jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ivan Malashin | D.S. Daibagya | В С Тынченко | Vladimir Nelyub | А. С. Бородулин | Andrei Gantimurov | А.S. Selyukov | S. A. Ambrozevich | Roman B. Vasiliev
Source: Scientific Reports (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach The study explore machine learning (ML) techniques to predict temperature-dependent photoluminescence (PL) spectra in colloidal CdSe nanoplatelets (NPLs), leveraging polynomial regression models trained on experimental data from 85 to 270 K spanning temperatures to forecast PL spectra backward to 0 K and forward to 300 K. 6th-degree polynomial models with Tweedie regression were optimal for band energy ([Formula: see text]) predictions up to 300 K, while 9th-degree models with LassoLars and Linear Regression regressors were suitable for backward predictions to 0 K. For exciton energy ([Formula: see text]), the Lasso model of degree 5 and the Ridge model of degree 4 performed well up to 300 K, while the Tweedie model of degree 2 and Theil-Sen model of degree 2 showed promise for predictions to 0 K. Furthermore, a GA-based approach was utilized to fit experimental data to theoretical model of Fan and Varshni equations, facilitating a comparative analysis with the ML-predicted curves. Photoluminescence ; Degree (music) ; Colloid ; Lasso (programming language) ; Polynomial regression ; Exciton ; Materials science ; Regression analysis ; Thermodynamics ; Biological system ; Energy (signal processing) ; Linear regression ; Statistical physics ; Mathematics ; Computer science ; Machine learning ; Physics ; Condensed matter physics ; Chemistry ; Physical chemistry ; Statistics ; Optoelectronics Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Youcef Megrouss | Souheyla Chetioui | Chafika Farah Kaouche | Salem Yahiaoui | Khaled Drim | Zohra Douaa Benyahlou | Mansour Azayez | Sid Ahmed Kaas | Mokhtaria Drıssı | Abdelkader Chouaıh
Source: Turkish computational and theoretical chemistry/Turkish computational and theoretical chemistry : (article, 2024-12-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Theoretical investigations, Molecular Docking, ADMET analysis, Molecular Dynamic Simulation, and Drug-Likeness Scoring of (E)-1-[2-(3,4-Dimethylphenyl) Diazen-2-Ium-1-Yl] Naphthalen-2-Olate as a Corticosteroid Side-Chain-Isomerase Inhibitor In this article, the optimized structure and their associated properties of the (E)-1-[2-(3,4-Dimethylphenyl)diazen-2-ium-1-yl]naphthalen-2-olate compound (EDNO) were obtained and evaluated using The density functional theory DFT at the (B3LYP/6-311G++(d,p)) level in the gas phase, To quantify the intermolecular interactions, Hirshfeld surface(HS) analysis was used, HS and 2D fingerprints indicate H⋅⋅⋅H (52%) and C− H⋅⋅⋅C (26.7%) as the most relevant intermolecular interactions in the crystal packing of EDNO. the reduced density gradient (RDG) method was used to reveal and distinguish between attractive interactions such hydrogen bonds, repulsive interactions and van der Waals interactions. Further, molecular docking, binding free energy calculations, and ADMET prole of the title compound was carried out to determine the binding affinity and toxicity. A 100 ns molecular dynamics (MD) simulation was performed to evaluate the binding stability of the compound EDNO/2WV2 complex using Desmond. Binding free energy of the complex was computed for 100 trajectory frames using the MM-GBSA approach Docking (animal) ; Chemistry ; Molecular dynamics ; Computational biology ; Pharmacology ; Computational chemistry ; Medicine ; Biology Nonlinear Optical Materials Research ; Chemical synthesis and pharmacological studies ; Free Radicals and Antioxidants
Authors: Antonio Carnevali | M. Rosa Palacín | Clare P. Grey | Alejandro A. Franco
Source: Energy storage materials (article, 2024-02-15)
Field: Materials Science — Materials Chemistry

VOLTA: A tool for battery screening bridging the gap between virtual electrode materials and practical applications We present a battery screening Python pipeline, VOLTA. It allows for a novel battery active material explorative workflow, prioritizing the cell level performance indicators, such as cell capacity and voltage profile. This is achieved by the construction of a starting dataset of both observed and virtual active materials from the Materials Project, the implementation of the physics-based ARTISTIC project pipeline for the assessment of practical electrode properties like porosity and thickness, and the coupling of the electrodes into virtual cells, whose figures of merit are calculated, like voltage and capacity. The screening can be conducted by applying filters to these cell-level properties, achieving a indirect selection of the most suitable active materials. The approach is validated through comparison to current commercial battery technology, and we demonstrate that VOLTA is able to identify promising electrode materials for high energy batteries, like the wellknown LiCoO2, LiNiO2 and graphite. We also illustrate a case-study, where the pipeline is used to identify suitable low-voltage, realistic virtual batteries obtained by combining entries of the Materials Project battery database (a battery revealing type of task). Pipeline (software) ; Bridging (networking) ; Electrode ; Battery (electricity) ; Materials science ; Workflow ; Computer science ; Voltage ; Electrical engineering ; Engineering ; Physics ; Database ; Operating system Machine Learning in Materials Science ; Advancements in Battery Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Keiichi Okubo | Jaydeep Thik | Tomoya Yamaguchi | Chen Ling
Source: Digital Discovery (article, 2024-01-01)
Field: Chemistry — Electrochemistry

Computer vision enabled high-quality electrochemical experimentation We have developed a convolutional neural network-based method that utilizes microscopic images of prepared electrodes to directly assess sample quality. This method paves the way towards high-quality, automated electrochemical experimentation. Quality (philosophy) ; Computer science ; Philosophy ; Epistemology Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: Ting Peng | Ruiqing Wu | Bohai Wang | Tomasz Liśkiewicz | Shengwei Shi
Source: Materials (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Long-Term Storage of Ti3C2Tx Aqueous Dispersion with Stable Electrochemical Properties MXenes possess high metallic conductivity and excellent dispersion quality and pseudocapcitance. Their good hydrophilicity makes them particularly suitable as eco-friendly inks for printing applications. However, MXenes are prone to oxidization in aqueous dispersions, and it is very important to improve their stability. Here, the long-term storage of MXene aqueous dispersions was realized by the introduction of sodium L-ascorbate (NaAsc) as the antioxidant. The preserved MXenes exhibited very stable electrochemical properties. Even after 60-day storage, the supercapacitor with preserved MXenes as the electrode still demonstrated an excellent specific capacitance of 381.1 F/g at a scan rate of 5 mV/s and a good retention rate of 92.6% after 10,000 consecutive cyclic voltammetry measurements, which was nearly the same as that of fresh MXenes. The results indicate a facile and efficient method to realize the long-term storage of MXene aqueous dispersions for mass use in future energy storage. MXenes ; Materials science ; Supercapacitor ; Aqueous solution ; Energy storage ; Cyclic voltammetry ; Electrochemistry ; Chemical engineering ; Capacitance ; Dispersion (optics) ; Electrode ; Nanotechnology ; Chemistry ; Organic chemistry MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Supercapacitor Materials and Fabrication
Authors: Mgcini Keith Phuthi | Yang Huang | Michael Widom | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2024-06-18)
Field: Materials Science — Materials Chemistry

Vibrational Entropy and Free Energy of Solid Lithium using Covariance of Atomic Displacements Enabled by Machine Learning Vibrational properties of solids are key to determining stability, response and functionality. However, they are challenging to computationally predict at Ab-Initio accuracy, even for elemental systems. Ab-Initio methods for modeling atomic interactions are limited in the system sizes and simulation times that can be achieved. Due to these limitations, Machine Learning Interatomic Potentials (MLIPs) are gaining popularity and success as a faster, more scalable approach for modeling atomic interactions, potentially at Ab-Initio accuracy. Even with faster potentials, methodologies for predicting entropy, free energy and vibrational properties vary in accuracy, cost and difficulty to implement. Using the Covariance of Atomic Displacements (CAD) to predict entropy, free energy and finite-temperature phonon dispersions is a promising approach but thorough benchmarking has been hampered by the cost of Ab-Initio methods for sampling. In this work, we use a MLIP and the CAD to characterize the convergence of the predicted properties and determine optimal sampling strategies. We focus on solid lithium at zero pressure, showing that the MLIP-CAD approach reproduces experimental entropy, phonon dispersions and the martensitic transition while also comparing to more established methods. Covariance ; Entropy (arrow of time) ; Statistical physics ; Lithium (medication) ; Physics ; Materials science ; Computer science ; Theoretical physics ; Thermodynamics ; Mathematics ; Psychology ; Statistics Machine Learning in Materials Science ; Surface and Thin Film Phenomena
Authors: Antoine Baron | Enrique H. Balaguera | Sébastien Pecqueur
Source: ECS Advances (article, 2024-12-02)
Field: Materials Science — Polymers and Plastics

Correlation between Electrochemical Relaxations and Morphologies of Conducting Polymer Dendrites Conducting Polymer Dendrites (CPD) can engrave sophisticated patterns of electrical interconnects in their morphology with low-voltage spikes and few resources: they may unlock in operando manufacturing functionalities for electronics using metamorphism conjointly with electron transport as part of the information processing. The relationship between structure and information transport remains unclear and hinders the exploitation of the versatility of their morphologies to store and process electrodynamic information. This study details the evolution of CPD's circuit parameters with their growth and shape. Through electrochemical impedance spectroscopy, multiple distributions of relaxation times are evidenced and evolve specifically upon growth. Correlations are established between dispersive capacitances of dendritic morphologies and growth duration, independently from exogenous physical variables: distance, evaporation or aging. Deviation of the anomalous capacitance from the conventional Debye dielectric relaxation can be programmed, as the growth controls the dispersion coefficient of the dendrite's constant-phase elements relaxation. These results suggest that the fading-memory time window of pseudo-capacitive interconnects can practically be conditioned using CPD morphogenesis as an in materio learning mechanism. This study confirms the perspective of using electrochemistry for unconventional electronics, engraving information in the physics of conducting polymer objects, and storing information in their morphology, accessible by impedance spectral analysis. Materials science ; Electrochemistry ; Polymer ; Correlation ; Conductive polymer ; Chemical physics ; Nanotechnology ; Chemical engineering ; Composite material ; Chemistry ; Mathematics ; Physical chemistry ; Electrode ; Geometry ; Engineering Conducting polymers and applications ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Ying Wang | Zhuoyi Li | Yan Liu | Penghao Pei
Source: Polymers (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Dynamic Thermo-Mechanical Properties of Carbon Nanotube Resin Composite Films In this paper, we prepared carbon nanotube (CNT) epoxy composite films and conducted tensile experiments at various temperatures (-40 °C, -10 °C, 20 °C, and 50 °C) and frequencies (1 Hz, 10 Hz, and 20 Hz) using Dynamic Mechanical Analysis (DMA). This study reveals the effects of temperature and frequency on the mechanical properties of CNT films and CNT epoxy composite films. The results indicate that the energy storage modulus of the pure CNT film is approximately 13 times greater than that of the composite material at 20 °C. Additionally, the loss factor of the composite material is about 25 times that of pure epoxy resin and 7 times that of pure CNT film. These findings suggest that the presence of epoxy resin reduces the elastic deformation capacity of the CNT film while enhancing its damping properties. The mechanical properties of CNT films and CNT epoxy composites at varying temperatures and frequencies investigated in this work offer valuable insights for future applications and studies of CNT films and CNT epoxy composites in diverse environments. Materials science ; Epoxy ; Composite material ; Carbon nanotube ; Composite number ; Dynamic mechanical analysis ; Ultimate tensile strength ; Deformation (meteorology) ; Modulus ; Polymer Carbon Nanotubes in Composites ; Smart Materials for Construction ; Polymer composites and self-healing
Authors: Samaa H. Al-Sultani | Ali Al‐Shathr | Bashir Y. Al-Zaidi
Source: Reactions (article, 2024-12-06)
Field: Chemistry — Inorganic Chemistry

Toluene Alkylation Reactions over Y-Type Zeolite Catalysts: An Experimental and Kinetic Study The present study demonstrated an improvement in both 1-heptene conversion and mono-heptyltoluene selectivity. It simultaneously depicted the isomerization reactions of 1-heptene and toluene alkylation over Y zeolite catalysts having a Si/Al of 3.5 and a surface area of 817 m2/g. The physical properties of the fresh zeolite catalyst were characterized using XRD, FTIR, XRF, TPD, and N2 adsorption–desorption spectroscopy. The experimental part was carried out in a 100 mL glass flask connected to a reflux condenser at different reaction temperatures ranging from 70 to 90 °C, toluene:1-heptene ratios of 3–8, and catalyst weights of 0.25–0.4 g. The highest conversion of ~96% was obtained at the highest toluene:1-heptene ratio (i.e., 8:1), 0.25 g of zeolite Y, at 180 min of reaction time and under a reaction temperature of 90 °C. However, the selectivity of 2-heptyltoluene reached its highest value of ~25% under these conditions. Likewise, the kinetic modeling developed in this study helped describe the proposed reaction mechanism by linking the experimental results with the predicted results. The kinetic parameters were determined by nonlinear regression analysis using the MATLAB® package genetic algorithm. The ordinary differential equations were integrated with respect to time using the fourth-order Runge–Kutta method, and the resulting mole fractions were fitted against the experimental data. The mean relative error (MRE) values were calculated from the experimental and predicted results, which showed a reasonable agreement with the average MRE being ~11.7%. The calculated activation energies showed that the reaction rate follows the following order: coking (55.9–362.7 kJ/mol) &gt; alkylation (73.1–332.1 kJ/mol) &gt; isomerization (69.3–120.2 kJ/mol), indicating that isomerization reactions are the fastest compared to other reactions. A residual activity deactivation model was developed to measure the deactivation kinetic parameters, and the deactivation energy value obtained was about 48.2 kJ/mol. Toluene ; Alkylation ; Zeolite ; Catalysis ; Kinetic energy ; Chemistry ; Organic chemistry ; Physics Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Garbas A. dos Santos | Kélrie H. A. Mendes | Sarah G. G. de Oliveira | Gabriel J. P. Tonon | Neide Paloma Gonçalves Lopes | Thiago Henrique Rodrigues da Cunha | Mário Guimarães | Rodrigo L. Lavall | Paulo F.R. Ortega
Source: Polymers (article, 2024-10-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Performance Dual-Redox-Mediator Supercapacitors Based on Buckypaper Electrodes and Hydrogel Polymer Electrolytes In recent years, the demand for solid, thin, and flexible energy storage devices has surged in modern consumer electronics, which require autonomy and long duration. In this context, hybrid supercapacitors have become strategic, and significant efforts are being made to develop cells with higher energy densities while preserving the power density of conventional supercapacitors. Motivated by these requirements, we report the development of a new high-performance dual-redox-mediator supercapacitor. In this study, cells were constructed using fully moldable buckypapers (BPs), composed of carbon nanotubes and cellulose nanofibers, as electrodes. We evaluated the compatibility of BPs with hydrogel polymer electrolytes, based on 1 mol L<sup>-1</sup> H<sub>2</sub>SO<sub>4</sub> and polyvinyl alcohol (PVA), supplemented with different redox species: methylene blue, indigo carmine, and hydroquinone. Solid cells were constructed containing two active redox species to maximize the specific capacity of each electrode. Considering the main results, the dual-redox-mediator supercapacitor exhibits high energy density of 32.0 Wh kg<sup>-1</sup> (at 0.8 kW kg<sup>-1</sup>) and is capable of delivering 25.9 Wh kg<sup>-1</sup> at high power demand (4.0 kW kg<sup>-1</sup>). Stability studies conducted over 10,000 galvanostatic cycles revealed that the PVA polymer matrix benefits the system by inhibiting the crossover of redox species within the cell. Buckypaper ; Supercapacitor ; Electrolyte ; Materials science ; Electrode ; Mediator ; Dual (grammatical number) ; Redox ; Polymer ; Nanotechnology ; Chemical engineering ; Capacitance ; Chemistry ; Composite material ; Carbon nanotube ; Engineering ; Metallurgy Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Transition Metal Oxide Nanomaterials
Authors: Ajaya Bhattarai | Ramesh L. Gardas | Manish Kumar Sah | Biraj Shah Thakuri | J. Pant
Source: Preprints.org (preprint, 2024-01-16)
Field: Materials Science — Materials Chemistry

The Multifaceted Perspective on the Role of Green Synthesis of Nanoparticles in Promoting a Sustainable Green Economy The current economic development paradigm, which is based on steadily rising resource consumption and pollution emissions, is no longer viable in a world with limited resources and ecological capacity. The &amp;quot;green economy&amp;quot; idea has presented this context with a chance to alter how society handles the interplay between the environmental and economic spheres. The related concept of &amp;quot;green nanotechnology&amp;quot; aims to use nano-innovations within the fields of materials science and engineering to generate products and processes that are economically and ecologically sustainable, enabling society to establish and preserve a green economy. Many different economic sectors are anticipated to be impacted by these applications, including those related to corrosion inhibitor nano fertilizers, nano remediation, biodegradation, heavy metal detection, biofuel, insecticides &amp;amp; pesticides, and catalytic CO2 reduction. These innovations might make it possible to use non-traditional water sources safely and to create construction materials that are enabled by nanotechnology, improving living and ecological conditions. Therefore, our aim is to highlight how nanotechnology is being used in the green economy and to present promises for nano applications in this domain. Additionally, we want to critically examine the practical difficulties these applications raise, especially in consideration of any possible implications for the health and safety of those employed within this innovative sector. In the end, it emphasizes how critical it is to attain a really sustainable advancement in nanotechnology. Green economy ; Context (archaeology) ; Sustainable development ; Business ; Environmental pollution ; Engineering ; Nanotechnology ; Ecology ; Environmental science ; Environmental protection ; Materials science Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering
Authors: David Enrique Arceo-Gómez | Javier Reyes Trujeque | Patricia Balderas‐Hernández | Andrés Carmona-Hernández | Araceli Espinoza-Vázquez | Ricardo Galván-Martínez | Ricardo Orozco-Cruz
Source: Surfaces (article, 2024-03-13)
Field: Materials Science — Materials Chemistry

Performance and Surface Modification of Cast Iron Corrosion Products by a Green Rust Converter (Mimosa tenuiflora Extract) One of the alternative materials used for conducting conservation treatment of iron artifacts is the rust converter, since it generates barrier properties and more stable oxides. The protective properties and surface modifications from using Mimosa tenuiflora extract as a green rust converter on a gray iron oxide layer were studied. The surface characterization was carried out using a Scanning Electron Microscope coupled to an energy-dispersive X-ray spectrometer (SEM-EDS), along with infrared spectroscopy (IR), Raman spectroscopy, X-ray Diffraction (XRD), and Water Contact Angle (WCA). Electrochemical characterization was performed with an Electrochemical Impedance Spectroscope (EIS) using 3.5 wt.% NaCl as the electrolyte. According to the results of the Raman spectroscopy and XRD, the layer of corrosion products formed after 90 days in the atmosphere was composed of goethite, lepidocrocite, maghemite, hematite, and magnetite. The surface of the corrosion products was transformed with the rust converter into an amorphous and microcracked layer. By IR, the Fe-O and C-O-Fe bonds associated with the iron chelate were found with absorption bands at 1540 and 1567 cm−1, respectively. By XRD, a modification of the magnetite crystallinity was observed. Finally, the Water Contact Angle and the protective capacity of the corrosion products were improved by the presence of the rust converter. Rust (programming language) ; Corrosion ; Metallurgy ; Materials science ; Traditional medicine ; Medicine ; Computer science Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; High-Temperature Coating Behaviors
Authors: Quinn Gallagher | Michael A. Webb
Source: Digital Discovery (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Data efficiency of classification strategies for chemical and materials design We benchmark the performance of space-filling and active learning algorithms on classification problems in materials science, revealing trends in optimally data-efficient algorithms. Computer science ; Biochemical engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mineral Processing and Grinding