jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sung Kyu Jang | Woosung Lee | Ga In Choi | J.H. Kim | Minji Kang | Seong-Ho Kim | Jong Hyun Choi | Seul-Gi Kim | Seoung‐Ki Lee | Hyeong‐U Kim | Hyeongkeun Kim
Source: Sensors (article, 2024-11-15)
Field: Chemistry — Spectroscopy

Data-Driven Analysis of High-Temperature Fluorocarbon Plasma for Semiconductor Processing The semiconductor industry increasingly relies on high aspect ratio etching facilitated by Amorphous Carbon Layer (ACL) masks for advanced 3D-NAND and DRAM technologies. However, carbon contamination in ACL deposition chambers necessitates effective fluorine-based plasma cleaning. This study employs a high-temperature inductively coupled plasma (ICP) system and Time-of-Flight Mass Spectrometry (ToF-MS) to analyze gas species variations under different process conditions. We applied Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) to identify key gas species, and used the First-Order Plus Dead Time (FOPDT) model to quantify dynamic changes in gas signals. Our analysis revealed the formation of COF3 at high gas temperatures and plasma power levels, indicating the presence of additional reaction pathways under these conditions. This study provides a comprehensive understanding of high-temperature plasma interactions and suggests new strategies for optimizing ACL processes in semiconductor manufacturing. Plasma ; Semiconductor ; Dram ; Semiconductor device fabrication ; Plasma processing ; Wafer ; Inductively coupled plasma ; Analytical Chemistry (journal) ; Etching (microfabrication) ; Optoelectronics ; Materials science ; Chemistry ; Nanotechnology ; Layer (electronics) ; Environmental chemistry Mass Spectrometry Techniques and Applications ; Laser-induced spectroscopy and plasma ; Ion-surface interactions and analysis
Authors: D. Reiser | Achim von Keudell
Source: Plasma Chemistry and Plasma Processing (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Reaction Mechanisms and Plasma-Catalyst Interaction in Plasma-Assisted Oxidation of n-Butane: A Data-Driven Approach Abstract Experimental investigations of n-butane oxidation under atmospheric-pressure plasma conditions and in He-dilution have provided detailed information on the power-dependence of the conversion of $$\text {C}_{4}\text {H}_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mtext>C</mml:mtext> <mml:mn>4</mml:mn> </mml:msub> <mml:msub> <mml:mtext>H</mml:mtext> <mml:mn>10</mml:mn> </mml:msub> </mml:mrow> </mml:math> into CO and $$\text {CO}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>CO</mml:mtext> <mml:mn>2</mml:mn> </mml:msub> </mml:math> at 450 K surface temperature. The rf-plasma discharge has been equipped with a $$\text {MnO}_{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>MnO</mml:mtext> <mml:mn>2</mml:mn> </mml:msub> </mml:math> -catalyst, and a significant impact on the reaction chain due to the presence of the catalyst surface could be observed. We report on ongoing data-based model development. Recently, a reaction kinetic model has been published, which agrees well with the experimental data (Stewig et al. in Plasma Sources Sci Technol 32:105006, 2023). However, that model could not clearly identify the main mechanisms in the interaction of plasma and catalyst. We show that various models can be found that explain the data similarly well. Detailed sensitivity analysis shows that only a maximum of three parameters can be identified in all the models considered for the currently limited data. Despite this limitation, we intend to continue the data analysis using more general models and introduce possible surface effects. Such unified models simultaneously describe the experimental data from both measurements with and without catalyst using a single set of physical parameters. To evaluate the hypotheses, we present numerical results for certain ranges of experimental parameters, which, in a subsequent experimental verification, allows to exclude or confirm one or another model. Butane ; Catalysis ; Algorithm ; Materials science ; Chemistry ; Computer science ; Organic chemistry Catalytic Processes in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: William E. Frazier | Yucheng Fu | Lei Li | Ram Devanathan
Source: Journal of Nuclear Materials (article, 2024-09-30)
Field: Materials Science — Metals and Alloys

Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (SS316L) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ’, and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate for periods of up to 3000 h. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model . Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus , yield stress, ultimate tensile strength , and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25 % volume fraction and mechanical properties within 6 % relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ∼1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept , that microstructural evolution under variable conditions can be predicted using a statistics-based model informed by a practicably obtainable dataset. The potential applications of this type of modeling framework are discussed. Cladding (metalworking) ; Materials science ; Nuclear engineering ; Degradation (telecommunications) ; Nuclear data ; Metallurgy ; Composite material ; Nuclear physics ; Physics ; Engineering ; Electronic engineering ; Neutron Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Material Properties and Failure Mechanisms
Authors: Zilin Ruan | J. Michael Gottfried
Source: Nature Synthesis (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Single-molecule chemistry with a smart robot Robot ; Molecule ; Chemistry ; Computer science ; Nanotechnology ; Human–computer interaction ; Artificial intelligence ; Materials science ; Organic chemistry Machine Learning in Materials Science ; Various Chemistry Research Topics ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jixin Hou | Xianyan Chen | Taotao Wu | Ellen Kuhl | Xianqiao Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Automated Data-Driven Discovery of Material Models Based on Symbolic Regression: A Case Study on Human Brain Cortex Computer science ; Regression ; Regression analysis ; Artificial intelligence ; Neuroscience ; Psychology ; Machine learning ; Statistics ; Mathematics Machine Learning in Materials Science ; Image Processing and 3D Reconstruction
Authors: Owais Ahmad | Rakesh Kumar Maurya | R. Mukherjee | Somnath Bhowmick
Source: Diffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena (article, 2024-06-11)
Field: Materials Science — Materials Chemistry

Integrated Phase Field and Machine Learning Study of Microstructure Evolution during Interface-Controlled Spinodal Decomposition This study leverages artificial intelligence (AI) to advance materials science, focusing on microstructural evolution in binary alloys during spinodal decomposition. Following the formulation of Zhu et al., we explore the microstructure evolution during interface-controlled spinodal decomposition. A comprehensive dataset captures the dynamic microstructural changes, highlighting the model's efficiency in analyzing complex data. The innovative use of an Autoencoder- ConvLSTM model enables precise, low-error microstructural transformation predictions, demonstrating AI’s potential in materials science research. This work provides a deeper understanding of material behaviors and offers new research directions. Spinodal decomposition ; Microstructure ; Spinodal ; Autoencoder ; Materials science ; Interface (matter) ; Transformation (genetics) ; Field (mathematics) ; Artificial intelligence ; Decomposition ; Phase (matter) ; Computer science ; Nanotechnology ; Deep learning ; Metallurgy ; Physics ; Chemistry ; Mathematics ; Composite material Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; High Temperature Alloys and Creep
Authors: Kun Miao | Zhenya Ding | Md Nayeem Hasan Kashem | Hyuntaek Lim | Wenwen Wang | Wei Li
Source: Journal of Polymer Science (article, 2024-06-04)
Field: Materials Science — Surfaces, Coatings and Films

A microfluidic platform for automatic continuous flow layer‐by‐layer assembly of polymer multilayered nanofilms Abstract Capillary flow layer‐by‐layer assembly is a microfluidic platform for high throughput preparation and screening of multilayered polymer nanofilms. However, it is time‐consuming and labor‐intensive, since the switching of polyelectrolyte solutions is done manually. As the emergence of flow chemistry and microdevices for artificial intelligence‐based lab automation, an automatic system that can switch polymer solutions and washing liquids is urgently needed for building functional LbL films inside microfluidic devices. Herein, we construct a continuous flow (ConF)‐layer‐by‐layer (LbL) device that can automatically fabricate polymer nanofilms inside a microfluidic device. By including a flow separator and a mixer (a model polyelectrolyte pair), chitosan and carboxymethyl cellulose can be deposited inside a microfluidic device using the ConF‐LbL system. Resulting nanofilms are studied by investigating thickness and surface roughness of the nanofilms and comparing with the other conventional LbL methods. The acid dissolution method was used to compare the differences in stability between nanofilms made from ConF‐LbL and static immersive method in similar conditions. Furthermore, by using one optimized film formulation for coating on the inside walls of a microfluidic chip, we successfully demonstrated the ability of this device to capture cancer cells in whole blood. This ConF‐LbL platform is expected to be a convenient tool to coat functional nanofilms inside microdevices for biomedical and sensor applications. Layer (electronics) ; Microfluidics ; Layer by layer ; Polymer ; Flow (mathematics) ; Materials science ; Nanotechnology ; Composite material ; Mechanics ; Physics Polymer Surface Interaction Studies ; Nanofabrication and Lithography Techniques ; Surface Modification and Superhydrophobicity
Authors: Mohanalingam Kathaperumal | Pragna Bhaskar | Austin J. Toro | Pratik Nimbalkar | Lila R. Dahal | Muhannad S. Bakir
Source:  (article, 2024-05-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Characterization of Warpage of Ultra-Low-K Dielectric Materials and Correlation with Modulus and Coefficient of Thermal Expansion Advanced high-performance computing (HPC) and artificial intelligence (AI) systems demand arger glass package systems that may extend to 200 mm × 200 mm package size. This large package sizes invariably require stringent properties of the insulation/build-up layers consisting of low to Ultra-low K (ULK) dielectric polymers. The inclusion of ULK polymer layers is of great importance to improve latency, signal integrity and energy per bit owing to their reduced capacitance. However, ULK polymers are typically without any added filler particles and hence need to be thoroughly characterized to realize a good control on yield ad reliability. Particularly in the case of fine line RDL (< 2 μm Lines/Spaces) both RDL critical dimensions yield and reliability depend upon the warpage, surface roughness of the dielectric insulation layer. Hence it is of great significance to measure the warpage of the ULK layer in order to understand the influence of warpage on yield of fine line RDL across the entire length of the package. By virtue of the molecular groups present in ULK polymers which consist of moderately fluorinated to perfluorinated moieties in the backbone, evaluation of potential correlation between warpage to yield, adhesion strength and surface roughness is of great importance. The ULK polymers investigated in this paper include photo-imageable and non-photo-imageable dielectric polymers. Thermal expansion ; Materials science ; Dielectric ; Composite material ; Characterization (materials science) ; Low-k dielectric ; Elastic modulus ; Modulus ; Electronic engineering ; Optoelectronics ; Nanotechnology ; Engineering Copper Interconnects and Reliability ; Ferroelectric and Piezoelectric Materials ; Semiconductor materials and devices
Authors: Shouxin Zhang | Chunhao Ye | Zhiwei Chen | He Huan | Dingding Yang
Source: Journal of Pipeline Systems Engineering and Practice (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Improving the Accuracy of Electrochemical Experiment Data for Artificial Intelligence–Based Carbon Steel Corrosion Analysis Carbon steel is an essential material for constructing pipelines in different industries, but serious casualties and significant economic loss could occur due to the corrosion of the pipeline steel. The artificial intelligence–based method has been shown successful in evaluating the corrosion problem in pipelines. The accuracy of the corrosion data used in the artificial intelligence–based method is crucial. The data can be collected from field tests and laboratory experiments. Compared with field tests, the corrosion data obtained through laboratory experiments, such as electrochemical measurements, are more comprehensive, but the results are often inaccurate due to the nonuniform surface state. This study aims to propose a method to improve the accuracy of electrochemical experiment data by modified the surface morphology. To achieve the objective, a technique was designed to prepare the uniform morphology. Moreover, different electrochemical measurements, texture analysis, micromorphological characteristics, and chemical composition analysis were performed under the nonuniform and uniform surface conditions in this work. It was found that the addition of 0.5×10−3 M Fe2+ was effective in preparing a homogenous electrode surface while almost having no additional impact on the corrosion process. The results proved that the surface morphology of the carbon steel electrode significantly influenced the results of the open circuit potential and potentiodynamic polarization. A uniform surface could improve the accuracy of electrochemical measurements. Corrosion ; Carbon steel ; Engineering ; Forensic engineering ; Materials science ; Environmental science ; Computer science ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Hiroki Kaifu | Sandra M. Troian | Artem Baskin
Source: Physical Review Research (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

How caged motion in the contact layer enhances thermal tunneling across a liquid/solid interface Ever more powerful and densely packed chips for applications like cryptocurrency mining and artificial intelligence generate such enormous heat fluxes that designers are pivoting from gas to liquid cooling to forestall damage from thermal runaway. Even with optimal flow patterns, however, the intrinsic thermal boundary resistance at the liquid/solid (L/S) interface poses an additional source of thermal impedance. There is a lingering misconception in the field that the higher the liquid contact density, the more frequent the L/S collision rate and the smaller the thermal slip length. Here we present an insightful counterexample based on nonequilibrium molecular dynamics simulations of a simple liquid confined between two face centered cubic crystals at different temperatures aligned with the [001], [011] or [111] facet plane. Measurements of various static and dynamic quantities of the contact layer reveal the ways in which long-range order, anisotropy of the L/S potential, and correlated motion act to reduce thermal boundary resistance. Systems with the smallest thermal slip length exhibit two distinct features: 2D caged motion with stringlike alignment of liquid particles, unlike that observed in glassy systems, and larger nonergodicity parameter with shorter, not longer, caging times. This trapping and release mechanism suggests a paradigm for the design of L/S interfaces to maximize thermal exchange across a classical L/S interface. Published by the American Physical Society 2024 Thermal ; Thermal resistance ; Slip (aerodynamics) ; Thermal contact conductance ; Interfacial thermal resistance ; Materials science ; Quantum tunnelling ; Anisotropy ; Condensed matter physics ; Non-equilibrium thermodynamics ; Mechanics ; Chemical physics ; Thermal contact ; Boundary layer ; Nanotechnology ; Chemistry ; Thermodynamics ; Physics ; Optics Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Phase Equilibria and Thermodynamics
Authors: Abdullah Al-Shamrani | Ahmed Abdulrazzaq Hadi | Abdullah Almutairi | Wafa Saleh ALkhweildy | Ghada Mansour Aldubiban | Albader Mohammed Moafa | Fatimah Ali Mohammad Alshehri | Mohammed hussein Alnami | A. Al-Qarni | Zaki Hamad Ayoub | Mohammed Ali Banajia | hameed nasser hameed Almutairi | FatimMohammed bin Ahmed Alnami | Ahmed Mousa Ali Safhi | Fahad Khalid Mohammed Aldossary
Source: Egyptian Journal of Chemistry (article, 2024-12-01)
Field: Materials Science — Biomaterials

Advancing Targeted Drug Delivery: The Role of Smart Polymers in Responsive Therapeutic Systems. Background:Targeted drug delivery systems have transformed modern therapeutics by enabling precise delivery of drugs to specific sites, thereby reducing systemic toxicity. Smart polymers, with their unique ability to respond to environmental and biological stimuli, have emerged as innovative materials for developing responsive therapeutic systems. These polymers enable controlled drug release in response to triggers such as pH, temperature, enzymatic activity, and light, providing a significant advantage over conventional delivery systems. Despite their potential, challenges such as scalability, biocompatibility, and regulatory hurdles remain barriers to widespread clinical adoption.Aim:This paper aims to explore the role of smart polymers in advancing targeted drug delivery systems, with a focus on their responsive mechanisms, therapeutic applications, and future potential.Methods:The paper synthesizes findings from peer-reviewed studies and experimental data to evaluate the design, function, and clinical applications of smart polymers. Key responsive mechanisms and their implementation in polymer-based systems such as hydrogels, micelles, and nanocarriers are analyzed. Comparative evaluations with non-responsive delivery systems and case studies of clinical applications are included.Results:Smart polymers demonstrate significant improvements in targeted delivery, including enhanced drug bioavailability, reduced systemic toxicity, and improved therapeutic outcomes. Successful applications are highlighted in oncology, diabetes management, and neurodegenerative disease treatments. Case studies reveal promising clinical trial outcomes, but challenges in cost, production, and regulatory compliance remain.Conclusion:Smart polymers represent a transformative advancement in drug delivery, providing responsive, patient-specific therapeutic solutions. Addressing current challenges through interdisciplinary innovation and integration with emerging technologies such as nanotechnology and artificial intelligence could unlock their full potential, improving the efficacy and accessibility of future therapies. Drug delivery ; Drug ; Targeted drug delivery ; Smart polymer ; Nanotechnology ; Business ; Medicine ; Pharmacology ; Chemistry ; Materials science ; Polymer Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; 3D Printing in Biomedical Research
Authors: Koushki, Mehdi | Amiri-Dashatan, Nasrin | Pourghadamyari, Hossein | Khodabandehloo, Hadi | Bagheri, Fatemeh | Farahani, Masoumeh | Tayebi, Lobat
Source: ODU Digital Commons (Old Dominion University) (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

An Overview of the Relationships Between the Food Industry and Nanotechnology Background and Objective: Due to the growth of the global population, food demands are increasing. Hence, the need to develop more efficient methods for producing better quality, safer, and more sustainable food seems essential. In the past decades, the use of nanoscale materials has increased greatly due to the unique chemical, physical, and biological characteristics of nanomaterials compared to bulk materials. This research presents nanotechnology role in improving sensorial properties (taste, appearance, and texture) and safety aspects as well as processing and packaging of foods. The use of nano-omics-based technologies and artificial intelligence-nanotechnology-based technologies in the food industry is also discussed. Results and Conclusion: Linking food science and nanotechnology as a multidisciplinary scientific field can help safeguard food security, production, processing, storage, and quality control. Although nanotechnology has addressed the challenges in different sectors of food biotechnology, there are still various problems and opportunities to improve the current conditions as well as the complications and health risks caused by nanotechnology. Food engineering ; Impact of nanotechnology ; Multidisciplinary approach ; Applications of nanotechnology ; Food industry ; Food safety ; Safeguard ; Societal impact of nanotechnology ; Quality (philosophy) Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Biological Research and Disease Studies