jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huan Liang | Wendi Tian | Hongtu Xu | Yuzhen Ge | Yang Yang | Enjian He | Zhijun Yang | Yixuan Wang | Shuhan Zhang | Guoli Wang | Qiulin Chen | Yen Wei | Yan Ji
Source: Polymers (article, 2024-11-20)
Field: Materials Science — Polymers and Plastics

Reprocessable Epoxy–Anhydride Resin Enabled by a Thermally Stable Liquid Transesterification Catalyst Introducing dynamic ester bonds into epoxy-anhydride resins enhances the reprocessability of the crosslinked network, facilitated by various types of transesterification catalysts. However, existing catalysts, such as metal salts and organic molecules, often struggle with dispersion, volatility, or structural instability issues. Here, we propose to solve such problems by incorporating a liquid-state, thermally stable transesterification catalyst into epoxy resins. This catalyst, an imidazole derivative, can be uniformly dispersed in the epoxy resin at room temperature. In addition, it shows high-temperature structural stability above at least 200 °C as the synergistic effects of the electron-withdrawing group and steric bulk can be leveraged. It can also effectively promote transesterification at elevated temperatures, allowing for the effective release of shear stress. This property enables the thermal recycling and reshaping of the fully crosslinked epoxy-anhydride resin. This strategy not only enhances the functionality of epoxy resins but also broadens their applicability across various thermal and mechanical environments. Epoxy ; Transesterification ; Catalysis ; Materials science ; Gelcoat ; Organic chemistry ; Chemical engineering ; Polymer science ; Composite material ; Chemistry ; Engineering Polymer composites and self-healing ; Carbon dioxide utilization in catalysis ; Epoxy Resin Curing Processes
Authors: Shimaa Farag Hamieda | Abeer Reffaee | Mona Saied
Source: Scientific Reports (article, 2024-06-06)
Field: Materials Science — Polymers and Plastics

Biophysical studies of modified PVC sheet based on sunflower oil for antistatic and blood bags applications In this work, the surface of polyvinyl chloride PVC sheet was modified by blending it with sunflower seed oil SSO to obtain PVC sheet/SSO films of ratios 100/0, 90/10, 80/20, 70/30, 60/40, and 50/50 (v/v)% using the solution casting method. Various techniques were used to characterize the prepared films, besides the use of hemolysis assays and blood clot formation tests. FTIR spectra revealed that there was a good interaction between the PVC sheet and the oil. The dielectric measurement indicated that SSO addition enhanced the dielectric properties of the sheet. The study of dielectric relaxation times confirmed the interaction between SSO and the sheet. DC conductivity increased to 6 × 10<sup>-6</sup> S/m, so it could be applied in antistatic applications. Also, SSO addition increased the value of the thermal stability. According to SEM micrographs, the film was roughened at a ratio of 60/40 and smoothed out at 50/50. This behavior was confirmed with roughness and contact angle measurement results, in which the film of ratio 60/40 had the highest value equal to (72.03°) and then decreased at 50/50 to (59.62°). These results were confirmed by XRD measurement as the crystallinity increased at the film ratio of 60/40 and decreased again at 50/50. Also, the ratio of 60/40 demonstrated a large decrease in thrombus weights along with a slight increase in hemolysis, which is within the acceptable range and has a high degree of biocompatibility, so this concentration is recommended to be used in blood bags applications. Materials science ; Antistatic agent ; Crystallinity ; Polyvinyl chloride ; Composite material ; Contact angle ; Thermal stability ; Polypropylene ; Casting ; Hemolysis ; Chemical engineering Polymer Science and PVC ; Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Shuan Chen | Ramil Babazade | Taewan Kim | Sunkyu Han | Yousung Jung
Source: Scientific Data (article, 2024-08-10)
Field: Materials Science — Materials Chemistry

A large-scale reaction dataset of mechanistic pathways of organic reactions Understanding organic reaction mechanisms is crucial for interpreting the formation of products at the atomic and electronic level, but still remains as a domain of knowledgeable experts. The lack of a large-scale dataset with chemically reasonable mechanistic sequences also hinders the development of reliable machine learning models to predict organic reactions based on mechanisms as human chemists do. Here, we present a high-quality and the first large-scale reaction dataset, denoted as mech-USPTO-31K, with chemically reasonable arrow-pushing diagrams validated by synthetic chemists, encompassing a wide spectrum of polar organic reaction mechanisms. We envision this dataset curated by applying a simple and flexible method that automatically generates reaction mechanisms using autonomously extracted reaction templates and expert-coded mechanistic templates to become an invaluable tool to develop future reaction outcome prediction models and discover new reactions. Computer science ; Template ; Scale (ratio) ; Arrow ; Biochemical engineering ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Anca Giorgiana Grigoras
Source: Polymers (article, 2024-04-21)
Field: Materials Science — Biomaterials

Investigation of Cellulose-Based Materials Applied in Life Sciences Using Laser Light Scattering Methods This review emphasizes the practical importance of laser light scattering methods for characterizing cellulose and its derivatives. The physicochemical parameters like molecular weights, the radius of gyration, hydrodynamic radius, and conformation will be considered when the reproducibility of polymer behavior in solution is necessary for the subsequent optimization of the property profile of a designed product. Since there are various sources of cellulose, and the methods of cellulose extraction and chemical modification have variable yields, materials with variable molecular weights, and size polydispersity will often result. Later, the molecular masses will influence other physicochemical properties of cellulosic materials, both in solution and solid state. Consequently, the most rigorous determination of these quantities is imperative. In this regard, the following are presented and discussed in this review: the theoretical foundations of the light scattering phenomenon, the evolution of the specific instrumentation and detectors, the development of the detector-coupling techniques which include a light scattering detector, and finally, the importance of the specific parameters of polymers in solution, resulting from the data analysis of light scattering signals. All these aspects are summarized according to the chemical classification of the materials: celluloses, esters of cellulose, co-esters of cellulose, alkyl esters of cellulose, ethers of cellulose, and other heterogeneous cellulose derivatives with applications in life sciences. Cellulose ; Radius of gyration ; Polymer ; Multiangle light scattering ; Dispersity ; Materials science ; Light scattering ; Scattering ; Molar mass ; Chemistry ; Chemical engineering ; Polymer chemistry ; Organic chemistry ; Optics ; Physics Advanced Cellulose Research Studies ; Electrohydrodynamics and Fluid Dynamics ; Dyeing and Modifying Textile Fibers
Authors: Andreas Karydis‐Messinis | Christina Kyriakaki | Eleni Triantafyllou | Kyriaki Tsirka | Christina Gioti | Dimitris Gkikas | Konstantinos Nesseris | Dimitrios A. Exarchos | Spyridoula Farmaki | Theodore E. Matikas | Aris E. Giannakas | Constantinos E. Salmas | Dimitrios Moschovas | Apostolos Avgeropoulos
Source: Preprints.org (preprint, 2024-02-02)
Field: Materials Science — Biomaterials

Development and Physicochemical Characterization of Edible Chitosan-Casein Hydrogel Membranes for Potential Use in Food Packaging The increasing global concern over plastic waste and its environmental impact has led to a growing interest in the development of sustainable packaging alternatives. This study focuses on the innovative use of expired dairy products as a potential resource for producing edible packaging materials. Expired dairy products, which are typically discarded, contribute to food waste and environmental pollution. By modifying these products to packaging materials, a dual-purpose solution addressing both waste reduction and sustainable packaging is evaluated. Expired milk and yogurt were selected as the primary raw materials due to their protein and carbohydrate content. The extracted casein was combined with various concentrations of chitosan while in some cases glycerol was used. The materials were characterized with attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy and X-ray analysis (XRD) to examine the interactions between the components of the formed binary blend. Scanning electron microscopy (SEM) was used to evaluate the surface morphology of the blends, while dynamic mechanical analysis (DMA) and tensile experiments were conducted to study the thermomechanical properties of the materials. Barrier properties, including water vapor transmission (WVTR) and oxygen permeability (OTR), were measured to assess the materials&amp;#039; potential to preserve food freshness. The findings reveal that expired dairy-based edible packaging materials exhibit promising mechanical properties, comparable to conventional plastic packaging. The barrier properties and especially the zero oxygen permeability of the membranes, indicate that these materials have the potential to effectively protect food products from external factors that could compromise quality and shelf life. The materials show encouraging signs of eco-friendliness, with a notable reduction in environmental persistence compared to conventional plastic packaging. Chitosan ; Characterization (materials science) ; Food packaging ; Membrane ; Casein ; Chemistry ; Food science ; Chemical engineering ; Nanotechnology ; Business ; Materials science ; Biochemical engineering ; Polymer science ; Engineering ; Biochemistry Nanocomposite Films for Food Packaging
Authors: Xingyu Shang | Lei Pang | Qinhao Bu | Qiaogen Zhang
Source: Journal of Applied Physics (article, 2024-02-08)
Field: Materials Science — Materials Chemistry

A multi-mobility model for polymer insulation: Role of high-mobility space charge on breakdown with high d<i>v</i>/d<i>t</i> voltages The failure of polymeric solid insulation under transient voltages is closely associated with space charge. In this study, DC and AC field dependent conduction characteristics of epoxy resin were investigated using the space charge limited current and high voltage broadband dielectric spectrum methods. The breakdown voltages (BDVs) were examined over a wide range of DC voltage ramp rates. The results reveal a threshold effect of AC conduction loss below 600 Hz with respect to the electric field. By analyzing charge migration laws, a mobility distribution was deduced, with an upper limit of 10−9m2V−1s−1, which is much higher than the measured DC mobility of 10−14m2V−1s−1. A multi-mobility bipolar charge transport model was proposed by introducing the obtained mobility distribution. Compared to the single-mobility model, high-mobility carriers induce larger unblocked space charge currents for high dv/dt voltages, leading to lower values of BDV, which is consistent with experiment results. As the voltage ramp rate decreases, both simulation and experiment demonstrate higher BDVs due to the field shielding effect of homocharges. Space charge ; Materials science ; Electric field ; Voltage ; Electron mobility ; Thermal conduction ; Condensed matter physics ; Charge (physics) ; Electrical engineering ; Optoelectronics ; Physics ; Composite material ; Electron High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Venkatesh Mishra | Satyendra Pratap Singh | Mamta Singh | Vishal Singh Chandel | Rajiv Manohar
Source: Heliyon (article, 2024-07-01)
Field: Chemistry — Inorganic Chemistry

Optical, rheological, and dielectric properties of coconut oil between 100 kHz and 30 MHz In this paper, the dielectric behaviour of coconut oil within the frequency range 100 kHz to 30 MHz between temperature 30 °C-50 °C has been observed. The measured values of the dielectric constant and dielectric loss show notable variation with frequency and temperature for pure coconut oil. It is noticed that the dielectric constant (<i>ε</i>՛) and dielectric loss (<i>ε</i>՛՛) of coconut oil decreases with increasing temperature. The variation in the dielectric constant and dielectric loss with frequency also showed decreasing trend with frequency. UV absorbance of coconut oil for wavelengths ranging from 200 nm to 800 nm and FTIR spectra have also been recorded. The viscosity of oil between temperature 30 °C-50 °C has also been reported. Dielectric ; Coconut oil ; Materials science ; Dielectric loss ; Rheology ; Analytical Chemistry (journal) ; Absorbance ; Viscosity ; Wavelength ; Composite material ; Optics ; Chemistry ; Optoelectronics ; Chromatography ; Physics Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Food Chemistry and Fat Analysis
Authors: Shifang Yang | Zexuan Zhang | Mingxi Zhu | Yunpeng Liu | Fangcheng Lv
Source: IET Nanodielectrics (article, 2024-06-19)
Field: Materials Science — Materials Chemistry

Microbial adhesion and nanocomposite‐loaded antifouling applications of insulating polymers in transmission lines‐a review Abstract In regions characterised by high humidity and abundant vegetation, the external insulation materials utilised in high‐voltage transmission and transformation are frequently fouled with a multitude of microorganisms, thereby jeopardising the reliable operation of the power grid. A comprehensive overview of the periodic growth regulation of parasitic algae is presented upon insulating composites across various regions of the world. Additionally, it highlights the methods for quantitative evaluation and accurate prediction of algae coverage degree. Also, the biological contaminates coating process and the artificial flashover test method are summarised, and the effects of algae on the reliability of inorganic and organic materials used for external insulation are compared. It emphasises the dynamic hygroscopic characteristics and cytoelectronegativity of cell secretions as the critical factors that negatively affect the hydrophobicity and flashover performance of silicone rubber insulators by algae. Furthermore, valuable insights into the long‐term inhibition of algae growth on polymeric insulators are provided using eco‐friendly antibiotic‐loaded silica aerogel nanocomposites. Biofouling ; Nanocomposite ; Materials science ; Adhesion ; Polymer ; Polymer nanocomposite ; Composite material ; Polymer science ; Nanotechnology ; Chemistry ; Membrane High voltage insulation and dielectric phenomena ; Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials
Authors: Xin He | Daniël M. Pelt | Jingran Gao | Barbara Gravendeel | P. C. Zhu | S. W. Chen | Jian Qiu | Frederic Lens
Source: IAWA Journal - KU Leuven/IAWA Journal (article, 2024-04-26)
Field: Chemistry — Organic Chemistry

Machine learning-based wood anatomy identification: towards anatomical feature recognition Summary Computer vision-based wood identification has been successfully applied to recognize tree species using digital images of wood sections or surfaces. However, this image-to-species approach can only recognize a limited number of species due to two main reasons: 1) the lack of a good reference database requiring high-quality standardized images from multiple individuals of hundreds or even thousands of traded timber species, and 2) species not included in the reference database cannot be identified without expert knowledge. Another bottleneck is that the feature extraction process used by these species recognition approaches is a black box, thereby creating a discrepancy between machine learning features and wood anatomical features. This discrepancy prevents wood anatomists from understanding how these machine-learning algorithms work. Here, we survey currently existing methods used in feature extraction, classification, and deep learning methods applied in wood identification along with their pitfalls and opportunities. As an example of how the field could move forward, we launch the idea of building an image-to-features-to-species identification approach based on microscopic wood images as well as text files comprising wood anatomical descriptions. If we can manage machine learning-based algorithms to recognize the main wood anatomical traits that experts use to identify species in a (semi-)automated way, this would boost wood identification in two ways: (1) extensive reference databases for each species would become less crucial as the databases are ordered at the trait level, (2) timber identification would become more feasible for species that have not yet been included in the reference database as long as wood anatomical descriptions are available. Identification (biology) ; Artificial intelligence ; Computer science ; Bottleneck ; Machine learning ; Feature (linguistics) ; Process (computing) ; Field (mathematics) ; Feature extraction ; Deep learning ; Pattern recognition (psychology) ; Biology ; Ecology ; Mathematics Wood and Agarwood Research ; Plant Pathogens and Fungal Diseases ; Forest Ecology and Biodiversity Studies
Authors: Zhijin Zhang | Bingbing Wang | Xuze Li | Shude Jing | Yuan Gao | Dong Zeng | Xingliang Jiang
Source: Polymers (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Study on the Aging Characteristics of a ±500 kV Composite Dead-End Insulator in Longtime Service Composite insulators have been widely used in power grids due to their excellent electrical-external-insulation performance. Long-term operation at high voltage levels accelerates the aging of composite insulators; however, there is a scarcity of research on aged composite insulators operating at 500 kV for over ten years. In this paper, the mechanical, electrical, and microscopic properties were tested on different sheds along a 500 kV composite insulator that had been running for 18 years. Additionally, the results were compared with a new insulator and the standards for live insulator operation. The results showed that the aging of the high-voltage end of composite insulators was the most serious. The results of the physical properties test indicated that the insulator's hardness was compliant but its tensile strength and break elongation did not meet standards. Under wet conditions, the pollution flashover voltage decreases by about 50% compared to the new insulator. Combined with the microscopic test results, the shed skeleton structure could be damaged and the filler might be lost during the aging process of polydimethylsiloxane (PDMS). The hardness of the insulator would increase by the precipitation of inorganic silicon; however, inorganic silicon might destroy the hydrophobicity and other properties of insulator sheds. These results can provide theoretical references for insulator life prediction and operation protection. Materials science ; Insulator (electricity) ; Composite material ; Composite number ; Polydimethylsiloxane ; Arc flash ; Voltage ; Forensic engineering ; Electrical engineering ; Engineering High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Power Transformer Diagnostics and Insulation
Authors: Zhongyuan Zhao | Feiyu Lian | Yuying Jiang
Source: Agriculture (article, 2024-09-08)
Field: Chemistry — Analytical Chemistry

Recognition of Rice Species Based on Gas Chromatography-Ion Mobility Spectrometry and Deep Learning To address the challenge of relying on complex biochemical methods for identifying rice species, a prediction model that combines gas chromatography-ion mobility spectroscopy (GC-IMS) with a convolutional neural network (CNN) was developed. The model utilizes the GC-IMS fingerprint data of each rice variety sample, and an improved CNN structure is employed to increase the recognition accuracy. First, an improved generative adversarial network based on the diffusion model (DGAN) is used for data enhancement to expand the dataset size. Then, on the basis of a residual network called ResNet50, a transfer learning method is introduced to improve the training effect of the model under the condition of a small sample. In addition, a new attention mechanism called Triplet is introduced to further highlight useful features and improve the feature extraction performance of the model. Finally, to reduce the number of model parameters and improve the efficiency of the model, a method called knowledge distillation is used to compress the model. The results of our experiments revealed that the recognition accuracy for identifying the 10 rice varieties was close to 96%; hence, the proposed model significantly outperformed traditional models such as principal component analysis and support vector machine. Furthermore, compared to the traditional CNN, our model reduced the number of parameters and number of computations by 53% and 55%, respectively, without compromising classification accuracy. The study also suggests that the combination of GC-IMS and our proposed deep learning method had better discrimination abilities for rice varieties than traditional chromatography and other spectral analysis methods and that it effectively identified rice varieties. Ion-mobility spectrometry ; Mass spectrometry ; Gas chromatography ; Ion chromatography ; Chemistry ; Gas chromatography–mass spectrometry ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; GABA and Rice Research
Authors: Can Ding | Yaolong Bai | Yinbo Ji | Pengcheng Ma
Source: IEEJ Transactions on Electrical and Electronic Engineering (article, 2024-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Network Modeling of Complex Hysteresis Loops in Ferromagnetic Materials To investigate the impact of diverse multivariate mixing excitation conditions on the hysteresis loop of ferromagnetic materials, this study initially constructs a magnetic performance testing system for electrical steel. This system is capable of generating mixed excitation utilizing a standard Epstein square‐circle setup. Subsequently, the study measures the magnetic properties of the oriented silicon steel sheets at various mixing AC frequencies of the hysteresis loop data. Secondly, a hybrid network model integrating a convolutional neural network (CNN) and a bi‐directional long short‐term memory network (BiGRU), augmented with an attention mechanism (AM), is proposed and utilized for predicting the hysteresis properties of oriented silicon steel wafers subjected to compound mixed‐frequency excitation. The model utilizes CNN to extract high‐dimensional data features reflecting the hysteresis characteristics of the loop, BiGRU to capture the temporal evolution patterns of the key feature vectors, an AM to weigh the feature parameters and emphasize the key features, and a Bayesian optimization (BO) algorithm based on neural network hyperparameters for automatic selection, enhancing prediction accuracy. In comparison with experimental observations, the method accurately predicts material hysteresis properties under non‐sinusoidal complex excitation conditions, outperforming existing deep‐learning network models. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. Hysteresis ; Ferromagnetism ; Artificial neural network ; Magnetic hysteresis ; Materials science ; Computer science ; Artificial intelligence ; Condensed matter physics ; Physics ; Magnetization ; Magnetic field Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels