyperspectral imaging ; Support vector machine ; Kiwi ; Machine learning ; Artificial intelligence ; Computer science ; Biological system ; Mathematics ; Food science ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Phytochemicals and Antioxidant Activities
Authors: Anas El Hamri | Y. Mouhib | Atmane Ourmiche | Mohammed Chigr | Nour‐Eddine El Mansouri
Source: Molecules (article, 2024-09-16)
Field: Materials Science — Polymers and Plastics

Study of the Effect of Cedar Sawdust Content on Physical and Mechanical Properties of Cement Boards The growing demand for sustainable building materials, amid escalating costs, has spurred interest in alternative solutions such as wood cement composites. This study explores the feasibility of producing wood cement boards (WCBs) using locally sourced cedar sawdust as a reinforcing agent. Boards with a thickness of 10 mm and a target density of 1200 kg/m<sup>3</sup> were manufactured under pressures ranging from 2 to 6 MPa for 24 h. Cedar sawdust, used as raw and untreated material, was incorporated into the mixture as a partial substitute for cement in varying proportions, ranging from 10% to 25% (by weight). The WCBs were cured for 28 days under ambient conditions. Physical properties including density, water absorption (WA), and thickness swelling (TS) were assessed, along with mechanical properties through flexural tests. The results showed that increasing cedar sawdust content decreased both density and mechanical performance while increasing WA and TS. Microstructural analysis (SEM and EDS) revealed significant porosity at higher sawdust contents, while lower contents had better matrix-reinforcement cohesion. Additionally, substantial levels of calcium and silicon were detected on the sawdust surface, indicating stabilized cement hydration products. These findings, supported by thermal (TGA and DSC) and FTIR analyses, clearly demonstrate that cement boards with 10% cedar sawdust exhibit favorable properties for non-structural applications, such as wall and partition cladding. Sawdust ; Materials science ; Cement ; Composite material ; Flexural strength ; Absorption of water ; Porosity ; Swelling ; Pulp and paper industry Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Jan Długosz | Phong B. Dao | Wiesław J. Staszewski | Tadeusz Uhl
Source: Sensors (article, 2024-03-20)
Field: Chemistry — Analytical Chemistry

Damage Detection in Glass Fibre Composites Using Cointegrated Hyperspectral Images Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting conditions during the imaging, which is a serious issue for gathering hyperspectral data in real-life scenarios. In this study, a data conditioning procedure is proposed for improving the results of damage detection with various classifiers. The developed procedure is based on the concept of signal stationarity and cointegration analysis, and achieves its goal by performing the detection and removal of the non-stationary trends in hyperspectral images caused by imperfect lighting. To evaluate the effectiveness of the proposed method, two damage detection tests have been performed on a damaged GFRP specimen: one using the proposed method, and one using an established damage detection workflow, based on the works of other authors. Application of the proposed procedure in the processing of a hyperspectral image of a damaged GFRP specimen resulted in significantly improved accuracy, sensitivity, and F-score, independently of the type of classifier used. Hyperspectral imaging ; Fibre-reinforced plastic ; Computer science ; Artificial intelligence ; Sensitivity (control systems) ; Pattern recognition (psychology) ; Glass fiber ; Classifier (UML) ; Computer vision ; Materials science ; Engineering ; Composite material ; Electronic engineering Spectroscopy and Chemometric Analyses ; Thermography and Photoacoustic Techniques ; Industrial Vision Systems and Defect Detection
Authors: Finan T. Hindam | Basma M. Eltanany | Amal M. Abou Al Alamein | Rasha M. El Nashar | Reham M. Arafa
Source: BMC Chemistry (article, 2024-05-09)
Field: Chemistry — Electrochemistry

