jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoxia Yang | Zhishuai Zheng | Hairong Zheng | Xiaoping Liu
Source: Russian Journal of Nondestructive Testing (article, 2024-10-01)
Field: Chemistry — Organic Chemistry

Deep Learning Method of Precious Wood Image Classification Based on Microscopic Computed Tomography Correctly identifying precious wood species is crucial for import and export trade and furniture material identification. This study utilizes nondestructive testing (microscopic computed tomography, Micro-CT) to capture microscopic images of the transverse, radial, and tangential sections of 24 precious wood species, creating a comprehensive dataset. The SLConNet deep learning model is developed, enhancing recognition accuracy through multi-scale convolution and an improved residual block structure. The experiment results show that the classification accuracy of the transverse, radial and tangential sections is 98.72, 96.75, and 95.36%, respectively, when the gain value is 0.8. The model outperforms traditional models like Alexnet, ResNet50, Inception-V3, and Xception. This research highlights the efficiency of nondestructive testing in obtaining a large number of microscopic wood images, compared to traditional anatomical methods. The SLConNet model showcases high accuracy in precision, recall, and specificity, suggesting its potential for widespread applications in wood classification. Computed tomography ; Structural material ; Artificial intelligence ; Tomography ; Deep learning ; Image (mathematics) ; Industrial computed tomography ; Materials science ; Computer science ; Computer vision ; Optics ; Metallurgy ; Radiology ; Medicine ; Physics Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Image Processing and 3D Reconstruction
Authors: Huiyuan Li | Zichen Jin | Xiaoning Jiang | Meiwen An | Jianlong Ji | Di Huang
Source: Applied Physics Letters (article, 2024-02-26)
Field: Materials Science — Polymers and Plastics

Influence of reaction cell electrodes on organic electrochemical transistors Organic electrochemical transistors (OECTs) hold great potential in various applications, including biosensing and neural network computation. Traditional “all-in-one” OECT device architecture faces the problems of unclear amplification mechanisms and complex side reactions, to name a few. The reaction cell OECT (RC-OECT) device architecture, proposed by Ting et al. [Adv. Funct. Mater. 31(19), 2010868 (2021)], effectively resolves these problems. Furthermore, in many applications of OECTs, such as high throughput (bio)sensing, an OECT device array instead of a single OECT is needed. Therefore, the size (area) of the OECT device, which represents the amount of occupied chip real estate and the integration of the device, matters. In this paper, we developed a hydrogen peroxide sensor based on the RC-OECT. We utilized an RC cathode modified by the poly(3,4-ethylenedioxythiophene) polystyrene sulfonate film and a Ag/AgCl OECT gate electrode for facilitating device integration, varied RC anode area, and achieved optimization of at least one of the two parameters, sensitivity and lower limit of detection (LLoD), in conjunction with the level of integration of the device. Multiple quantitative sensitivity metrics have been adopted in this work. We also evaluated the correlation between RC anode area and LLoD. In addition, a mechanistic analysis of the RC-OECT device structure, in terms of faradic and non-faradaic currents, was carried out to illustrate the interplay between sensing performance and the electrode area of the reaction cell. This mechanistic analysis provides insights for miniaturizing OECT devices with the RC-OECT architecture. Nanotechnology ; Conductive polymer ; Chemistry ; Electrode ; Materials science Conducting polymers and applications ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Mahmudul Hoque Mahmud | Anika Tun Naziba | Fahmidul Islam | Manika Tun Nafisa | Atiqur Rahman | Mohammad Nasir Uddin
Source:  (article, 2024-12-07)
Field: Chemistry — Analytical Chemistry

Enhanced Breast Cancer Detection Using PCF-SPR Sensor: A Machine Learning Approach This paper presents a highly sensitive biosensor using photonic crystal fiber (PCF) for breast cancer cell detection, leveraging the surface plasmon resonance (SPR) effect with a hybrid gold and TiO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> layer. Simulations using the finite element method show the sensor can distinguish between normal and cancerous cells, achieving a maximum wavelength sensitivity of 7142.86 nm/RIU for Michigan Cancer Foundation-7 (MCF-7) breast cancer cells. The sensor offers a rapid, efficient and cost-effective solution for breast cancer detection. Additionally, optimization of parameters like the gold and TiO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> film, air hole spacing and analyte refractive index using supervised machine learning achieved an R2 accuracy of 0.8947 and a low mean absolute error (MAE) of 0.0014. Computer science ; Cancer ; Cancer detection ; Breast cancer ; Artificial intelligence ; Medicine ; Internal medicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Jing Zhang | Yanru Zhao | Yan Jin | Xin Yin | Zizheng Ji | Haoran Zhang | Xiaosong Fu
Source: Computers and Electronics in Agriculture (article, 2024-09-03)
Field: Chemistry — Analytical Chemistry

Spiking-LSTM: A novel hyperspectral image segmentation network for Sclerotinia detection Hyperspectral imaging ; Artificial intelligence ; Segmentation ; Computer vision ; Image segmentation ; Pattern recognition (psychology) ; Computer science ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Shanliang Ma | Jiarui Gu | Jie Wang | Yang Shao | Zengqi Zhang | Xiaoming Liu
Source: Journal of Industrial and Engineering Chemistry (article, 2024-07-06)
Field: Materials Science — Materials Chemistry

In-depth insight into the driving factors of the compressive strength development of MKPC based on interpretable machine learning methods Compressive strength ; Artificial intelligence ; Engineering ; Computer science ; Materials science ; Composite material Magnesium Oxide Properties and Applications ; Ferroelectric and Piezoelectric Materials ; Advanced ceramic materials synthesis
Authors: Okechukwu D. Onukwuli | Ikechukwu A. Nnanwube | Fidelis Obidi Ochili | John Ifeanyi Obibuenyi
Source: Heliyon (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

Assessing the efficiency of danacid as corrosion inhibitor for aluminium in HCl medium: Experimental, theoretical and optimization studies The present study examined the corrosion protection of aluminium in 1M HCl by deploying expired danacid, with techniques such as gravimetric, electrochemical, and density functional theory (DFT). Inhibitor characterization was executed with Fourier transform infrared (FTIR) spectroscopy and gas chromatography mass spectrometry (GC-MS), which was supplemented by optimization of parameters with response surface methodology. The results of gravimetric study indicates that the inhibition efficiency (IE) rose with rise in danacid concentration and reduced with rise in temperature. FTIR results shows the presence of heteroatoms in the inhibitor while further analysis by GC-MS showed the presence of heteroatom-carrying compounds such as menthyl acetate, eicosyl vinyl ester, carbonic acid, dotriacontane, n-hexadecanoic acid, among others. The activation energy value obtained from this work suggest that the inhibitor molecule's adsorption on the surface of aluminium follows physical adsorption mechanism. Potentiodynamic polarization (PDP) results portrays the inhibitor as a mixed-type while the results of electrochemical impedance spectroscopy (EIS) tests gave an inhibition efficiency of 95.05 %. RSM results show optimum inhibition efficiency of 93.57 % while artificial neural network (ANN) gave improved predicted values. The DFT results further show that the danacid molecules could effectively inhibit alumina corrosion in aggressive media. Hence, danacid proved to be a viable inhibitor for aluminium corrosion. Dielectric spectroscopy ; Gravimetric analysis ; Fourier transform infrared spectroscopy ; Corrosion ; Adsorption ; Corrosion inhibitor ; Aluminium ; Chemistry ; Response surface methodology ; Central composite design ; Inorganic chemistry ; Electrochemistry ; Nuclear chemistry ; Chemical engineering ; Chromatography ; Organic chemistry ; Physical chemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Tran Doan Huan | Hieu‐Chi Dam | Christopher Künneth | Vu Ngoc Tuoc | Hiori Kino
Source: Chemistry of Materials (article, 2024-11-08)
Field: Materials Science — Materials Chemistry

Superconductor Discovery in the Emerging Paradigm of Materials Informatics The last two decades have witnessed a tremendous number of computational predictions of hydride-based (phonon-mediated) superconductors, mostly at extremely high pressures, i.e., hundreds of GPa. These discoveries were heavily driven by Migdal-Éliashberg theory (and its first-principles computational implementations) for electron-phonon interactions, the key concept of phonon-mediated superconductivity. Dozens of predictions were experimentally synthesized and characterized, triggering not only enormous excitement in the community but also some debates. In this Article, we review the computational-driven discoveries and the recent developments in the field from various essential aspects, including the theoretical, computational, and, specifically, artificial intelligence (AI)/machine learning (ML) based approaches emerging within the paradigm of materials informatics. While challenges and critical gaps can be found in all of these approaches, AI/ML efforts specifically remain in its infant stage for good reasons. However, opportunities exist when these approaches can be further developed and integrated in concerted efforts, in which AI/ML approaches could play more important roles. Informatics ; Nanotechnology ; Data science ; Materials informatics ; Materials science ; Superconductivity ; Engineering ethics ; Engineering physics ; Computer science ; Health informatics ; Engineering ; Physics ; Political science ; Condensed matter physics ; Electrical engineering ; Engineering informatics ; Health care Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Alireza Mohammad Karim
Source: Fluids (article, 2024-09-26)
Field: Materials Science — Surfaces, Coatings and Films

Droplet Contact Line Dynamics after Impact on Solid Surface: Future Perspectives in Healthcare and Medicine The physics of the moving contact line of an impacting droplet is widely applied in a variety of domains in rapidly advancing healthcare technology and medicine. The behavior of the dynamic contact line after impact of a biologically active droplet on a complex material surface involves complicated solid–liquid and liquid–gas interfacial interactions. Therefore, a deep understanding of such complex droplet contact line dynamics by applying the current physical models and state-of-the-art nanotechnology and artificial neural networks can be one of the ongoing promising interests in the field of interfacial physics. This review provides an overview of several scientific aspects of contact line dynamics of an impacting droplet and its influence on the current developed healthcare technology and medicine. Firstly, the potential applications in modern healthcare and personalized medicine are listed and discussed. Secondly, the theory of the moving contact line and the fundamental physical parameters related to the motion of impacting droplets are introduced. Afterwards, the current physical models of moving contact line dynamics are critically explained by emphasizing their limitations. Finally, current concerns and obstacles are summarized, and future perspectives and research directions are outlined to address poorly understood and conflicting issues. Solid surface ; Dynamics (music) ; Health care ; Line (geometry) ; Nanotechnology ; Computer science ; Materials science ; Physics ; Chemical physics ; Mathematics Surface Modification and Superhydrophobicity ; Nanomaterials and Printing Technologies ; Fluid Dynamics and Heat Transfer
Authors: Malvika Viswanathan | Leqi Yin | Yashwant Kurmi | Aqeela Afzal | Zhongliang Zu
Source: NMR in Biomedicine (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three-point method, and multiple-pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time. Synthetic data ; Magnetization transfer ; Computer science ; Transfer of learning ; Artificial intelligence ; Experimental data ; Biological system ; Pattern recognition (psychology) ; Magnetic resonance imaging ; Mathematics ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Chen Chen | Xiushan Wang | Pu Jing | Shunshan Jiao
Source: Journal of Food Engineering (article, 2024-05-30)
Field: Chemistry — Analytical Chemistry

Kinetic analysis and back propagation neural network model for shelf-life estimation of stabilized rice bran Bran ; Shelf life ; Artificial neural network ; Biological system ; Backpropagation ; Estimation ; Kinetic energy ; Environmental science ; Food science ; Chemistry ; Computer science ; Engineering ; Artificial intelligence ; Biology ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses
Authors: Jonathan W. Kaufman | Connor Wyckoff | Benjamin Lam | Katherine A. Acord | Tyriek Craigs | Christopher T. Kassner | Ashley Hilmas | Lisa M. Rueschhoff
Source: Additive manufacturing (article, 2024-06-01)
Field: Materials Science — Ceramics and Composites

Analysis of fiber-reinforced silicon carbide formed via material extrusion Materials science ; Composite material ; Extrusion ; Silicon carbide ; Fiber ; Flexural strength ; Rheology ; Thermal shock ; Extrusion moulding ; Bending Advanced ceramic materials synthesis ; Additive Manufacturing and 3D Printing Technologies ; Advanced materials and composites
Authors: Minsu Chong | Tae Gyu Rhee | Yeong Gwang Khim | Min‐Hyoung Jung | Young‐Min Kim | Hu Young Jeong | Heung‐Sik Kim | Young Jun Chang | Hyuk Jin Kim
Source: Applied Surface Science (article, 2024-06-18)
Field: Materials Science — Materials Chemistry

Machine-learning-empowered identification of initial growth modes for 2D transition metal dichalcogenide thin films Transition metal ; Thin film ; Materials science ; Identification (biology) ; Nanotechnology ; Condensed matter physics ; Chemical physics ; Optoelectronics ; Chemistry ; Physics ; Catalysis 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Memory and Neural Computing
Authors: Xiuying Liu | Jun-Ru Yu | Heng-Nan Deng
Source: Horticulturae (article, 2024-05-13)
Field: Chemistry — Analytical Chemistry

Non-Destructive Prediction of Anthocyanin Content of Rosa chinensis Petals Using Digital Images and Machine Learning Algorithms Anthocyanins are widely found in plants and have significant functions. The accurate detection and quantitative assessment of anthocyanin content are essential to assess its functions. The anthocyanin content in plant tissues is typically quantified by wet chemistry and spectroscopic techniques. However, these methods are time-consuming, labor-intensive, tedious, expensive, destructive, or require expensive equipment. Digital photography is a fast, economical, efficient, reliable, and non-invasive method for estimating plant pigment content. This study examined the anthocyanin content of Rosa chinensis petals using digital images, a back-propagation neural network (BPNN), and the random forest (RF) algorithm. The objective was to determine whether using RGB indices and BPNN and RF algorithms to accurately predict the anthocyanin content of R. chinensis petals is feasible. The anthocyanin content ranged from 0.832 to 4.549 µmol g−1 for 168 samples. Most RGB indices were strongly correlated with the anthocyanin content. The coefficient of determination (R2) and the ratio of performance to deviation (RPD) of the BPNN and RF models exceeded 0.75 and 2.00, respectively, indicating the high accuracy of both models in predicting the anthocyanin content of R. chinensis petals using RGB indices. The RF model had higher R2 and RPD values, and lower root mean square error (RMSE) and mean absolute error (MAE) values than the BPNN, indicating that it outperformed the BPNN model. This study provides an alternative method for determining the anthocyanin content of flowers. Anthocyanin ; Petal ; RGB color model ; Pigment ; Content (measure theory) ; Mean squared error ; Mathematics ; Artificial intelligence ; Botany ; Biological system ; Computer science ; Food science ; Chemistry ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Plant Gene Expression Analysis
Authors: Cong He | Xiaogang Jiang | Yande Liu
Source: Journal of Food Process Engineering (article, 2024-03-01)
Field: Chemistry — Analytical Chemistry

Research on the upgrade and maintenance method of apple soluble solids content models Abstract In this paper, 3 years (2020, 2021, and 2022) of Bingtangxin apples were used to study the upgrade and maintenance method of apple soluble solids content (SSC) model using near infrared (NIR) spectroscopy. Modeling by partial least squares (PLS) and one‐dimensional convolutional neural network (1D‐CNN) algorithm. The “base model” is built with apples in 2020 and then upgrade and maintain it. The upgraded model is used to predict the sample in 2021 and 2022. Three methods were used to upgrade and maintenance the model, which are based on updated data, direct correction‐based method, and similar band‐based method. Among the three upgrade methods, the model maintenance method using updated data had the greatest improvement in prediction correlation coefficients ( R p ) in both years, and both obtained good predictions. The use of direct correction resulted in the greatest reduction in the root mean square error of prediction (RMSEP) in both years. However, the R p of the model is not improved much by using the direct correction method. The model maintenance method using similar bands combined with different modeling methods also gives good prediction results. Finally, after comparison, the model maintenance method of selecting similar bands is preferred. In the case of limited improvement in the effect of using the similar band method, the model is maintained using the method of adding updated data. The investigation of model upgrade and maintenance methods can reduce human and material consumption and make apple SSC models more versatile. Practical applications NIR technology is the first choice for online fruit quality inspection, but its inspection process requires constant upgrading and maintenance with models. The quality of apples varies from year to year, and the model built with samples from a single year does not work well in predicting samples from other years, resulting in the need to upgrade and maintain the fruit quality detection model. However, sometimes, although a large amount of experimental material is lost, it still results in a generic calibration model that is too cumbersome and leads to poor prediction results. At this time, finding an effective model upgrade and maintenance method can both reduce the sample consumption and make the model prediction accuracy improve, and also reduce the model complexity and make the maintained model more robust. Upgrade ; Content (measure theory) ; Chemistry ; Food science ; Computer science ; Process engineering ; Mathematics ; Engineering ; Operating system Spectroscopy and Chemometric Analyses ; Food Quality and Safety Studies ; Fermentation and Sensory Analysis