jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ika Priyanti | Doonyapong Wongsawaeng | Kanokwan Ngaosuwan | Worapon Kiatkittipong | Peter Hosemann | Suttichai Assabumrungrat
Source: Scientific Reports (article, 2024-06-06)
Field: Materials Science — Surfaces, Coatings and Films

Corona discharge plasma for green de-inking of inkjet printer ink This work features a new corona discharge plasma technology for de-inking yellow, blue, and red colors on various papers. This work was developed to minimize the chemical and environmental impacts of de-inking processes. A nonchemical contribution, operating at room temperature and atmospheric pressure, reduces the environmental impact of the process. The deinkability factor (DEM<sub>Lab</sub>) values for all papers are determined with the optimal assessment results provided by a 36-mm variation gap at 2-min (blue) and 10-min (yellow and red) plasma exposure times, followed by applied voltages of 20 kV (yellow), 16 kV (blue), and 20 kV (red). The corona discharge plasma led to 48.58% (yellow printed paper), 64.11% (blue printed paper), and 41.11% (red printed paper) deinkability without altering the physical properties of the paper itself. The change in the tensile strength for the plasma-exposed paper was relatively little, less than 10%, compared to that of common recycling. The tensile strength of the untreated white paper was 5065 ± 487.44 N/mm<sup>2</sup>, and that of the plasma-treated printed paper was 4593 ± 248.47 N/mm<sup>2</sup>. It appears that there is little impact on the physicochemical properties of paper induced by the corona plasma treatment during the de-inking process. Plasma ; Corona discharge ; Ultimate tensile strength ; Materials science ; Corona (planetary geology) ; Atmospheric-pressure plasma ; Environmental science ; Composite material ; Voltage ; Electrical engineering ; Physics ; Engineering Surface Modification and Superhydrophobicity ; Plasma Applications and Diagnostics ; Recycling and Waste Management Techniques
Authors: Ruqing Ye | Xiaoming Chen
Source: Micromachines (article, 2024-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromigration Analysis for Interconnects Using Improved Graph Convolutional Network with Edge Feature Aggregation Electromigration (EM) is a critical reliability issue in integrated circuits and is becoming increasingly significant as fabrication technology nodes continue to advance. The analysis of the hydrostatic stress, which is paramount in electromigration studies, typically involves solving complex physical equations (partial differential equations, or PDEs in this case), which is time consuming, inefficient and not practical for full-chip EM analysis. In this paper, a novel approach is proposed, conceptualizing circuit interconnect trees as a graph within a graph neural network framework. Using finite element solution software, ground truth hydrostatic stress values were obtained to construct a dataset of interconnected trees with hydrostatic stress values for each node. An improved Graph Convolutional Network (GCN) augmented with edge feature aggregation and attention mechanism was then trained employing the dataset, yielding a model capable of predicting hydrostatic stress values for nodes in an interconnect tree. The results show that our model demonstrated a 15% improvement in the Root Mean Square Error (RMSE) compared to the original GCN model and improved the solution speed greatly compared to traditional finite element software. Electromigration ; Computer science ; Graph ; Node (physics) ; Finite element method ; Interconnection ; Mean squared error ; Algorithm ; Software ; Theoretical computer science ; Mathematics ; Engineering ; Statistics Copper Interconnects and Reliability ; Corrosion Behavior and Inhibition ; Electronic Packaging and Soldering Technologies
Authors: Revanth Mattey | Susanta Ghosh
Source: arXiv (Cornell University) (preprint, 2024-05-09)
Field: Materials Science — Materials Chemistry

Gradient Flow Based Phase-Field Modeling Using Separable Neural Networks The $L^2$ gradient flow of the Ginzburg-Landau free energy functional leads to the Allen Cahn equation that is widely used for modeling phase separation. Machine learning methods for solving the Allen-Cahn equation in its strong form suffer from inaccuracies in collocation techniques, errors in computing higher-order spatial derivatives through automatic differentiation, and the large system size required by the space-time approach. To overcome these limitations, we propose a separable neural network-based approximation of the phase field in a minimizing movement scheme to solve the aforementioned gradient flow problem. At each time step, the separable neural network is used to approximate the phase field in space through a low-rank tensor decomposition thereby accelerating the derivative calculations. The minimizing movement scheme naturally allows for the use of Gauss quadrature technique to compute the functional. A `$tanh$' transformation is applied on the neural network-predicted phase field to strictly bounds the solutions within the values of the two phases. For this transformation, a theoretical guarantee for energy stability of the minimizing movement scheme is established. Our results suggest that bounding the solution through this transformation is the key to effectively model sharp interfaces through separable neural network. The proposed method outperforms the state-of-the-art machine learning methods for phase separation problems and is an order of magnitude faster than the finite element method. Balanced flow ; Artificial neural network ; Separable space ; Computer science ; Flow (mathematics) ; Field (mathematics) ; Phase (matter) ; Artificial intelligence ; Mechanics ; Mathematics ; Physics ; Mathematical analysis ; Pure mathematics Solidification and crystal growth phenomena ; Model Reduction and Neural Networks ; Fluid Dynamics and Turbulent Flows
Authors: Habibe Elif Gülşen Akbay | Öyküm Başgöz | Ömer Güler
Source: Biomass Conversion and Biorefinery (article, 2024-04-13)
Field: Chemistry — Spectroscopy

A novel additive for enhancing biomass energy production from agro-industrial wastes: synthesis of hydrophobic nanoporous silica aerogel and its effect on methane production Abstract Anaerobic digestion (AD) is one of the most preferred processes for the treatment of organic waste. However, additional processes such as co-digestion, pretreatment, and additive addition continue to be explored to remove the limits on the applicability of AD. This study investigated the effects of hydrophobic nanoporous silica aerogel (NpSA) synthesized from waste rice husks on the anaerobic co-digestion (AnCD) of the mixture consisting of sewage sludge and fruit processing industry wastes. All bioreactors containing NpSA-free, 0.1 g, 0.2 g, 0.5 g, and 1 g NpSA (0.03–0.3 g NpSA /gVS added ) were operated in a mesophilic-batch process. Biogas and methane yields increased from 346 mL/gVS (NpSA-free) to 387 mL/gVS and from 231 mL/gVS (NpSA-free) to 288 mL/gVS, respectively, with 0.1 g NpSA addition. NpSA additive increased biogas production in all bioreactors compared to the blank. However, biogas production rate and methane content increased faster at lower doses of NpSA. Maximum soluble chemical oxygen demand (sCOD), protein, carbohydrate, and volatile solid (VS) reductions were between 45–71%, 35–54%, 44–65%, and 34–91% for NpSA added mixtures, respectively. The hydrophobic NpSA additive was effective in improving the AnCD performance and biogas/methane production. Experimental results fit the kinetic models frequently preferred in such AD processes. In addition, the possible energy and financial potential of the produced methane were also discussed, and it was determined that the direct sale of methane gas produced by the addition of NpSA in the global market could provide 1.4 $/L mixture more financial gain than the mixture NpSA-free. Graphical Abstract Biogas ; Methane ; Nanoporous ; Anaerobic digestion ; Waste management ; Chemistry ; Pulp and paper industry ; Environmental science ; Organic chemistry Aerogels and thermal insulation ; Anaerobic Digestion and Biogas Production ; Supercapacitor Materials and Fabrication
Authors: Jirapa Tangsritrakul | C. Wichittanakom | Chatree Saiyasombat
Source: Journal of Metals Materials and Minerals (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Composition-driven phase coexistence and functional properties of the (1-x)BZT-(x)BCT ceramics near the phase convergence region The concept of composition-induced phase transformation in Lead Zirconate Titanate (PZT) at the Morphotropic Phase Boundary (MPB) has been employed to improve functional properties of the (1-x)BZT-(x)BCT ceramic. However, it was observed that the phase diagram of the (1-x)BZT-(x)BCT ceramic is different to the PZT. As a result, the nature of the superior functional properties found in (1-x)BZT-(x)BCT ceramic is unlike PZT and still unclear so far. In this work, functional properties; dielectric, ferroelectric, energy storage, and piezoelectric properties, of the (1-x)BZT-(x)BCT ceramics where x = 0.3 mol% to 0.6 mol% were evaluated at room temperature in comparison to the identification of phase coexistence using synchrotron x-ray powder diffraction (SXPD). This work found that changes of BCT content had a strong impact on the observed coexisting phases and functional properties. Moreover, the composition that showed the highest piezoelectric properties did not present the largest of saturation polarization. This implies that the functional properties of the (1-x)BZT-(x)BCT ceramics are not dependent on the presence of polarizations under the application of electric field. The contribution of non-180° domain switching also plays a vital role, especially in the piezoelectric properties. These findings would help to extend our knowledge of the nature of the (1-x)BZT-(x)BCT ceramic. Materials science ; Phase (matter) ; Ceramic ; Convergence (economics) ; Condensed matter physics ; Metallurgy ; Physics ; Quantum mechanics Ferroelectric and Piezoelectric Materials ; Shape Memory Alloy Transformations ; Microwave Dielectric Ceramics Synthesis
Authors: Marcela Gašparová | Branislav Pavílek | Pavol Tisovský | Simona Ilková | Zita Tokárová
Source: Preprints.org (preprint, 2024-05-20)
Field: Chemistry — Spectroscopy

Low-Field NMR as a Vital Spectral Tool in Organic Chemistry The growing interest in benchtop NMR systems is visible in a vast number of applications in science and industry. Low-field nuclear magnetic resonance (LF-NMR) became an instant in technologies that productions are dependent on the reliable structure identification, grade and purity control, chemical process inspection or reaction monitoring. Beyond the benefits of being cost-, space-, maintenance- and user friendly, insufficient sensitivity and low signal resolution are the main drawbacks, due to which the low-field NMR (LF-NMR) in the fields of organic chemistry and synthesis remains limited to solely academic teaching purposes. Herein, we discuss the possibilities of LF-NMR for the needs of organic chemistry and synthesis on a set of compounds 1-4 achieved as the products of student’s laboratory course and organic derivatives 5-8 that are from our research interests. Chemical structure determination of all compounds 1-8 presented herein was performed using the benchtop-type NMR instrument operating at 60 MHz (Pulsar Oxford Ltd.). Structure of novel compounds 5-7 prepared under the aims of our research was characterized by combined 1H/19LF-NMR and compared with an appropriate high-field NMR data from Bruker Ascend Instrument (400 MHz). Finally, possibilities of the low-field NMR as an analytical tool for the research purposes in the fields of organic chemistry and synthesis are documented on a set of 1H and 2D 1H-1H COSY for azomethine 8. Field (mathematics) ; Chemistry ; Nuclear magnetic resonance ; Physics ; Mathematics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Atomic and Subatomic Physics Research
Authors: A.V. Nikitin | A. E. Protasevich | M. Rey | Vladimir G. Tyuterev
Source: Chemical Physics (article, 2024-10-15)
Field: Chemistry — Spectroscopy

Extending the functional form of the methane PES in redundant coordinates for highly excited vibrational energy levels calculation Chemistry ; Excited state ; Methane ; Density functional theory ; Computational chemistry ; Energy (signal processing) ; Atomic physics ; Organic chemistry ; Quantum mechanics ; Physics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Atmospheric Ozone and Climate
Authors: André Orth | Hawo Höfer | Alexei Nefedov | Mehrdad Jalali | Christof Wöll | Markus Reischl
Source: Current Directions in Biomedical Engineering (article, 2024-12-01)
Field: Materials Science — Surfaces, Coatings and Films

ML-Based XPS Quantification Supported by Synthetic Dataset Generation Abstract With growing interest in laboratory automation and high-throughput systems, the amount of generated experimental data is rapidly increasing while analysis methods still require many manual work hours from experts. This is prevalent in X-ray photoelectron spectroscopy (XPS), where quantification is a complex, time-consuming, and error-prone task. We therefore propose a neural network-based workflow to make this process more approachable. As training data availability ranges from insufficient to non-existent, our workflow creates a synthetic dataset containing XPS signals and corresponding area percentages based on binding energies supplied by the user. As a result, no previous measurements are needed. After training on the synthetic data, the neural network can predict area percentages of the known binding energies with high confidence. This workflow can therefore be adapted for XPS quantification tasks to filter significant data and supervise processes. Moreover, this enables non-experts to analyze spectra and can help experts to reduce focus on important spectra. X-ray photoelectron spectroscopy ; Computer science ; Chemical engineering ; Engineering Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Mrigi Munjal | Jaewan Lee | Chang-Young Park | Sehui Han
Source: arXiv (Cornell University) (preprint, 2024-12-05)
Field: Materials Science — Materials Chemistry

Lattice Lingo: Effect of Textual Detail on Multimodal Learning for Property Prediction of Crystals Most prediction models for crystal properties employ a unimodal perspective, with graph-based representations, overlooking important non-local information that affects crystal properties. Some recent studies explore the impact of integrating graph and textual information on crystal property predictions to provide the model with this "missing" information by concatenation of embeddings. However, such studies do not evaluate which type of textual information is actually beneficial. We concatenate graph representations with text representations derived from textual descriptions with varying levels of detail. These descriptions, generated using the Robocrystallographer package, encompass global (e.g., space group, crystal type), local (e.g., bond lengths, coordination environment), and semiglobal (e.g., connectivity, arrangements) information about the structures. Our approach investigates how augmenting graph-based information with various levels of textual detail influences the performance for predictions for shear modulus and bulk modulus. We demonstrate that while graph representations can capture local structural information, incorporating semiglobal textual information enhances model performance the most. Global information can support performance further in the presence of semiglobal information. Our findings suggest that the strategic inclusion of textual information can enhance property prediction, thereby advancing the design and discovery of advanced novel materials for battery electrodes, catalysts, etc. Property (philosophy) ; Lattice (music) ; Artificial intelligence ; Computer science ; Natural language processing ; Philosophy ; Physics ; Epistemology Machine Learning in Materials Science ; Text and Document Classification Technologies
Authors: David Moss
Source: Preprints.org (preprint, 2024-03-22)
Field: Materials Science — Materials Chemistry

2D Materials for Integrated Optical Polarizers Optical polarizers are essential components for the selection and manipulation of light polarization states in optical systems. Over the past decade, the rapid advancement of photonic technologies and devices has led to the development of a range of novel optical polarizers, opening avenues for many breakthroughs and expanding applications across diverse fields. Particularly, two-dimensional (2D) materials, known for their atomic thin film structures and unique optical properties, have become attractive for implementing optical polarizers with high performance and new features that were not achievable before. This paper reviews recent progress in 2D-material-based optical polarizers. First, an overview of key properties of various 2D materials for realizing optical polarizers is provided. Next, the state-of-the-art optical polarizers based on 2D materials, which are categorized into spatial-light devices, fiber devices, and integrated waveguide devices, are reviewed and compared. Finally, we discuss the current challenges of this field as well as the exciting opportunities for future technological advances. Polarizer ; Materials science ; Computer science ; Optics ; Physics ; Birefringence 2D Materials and Applications ; Photonic and Optical Devices
Authors: Thompson Odion Igunma | Adeoye Taofik Aderamo | Olisakwe Henry Chukwuemeka
Source: Open Access Research Journal of Engineering and Technology (review, 2024-10-13)
Field: Materials Science — Materials Chemistry

Ceramic matrix composites for corrosion-resistant next-generation nuclear reactor systems: A conceptual review of enhancements in durability against molten salt attack Ceramic matrix composites (CMCs) are emerging as a key material class for enhancing corrosion resistance in next-generation nuclear reactor systems, particularly in high-temperature molten salt environments. These environments, critical for advanced nuclear reactors such as molten salt reactors (MSRs), present severe challenges, including aggressive chemical attacks that degrade traditional structural materials over time. This conceptual review explores the development and application of CMCs to improve the durability and corrosion resistance of nuclear reactors exposed to molten salt attacks. CMCs, which consist of ceramic fibers embedded in a ceramic matrix, offer significant advantages, such as high thermal stability, mechanical strength, and improved corrosion resistance compared to conventional materials. This review examines how CMCs can be tailored to withstand harsh operational conditions, with a focus on the selection of ceramic phases, fiber-matrix interactions, and innovative fabrication techniques that enhance their protective capabilities. Key challenges addressed include the optimization of composite design to resist molten salt corrosion, the effects of temperature on the material properties, and the long-term stability of CMCs under extreme conditions. Advances in surface treatments, coatings, and the development of hybrid CMC systems are also discussed, highlighting their potential to further enhance durability. The review outlines the use of advanced characterization techniques, such as high-temperature corrosion testing and in situ microscopy, to evaluate CMC performance in molten salt environments. Additionally, it identifies knowledge gaps in current research, emphasizing the need for long-term studies on CMC behavior under realistic reactor conditions. This review concludes by proposing future research directions and technological advancements required to integrate CMCs into next-generation nuclear reactor designs, aiming to improve system reliability and operational safety. Durability ; Corrosion ; Molten salt ; Materials science ; Ceramic ; Salt (chemistry) ; Ceramic matrix composite ; Composite material ; Matrix (chemical analysis) ; Metallurgy ; Chemistry Nuclear materials and radiation effects ; Nuclear Materials and Properties ; Graphite, nuclear technology, radiation studies
Authors: Hayri İncekara
Source: Intelligent Methods in Engineering Sciences (article, 2024-09-30)
Field: Chemistry — Analytical Chemistry

Classification of Raisin Grains Using Different Artificial Neural Network Methods In addition to its nutritional properties, raisins are also a beneficial food in terms of health due to its vitamins, minerals, antioxidants and phenolic compounds. Turkey ranks first in global raisin production with a production capacity of 24%. Many problems are encountered in the classification of raisins according to their type and quality by traditional methods. In order to overcome these problems, artificial intelligence systems, whose usage area is increasing day by day, are utilized. In this study, raisin grains were classified using 3 different Artificial Neural Network (ANN) methods using the ‘Raisin’ dataset from the UCI Machine Learning Repository. Performance measurements of Competitive Layer Neural Network (CLNN), Pattern Recognition Artificial Neural Network (PRNN) and Self-Organizing Map (SOM) methods used in classification were performed. In the obtained performance measurements, PRNN has the highest success, while SOM is weaker compared to the other two methods. CLNN, on the other hand, remains at similar levels to PRNN and offers a good alternative to PRNN. Artificial neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses
Authors: Shanshan Liu | Ziheng Wang | Zhen Gao | Pedro Reviriego | Farzad Niknia | Xiaochen Tang | Jun Zhou | Fabrizio Lombardi
Source: IEEE Nanotechnology Magazine (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Are Emerging Machine Learning Models Dependable at the Nanoscales? Machine learning models by emerging neural networks (NNs) are being developed to solve advanced and complex tasks; for example, multi-branch NNs (such as Siamese and Triplet networks) have been proposed to perform similarity checking tasks, and large-scale models (such as Bidirectional Encoder Representations from Transformers (BERT) and Stable Diffusion) have been developed for nature language and image processing. These emerging NNs that often have a large model size, are potentially resilient to small errors during computation; however, when considering random errors originating from the underlying hardware implementation (such as soft errors on nanoscale circuits induced by radiation or voltage scaling), their complexity poses an obvious question: can these models still operate dependably when implemented by nano-scaled CMOS technologies? This article explores this concern by analyzing and evaluating the dependability of emerging NNs in the presence of hardware induced errors; the results are expected to provide insights for relevant error protection techniques when applying these models in safety-critical applications. Nanotechnology ; Computer science ; Materials science Machine Learning in Materials Science