jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sihyun Park | Sangho Jeon | Na- Hyun Kwon | Muhyeok Kwon | Jae-Ho Shin | Wonchan Kim | Jeong Gu Lee
Source: European Journal of Agronomy (review, 2024-06-10)
Field: Chemistry — Analytical Chemistry

Application of near-infrared spectroscopy to predict chemical properties in clay rich soil: A review Infrared spectroscopy ; Environmental science ; Spectroscopy ; Infrared ; Clay minerals ; Soil science ; Agronomy ; Materials science ; Mineralogy ; Chemistry ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Geochemistry and Geologic Mapping
Authors: Ashish Masarkar | Auhin Kumar Maparu | Yaswanth Sai Nukavarapu | Beena Rai
Source: ACS Applied Nano Materials (article, 2024-08-19)
Field: Materials Science — Materials Chemistry

Predicting Cytotoxicity of Nanoparticles: A Meta-Analysis Using Machine Learning Cytotoxicity evaluation of nanoparticles (NPs) is regarded as a crucial step for their successful application in the biomedical industry. However, conventional experimental methodologies for cytotoxicity measurements are often expensive, time-consuming, and demand intense training in cell culture. In this study, we developed generalized machine learning (ML) models for both qualitative and quantitative prediction of cytotoxicity across a wide variety of NPs. In particular, a meta-analysis of cytotoxicity data was conducted from published literature on metallic, metal oxide, polymer, and carbon-based NPs, leading to the development of random forest-based regression and classification models for predicting cell viability from physicochemical properties of NPs, cellular attributes, and testing conditions. Our feature importance analysis showed that accurately predicting the cytotoxicity of NPs using the regression model requires knowledge of their composition, concentration, zeta potential, and size, as well as exposure time, toxicity assay, and tissue type. Interestingly, among these attributes, the information about composition of NPs or tissue type was not needed for achieving high accuracy in the qualitative prediction of cytotoxicity using the classification model, indicating its superior robustness compared to the regression model. These findings may encourage future researchers to employ ML models more effectively and frequently to reliably assess the safety of NPs. Cytotoxicity ; Nanoparticle ; Computer science ; Artificial intelligence ; Chemistry ; Nanotechnology ; Materials science ; Biochemistry ; In vitro Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Inhyo Lee | Jaejun Lee | Minseon Kim | Jaejung Park | Heekyu Kim | Seung‐Chul Lee | Kyoungmin Min
Source: ACS Applied Materials & Interfaces (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal–Organic Frameworks Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability. Through Shapley additive explanations (SHAP) analysis, we found that Co and Ln could enhance the structural stability of MOFs. We validated these findings using newly generated hypothetical MOFs. Notably, we adopted an interpretable machine learning technique to analyze the contribution of remarkably diverse metal elements in the 20,342 MOFs to the mechanical properties of each MOF. We anticipate that this research will serve as a valuable tool for future studies on identifying mechanically robust MOFs suitable for various industrial applications. Materials science ; Metal-organic framework ; Metal ; Nanotechnology ; Metallurgy ; Organic chemistry ; Adsorption Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Omid Banapour Ghaffari | Bijan Eftekhari Yekta | Morteza Zakeri‐Nasrabadi
Source: Materialia (article, 2024-01-27)
Field: Materials Science — Ceramics and Composites

Estimating “depth of layer” (DOL) in ion-exchanged glasses using explainable machine learning Materials science ; Layer (electronics) ; Ion ; Engineering physics ; Chemical engineering ; Analytical Chemistry (journal) ; Optoelectronics ; Nanotechnology ; Chromatography ; Organic chemistry ; Engineering Glass properties and applications ; Pigment Synthesis and Properties ; Luminescence Properties of Advanced Materials
Authors: Jinlong Li | Zenghua Liu | Yang Zheng | Zongjian Zhang | Cunfu He
Source: Nondestructive Testing And Evaluation (article, 2024-06-19)
Field: Materials Science — Materials Chemistry

Quantitative evaluation of spheroidisation in 15CrMo steel based on deep learning 15CrMo steel has good mechanical properties and is widely used in high-temperature pressure components. After long term service, it is easy to cause material spheroidisation. The metallographic and mechanical property tests are used to evaluate the spheroidisation of specimens. Ultrasonic backscattering signal is sensitive to microstructure and contains more microstructure information, which can be used for the evaluation of spheroidisation. However, the ultrasonic backscattering signal is nonlinear, and feature extraction is difficult. In this study, ultrasonic testing is used to scan different spheroidisation specimens, and the ultrasonic backscattering signal is extracted as the input to the deep learning model, which is used to extract features from the backscattering signal. The models are evaluated using classification and regression evaluation metrics. The results show that the proposed CNN-LSTM model has good identification performance for the classification of spheroidisation and the prediction of mechanical properties. The classification accuracy, recall, precision, and F1-score are all 1. Additionally, the maximum predicted RMSE and MAE values are only 2.33 MPa and 1.70 MPa, and the minimum R2 is only 0.97. The worst prediction is the tensile strength, with an average value of 442.2 MPa and a maximum value of 481.1 MPa. Artificial intelligence ; Materials science ; Computer science ; Metallurgy Material Properties and Failure Mechanisms ; Metallurgy and Material Forming ; Engineering Diagnostics and Reliability
Authors: Fuxiang Wang | Qiying Li | Weigang Deng | Chunguang Wang | Lei Han
Source: Foods (article, 2024-07-01)
Field: Chemistry — Analytical Chemistry

Detection of Anthocyanins in Potatoes Using Micro-Hyperspectral Images Based on Convolutional Neural Networks The color potato has the function of both a food and vegetable. The color potato not only contains various amino acids and trace elements needed by the human body but also contains anthocyanins. Anthocyanins have many functions, such as antioxidation, inflammation inhibition, vision improvement, and cancer prevention, so colored potatoes are deeply loved by consumers and have good market prospects. However, at present, the detection of anthocyanin content in color potatoes mainly depends on chemical methods, which are time-consuming and laborious, so it is necessary to study a fast and accurate detection method. In this study, microscopic hyperspectral equipment was used to collect the spectral information of the outer skin and inner skin of potatoes. The original spectrum, pretreatment spectrum, and characteristic spectrum variables of the outer skin and inner skin were predicted by the convolution neural network (CNN) algorithm and partial least squares regression (PLS) algorithm, respectively, and the performance of the model was evaluated by the prediction set correlation coefficient (Rp), prediction set root mean square error (RMSEP), correction set correlation coefficient (Rc), correction set root mean square error (RMSEC), and residual prediction deviation (RPD). The results revealed that the inner skin Raw + CNN model constructed under raw spectral data is optimal with Rc = 0.9508, RMSEC = 0.0374%, Rp = 0.9461, RMSEP = 0.2361% and RPD = 4.4933. The inner skin Savitzky-Golay (SG) + Detrend (DET) + CNN model constructed from pre-processed spectral data is optimal with Rc = 0.9499, RMSEC = 0.0359%, Rp = 0.9439, RMSEP = 0.2384%, RPD = 4.6516. The inner skin DET + competitive adaptive reweighted sampling (CARS) +CNN model constructed from the feature-based spectral data was optimal with Rc = 0.9527, RMSEC = 0.0708%, Rp = 0.9457, RMSEP = 0.2711%, and RPD = 4.1623. It can be seen that the Rp, RMSEP, Rc, RMSEC, and RPD values for modeling the spectral information of the inner skin were higher than those of the outer skin under the three different spectral data. The prediction accuracy of the model built by the CNN algorithm was better than the conventional algorithm PLS, the application of the CNN algorithm in inner skin can achieve accurate prediction of anthocyanin content in potato. Hyperspectral imaging ; Mean squared error ; Partial least squares regression ; Mathematics ; Convolutional neural network ; Test set ; Biological system ; Residual ; Correlation coefficient ; Pattern recognition (psychology) ; Artificial intelligence ; Chemistry ; Computer science ; Algorithm ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Haojie Mei | Luyao Cheng | Liang Chen | Feifei Wang | Guijin Yang | Jin-Fu Li | Lingti Kong
Source: Acta Materialia (article, 2024-12-08)
Field: Materials Science — Materials Chemistry

Crack propagation in BCC Fe and the influence of Zn: An atomistic exploration The intricate mechanism underlying brittle fractures in steel materials, particularly Zn-induced liquid metal embrittlement in BCC Fe, remains poorly understood. Accordingly, systematic investigations on crack propagation in BCC Fe, leveraging the anisotropic linear elastic fracture mechanics and a state-of-the-art Fe-Zn binary machine learning moment tensor potential, were performed in this work. Both theoretical analyses and atomic simulations reveal that the ( 001 ) [ 110 ] crack is intrinsically brittle, whereas the ( 110 ) [ 1 1 ¯ 0 ] crack can be blunted due to the emission of 〈 111 〉 dislocations, exhibiting a certain degree of toughness. At elevated temperatures, the reduced unstable stacking fault energy promotes dislocation emission. Zn doping facilitates the ( 001 ) [ 110 ] crack to propagate at lowered loads and enhances the dislocation emission of the ( 110 ) [ 1 1 ¯ 0 ] crack. Notably, our atomic simulations reveal a novel mechanism of Zn-induced liquid metal embrittlement: while Zn enhances dislocation emission at the crack tip, it fails to effectively blunt the crack. Instead, Zn infiltrates the crack surface via the steps caused by dislocation emission, ultimately driving brittle crack propagation. Materials science ; Metallurgy Fusion materials and technologies ; Microstructure and mechanical properties ; Powder Metallurgy Techniques and Materials
Authors: Longguo Wu | Ling Ma | Wencheng Pan | Yao Zhang | Yu Tian | Yanzhe Zhu | Xingwu Tian | Yiyang Zhang | Yune Cao
Source: Microchemical Journal (article, 2024-09-12)
Field: Chemistry — Analytical Chemistry

Optimal antioxidant enzyme activity estimation in melon plant leaves based on microhyperspectral imaging technique Melon ; Antioxidant ; Enzyme ; Chemistry ; Enzyme assay ; Food science ; Biochemistry ; Botany ; Biology ; Horticulture Spectroscopy and Chemometric Analyses ; Medicinal Plants and Neuroprotection ; Phytochemicals and Antioxidant Activities
Authors: Gilda Aiello | Davide Tosi
Source: Journal of Agriculture and Food Research (article, 2024-04-25)
Field: Chemistry — Analytical Chemistry

An Artificial Intelligence-based tool to predict “unhealthy” wine and olive oil The application of Artificial Intelligence (AI) in the agri-food industry has witnessed significant advancements, particularly in healthy food, quality assessment, and geographical origin determination of agri-products such as wine and olive oils, whose international market is constantly growing. This paper explores the applicability of Machine Learning (ML) models for predicting “unhealthy” wines and olive oils. As for the healthy factor, two main characteristics of wines and olive oils were evaluated: their quality and their geographical origin. In the study, 3 ML algorithms were compared (Random Forest, Linear Discriminant Analysis, and K-Nearest Neighbors) to predict just by observing their chemical characteristics: i) the quality of red and white wine and ii) the geographical origins of wine and olive oils. Real datasets were used for these case studies. The Synthetic Minority Over-sampling Technique (SMOTE) was used to manage imbalanced data, and well-adopted AI metrics were collected to evaluate the accuracy of the predictions made by the 3 ML algorithms. 3 datasets were analyzed: dataset #01 contains ∼7000 data entries related to 2 types of wine (red and white Portuguese wines) and 11 wine's chemical characteristics; dataset #02 contains 178 data entries related to 3 different Italian vineyards and 13 wine's chemical characteristics; dataset #03 contains 572 data entries related to 9 different Italian geographical regions for olive oil and 7 fatty acid characteristics. A total of ∼7700 data points, 5 wines and 9 olive oil types, and 31 chemical characteristics were analyzed. 12 quality metrics were collected per each dataset analysis. The designed ML models achieved good accuracy (>0.65) in predicting the wine quality, a very high accuracy (>0.99) in predicting its vineyard, and a very good accuracy (>0.97) in predicting the origin of olive oil. Moreover, an AI-based tool has been developed that stakeholders can use to predict the quality and geographical origin of wine and olive oil to avoid possible fraudulent and unhealthy product labels. The obtained prediction results showcase AI and ML's potential to help the agri-food industry, providing valuable insights and innovations for producers, distributors, and consumers alike. Integrating AI and ML in the agri-food industry may enhance product quality, consumer trust, and market transparency. Wine ; Olive oil ; Artificial intelligence ; Computer science ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Wine Industry and Tourism ; Horticultural and Viticultural Research
Authors: Hesam Salimi Shahraki | Anees Ahmad | Qurtulen | Rani Bushra | Momina Momina
Source: Inorganic Chemistry Communications (article, 2024-06-04)
Field: Materials Science — Materials Chemistry

Biomass derived carbon dots for enhanced wastewater remediation: ANN-optimized synthesis and cellular imaging applications Environmental remediation ; Biomass (ecology) ; Wastewater ; Environmental science ; Nanotechnology ; Materials science ; Environmental engineering ; Ecology ; Contamination ; Biology Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Advanced Nanomaterials in Catalysis
Authors: Víctor M. Rentería T. | Manasa Krishna Pandreka | G. B. | M Yamini | Abhishek Gannarapu | Edward Raju Gope | D Raghava | Nageswara Rao K
Source: Journal of Pharma Insights and Research. (article, 2024-12-05)
Field: Chemistry — Spectroscopy

Advances in High-Performance Liquid Chromatography (HPLC) and Ultra-Performance Liquid Chromatography (UPLC) High-Performance Liquid Chromatography (HPLC) and Ultra-Performance Liquid Chromatography (UPLC) are fundamental analytical techniques that have revolutionized chemical analysis across various fields. These chromatographic methods offer exceptional separation capabilities, high resolution, and precise quantification of complex mixtures. HPLC has evolved significantly since its inception, with improvements in column technology, detection systems, and automation. UPLC, introduced as an advancement to conventional HPLC, operates at significantly higher pressures using sub-2-μm particle sizes, resulting in enhanced resolution, speed, and sensitivity. Both techniques have found extensive applications in pharmaceutical analysis, environmental monitoring, food safety, clinical diagnostics, and biomedical research. The implementation of various detection methods, including UV-visible, fluorescence, mass spectrometry, and electrochemical detection, has expanded their analytical capabilities. Recent developments in column technology, such as core-shell particles and monolithic columns, have further improved separation efficiency. The integration of artificial intelligence and machine learning has enhanced method development and data analysis. This review discusses the fundamental principles, technological advancements, and diverse applications of HPLC and UPLC. Additionally, it discusses current challenges, emerging trends, and future prospects in chromatographic science, including green chromatography initiatives and miniaturization efforts. The continuous evolution of these techniques contributes significantly to analytical chemistry's advancement, promising even more sophisticated applications in various scientific disciplines. High-performance liquid chromatography ; Chromatography ; Resolution (logic) ; Miniaturization ; Chemistry ; Nanotechnology ; Computer science ; Process engineering ; Materials science ; Artificial intelligence ; Engineering Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ariyan Zare Ghadi | Ahmad Syauqi | Boram Gu | Hankwon Lim
Source: International Journal of Hydrogen Energy (article, 2024-07-20)
Field: Chemistry — Organic Chemistry

Highly accurate heat release rate marker detection in NH3–CH4 cofiring through machine learning and domain knowledge-based selection integration Selection (genetic algorithm) ; Domain (mathematical analysis) ; Cofiring ; Computer science ; Chemistry ; Machine learning ; Process engineering ; Engineering ; Mathematics ; Physical chemistry Chemical Thermodynamics and Molecular Structure ; Spectroscopy and Laser Applications ; Advanced Combustion Engine Technologies
Authors: Jonathan T. Willman | Joseph M. Gonzalez | Kien Nguyen-Cong | Sébastien Hamel | Vincenzo Lordi | Ivan Oleynik
Source: The Journal of Chemical Physics (article, 2024-08-28)
Field: Materials Science — Materials Chemistry

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. This study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure-temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers. Molecular dynamics ; Transferability ; Interatomic potential ; Atom (system on chip) ; Density functional theory ; Statistical physics ; Computer science ; Chemistry ; Physics ; Computational chemistry ; Parallel computing Machine Learning in Materials Science ; High-pressure geophysics and materials ; Diamond and Carbon-based Materials Research
Authors: Chunmei Zhang | Xuanyu Wu | Chengyuan Zha | Lei Li | Hao Lü | Ting Chen | Shan Zhang | Huanhuan Xing | Mengdi Zhao | Wei Sun | Wei Chen | Chunxian Guo | Chang Ming Li
Source: ACS Applied Nano Materials (article, 2024-08-16)
Field: Materials Science — Materials Chemistry

MXene/Co<sub>3</sub>O<sub>4</sub> Layered Composites for Acetone Gas Sensing Accordion-like multilayered Ti3C2Tx MXene has garnered significant attention due to its high conductivity and sensitivity, thereby being promising for acetone sensing. However, the limited stability of thin-layered MXene hinders its applications at high temperature. In this study, MXene/Co3O4 was synthesized via a self-assembly method by coating thin-layered MXene with Co3O4 nanosheets (Co3O4 NS) to enhance acetone sensing performance, especially at a high temperature of up to 200 °C. An interfacial electric field arises between Ti3C2Tx MXene and Co3O4 NS because of their different work functions. Due to a synergistic effect of the interfacial electric field and the surface-coated Co3O4, MXene/Co3O4 achieves the highest response intensity for acetone concentrations ranging from 0.1 to 1000 ppm, surpassing the individual sensitivities of Co3O4 NS and MXene alone. Remarkably, the detection limit of MXene/Co3O4 (0.1 ppm) outperforms those of Co3O4 NS and MXene. Employing the AdaBoost algorithm for machine learning, MXene/Co3O4 demonstrated 99.76% accuracy in distinguishing acetone from six other gases, holding great potential for practical gas sensing applications. The acetone adsorption energy of the MXene/Co3O4 heterojunction calculated by density functional theory is −0.97 eV, indicating easier adsorption than MXene and Co3O4 NS. This study presents a facile approach to fabricate MXene/metal oxide-based materials for acetone sensing, offering promising prospects in gas sensor development and showing universal significance for gas sensing fabrications. Acetone ; Materials science ; Adsorption ; Heterojunction ; Electric field ; Composite number ; Detection limit ; Chemical engineering ; Nanotechnology ; Composite material ; Optoelectronics ; Chemistry ; Chromatography ; Organic chemistry MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; Advanced Memory and Neural Computing
Authors: Zheyu Zhang | Hao Zhong | Stavros Avramidis | Shuangshuang Wu | Wenshu Lin | Yaoxiang Li
Source: Wood Science and Technology (article, 2024-12-06)
Field: Chemistry — Analytical Chemistry

Transfer learning for predicting wood density of different tree species: calibration transfer from portable NIR spectrometer to hyperspectral imaging Hyperspectral imaging ; Transfer of learning ; Artificial intelligence ; Calibration ; Computer science ; Spectrometer ; Artificial neural network ; Tree (set theory) ; Near-infrared spectroscopy ; Remote sensing ; Overfitting ; Biological system ; Machine learning ; Pattern recognition (psychology) ; Environmental science ; Mathematics ; Optics ; Statistics ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection