jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Małgorzata Kowalczyk
Source: Materials (article, 2024-01-19)
Field: Materials Science — Materials Chemistry

Analysis of Cutting Forces and Geometric Surface Structures in the Milling of NiTi Alloy This paper presents a study of the total cutting force used and selected parameters of the geometric structure of the surface (e.g., <i>Sa</i>, <i>Sz</i>) during the end milling process of NiTi alloy. The input parameters included are cutting speed (<i>v<sub>c</sub></i>), feed per tooth (<i>f<sub>z</sub></i>), and radial depth of cut (<i>a<sub>e</sub></i>). A Box-Behnken experimental design was employed to conduct the research. The obtained experimental results were utilized within the framework of a response surface methodology (RSM) to develop mathematical and statistical models capable of predicting cutting force components and selected 3D surface parameters. These models provide valuable insights into the relationships between the cutting parameters and the output variables, facilitating the optimization of the NiTi alloy milling process. The findings of this study contribute to a better understanding of the behavior of NiTi alloy during the milling process and offer information for process optimization. By employing a Box-Behnken experimental design, it was possible to investigate the effects of different parameter combinations on the components of total cutting force and selected 3D surface parameters according to ISO 25178, thus aiding in the identification of optimal milling conditions to achieve desired outcomes in the machining of NiTi alloy. Nickel titanium ; Response surface methodology ; Materials science ; Alloy ; Machining ; Process (computing) ; Box–Behnken design ; Mechanical engineering ; Titanium alloy ; Process variable ; Design of experiments ; Metallurgy ; Shape-memory alloy ; Computer science ; Mathematics ; Engineering ; Statistics Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Material Selection and Properties
Authors: Sehui Chang | Duk‐Jo Kong | Young Min Song
Source: Nanophotonics (review, 2024-02-29)
Field: Materials Science — Materials Chemistry

Advanced visual components inspired by animal eyes Artificial vision systems pervade our daily lives as a foremost sensing apparatus in various digital technologies, from smartphones to autonomous cars and robotics. The broad range of applications for conventional vision systems requires facile adaptation under extreme and dynamic visual environments. However, these current needs have complicated individual visual components for high-quality image acquisition and processing, which indeed leads to a decline in efficiency in the overall system. Here, we review recent advancements in visual components for high-performance visual processing based on strategies of biological eyes that execute diverse imaging functionalities and sophisticated visual processes with simple and concise ocular structures. This review first covers the structures and functions of biological eyes (i.e., single-lens eyes and compound eyes), which contain micro-optic components and nanophotonic structures. After that, we focus on their inspirations in imaging optics/photonics, light-trapping and filtering components, and retinomorphic devices. We discuss the remaining challenges and notable biological structures waiting to be implemented. Computer science ; Nanotechnology ; Nanomaterials ; Human–computer interaction ; Materials science Photochromic and Fluorescence Chemistry ; Advanced Memory and Neural Computing ; Photoreceptor and optogenetics research
Authors: Madison H. McMinn | Ximin Hu | Katherine Poisson | Phillip Berger | Paola Pimentel | Xinwen Zhang | Pranali Ashara | Ella L. Greenfield | Jessica Eig | Zhenyu Tian
Source: Environmental Science Processes & Impacts (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Emerging investigator series: in-depth chemical profiling of tire and artificial turf crumb rubber: aging, transformation products, and transport pathways Crumb rubber generated from end-of-life tires (ELTs) poses a threat to environmental and human health based on its widespread use. Of particular concern is the use of ELT crumb rubber as infill for artificial turf fields, as people are unknowingly exposed to complex mixtures of chemicals when playing on these fields. Additionally, there is concern regarding transport of rubber-related chemicals from artificial turf into the environment. However, existing knowledge does not fully elucidate the chemical profile, transformation products, and transport pathways of artificial turf crumb rubber across different ages. To address these knowledge gaps, we utilized a multi-faceted approach that consisted of targeted quantitation, chemical profiling, and suspect screening <i>via</i> ultra-high performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). We collected and processed 3 tire and 11 artificial turf crumb rubber samples <i>via</i> solvent extraction, leaching, and a bioaccessibility-based extraction. Nineteen rubber-derived chemicals were quantified using parallel reaction monitoring and isotope dilution techniques. In solvent extracts, the most abundant analytes were 1,3-diphenylguanidine (0.18-1200 μg g<sup>-1</sup>), <i>N</i>-(1,3-dimethylbutyl)-<i>N</i>'-phenyl-<i>p</i>-phenylenediamine (6PPD, 0.16-720 μg g<sup>-1</sup>), 2-mercaptobenzothiazole (0.47-140 μg g<sup>-1</sup>), and benzothiazole (0.84-150 μg g<sup>-1</sup>). Chemical profiling assessed changes in sample diversity, abundance, polarity, and molecular mass. Suspect screening identified 81 compounds with different confidence levels (16 at level 1, 53 with level 2, 7 at level 3, and 5 at level 4). The formation rate of transformation products and clustering analysis results identified time-based trends in artificial turf field samples. We found that the first two years of aging may be critical for the potential environmental impact of artificial turf fields. Our analysis provides insight into the chemical complexity of artificial turf crumb rubber samples ranging from 0-14 years in age. Profiling (computer programming) ; Crumb rubber ; Natural rubber ; Environmental science ; Forensic engineering ; Engineering ; Computer science ; Materials science ; Composite material Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Risk and Safety Analysis
Authors: Sayan Pal | Arun Pankajakshan | Maximilian O. Besenhard | Nicholas Snead | Juan Almeida | Shorooq Abukhamees | Duncan Craig | Federico Galvanin | Asterios Gavriilidis | Luca Mazzei
Source: Organic Process Research & Development (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Automated Continuous Crystallization Platform with Real-Time Particle Size Analysis via Laser Diffraction The fourth industrial revolution is gaining momentum in the pharmaceutical industry. However, particulate processes and suspension handling remain big challenges for automation and the implementation of real-time particle size analysis. Moreover, the development of antisolvent crystallization processes is often limited by the associated time-intensive experimental screenings. This work demonstrates a fully automated modular crystallization platform that overcomes these bottlenecks. The system combines automated crystallization, sample preparation, and immediate crystal size analysis via online laser diffraction (LD) and provides a technology for rapidly screening crystallization process parameters and crystallizer design spaces with minimal experimental effort. During the LD measurements, to avoid multiple scattering events, crystal suspension samples are diluted automatically. Multiple software tools, i.e., LabVIEW, Python, and PharmaMV, and logic algorithms are integrated in the platform to facilitate automated control of all the sensors and equipment, enabling fully automated operation. A customized graphical user interface is provided to operate the crystallization platform automatically and to visualize the measured crystal size and the crystal size distribution of the suspension. Antisolvent crystallization of ibuprofen, with ethanol as solvent and water with Soluplus (an additive) as antisolvent, is used as a case study. The platform is demonstrated for antisolvent crystallization of small ibuprofen crystals in a confined impinging jet crystallizer, performing automated preplanned user-defined experiments with online LD analysis. Crystallization ; Particle size ; Materials science ; Diffraction ; Particle (ecology) ; Laser ; Nanotechnology ; Chemical engineering ; Computer science ; Process engineering ; Optics ; Physics ; Engineering ; Geology Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Minerals Flotation and Separation Techniques
Authors: Stefano Rodinò | Marco Siciliano | Elio Matteo Curcio | Francesco Lamonaca | Domenico Luca Carnì | Giuseppe Carbone | Carmine Maletta
Source: Experimental Mechanics (article, 2024-02-21)
Field: Materials Science — Materials Chemistry

Development of an Automated Experimental System for Thermomechanical and Electrical Characterization of NiTi Shape Memory Alloys Abstract Background Comprehensive datasets quantifying the coupled thermo-mechanical and electrical properties of shape memory alloys (SMAs) are lacking, as are standardized techniques for robust characterization. This hampers accurate modeling and design of SMA-based components. Objective: This work develops an automated experimental system to enable simultaneous measurement of stress-strain-temperature behavior and electrical resistivity evolution in NiTi SMA wires under controlled stress conditions. Methods: Customized test frames apply precise mechanical stresses while allowing for in situ electrical measurements and infrared imaging during complete thermal cycling protocols. Specialized instrumentation including a Keithley 2002 multimeter, Agilent E3631A programmable power supply, and FLIR A615 thermal camera are integrated with LabVIEW-based software routines for complete automation of the characterization process. Rigorous metrology principles are implemented throughout the measurement procedure to improve accuracy, repeatability, and consistency compared to prior manual techniques. Results: Extensive datasets are generated which reveal pronounced stress-dependencies in key SMA material parameters including transformation temperatures, recoverable strain, and electrical resistivity. A 3D regression model describes the comprehensive relationship between resistivity, temperature, and applied stress across the entire characterization domain. Conclusions: The automated measurement framework and methodology establishes a foundation for high-fidelity, reliable acquisition of coupled SMA property data. This will enable more accurate modeling and design of components and systems incorporating SMA actuation or sensing functions. SMA* ; Shape-memory alloy ; Nickel titanium ; Characterization (materials science) ; Solid mechanics ; Automation ; Computer science ; Metrology ; Mechanical engineering ; Materials science ; Consistency (knowledge bases) ; Data acquisition ; Engineering ; Artificial intelligence Shape Memory Alloy Transformations
Authors: Yuriy Sinchuk | Yanneck Wielhorski | Arturo Mendoza | Samy Blusseau | Santiago Velasco-Forero
Source: Composites Part A Applied Science and Manufacturing (article, 2024-08-06)
Field: Materials Science — Polymers and Plastics

Automatic yarn path extraction of large 3D interlock woven fabrics with confidence estimation Yarn ; Weighting ; Artificial intelligence ; Computer science ; Hyperparameter ; Sampling (signal processing) ; Path (computing) ; Tracking (education) ; Pattern recognition (psychology) ; Computer vision ; Materials science ; Filter (signal processing) ; Composite material Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Industrial Vision Systems and Defect Detection
Authors: William P. Carbery | Camille A. Farfan | Ronald Ulbricht | Daniel B. Turner
Source: Nature Communications (article, 2024-10-05)
Field: Materials Science — Materials Chemistry

The phonon-modulated Jahn–Teller distortion of the nitrogen vacancy center in diamond The negatively charged nitrogen vacancy (NV) center in diamond is an optically accessible material defect with a unique combination of spin and optical properties that has attracted interest in quantum-information sciences and as a design candidate for nanoscale quantum sensors. Here, we present time-resolved nonlinear optical spectroscopy measurements, conducted with ultrabroadband laser pulses, that reveal strong modulation of the excited-state by the longitudinal optical (LO) phonon of the diamond lattice. The LO phonon and its overtones geometrically distort neighboring NV centers, driving long lived (3.5 ps) excited state relaxation of coupled NV centers after the initial excitation and ultrafast (<150 fs) decay of the Jahn-Teller distortion. These observations elevate the LO phonon to an important tuning mode of the Jahn-Teller conical intersection and help resolve previous spectroscopy experiments that noted longer-lived excited-state dynamics. Excited state ; Phonon ; Conical intersection ; Diamond ; Jahn–Teller effect ; Spectroscopy ; Condensed matter physics ; Raman spectroscopy ; Nitrogen-vacancy center ; Excitation ; Relaxation (psychology) ; Atomic physics ; Physics ; Materials science ; Molecular physics ; Optics ; Quantum mechanics Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Advanced Fiber Laser Technologies
Authors: Asitha Kottahachchi Kankanamge Don | Ibrahim Khalil
Source: ACM Transactions on Quantum Computing (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework In the evolving landscape of data privacy regulations, the challenge of providing extensive data for robust deep classification models arises. The accuracy of these models relies on the amount of training data, due to the multitude of parameters that require tuning. Unfortunately, obtaining such ample data proves challenging, particularly in domains like medical applications, where there is a pressing need for robust models for early disease detection but a shortage of labeled data. Nevertheless, the classical supervised contrastive learning models, have shown the potential to address this challenge up to a certain limit, by utilizing deep encoder models. However, recent advancements in quantum machine learning enable the extraction of meaningful representations from extremely limited and simple data. Thus, replacing classical counterparts in classical or hybrid quantum-classical supervised contrastive models enhances feature learning capability with minimal data. Therefore, this work proposes the Q-SupCon model, a fully quantum-powered supervised contrastive learning model comprising a quantum data augmentation circuit, quantum encoder, quantum projection head, and quantum variational classifier, enabling efficient image classification with minimal labeled data. Furthermore, the novel model attains 80%, 60%, and 80% test accuracy on MNIST, KMNIST, and FMNIST datasets, marking a significant advancement in addressing the data scarcity challenge. Computer science ; Artificial intelligence ; MNIST database ; Deep learning ; Supervised learning ; Machine learning ; Quantum ; Autoencoder ; Artificial neural network Electronic and Structural Properties of Oxides ; Stochastic Gradient Optimization Techniques ; Adversarial Robustness in Machine Learning
Authors: Irvin Séchepée | Clara Dubray | Vincent Velay | Hiroaki Matsumoto
Source: Journal of Alloys and Compounds (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Effects of grain size and β fraction on the deformation modes of a Ti-6Al-2Sn-4Zr-2Mo-Si alloy with equiaxed (α + β) microstructures: Slip trace analysis and multiscale simulation of polycrystal plasticity Equiaxed crystals ; Materials science ; Grain size ; Slip (aerodynamics) ; Microstructure ; Ultimate tensile strength ; Plasticity ; Volume fraction ; Metallurgy ; Critical resolved shear stress ; Alloy ; Titanium alloy ; Deformation mechanism ; Composite material ; Thermodynamics ; Shear rate Titanium Alloys Microstructure and Properties ; Microstructure and mechanical properties ; Magnesium Alloys: Properties and Applications
Authors: Wei Dong | Yuichi Sugai | Yongjun Wang | Hemeng Zhang | Xiaoming Zhang | Kyuro Sasaki
Source: ACS Omega (article, 2024-04-26)
Field: Chemistry — Spectroscopy

Experimental Study on Enhanced Methane Detection Using an MEMS-Pyroelectric Sensor Integrated with a Wavelet Algorithm An optical sensing approach that balances portability with cost efficiency has been designed for the reliable monitoring of fugitive methane (CH<sub>4</sub>) emissions. Employing a LiTaO<sub>3</sub>-based pyroelectric detector integrated with micro-electro-mechanical systems and a broad infrared source, the developed gas sensor adeptly measured CH<sub>4</sub> concentrations with a low limit of detection of about 5.6 ppm<sub>v</sub> and showed rapid response times with <i>t</i><sub>90</sub> consistently under 3 s. Notably, the novelty of our method lies in its precise control and reduction of CH<sub>4</sub> levels, enhanced by wavelet denoising. This technique, optimized through meticulous grid search, effectively mitigated noise interference noticeable at CH<sub>4</sub> levels below 10 ppm<sub>v</sub>. Postdenoising, nonlinear regression analyses based on the modified Beer-Lambert equation returned <i>R</i><sup>2</sup> values of 0.985 and 0.982 for the training and validation sets, respectively. In conclusion, this gas sensor has been shown to be able to meet the requirements for early warning of CH<sub>4</sub> leakage on the surface in various carbon capture, utilization, and storage projects such as enhanced oil or gas recovery projects using CO<sub>2</sub> injection. Methane ; Microelectromechanical systems ; Wavelet ; Pyroelectricity ; Materials science ; Computer science ; Algorithm ; Nanotechnology ; Artificial intelligence ; Optoelectronics ; Chemistry Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: Pappu Kumar Yadav | Thomas F. Burks | Jianwei Qin | Moon J. Kim | Quentin Frederick | Megan M. Dewdney | Mark A. Ritenour
Source: Journal of Applied Remote Sensing (article, 2024-02-08)
Field: Chemistry — Analytical Chemistry

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers Citrus black spot (CBS) is a fungal disease caused by Phyllosticta citricarpa that poses a quarantine threat and can restrict market access to fruits. It manifests as lesions on the fruit surface and can result in premature fruit drops, leading to reduced yield. Another significant disease affecting citrus is canker, which is caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri); it causes economic losses for growers due to fruit drops and blemishes. Early detection and management of groves infected with CBS or canker through fruit and leaf inspection can greatly benefit the Florida citrus industry. However, manual inspection and classification of disease symptoms on fruits or leaves are labor-intensive and time-consuming processes. Therefore, there is a need to develop a computer vision system capable of autonomously classifying fruits and leaves, expediting disease management in the groves. This paper aims to demonstrate the effectiveness of convolutional neural network (CNN) generated features and machine learning (ML) classifiers for detecting CBS infected fruits and leaves with canker symptoms. A custom shallow CNN with radial basis function support vector machine (RBF SVM) achieved an overall accuracy of 92.1% for classifying fruits with CBS and four other conditions (greasy spot, melanose, wind scar, and marketable), and a custom Visual Geometry Group 16 (VGG16) with the RBF SVM classified leaves with canker and four other conditions (control, greasy spot, melanoses, and scab) at an overall accuracy of 93%. These preliminary findings demonstrate the potential of utilizing hyperspectral imaging (HSI) systems for automated classification of citrus fruit and leaf diseases using shallow and deep CNN-generated features, along with ML classifiers. Hyperspectral imaging ; Convolutional neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Contextual image classification ; Artificial neural network ; Machine learning ; Remote sensing ; Image (mathematics) ; Geology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Tao Li | Hong Cao | Jiahua He | Sheng‐You Huang
Source: Nature Communications (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps Cryo-electron microscopy (cryo-EM) is one of the most powerful experimental methods for macromolecular structure determination. However, accurate DNA/RNA structure modeling from cryo-EM maps is still challenging especially for protein-DNA/RNA or multi-chain DNA/RNA complexes. Here we propose a deep learning-based method for accurate de novo structure determination of DNA/RNA from cryo-EM maps at <5 Å resolutions, which is referred to as EM2NA. EM2NA is extensively evaluated on a diverse test set of 50 experimental maps at 2.0-5.0 Å resolutions, and compared with state-of-the-art methods including CryoREAD, ModelAngelo, and phenix.map_to_model. On average, EM2NA achieves a residue coverage of 83.15%, C4' RMSD of 1.06 Å, and sequence recall of 46.86%, which outperforms the existing methods. Moreover, EM2NA is applied to build the DNA/RNA structures with 10 to 5347 nt from an EMDB-wide data set of 263 unmodeled raw maps, demonstrating its ability in the blind model building of DNA/RNA from cryo-EM maps. EM2NA is fast and can normally build a DNA/RNA structure of <500 nt within 10 minutes. Nucleic acid ; Computational biology ; Computer science ; Biology ; Genetics Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Genetics, Bioinformatics, and Biomedical Research