jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: H Wang | Zhongze Han | Xiaoshuang Xiong | Xuewei Song | Chen Shen
Source: Machines (article, 2024-05-02)
Field: Materials Science — Polymers and Plastics

Enhancing Yarn Quality Wavelength Spectrogram Analysis: A Semi-Supervised Anomaly Detection Approach with Convolutional Autoencoder Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails the examination of component defects through Wavelet Spectrogram Analysis (WSA). Conventional detection techniques heavily rely on manual experience and lack generality. To address this limitation, this current study leverages machine learning technology to formulate a semi-supervised anomaly detection approach employing a convolutional autoencoder. This method trains deep neural networks with normal data and employs the reconstruction mode of a convolutional autoencoder in conjunction with Kernel Density Estimation (KDE) to determine the optimal threshold for anomaly detection. This facilitates the differentiation between normal and abnormal operational modes without the necessity for extensive labeled fault data. Experimental results from two sets of industrial data validate the robustness of the proposed methodology. In comparison to conventional Autoencoder and prevalent machine learning techniques, the proposed approach demonstrates superior performance across evaluation metrics such as Accuracy, Recall, Area Under the Curve (AUC), and F1-score, thereby affirming the feasibility of the suggested model. Autoencoder ; Spectrogram ; Anomaly detection ; Pattern recognition (psychology) ; Artificial intelligence ; Anomaly (physics) ; Computer science ; Convolutional neural network ; Quality (philosophy) ; Deep learning ; Physics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Video Surveillance and Tracking Methods
Authors: Isaac Y. Miranda-Valdez | Tero Mäkinen | Sebastian Coffeng | Axel Päivänsalo | Luisa Jannuzzi | Leevi Viitanen | Juha Koivisto | Mikko J. Alava
Source: Materials Horizons (article, 2024-12-06)
Field: Materials Science — Biomaterials

Accelerated design of solid bio-based foams for plastics substitutes Biobased substitutes for plastics are a future necessity. However, the design of substitute materials with similar or improved properties is a known challenge. Here we show an example case of optimizing the mechanical properties of a fully biobased methylcellulose-fiber composite material. We tackle the process-structure-property paradigm using Bayesian optimization with Gaussian process regression to map the processed material composition to the final mechanical properties of new bio-based solid foams. We exploited the fast-to-measure rheological properties of the liquid biofiber suspensions processed into foams to show how these collapse to an auxiliary sub-space of low dimensionality for design. The optimal compositions for methylcellulose-fiber foams shown here correspond to two distinct cases: high methylcellulose content for the formation of strong closed-cell foams, and high fiber contents with approximately equal amounts of methylcellulose for the formation of methylcellulose-bound fiber networks. The novel approach is transferable to other biobased foam compositions with different fibers and additives. This new approach allows the rational design of bio-based plastics replacements by encompassing desired final material properties, descriptors of materials processed, and knowledge of the process. Materials science ; Nanotechnology ; Polymer science ; Composite material biodegradable polymer synthesis and properties
Authors: Robin Delbart | Colin Robert | Thuy Quynh Truong Hoang | Francisca Martínez-Hergueta
Source: Composites Part A Applied Science and Manufacturing (article, 2024-07-26)
Field: Materials Science — Polymers and Plastics