A voltammetric method coupled with chemometrics for determination of a ternary antiparkinson mixture in its dosage form: greenness assessment An electroanalytical methodology was developed by direct differential pulse voltammetric (DPV) measurement of Levodopa (LD), Carbidopa (CD) and Entacapone (ENT) mixture using bare glassy carbon electrode (GCE) in Britton Robinson (BR) buffer (pH = 2.0). A multivariate calibration model was then applied to the exported preprocessed voltammetric data using partial least square (PLS) as a chemometric tool. Additionally, the model was cross-validated and the number of latent variables (LVs) were determined to produce a reliable model for simultaneous quantitation of the three drugs either in their synthetic mixtures or in their marketed pharmaceutical formulation with high accuracy and precision. Data preprocessing was used to tackle the problem of lacking bi-linearity which is commonly found in electrochemical data. The proposed chemometric model was able to provide fast and reliable technique for quantitative determination of antiparkinson drugs in their dosage forms. This was successfully achieved by utilizing sixteen mixtures as calibration set and nine mixtures as validation set. The percent recoveries for LD, CD and ENT were found to be 100.05% ± 1.28%, 100.04% ± 0.53% and 99.99% ± 1.25%, respectively. The obtained results of the proposed method were statistically compared to those of a previously reported High Performance Liquid Chromatography (HPLC) methodology. Finally, the presented analytical method strongly supports green analytical chemistry regarding the minimization of potentially dangerous chemicals and solvents, as well as reducing energy utilization and waste generation. Chemometrics ; Chromatography ; Ternary operation ; Chemistry ; Calibration ; Dosage form ; Voltammetry ; Analytical Chemistry (journal) ; Mathematics ; Computer science ; Electrochemistry ; Electrode ; Statistics Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Methods in Pharmaceuticals
Authors: Lili Fu | Shuyan Liu | Dongyan Huang | Jingli Wang | Xinming Jiang | Gang Wang
Source: Agriculture (article, 2024-04-11)
Field: Chemistry — Analytical Chemistry

Utilizing the Fusion Characteristics of Multispectral and Electronic Noses to Detect Soil Main Nutrient Content Soil nutrients are essential for plant growth, and it is crucial to accurately detect their levels. However, current methods for detecting soil nutrients still have low accuracy and high costs. In this study, we propose a multi-source fusion feature approach that combines multispectral image technology with electronic nose gas response technology to achieve rapid, high-precision, and cost-effective detection of soil nutrients, including soil organic matter (SOM), total nitrogen (TN), available potassium (AK), and available phosphorus (AP). To begin, a multispectral camera was used to collect spectral images and extract seven index features. Additionally, an electronic nose was used to collect soil pyrolysis gases to obtain the response curve and extract seven response features. These two sets of features were effectively fused to form a 106 × 98 fusion feature space. Secondly, principal component analysis (PCA) and Pearson correlation coefficient (PCC) algorithms were used to optimize and reduce the dimensionality of the fused feature space. Finally, a predictive model of the relationship between the feature space and the nutrient content of the soil samples was established using the random forest (RF) and partial least squares regression (PLSR) algorithms. The predictive performance of the model was used to evaluate the accuracy of soil nutrient detection. The results showed that the PLSR modeling of the optimized feature space of PCA achieved high accuracy in predicting the levels of main soil nutrients, with R2 values for SOM, TN, AK, and AP of 0.96, 0.95, 0.84, and 0.73, and the RMSE values of 0.56, 0.07, 3.21, and 3.70, respectively. Compared to using only electronic nose gas response technology, the soil nutrient detection with multi-source data features method in this study not only improved the accuracy for SOM and TN but also for metal elements AK and AP in soil. The spectral index features proposed in this study were able to compensate for the limitations of the electronic nose response features, and modeling with the fusion feature space resulted in an accurate prediction of SOM, TN, AK, and AP levels. Multispectral image ; Soil nutrients ; Environmental science ; Nutrient ; Water content ; Fusion ; Content (measure theory) ; Remote sensing ; Soil science ; Biology ; Soil water ; Mathematics ; Engineering ; Geology ; Ecology ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: Lydia Sue Serafin | Ethan R. Cluff | Gus L. W. Hart | Eric R. Homer
Source: Acta Materialia (article, 2024-10-03)
Field: Materials Science — Materials Chemistry

Grain boundary solute segregation across the 5D space of crystallographic character Materials science ; Character (mathematics) ; Grain boundary ; Crystallography ; Space (punctuation) ; Metallurgy ; Microstructure ; Geometry ; Mathematics Microstructure and mechanical properties ; Mineral Processing and Grinding ; Rock Mechanics and Modeling
Authors: Zimei Zhang | Jianwei Xiao | Wenjie Wang | Magdalena Zielińska | Shanyu Wang | Ziliang Liu | Zhian Zheng
Source: Agriculture (article, 2024-03-21)
Field: Chemistry — Analytical Chemistry

Automated Grading of Angelica sinensis Using Computer Vision and Machine Learning Techniques Angelica sinensis (Oliv.) Diels, a member of the Umbelliferae family, is commonly known as Danggui (Angelica sinensis, AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for efficient market management and consumer health. The commonly used method to classify AS grades depends on the evaluator’s observation and experience. However, this method has issues such as unquantifiable parameters and inconsistent identification results among different evaluators, resulting in a relatively chaotic classification of AS in the market. To address these issues, this study introduced a computer vision-based approach to intelligently grade AS. Images of AS at five grades were acquired, denoised, and segmented, followed by extraction of shape, color, and texture features. Thirteen feature parameters were selected based on difference and correlation analysis, including tail area, whole body area, head diameter, G average, B average, R variances, G variances, B variances, R skewness, G skewness, B skewness, S average, and V average, which exhibited significant differences and correlated with grades. These parameters were then used to train and test both the traditional back propagation neural network (BPNN) and the BPNN model improved with a growing optimizer (GOBPNN). Results showed that the GOBPNN model achieved significantly higher average testing precision, recall, F-score, and accuracy (97.1%, 95.9%, 96.5%, and 95.0%, respectively) compared to the BPNN model. The method combining machine vision technology with GOBPNN enabled efficient, objective, rapid, non-destructive, and cost effective AS grading. Grading (engineering) ; Artificial intelligence ; Computer science ; Natural language processing ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Traditional Chinese Medicine Studies
Authors: Murphy M. Peksen
Source: Applied Sciences (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

CFD and Artificial Intelligence-Based Machine Learning Synergy for the Assessment of Syngas-Utilizing Pre-Reformer in r-SOC Technology Advancement This study demonstrates the significant advantages of integrating computational fluid dynamics (CFD) with artificial intelligence (AI)-based machine learning (ML) to optimize the pre-reforming process for reversible solid oxide cell (r-SOC) technologies. It places a distinct focus on the relationship between process variables, aiming to enhance the preparation of quality r-SOC-ready fuel, which is an indispensable element for successful operation. Evaluating the intricate thermochemistry of syngas-containing reforming processes involves employing an experimentally validated CFD model. The model serves as the foundation for gathering essential data, crucial for the development and training of AI-based machine learning models. The developed model forecasts and optimizes reforming processes across diverse fuel compositions, encompassing oxygen-containing syngas blends and controlled feedstock outlet process conditions. Impressively, the model’s predictions align closely with CFD outcomes with an error margin as low as 0.34%, underscoring its accuracy and reliability. This research significantly contributes to a deeper understanding and the qualitative enhancement of preparing high-quality syngas for SOC under improved process conditions. Enabling the early availability of valuable information drives forward sustainable research and ensures the safe, consistent operation assessment of r-SOC. Additionally, this strategic approach substantially reduces the need for resource-intensive experiments. Syngas ; Process engineering ; Computer science ; Manufacturing engineering ; Artificial intelligence ; Engineering ; Chemistry ; Catalysis ; Organic chemistry Catalytic Processes in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Metallurgical Processes and Thermodynamics