jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zheng-Wei Liu | Jiali Li | Tingyu Zhang | Shuai Chen | Dongli Xin | Kai Liu | Kui Chen | Yong‐Chao Liu | Chuanming Sun | Guoqiang Gao | Guangning Wu
Source: Applied Sciences (article, 2024-05-30)
Field: Materials Science — Materials Chemistry

Accurate Identification of Partial Discharge Signals in Cable Terminations of High-Speed Electric Multiple Unit Using Wavelet Transform and Deep Belief Network Cable termination serves as a crucial carrier for high-speed train power transmission and a weak part of the cable insulation system. Partial discharge detection plays a significant role in evaluating insulation status. However, field testing signals are often contaminated by external corona interference, which affects detection accuracy. This paper proposes a classification model based on wavelet transform (WT) and deep belief network (DBN) to accurately and rapidly identify corona discharge in the partial discharge signals of vehicle-mounted cable terminals. The method utilizes wavelet transform for noise reduction, employing the sigmoid activation function and analyzing the impact of WT on DBN classification performance. Research indicates that this method can achieve an accuracy of over 89% even with limited training samples. Finally, the reliability of the proposed classification model is verified using measured mixed signals. Partial discharge ; Wavelet transform ; Computer science ; Reliability (semiconductor) ; Wavelet ; Noise reduction ; Acoustics ; Power cable ; Artificial intelligence ; Electronic engineering ; Pattern recognition (psychology) ; Engineering ; Electrical engineering ; Power (physics) ; Materials science ; Voltage ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Image Enhancement Techniques
Authors: Rami Shafei | Philipp Strobel | Peter J. Schmidt | Dimitrios Maganas | Wolfgang Schnick | Frank Neese
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A theoretical spectroscopy study of the photoluminescence properties of narrow band Eu<sup>2+</sup>-doped phosphors containing multiple candidate doping centers. Prediction of an unprecedented narrow band red phosphor We have previously presented a computational protocol that is based on an embedded cluster model and operates in the framework of TD-DFT in conjunction with the excited state dynamics (ESD) approach. The protocol is able to predict the experimental absorption and emission spectral shapes of Eu<sup>2+</sup>-doped phosphors. In this work, the applicability domain of the above protocol is expanded to Eu<sup>2+</sup>-doped phosphors bearing multiple candidate Eu doping centers. It will be demonstrated that this protocol provides full control of the parameter space that describes the emission process. The stability of Eu doping at various centers is explored through local energy decomposition (LED) analysis of DLPNO-CCSD(T) energies. This enables further development of the understanding of the electronic structure of the targeted phosphors, the diverse interactions between Eu and the local environment, and their impact on Eu doping probability, and control of the emission properties. Hence, it can be employed to systematically improve deficiencies of existing phosphor materials, defined by the presence of various intensity emission bands at undesired frequencies, towards classes of candidate Eu<sup>2+</sup>-doped phosphors with desired narrow band red emission. For this purpose, the chosen study set consists of three UCr<sub>4</sub>C<sub>4</sub>-based narrow-band phosphors, namely the known alkali lithosilicates RbNa[Li<sub>3</sub>SiO<sub>4</sub>]<sub>2</sub>:Eu<sup>2+</sup> (RNLSO2), RbNa<sub>3</sub>[Li<sub>3</sub>SiO<sub>4</sub>]<sub>4</sub>:Eu<sup>2+</sup> (RNLSO) and their isotypic nitridolithoaluminate phosphors consisting of CaBa[LiAl<sub>3</sub>N<sub>4</sub>]<sub>2</sub>:Eu<sup>2+</sup> (CBLA2) and the proposed Ca<sub>3</sub>Ba[LiAl<sub>3</sub>N<sub>4</sub>]<sub>4</sub>:Eu<sup>2+</sup> (CBLA), respectively. The theoretical analysis presented in this work led us to propose a modification of the CBLA2 phosphor that should have improved and unprecedented narrow band red emission properties. Finally, we believe that the analysis presented here is important for the future rational design of novel Eu<sup>2+</sup>-doped phosphor materials, with a wide range of applications in science and technology. Phosphor ; Doping ; Photoluminescence ; Excited state ; Spectroscopy ; Materials science ; Cluster (spacecraft) ; Optoelectronics ; Atomic physics ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Computer science Luminescence Properties of Advanced Materials ; Advanced Chemical Physics Studies ; Radiation Detection and Scintillator Technologies
Authors: Chanik Kang | Joonhyuk Seo | Ikbeom Jang | Hsiao L. Chung
Source: iScience (article, 2024-12-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Adjoint method-based Fourier neural operator surrogate solver for wavefront shaping in tunable metasurfaces We present a Fourier neural operator (FNO)-based surrogate solver for the efficient optimization of wavefronts in tunable metasurface controls. Existing methods, including the Gerchberg-Saxton algorithm and the adjoint optimization, are often computationally demanding due to their iterative processes, which require numerical simulations at each step. Our surrogate solver overcomes this limitation by providing highly accurate gradient estimations with respect to changes in tunable meta-atoms without the need for direct simulations. This approach substantially reduces both computational time and cost in wavefront shaping applications. The proposed solver demonstrates a residual of 0.02 when compared to the normalized figure of merit achieved by the optimized structure obtained through the adjoint method, and its inference time is 887.5 times faster than conventional simulation-based methods. This advancement enables ultra-fast wavefront shaping across a range of applications, including optical wavefront shaping, reconfigurable intelligent metasurfaces, and biomedical imaging. Wavefront ; Solver ; Fourier transform ; Operator (biology) ; Surrogate model ; Computer science ; Fast Fourier transform ; Physics ; Chemistry ; Optics ; Mathematics ; Algorithm ; Mathematical analysis ; Machine learning Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Thermal Radiation and Cooling Technologies
Authors: Qianjie Wang | Guangyao Gao | Fei Fang | Qianyi Wang | Peter K. Lundquist | Liangliang Sun
Source: Analytica Chimica Acta (article, 2024-08-28)
Field: Chemistry — Spectroscopy

A simple and efficient approach for preparing cationic coating with tunable electroosmotic flow for capillary zone electrophoresis-mass spectrometry-based top-down proteomics Chemistry ; Capillary electrophoresis ; Capillary action ; Mass spectrometry ; Chromatography ; Capillary electrophoresis–mass spectrometry ; Cationic polymerization ; Coating ; Tandem mass spectrometry ; Electrophoresis ; Tandem ; Resolution (logic) ; Analytical Chemistry (journal) ; Electrospray ionization ; Polymer chemistry Mass Spectrometry Techniques and Applications ; Microfluidic and Capillary Electrophoresis Applications ; Advanced Proteomics Techniques and Applications
Authors: Yilin Mao | He Li | Yang Xu | Shuangshuang Wang | Xinyue Yin | Kai Fan | Zhaotang Ding | Yu Wang
Source: Journal of the Science of Food and Agriculture (article, 2024-07-19)
Field: Chemistry — Analytical Chemistry

Early detection of gray blight in tea leaves and rapid screening of resistance varieties by hyperspectral imaging technology This study provides a core algorithm for a GB disease instrument with detection capabilities, which is of great importance for the early prevention of GB disease in tea plantations. © 2024 Society of Chemical Industry. Hyperspectral imaging ; Artificial intelligence ; Support vector machine ; Computer science ; Pattern recognition (psychology) ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Leaf Properties and Growth Measurement
Authors: J. Zakrzewski | Michael Pawlak | Osamu Matsuda | D. M. Todorovic | Junyan Liu
Source: Journal of Applied Physics (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Semiconductor physics: Plasma, thermal, elastic, and acoustic phenomena Plasma ; Semiconductor ; Physics ; Thermal ; Materials science ; Condensed matter physics ; Thermodynamics ; Nuclear physics ; Optoelectronics Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Thermal Radiation and Cooling Technologies
Authors: N.S. Bowen | Cameron Guyer | Terence Rippon | Michael Daly | Peng Gao | Vito Galati | Sal Lograsso | Stephen Johnston | Davide Masato
Source: Polymer Engineering and Science (article, 2024-02-26)
Field: Materials Science — Polymers and Plastics

Mechanical and crystallization properties of hot runner injection molded virgin and recycled polypropylene Abstract As the demand for sustainable and environmentally conscious practices continues to grow, recycled plastics have become increasingly popular for plastics manufacturers. However, reprocessing these materials can result in inconsistencies in both process and product quality, leading to a loss in mechanical properties. To address this issue, this work investigates the injection molding of recycled and virgin polypropylene (PP). Using a multi‐cavity hot runner mold, correlations between processing parameters, such as melt temperature, mold temperature, pack pressure, soak time, and mechanical properties, were systematically investigated. A multivariate analysis approach was used to model the variables for each material. Results indicated significant correlations between processing, mechanical properties, and morphology. In particular, for the PP, the melt temperature affected the formation of β‐crystals and their transition to α‐crystals upon tensile testing. Different morphology was observed for the recycled polypropylene (rPP), in which crystallization was affected by polyethylene (PE) contamination. The results discuss the injection molding polymer, structure, and properties, relationship providing an approach to understanding and optimizing the mechanical properties of rPP. Highlights Polyethylene contamination in polypropylene significantly influences crystallinity. Recycling contamination reduces the polypropylene's ability to form β crystals. Virgin polypropylene mechanical properties can be tailored through processing conditions. Recycled polypropylene mechanical properties suffer at higher temperatures and residence times. Polypropylene α/β morphologies can be visualized after mechanical testing. Polypropylene ; Materials science ; Crystallinity ; Molding (decorative) ; Composite material ; Polyethylene ; Mold ; Crystallization ; Injection moulding ; Chemical engineering Polymer crystallization and properties ; biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites
Authors: Qi An | Cheng Xing | Zhipeng Wang | Shuang Li | Wenwen Wang | Shiying Yang | Linglei Kong | Dezhi Yang | Li Zhang | Guanhua Du | Yang Lü
Source: Pharmaceutics (article, 2024-03-11)
Field: Chemistry — Spectroscopy

Metformin-Mediated Improvement in Solubility, Stability, and Permeability of Nonsteroidal Anti-Inflammatory Drugs Nonsteroidal anti-inflammatory drugs (NSAIDs) are class II biopharmaceutics classification system drugs. The poor aqueous solubility of NSAIDs can lead to limited bioavailability after oral administration. Metformin (MET), a small-molecule compound, can be used in crystal engineering to modulate the physicochemical properties of drugs and to improve the bioavailability of orally administered drugs, according to the literature research and preliminary studies. We synthesized two drug-drug molecular salts (ketoprofen-metformin and phenylbutazone-metformin) with NSAIDs and thoroughly characterized them using SCXRD, PXRD, DSC, and IR analysis to improve the poor solubility of NSAIDs. In vitro evaluation studies revealed that the thermal stability and solubility of NSAIDs-MET were substantially enhanced compared with those of NSAIDs alone. Unexpectedly, an additional increase in permeability was observed. Since the structure determines the properties, the structure was analyzed using theoretical calculations to reveal the intermolecular interactions and to explain the reason for the change in properties. The salt formation of NSAIDs with MET could substantially increase the bio-absorption rate of NSAIDs, according to the in vivo pharmacokinetic findings, which provides an experimental basis for developing new antipyretic and analgesic drugs with rapid onset of action. Biopharmaceutics Classification System ; Bioavailability ; Pharmacology ; Biopharmaceutics ; Drug ; Solubility ; Chemistry ; Pharmacokinetics ; Antipyretic ; In vivo ; Analgesic ; Medicine ; Organic chemistry ; In vitro ; Biochemistry Analytical Chemistry and Chromatography ; Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: Kun Tao | Rongrong Chen | L. H. Yang | Jin Gao | Desheng Xue | Chenglong Jia
Source: APL Materials (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

The 1/4 occupied O atoms induced ultra-flatband and the one-dimensional channels in the Pb10−<i>x</i>Cu<i>x</i>(PO4)6O4 (x = 0, 0.5) crystal The search for room-temperature superconductors has been a long-standing goal in condensed matter physics. In this study, we investigate the electronic and geometric properties of lead apatite with and without Cu doping within the framework of the density functional theory. Based on our calculations, we found that without Cu doping, the lead apatite PPOO shows an insulator character with flatbands that straddle the Fermi level. Once we introduce O1 vacancies, the flatbands disappear. Furthermore, we analyze the effects of Cu doping on the crystal structure and the electronic band structure of the material PCPOO4. Our calculations reveal the presence of one-dimensional channels induced by fully occupied O1 atoms that are only 1/4 occupied in the literature, which may play a crucial role in the realization of room-temperature superconductivity. Based on our findings, we propose a possible solution to improve the quality of superconductivity by annealing the material in an oxygen atmosphere. These results contribute to a better understanding of the unusual properties of Cu-doped lead apatite PCPOO4 and will pave the way for further exploration of its potential as a room-temperature superconductor. Doping ; Superconductivity ; Condensed matter physics ; Apatite ; Materials science ; Density functional theory ; Electronic structure ; Fermi level ; Crystal (programming language) ; Chemical physics ; Physics ; Chemistry ; Mineralogy ; Computational chemistry ; Electron ; Quantum mechanics Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics ; Iron-based superconductors research
Authors: Seda Şahin | Ayşe Torun
Source: Agriculture (article, 2024-01-31)
Field: Chemistry — Analytical Chemistry

Comparison of Engine Performance and Emission Values of Biodiesel Obtained from Waste Pumpkin Seeds with Machine Learning This study was primarily conducted to investigate the potential use of pumpkin seed oil in biodiesel production. Initially, the fatty acid composition of oils extracted from discarded pumpkin seeds was determined. Then, biodiesel produced from discarded pumpkin seed oil was tested in an engine test setup. The performance and emission values of a four-cylinder diesel engine fueled with diesel (D100), biodiesel (PB100), and blended fuels (PB2D98, PB5D95, and PB20D80) were determined. Furthermore, three distinctive machine learning algorithms (artificial neural networks, XGBoost, and random forest) were employed to model engine performance and emission parameters. Models were generated based on the data from the PB100, PB2D98, and PB5D95 fuels, and model performance was assessed through the R2, RMSE, and MAPE metrics. The highest torque value (333.15 Nm) was obtained from 1200 rpm of D100 fuel. PB2D98 (2% biodiesel–98% diesel) had the lowest specific fuel consumption (194.33 g HPh−1) at 1600 rpm. The highest BTE (break thermal efficiency) value (30.92%) was obtained from diesel fuel at 1400 rpm. Regarding the blended fuels, PB2D98 exhibited the most fuel-efficient performance. Overall, in terms of engine performance and emission values, PB2M98 showed the closest results to diesel fuel. A comparison of machine learning algorithms revealed that artificial neural networks (ANNs) generally performed the best. However, the XGBoost algorithm proved to be more successful than other algorithms at predicting the performance and emissions of PB20D80 fuel. The present findings demonstrated that the XGBoost algorithm could be a more reliable option for predicting engine performance and emissions, especially for data-deficient fuels such as PB20D80. Biodiesel ; Pumpkin seed ; Environmental science ; Botany ; Pulp and paper industry ; Waste management ; Agricultural engineering ; Engineering ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Biodiesel Production and Applications
Authors: Valentin Munteanu | Vladimir Starostin | Alessandro Greco | Linus Pithan | Alexander Gerlach | Alexander Hinderhofer | Stefan Kowarik | Frank Schreiber
Source: Journal of Applied Crystallography (article, 2024-03-31)
Field: Materials Science — Materials Chemistry

Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters. Underdetermined system ; Artificial neural network ; Inverse problem ; Computer science ; Ambiguity ; Algorithm ; Inverse ; Neutron ; Parameter space ; Inference ; Range (aeronautics) ; Artificial intelligence ; Mathematics ; Physics ; Mathematical analysis ; Materials science ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Chris A. Brosey | Todd Link | Runze Shen | Davide Moiani | Kathryn Burnett | Greg L. Hura | Darin E. Jones | John A. Tainer
Source: Nature Chemical Biology (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Chemical screening by time-resolved X-ray scattering to discover allosteric probes Drug discovery relies on efficient identification of small-molecule leads and their interactions with macromolecular targets. However, understanding how chemotypes impact mechanistically important conformational states often remains secondary among high-throughput discovery methods. Here, we present a conformational discovery pipeline integrating time-resolved, high-throughput small-angle X-ray scattering (TR-HT-SAXS) and classic fragment screening applied to allosteric states of the mitochondrial import oxidoreductase apoptosis-inducing factor (AIF). By monitoring oxidized and X-ray-reduced AIF states, TR-HT-SAXS leverages structure and kinetics to generate a multidimensional screening dataset that identifies fragment chemotypes allosterically stimulating AIF dimerization. Fragment-induced dimerization rates, quantified with time-resolved SAXS similarity analysis (k<sub>VR</sub>), capture structure-activity relationships (SAR) across the top-ranked 4-aminoquinoline chemotype. Crystallized AIF-aminoquinoline complexes validate TR-SAXS-guided SAR, supporting this conformational chemotype for optimization. AIF-aminoquinoline structures and mutational analysis reveal active site F482 as an underappreciated allosteric stabilizer of AIF dimerization. This conformational discovery pipeline illustrates TR-HT-SAXS as an effective technology for targeting chemical leads to important macromolecular states. Allosteric regulation ; Scattering ; Chemistry ; Computational biology ; Nanotechnology ; Biophysics ; Physics ; Materials science ; Biology ; Optics ; Biochemistry ; Enzyme Enzyme Structure and Function ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Kamila Iskhakova | Hanna Ćwieka | Svenja Meers | Heike Helmholz | Anton Davydok | Malte Storm | Ivo M. Baltruschat | Silvia Galli | Daniel Pröfrock | Olga Will | Mirko Gerle | Timo Damm | Sandra Sefa | Weilue He | Keith MacRenaris | Malte Soujon | Felix Beckmann | Julian Moosmann | Thomas O'Hallaran | Roger J. Guillory | D. C. Florian Wieland | Berit Zeller‐Plumhoff | Regine Willumeit‐Römer
Source: Bioactive Materials (article, 2024-09-03)
Field: Materials Science — Biomaterials

Multi-modal investigation of the bone micro- and ultrastructure, and elemental distribution in the presence of Mg-xGd screws at mid-term healing stages Magnesium (Mg) - based alloys are becoming attractive materials for medical applications as temporary bone implants for support of fracture healing, e.g. as a suture anchor. Due to their mechanical properties and biocompatibility, they may replace titanium or stainless-steel implants, commonly used in orthopedic field. Nevertheless, patient safety has to be assured by finding a long-term balance between metal degradation, osseointegration, bone ultrastructure adaptation and element distribution in organs. In order to determine the implant behavior and its influence on bone and tissues, we investigated two Mg alloys with gadolinium contents of 5 and 10 wt percent in comparison to permanent materials titanium and polyether ether ketone. The implants were present in rat tibia for 10, 20 and 32 weeks before sacrifice of the animal. Synchrotron radiation-based micro computed tomography enables the distinction of features like residual metal, degradation layer and bone structure. Additionally, X-ray diffraction and X-ray fluorescence yield information on parameters describing the bone ultrastructure and elemental composition at the bone-to-implant interface. Finally, with element specific mass spectrometry, the elements and their accumulation in the main organs and tissues are traced. The results show that Mg-xGd implants degrade <i>in vivo</i> under the formation of a stable degradation layer with bone remodeling similar to that of Ti after 10 weeks. No accumulation of Mg and Gd was observed in selected organs, except for the interfacial bone after 8 months of healing. Thus, we confirm that Mg-5Gd and Mg-10Gd are suitable material choices for bone implants. Osseointegration ; Materials science ; Biocompatibility ; Titanium ; Biomedical engineering ; Bone tissue ; Bone healing ; Implant ; Biomaterial ; Bone mineral ; Osteoporosis ; Metallurgy ; Anatomy ; Nanotechnology ; Surgery ; Medicine ; Pathology Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Hydrogen Storage and Materials