Development of an electro-thermo-mechanical 4D printed multi-shape smart actuator: Experiments and simulation This investigation presents an experimental and numerical approach to developing 4D printed multi-shape actuators with an integrated electrical self-triggering system. It employs a multifunctional shape memory carbon black Polylactic Acid (PLA) layer embedded within rubbery thermoplastic polyurethane (TPU). Notably, these thermo-responsive actuators are programmed and triggered using Joule’s effect, eliminating the need for an external heat source and enabling precise control of temperature gradients. Two distinct motion mechanisms are employed: the differences in thermal expansion coefficient between the PLA/TPU polymers below the glass transition temperature, and the shape memory effect of the PLA layer above the glass transition temperature. As a result, a diverse range of motion responses can be achieved by adjusting the potential differences. A coupled electro-thermo-mechanical finite element model is used to gain further insight into the motion mechanisms, offering predictive capabilities beyond the ones reported by previous models. The developed technology provides enhanced actuation capabilities to conventional bi-shape SMP actuators, offering a versatile range of bending cycles. Furthermore, Joule’s effect enables the implementation of closed-loop control systems, which is essential for developing autonomous robotic systems. Materials science ; Actuator ; Shape-memory polymer ; Thermoplastic polyurethane ; Polylactic acid ; Shape-memory alloy ; Joule heating ; Smart material ; Composite material ; Bending ; Glass transition ; Finite element method ; Mechanical engineering ; Thermal ; Polymer ; Computer science ; Structural engineering ; Elastomer Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Xiuying Xu | Changhao Fu | Yingying Gao | Kang Ye | Wei Zhang
Source: Agriculture (article, 2024-03-13)
Field: Chemistry — Analytical Chemistry

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm−1 was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10−8 were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis. Identification (biology) ; Zea mays ; Spectroscopy ; Environmental science ; Remote sensing ; Agronomy ; Biological system ; Botany ; Geography ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Water Quality Monitoring and Analysis
Authors: Hung K. Nguyen | Bede Pittenger | K. Nakajima
Source: Nano Letters (article, 2024-07-17)
Field: Materials Science — Materials Chemistry

Mapping the Nanoscale Heterogeneous Responses in the Dynamic Acceleration of Deformed Polymer Glasses Understanding the evolution of local structure and mobility of disordered glassy materials induced by external stress is critical in modeling their mechanical deformation in the nonlinear regime. Several techniques have shown acceleration of molecular mobility of various amorphous glasses under macroscopic tensile deformation, but it remains a major challenge to visualize such a relationship at the nanoscale. Here, we employ a new approach based on atomic force microscopy in nanorheology mode for quantifying the local dynamic responses of a polymer glass induced by nanoscale compression. By increasing the compression level from linear elastic to plastic deformation, we observe an increase in the mechanical loss tangent (tan δ), evidencing the enhancement of polymer mobility induced by large stress. Notably, tan δ images directly reveal the preferential effect of the large compression on the dynamic acceleration of nanoscale heterogeneities with initially slow mobility, which is clearly different from that induced by increasing temperature. Nanoscopic scale ; Acceleration ; Polymer ; Materials science ; Nanotechnology ; Physics ; Composite material ; Classical mechanics Material Dynamics and Properties ; Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies
Authors: Huangqingbo Sun | Jiayi Li | Robert F. Murphy
Source: Bioinformatics (article, 2024-02-01)
Field: Chemistry — Spectroscopy

Expanding the coverage of spatial proteomics: a machine learning approach All code and intermediate results are available in a Reproducible Research Archive at https://github.com/murphygroup/CODEXPanelOptimization. Computer science ; Set (abstract data type) ; Source code ; Sample (material) ; Code (set theory) ; Data mining ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) Advanced Proteomics Techniques and Applications ; Cell Image Analysis Techniques ; Single-cell and spatial transcriptomics
Authors: Chinyere O. Nwankwo | Jeffrey Mahachi | David O. Olukanni | Innocent Musonda
Source: Polymer Composites (review, 2024-04-26)
Field: Materials Science — Polymers and Plastics

Africa's natural fibres used in polymer composites: A systematic review Abstract Environmental concerns and the depletion of petroleum resources have spurred research into natural fibres as sustainable alternatives to synthetic fibres as the reinforcement phase in polymer composites. Given that the local availability of materials is a crucial component in the sustainability framework, there is a need to map out the fibres used to develop natural fibre composites across geographical regions to optimize local resource utilization. Through a systematic review of publications sourced from Scopus and Web of Science databases, this study examines the contributions made by Africa to develop green polymer composites. The bibliometric data from both databases were systematically merged, and bibliometric analysis was carried out to identify trends and relevant relationships and provide a more general insight into Africa's progress in the natural fibre polymer composite field. A meta‐analysis was then conducted to identify the natural fibres exclusively sourced from Africa that have been used to develop polymer composites. The study also discussed natural fibre classifications with respect to fibre type and form. Sisal, palm varieties (particularly date palm), alfa, jute and members of the Musaceae family (i.e., banana, plantain and enset) were found to be the most used African‐sourced fibres. This study is a step to creating a more extensive global natural fibre database that seeks to provide more precise knowledge, enhance research efficiency, and ensure the utilization of local materials in creating more sustainable composites. Highlights The use of local materials is a key component of sustainable development A natural fibre database can foster sustainability in the composite industry Egypt, Morocco, and Tunisia are leading Africa's composite research Research prioritizes composite material science over practical applications Sisal, date palm, alfa and jute are the most used natural fibres in Africa Sustainability ; SISAL ; Composite material ; Materials science ; Natural resource ; Natural (archaeology) ; Scopus ; Geography Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Innovations in Concrete and Construction Materials
Authors: Adrian Arendowski
Source: Metabolites (review, 2024-03-20)
Field: Chemistry — Spectroscopy

Matrix- and Surface-Assisted Laser Desorption/Ionization Mass Spectrometry Methods for Urological Cancer Biomarker Discovery—Metabolomics and Lipidomics Approaches Urinary tract cancers, including those of the bladder, the kidneys, and the prostate, represent over 12% of all cancers, with significant global incidence and mortality rates. The continuous challenge that these cancers present necessitates the development of innovative diagnostic and prognostic methods, such as identifying specific biomarkers indicative of cancer. Biomarkers, which can be genes, proteins, metabolites, or lipids, are vital for various clinical purposes including early detection and prognosis. Mass spectrometry (MS), particularly soft ionization techniques such as electrospray ionization (ESI) and laser desorption/ionization (LDI), has emerged as a key tool in metabolic profiling for biomarker discovery, due to its high resolution, sensitivity, and ability to analyze complex biological samples. Among the LDI techniques, matrix-assisted laser desorption/ionization (MALDI) and surface-assisted laser desorption/ionization (SALDI) should be mentioned. While MALDI methodology, which uses organic compounds as matrices, is effective for larger molecules, SALDI, based on the various types of nanoparticles and nanostructures, is preferred for smaller metabolites and lipids due to its reduced spectral interference. This study highlights the application of LDI techniques, along with mass spectrometry imaging (MSI), in identifying potential metabolic and lipid biomarkers for urological cancers, focusing on the most common bladder, kidney, and prostate cancers. Mass spectrometry ; Lipidomics ; Metabolomics ; Surface-enhanced laser desorption/ionization ; Biomarker discovery ; Chemistry ; Biomarker ; Mass spectrometry imaging ; Ambient ionization ; MALDI imaging ; Matrix-assisted laser desorption electrospray ionization ; Prostate cancer ; Electrospray ionization ; Matrix-assisted laser desorption/ionization ; Ionization ; Desorption ; Cancer ; Medicine ; Chromatography ; Proteomics ; Internal medicine ; Sample preparation in mass spectrometry ; Biochemistry ; Chemical ionization Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Miroslav Müller | Viktor Kolář | Rajesh Mishra
Source: Polymers (article, 2024-10-10)
Field: Materials Science — Polymers and Plastics

Mechanical and Thermal Degradation-Related Performance of Recycled LDPE from Post-Consumer Waste This paper presents research aimed at laboratory experiments on static and cyclic fatigue testing of low-density polyethylene (LDPE) recovered from post-consumer waste in order to develop a recycled product exhibiting satisfactory mechanical and thermo-mechanical properties. The results of the cyclic fatigue tests set up to 80% of the maximum load in static tensile testing demonstrated satisfactory functionality of the recycled material developed by using the injection molding process. There was no significant change in the tensile strength under static and cyclic fatigue tests. Under cyclic loading, there was a quasi-static effect manifested by plastic deformation, and the displacement increased significantly. The static and cyclic tensile tests indicated improvement in the mechanical performance of the recycled LDPE as compared to the virgin material, owing to the high quality of the regranulates. Fourier Transform Infrared Spectroscopy (FTIR) was conducted to analyze the functional groups in virgin and recycled LDPE samples. The analysis showed no significant change in the transmittance spectra. The thermal degradation performance was also analyzed by Differential Scanning Calorimetry (DSC) and Dynamic Mechanical Analysis (DMA). The results were quite similar for both virgin and recycled LDPE. Low-density polyethylene ; Materials science ; Differential scanning calorimetry ; Composite material ; Ultimate tensile strength ; Fourier transform infrared spectroscopy ; Polyethylene ; Tensile testing ; Chemical engineering Polymer crystallization and properties ; Recycling and Waste Management Techniques ; Microplastics and Plastic Pollution
Authors: Billy K. Poon | Thomas C. Terwilliger | Paul D. Adams
Source: Protein Science (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

The <scp>Phenix‐AlphaFold</scp> webservice: Enabling <scp>AlphaFold</scp> predictions for use in <scp>Phenix</scp> Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo-electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model-building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix-AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix. Pipeline (software) ; Software ; Suite ; Computer science ; Operating system Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Nan Tian | Guifang Zhang | Peng Yan | Pengchao Li | Zhenhua Feng | Xiaoliang Wang
Source: Materials (article, 2024-01-31)
Field: Materials Science — Materials Chemistry

Simulation and Experimental Study on the Effect of Superheat on Solidification Microstructure Evolution of Billet in Continuous Casting The control of the solidification structure of a casting billet is directly correlated with the quality of steel. Variations in superheat can influence the transition from columnar crystals to equiaxed crystals during the solidification process, subsequently impacting the final solidification structure of the billet. In this study, a model of microstructure evolution during billet solidification was established by combining simulation and experiment, and the dendrite growth microstructure evolution during billet solidification under different superheat was studied. The results show that when the superheat is 60 K, the complete solidification time of the casting billet from the end of the 50 mm section is 252 s, when the superheat is 40 K, the complete solidification time of the casting billet is 250 s, and when the superheat is 20 K, the complete solidification time of the casting billet is 245 s. When the superheat is 20 K, the proportion of the equiaxed crystal region is higher-the highest value is 53.35%-and the average grain radius is 0.84556 mm. The proportion of the equiaxed crystal region decreases with the increase of superheat. When the superheat is 60 K, the proportion of the equiaxed crystal region is the lowest-the lowest value is 46.27%-and the average grain radius is 1.07653 mm. Proper reduction of superheat can obviously reduce the size of equiaxed crystal, expand the area of equiaxed crystal and improve the quality of casting billet. Equiaxed crystals ; Superheating ; Microstructure ; Materials science ; Metallurgy ; Dendrite (mathematics) ; Casting ; RADIUS ; Continuous casting ; Crystal (programming language) ; Thermodynamics ; Geometry ; Mathematics Solidification and crystal growth phenomena ; Metallurgical Processes and Thermodynamics ; Aluminum Alloy Microstructure Properties
Authors: Rajendra Hamad | Subir Kumar Chakraborty
Source: Scientific Reports (article, 2024-07-08)
Field: Chemistry — Analytical Chemistry

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis–NIR–SWIR hyperspectral imaging The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R<sup>2</sup> > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R<sup>2</sup> > 0.70) were less accurate while using the Vis-NIR spectral data. Hyperspectral imaging ; Partial least squares regression ; Brassica ; Near-infrared spectroscopy ; Microwave ; Oleic acid ; Environmental science ; Chemistry ; Materials science ; Remote sensing ; Agronomy ; Botany ; Mathematics ; Biology ; Computer science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies