jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junwei Zhao | Zhengfei Kuang | Rui Long | Zhichun Liu | Wei Liu
Source: Energy Storage and Saving (article, 2023-12-11)
Field: Materials Science — Materials Chemistry

Impacts of thermal and electric contact resistance on the material design in segmented thermoelectric generators Segmented thermoelectric generators (STEGs) can exhibit present superior performance than those of the conventional thermoelectric generators. Thermal and electrical contact resistances exist between the thermoelectric material interfaces in each thermoelectric leg. This may significantly hinder performance improvement. In this study, a five-layer STEG with three pairs of TE materials was investigated considering the thermal and electrical contact resistances on the material contact surface. The STEG performance under different contact resistances with various combinations of TE materials were analyzed. The relationship between the material sequence and performance indicators under different contact resistances is established by machine learning. Based on the genetic algorithm, for each contact resistance combination, the optimal material sequences were identified by maximizing the electric power and energy conversion efficiency. To reveal the underlying mechanism that determines the heat-to-electrical performance, the total electrical resistance, output voltage, ZT value, and temperature distribution under each optimized scenario were analyzed. The STEG can augment the heat-to-electricity performance only at small contact resistances. A large contact resistance significantly reduces the performance. At an electrical contact resistance of RE = 10–3 K∙m2∙W−1 and thermal contact resistance of RT = 10–8 Ω∙m2, the maximum electric power was reduced to 5.71 mW (90.86 mW without considering the contact resistance). And the maximum energy conversion efficiency is lowered to 2.54% (12.59% without considering the contact resistance). Contact resistance ; Materials science ; Thermoelectric effect ; Thermal contact conductance ; Electrical contacts ; Thermal resistance ; Thermal contact ; Thermoelectric generator ; Electrical resistance and conductance ; Composite material ; Energy conversion efficiency ; Thermoelectric materials ; Electric potential energy ; Contact area ; Voltage ; Thermal ; Electrical engineering ; Thermal conductivity ; Power (physics) ; Optoelectronics ; Layer (electronics) ; Thermodynamics ; Engineering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Innovative Energy Harvesting Technologies
Authors: Albert Argilaga | Chaofa Zhao
Source: Powder Technology (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Rupture distances and capillary forces of liquid bridges: Closed-form expressions and ANNs-trained prediction models Capillary action ; Range (aeronautics) ; Particle swarm optimization ; Artificial neural network ; Computer science ; Algorithm ; Mechanics ; Engineering ; Artificial intelligence ; Materials science ; Physics Pickering emulsions and particle stabilization ; Enhanced Oil Recovery Techniques ; Fluid Dynamics and Heat Transfer
Authors: Dulyawat Doonyapisut | Byeongkyu Kim | Jung Kyu Kim | Eunseok Lee | Chan‐Hwa Chung
Source: Engineering Applications of Artificial Intelligence (article, 2023-08-26)
Field: Materials Science — Materials Chemistry

Deep generative learning for exploration in large electrochemical impedance dataset Computer science ; Artificial intelligence ; Deep learning ; Task (project management) ; Visualization ; Representation (politics) ; Generative model ; Pattern recognition (psychology) ; Machine learning ; Data mining ; Generative grammar Machine Learning in Materials Science ; Topic Modeling ; Ferroelectric and Negative Capacitance Devices
Authors: Shungo Natsui | Yuko Goto | Junichi Takahashi | Hiroshi Nogami
Source: Chemical Engineering Science (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Pattern analysis of the combustions of various copper concentrate tablets using high-speed microscopy and video-based deep learning The combustion behavior of the complex, ever-changing Cu concentrate with SiO2 flux in a flash smelting shaft should be comprehensively understood to improve the efficiency and energy consumption of smelting. To characterize the combustion behavior of each sample, combustion studies involving high-speed digital microscopy and thermal measurements were performed using numerous small Cu concentrate tablets. Generally, two temperature ranges of heating retardation were observed during combustion, at approximately 800–1000 and 1150–1200 °C. A novel video-based Cu concentrate classification system was used to successfully recognize the different combustion patterns of tablets with Cu concentrate-SiO2 mixtures under oxidation gas. This classification system also enabled the calculation of the chemical composition of the concentrate by transforming the network output into a probability distribution. The algorithm based on deep learning employed in this study could learn the combustion behaviors of SiO2-containing Cu concentrates using time-series images extracted from video data. Combustion ; Smelting ; Copper ; Materials science ; Flash (photography) ; Flash smelting ; Metallurgy ; Process engineering ; Artificial intelligence ; Computer science ; Analytical Chemistry (journal) ; Biological system ; Chemistry ; Engineering ; Chromatography ; Optics ; Physics Crystallization and Solubility Studies ; Thermography and Photoacoustic Techniques ; Spectroscopy and Chemometric Analyses
Authors: P.O. Oghenerukevwe | C.I. Ajuwa | Olusegun David Samuel | T.F. Adepoju
Source: Case Studies in Chemical and Environmental Engineering (article, 2023-09-01)
Field: Materials Science — Materials Chemistry

Carica papaya leaf extract (CPLE) as green corrosion inhibitor for AISI 4140 steel in 15% hydrochloric acid medium This study investigated the potential of Carica papaya leaf extract (CPLE) as the corrosion inhibition and un-inhibition of AISI 4140 steel tubing in a 15% HCl acidizing oilfield environment. The C. papaya leaf was extracted using continuous extraction method with ethanol. The selected mild steel elemental compositions were determined using atomic mass spectrometry analyzer. Process modeling and optimization of process condition was carried out by response surface methodology impose central composite rotatable design (RSM-CCRD) and artificial neural network impose genetic algorithm (ANN-GA) with constraint variables: inhibition conc.; X1, immersion time; X2, and temperature; X3, respectively. The thermodynamic parameters were evaluated using corrosion rate and transitional state equations, while the fitness of absorption isothermal study was examined via Langmuir, Temkin, Frumkin, and Flory-Huggin equations. Results shows that iron (Fe) is the predominant element in the alloy steel used in this study with other elements present in small quantities. Statistical analysis by RSM-CCRD validated the minimum CR and optimum IE yields of 35.90 (mm/yr) and 69.54%, while ANN-GA validated the minimum CR and optimum IE yields of 26.10 (mm/yr) and 69.82%, respectively. Based on thermodynamic parameter, the Gibb's free energy change of absorption was negative and fell within the thresholds of chemisorptions. The fitness of absorption was established by Langmuir isotherm. The study concluded that the modeling and optimization of absorption of CPLE on AISI 4140 steel in 15% HCl medium is feasible and can be used in the industries. Response surface methodology ; Carica ; Corrosion ; Hydrochloric acid ; Materials science ; Desorption ; Isothermal process ; Atomic absorption spectroscopy ; Extraction (chemistry) ; Central composite design ; Metallurgy ; Absorption (acoustics) ; Nuclear chemistry ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Adsorption ; Thermodynamics ; Composite material ; Botany ; Physics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Komal Sharma | Ganesh Kumar Sethi | Rajesh K. Bawa
Source: Multimedia Tools and Applications (article, 2023-02-10)
Field: Chemistry — Analytical Chemistry

Automatic Rice Variety Identification System: state-of-the-art review, issues, challenges and future directions Variety (cybernetics) ; Computer science ; Identification (biology) ; Task (project management) ; Field (mathematics) ; Quality (philosophy) ; Data science ; Artificial intelligence ; Systems engineering ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Aakrati Jain | Risa Miyazawa | Sathya Raghavan | Prabudhya Roy Chowdhury | Mukta Farooq | Arvind Kumar | Katsuyuki Sakuma
Source:  (article, 2023-05-01)
Field: Materials Science — Materials Chemistry

Thermal Characterization of 3-D Stacked Heterogeneous Integration (HI) Package for High-Power Computing Applications 3-D stacking of chiplets in Heterogeneous Integration (HI) packages with interconnects to supply power and transfer data is gaining popularity for High-Performance Computing (HPC) applications. Key advantages driving interest in this approach include vertical stacking which increases bandwidth and reduces energy and parasitic losses for high-power computing applications. However, chip stacking may increase the package height and increase the thermal and mechanical stresses on the package. Furthermore, compute intensive HPC applications like Artificial Intelligence (AI) and machine learning (ML) algorithms drive significant thermal management challenges. Therefore, it is critical to characterize the thermal performance of stacked silicon packages to predict the cooling requirements for optimal functioning in high-power computing applications. In this study, a numerical simulation model is built for a lidded 3-D package with a two-chip stack. The model is validated against experiments for a 2-D package architecture with a spreader. A comprehensive case study then is performed by applying different boundary conditions, changing physical parameters, and by making geometrical modifications, with the overall goal being to independently analyze the effects of each on thermal performance compared to that of a baseline case. The boundary conditions studied include variable uniform power map in one or both chips, non-uniform heat dissipation, and convection heat transfer coefficient of the spreader. The physical parameters include chip thickness, TIM material, and lid thickness. The geometrical modifications include logic chip on memory and vice-versa, face-to-face vs. face-to-back bonding, pitch of interconnect, number of chips in stack, and number of chiplets in a stack layer. Based on the thermal performance for these conditions, required cooling techniques are suggested for improved functioning of the overall 3-D assembly. Candidate approaches to reducing thermal resistance and package temperature are also prescribed. Computer science ; Stacking ; Three-dimensional integrated circuit ; Stack (abstract data type) ; Thermal resistance ; Junction temperature ; Heat transfer ; Chip ; Electronic engineering ; Computational science ; Mechanical engineering ; Power (physics) ; Engineering Thermal properties of materials ; 3D IC and TSV technologies ; Heat Transfer and Optimization
Authors: Wei Zhang | Li Zhou | Tinggui Yan | Mohan Chen
Source: The Journal of Physical Chemistry B (article, 2023-10-09)
Field: Chemistry — Inorganic Chemistry

Speciation of La<sup>3+</sup>–Cl<sup>–</sup> Complexes in Hydrothermal Fluids from Deep Potential Molecular Dynamics The stability of rare earth element (REE) complexes plays a crucial role in quantitatively assessing their hydrothermal migration and transformation. However, reliable data are lacking under high-temperature hydrothermal conditions, which hampers our understanding of the association behavior of REE. Here a deep learning potential model for the LaCl<sub>3</sub>-H<sub>2</sub>O system in hydrothermal fluids is developed based on the first-principles density functional theory calculations. The model accurately predicts the radial distribution functions compared to <i>ab initio</i> molecular dynamics (AIMD) simulations. Furthermore, species of La-Cl complexes, the dissociation pathway of the La-Cl complexes dissociation process, and the potential of mean forces and corresponding association constants (log<i>K</i>) for LaCl<sub><i>n</i></sub><sup>3-<i>n</i></sup> (<i>n</i> = 1-4) are extensively investigated under a wide range of temperatures and pressures. Empirical density models for log<i>K</i> calculation are fitted with these data and can accurately predict log<i>K</i> data from both experimental results and AIMD simulations. The distribution of La-Cl species is also evaluated across a wide range of temperatures, pressures, and initial chloride concentration conditions. The results show that La-Cl complexes are prone to forming in a low-density solution, and the number of bonded Cl<sup>-</sup> ions increases with rising temperature. In contrast, in a high-density solution, La<sup>3+</sup> dominates and becomes the more prevalent species. Hydrothermal circulation ; Dissociation (chemistry) ; Molecular dynamics ; Chemistry ; Density functional theory ; Ion ; Ab initio ; Chloride ; Thermodynamics ; Physical chemistry ; Chemical physics ; Computational chemistry ; Physics ; Chemical engineering Radioactive element chemistry and processing ; Extraction and Separation Processes ; Metal Extraction and Bioleaching
Authors: Sudeep Koppayithodi | Palash Jana | Subhajit Bandyopadhyay
Source: Chemistry - A European Journal (article, 2023-03-30)
Field: Chemistry — Spectroscopy

Highly Selective and Quantitative Point‐of‐Care Diagnostic Method for Adrenaline Adrenaline, also known as epinephrine, is a neurotransmitter/hormone that is an important target in diagnostics. Development of an effective method for detecting it in the presence of other neurotransmitters is a challenging task. The electrochemical and fluorescent techniques commonly used have low selectivity in distinguishing among catecholamines. Herein, a small-molecule organic probe with an activated furfural moiety is reported to exploit the nucleophilicity of epinephrine to generate a bright-colored donor-acceptor Stenhouse adduct. Among nine common neurotransmitters or their analogues, only epinephrine was found to generate a unique colour change discernible with the naked eye, whereas the other ones remain unaffected. Under various in-field detection conditions, including solution, droplet, and paper strip-based detection, the colour change were also noticeable. The low detection limit of 1.37 nM and a limit of quantitation of 4.37 nM were achieved with simple UV/Vis methods in addition to the sub-ppm level sensing under visual conditions with naked eyes. The probe could be used for practical colorimetric measurements as a point-of-care tool without any complex and expensive machinery, making this approach accessible to all. In addition, using a simple smartphone, the determination of epinephrine concentrations is possible by using machine-learning techniques. Epinephrine ; Detection limit ; Chemistry ; Naked eye ; Combinatorial chemistry ; Chromatography ; Endocrinology ; Biology Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques ; Luminescence and Fluorescent Materials
Authors: Indrani Choudhuri | Jingyun Ye | Donald G. Truhlar
Source: Chemical Physics Reviews (article, 2023-08-07)
Field: Chemistry — Inorganic Chemistry

Computational quantum chemistry of metal–organic frameworks Metal–organic frameworks (MOFs) have premium exceptional properties for a variety of functions, such as gas separation and storage and catalysis. The large variety of possible inorganometallic nodes and organic linkers provide an almost unlimited number of combinations for assembling MOFs, which makes the experimental characterization and examination of all potentially useful combinations practically impossible. Furthermore, experimental studies of MOFs typically fall short in uncovering crucial details regarding their mechanisms of action or the molecular details responsible for their functional properties, such as the nature of adsorbate binding or the structures of transition states. Computational modeling has, therefore, become an efficient and important tool for strategizing the functionalization of MOFs and explicating the mechanisms of their functions. Here, we review the computational methodologies used for computational studies of MOFs, especially Kohn–Sham density functional theory and combined quantum mechanical and molecular mechanical methods for calculating their structural, electronic, and magnetic properties, as well as for understanding the mechanisms of MOFs' applications to magetic devices, thermal conduction, gas adsorption, separation, storage, and sensing, thermal catalysis, photocatalysis, and electrocatalysis. Metal-organic framework ; Nanotechnology ; Electrocatalyst ; Characterization (materials science) ; Density functional theory ; Variety (cybernetics) ; Quantum ; Chemistry ; Adsorption ; Biochemical engineering ; Materials science ; Computer science ; Computational chemistry ; Physics ; Physical chemistry ; Quantum mechanics ; Electrochemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; X-ray Diffraction in Crystallography
Authors: Peichen Zhong | Fengyu Xie | Luis Barroso-Luque | Liliang Huang | Gerbrand Ceder
Source: PRX Energy (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Modeling Intercalation Chemistry with Multiredox Reactions by Sparse Lattice Models in Disordered Rocksalt Cathodes Modern battery materials can contain many elements with substantial site disorder, and their configurational state has been shown to be critical for their performance. The intercalation voltage profile is a critical parameter to evaluate the performance of energy storage. The application of commonly used cluster expansion techniques to model the intercalation thermodynamics of such systems ab initio is challenged by the combinatorial increase in configurational degrees of freedom as the number of species grows. Such challenges necessitate the efficient generation of lattice models without overfitting and proper sampling of the configurational space under the requirement of charge balance in ionic systems. In this work, we introduce a combined approach that addresses these challenges by (1) constructing a robust cluster expansion Hamiltonian using the sparse regression technique, including -norm regularization and structural hierarchy; and (2) implementing semigrand-canonical Monte Carlo to sample charge-balanced ionic configurations using the table-exchange method and an ensemble average approach. These techniques are applied to a disordered rocksalt oxyfluoride (LMNOF) that is part of a family of promising earth-abundant cathode materials. The simulated voltage profile is found to be in good agreement with experimental data and particularly provides a clear demonstration of the and oxygen contributions to the redox potential as a function of content. Cluster expansion ; Statistical physics ; Chemistry ; Ionic bonding ; Thermodynamics ; Computer science ; Physics ; Ion ; Quantum mechanics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Balaraman Vedhanarayanan | K. C. Seetha Lakshmi | Tsung‐Wu Lin
Source: Batteries (article, 2023-09-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interfacial Tuning of Polymeric Composite Materials for High-Performance Energy Devices Polymeric composite materials attracted attention when pristine polymers alone could not fulfill the necessity of high-performance functional materials for wide applications. Mixing two or more polymers (blends) together or compositing the polymers with inorganic compounds/carbon-based nanomaterials greatly solved the problem associated with the mechanical, thermal, and electronic properties along with the chemical stability, which paves a new pathway for optimizing the functional properties of active materials. However, a mere mixing of individual components sometimes would not provide enhanced properties due to the formation of phase-separated, larger domains of components. In particular, the grain boundaries of components, also known as “interfaces”, actually determine the properties of these composite materials. The tuning of interfacial properties is significant to achieve composites with higher electrical conductivity and better charge transfer kinetics if they are targeted toward high-performance energy devices. This review aims to provide an overview of recent advancements in the area of polymeric composite materials with tuned interfacial characteristics towards energy conversion (solar cells, photocatalytic hydrogen production, and nanogenerators) and energy storage (supercapacitors and metal-ion batteries) devices with very recent representative examples. Materials science ; Polymer ; Composite number ; Supercapacitor ; Compositing ; Nanomaterials ; Nanotechnology ; Energy storage ; Composite material ; Mixing (physics) ; Electrode ; Computer science ; Electrochemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Battery Materials and Technologies
Authors: José A. Rodríguez | Carlos Jimenez‐Orozco | Elizabeth Flórez | Francesc Viñes | Francesc Illas
Source: The Journal of Physical Chemistry C (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

C<sub>1</sub> Chemistry on Metal Carbide Nanoparticles: Boosting the Conversion of CO<sub>2</sub> and CH<sub>4</sub> The studies described in this Perspective show that transition metal carbide (TMC) nanoparticles can be very useful for the activation of three molecules located at the heart of C1 chemistry: H2, CH4, and CO2. They also can play a major role in the trapping and conversion of two major greenhouse gases. A combination of experiment and theory has shed light on the physical and chemical properties of these systems, which can be very different from those of bulk carbides. Molecular clusters of these compounds, which can be inserted inside the cages of zeolites or carbon nanotubes, have unsaturated metal and carbon atoms that frequently work in a cooperative way when dealing with hard-to-activate molecules, such as CH4 and CO2. These molecular clusters can evolve into nanoparticles of small to medium size (<15 nm) that have unique carbon/metal ratios and structures not seen in the bulk metal carbides. Even when their structures are cuts from bulk lattices, the TMC nanoparticles have corner or edge atoms that are active for the cleavage and conversion of C–H and C–O bonds. Here, we cover experimental and theoretical studies with well-defined metal carbide nanoparticles prepared by different methods, free and supported on diverse substrates. The Perspective ends with a discussion of current challenges and potential applications. Carbide ; Nanoparticle ; Molecule ; Metal ; Nanotechnology ; Carbon fibers ; Transition metal ; Materials science ; Carbon nanotube ; Chemistry ; Chemical physics ; Chemical engineering ; Catalysis ; Organic chemistry ; Metallurgy ; Composite material Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies