jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hanim Zuhrotul Amanah | Sri Rahayoe | Eni Harmayani | Reza Adhitama Putra Hernanda | Khoirunnisaa | Ajeng Siti Rohmat | Hoonsoo Lee
Source: Open Agriculture (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Construction of a sustainable model to predict the moisture content of porang powder (<i>Amorphophallus oncophyllus</i>) based on pointed-scan visible near-infrared spectroscopy Abstract The moisture content of porang powder (PP) is an inherent quality parameter. Therefore, several analytical methods, such as oven drying and Karl–Fischer titration, were applied to determine the content. However, these techniques are noted to have various disadvantages, such as being time-consuming, requiring sample preparation, being labor-intensive, and producing chemical waste. This study aims to investigate the potential of visible near-infrared (Vis-NIR) spectroscopy as a nondestructive and sustainable analytical technology to predict moisture content in PP. In this study, we developed a traditional machine learning algorithm, a partial least squares regression (PLSR), in tandem with two spectral bands, which are Vis-NIR (400–1,000 nm) and NIR (954–1,700 nm). To upgrade the performance of PLSR, we applied seven preprocessing techniques: mean normalization, maximum normalization, range normalization, multiplicative scatter correction, standard normal variate (SNV), and Savitzky–Golay first and second derivatives. We found that PLSR using NIR spectral bands was more effective; the preprocessed mean normalization exhibited the best results with a coefficient of determination <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mo>(</m:mo> <m:msubsup> <m:mi>R</m:mi> <m:mi>p</m:mi> <m:mn>2</m:mn> </m:msubsup> <m:mo>)</m:mo> </m:math> \left({R}_{p}^{2}) of 0.96 and a standard error prediction (SEP) of 0.56 using five latent variables. Furthermore, we also extracted 39 optimum wavelengths using variable importance in projection and achieved better performance ( <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msubsup> <m:mi>R</m:mi> <m:mi>p</m:mi> <m:mn>2</m:mn> </m:msubsup> </m:math> {R}_{p}^{2} = 0.95, SEP = 0.56%wb, and 5 LVs) via SNV preprocessed NIR spectra. Partial least squares regression ; Normalization (sociology) ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Mathematics ; Spectroscopy ; Water content ; Multiplicative function ; Materials science ; Chemistry ; Statistics ; Physics ; Chromatography ; Optics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Industrial Vision Systems and Defect Detection
Authors: Ebenezer O. Olaniyi | Yuzhen Lu | Xin Zhang | Anuraj T. Sukumaran | Hudson T. Thames | Diksha Pokhrel
Source: Food Analytical Methods (article, 2024-02-29)
Field: Chemistry — Analytical Chemistry

Non-Destructive Assessment of Microbial Spoilage of Broiler Breast Meat Using Structured Illumination Reflectance Imaging with Machine Learning Meat spoilage ; Principal component analysis ; Artificial intelligence ; Food spoilage ; Broiler ; Linear discriminant analysis ; Pattern recognition (psychology) ; Mathematics ; Reflectivity ; Food science ; Support vector machine ; Computer science ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Willis O’Leary | Manuel Grumet | Waldemar Kaiser | Tomáš Bučko | Jennifer L. M. Rupp | David A. Egger
Source: Journal of the American Chemical Society (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors The successful design of solid-state photo- and electrochemical devices depends on the careful engineering of point defects in solid-state ion conductors. Characterization of point defects is critical to these efforts, but the best-developed techniques are difficult and time-consuming. Raman spectroscopy─with its exceptional speed, flexibility, and accessibility─is a promising alternative. Raman signatures arise from point defects due to local symmetry breaking and structural distortions. Unfortunately, the assignment of these signatures is often hampered by a shortage of reference compounds and corresponding reference spectra. This issue can be circumvented by calculation of defect-induced Raman signatures from first principles, but this is computationally demanding. Here, we introduce an efficient computational procedure for the prediction of point defect Raman signatures in solid-state ion conductors. Our method leverages machine-learning force fields and "atomic Raman tensors", i.e., polarizability fluctuations due to motions of individual atoms. We find that our procedure reduces computational cost by up to 80% compared to existing first-principles frozen-phonon approaches. These efficiency gains enable synergistic computational-experimental investigations, in our case allowing us to precisely interpret the Raman spectra of Sr(Ti<sub>0.94</sub>Ni<sub>0.06</sub>)O<sub>3-δ</sub>, a model oxygen ion conductor. By predicting Raman signatures of specific point defects, we determine the nature of dominant defects and unravel impacts of temperature and quenching on <i>in situ</i> and <i>ex situ</i> Raman spectra. Specifically, our findings reveal the temperature-dependent distribution and association behavior of oxygen vacancies and nickel substitutional defects. Overall, our approach enables rapid Raman-based characterization of point defects to support defect engineering in novel solid-state ion conductors. Chemistry ; Raman spectroscopy ; Characterization (materials science) ; Solid-state ; Ion ; Electrical conductor ; Chemical physics ; Spectroscopy ; Molecular physics ; Nanotechnology ; Physical chemistry ; Quantum mechanics ; Physics ; Organic chemistry Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Semiconductor materials and devices
Authors: Stephen T. Schaefer | Davi Fébba | Kingsley Egbo | Glenn Teeter | Andriy Zakutayev | M. Brooks Tellekamp
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapid screening of molecular beam epitaxy conditions for monoclinic (In<sub><i>x</i></sub>Ga<sub>1−<i>x</i></sub>)<sub>2</sub>O<sub>3</sub> alloys High-throughput MBE with cyclical growth and in situ etch increases experimental throughput by approximately 6× and substrate utilization by &gt;40×. Monoclinic crystal system ; Molecular beam epitaxy ; Substrate (aquarium) ; Epitaxy ; Materials science ; Crystallography ; Throughput ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Crystal structure ; Computer science ; Geology ; Organic chemistry ; Operating system Ga2O3 and related materials ; ZnO doping and properties ; GaN-based semiconductor devices and materials
Authors: Rangsiman Ketkaew | Fabrizio Creazzo | Kevin Sivula | Sandra Luber
Source: Chem Catalysis (article, 2024-08-29)
Field: Chemistry — Electrochemistry

A metadynamics study of water oxidation reactions at (001)-WO3/liquid-water interface The WO 3 surface catalyst offers the acceleration of the conversion of water into oxygen and protons. In this work, we study the oxygen evolution reaction (OER) and H 2 O 2 formation as competitive reactions on the (001) WO 3 surface using stateof-the-art simulation techniques: metadynamics and ab initio molecular dynamics. We calculate the free energy surfaces of the OER and H 2 O 2 formation. This research paves the way for further investigation and improvement of efficient catalysts for water oxidation catalysis. Metadynamics ; Liquid water ; Interface (matter) ; Chemical engineering ; Chemistry ; Materials science ; Inorganic chemistry ; Physical chemistry ; Computational chemistry ; Thermodynamics ; Adsorption ; Physics ; Molecular dynamics ; Engineering Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Analytical Chemistry and Sensors
Authors: Christiana Dupe Adegbesan | Samuel Daniel | Cédric Karel Fonzeu Monguen | Hannington Nevin Otieno | Zhen‐Yu Tian
Source: Engineered Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Catalytically Active Cu-Fe/Kaolin Hybrid Catalyst for Semi-Hydrogenation of Acetylene The significance of the work in this submitted manuscript is the synthesis of relatively inexpensive catalyst (CuxFe/kaolin) from nonprecious metal and with excellent selectivity, high conversion and yield. Acetylene ; Catalysis ; Selectivity ; Yield (engineering) ; Metal ; Materials science ; Chemistry ; Chemical engineering ; Nuclear chemistry ; Organic chemistry ; Metallurgy ; Engineering Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Jelle Vekeman | Dries Vandenabeele | Nikolaus Doppelhammer | Elisabeth Vandeurzen | Eric Breynaert | Christine E. A. Kirschhock | Toon Verstraelen
Source: Chemistry of Materials (article, 2024-04-09)
Field: Chemistry — Inorganic Chemistry

Simple Molecular Model for Hydrated Silicate Ionic Liquids, a Realistic Zeolite Precursor Despite the widespread use of zeolites in the chemical industry, their formation process is not fully understood due to the complex and heterogeneous structure of traditional synthesis media. Hydrated silicate ionic liquids (HSILs) have been proposed as an alternative. They are truly homogeneous and transparent mixtures with a low viscosity, facilitating experimental characterization. Interestingly, their homogeneous nature and simple speciation bring realistic molecular models of a zeolite growth liquid within reach for the first time. In this work, a simple molecular model is developed that gives insight into the crucial role of the alkali cations (sodium, potassium, rubidium, and cesium). Thereby, molecular dynamics simulations are combined with experimental measurements to demonstrate that the HSIL liquid structure strongly depends on the charge density and concentration of the alkali cation. As the water content increases, it transitions from a glassy network with fast ion exchange to an aqueous solution containing long-lasting solvated ion pairs. Furthermore, simulations reveal that the cation is capable of bringing several silicate monomers together in a glassy network, displaying perfect orientations for condensation reactions that underlie zeolite formation. This work is an important step toward the development of molecular models that can fully describe the early nucleation process of zeolites in combination with experiments. Zeolite ; Silicate ; Simple (philosophy) ; Ionic bonding ; Ionic liquid ; Chemical engineering ; Materials science ; Inorganic chemistry ; Chemistry ; Organic chemistry ; Catalysis ; Ion ; Engineering Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Ionic liquids properties and applications
Authors: Johann Lüder | Manabu Ihara | Sergei Manzhos
Source: Electronic Structure (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements Abstract We present a machine-learned (ML) model of kinetic energy for orbital-free density functional theory (OF-DFT) suitable for bulk light weight metals and compounds made of group III–V elements. The functional is machine-learned with Gaussian process regression (GPR) from data computed with Kohn-Sham DFT with plane wave bases and local pseudopotentials. The dataset includes multiple phases of unary, binary, and ternary compounds containing Li, Al, Mg, Si, As, Ga, Sb, Na, Sn, P, and In. A total of 433 materials were used for training, and 18 strained structures were used for each material. Averaged (over the unit cell) kinetic energy density is fitted as a function of averaged terms of the 4th order gradient expansion and the product of the density and effective potential. The kinetic energy predicted by the model allows reproducing energy-volume curves around equilibrium geometry with good accuracy. We show that the GPR model beats linear and polynomial regressions. We also find that unary compounds sample a wider region of the descriptor space than binary and ternary compounds, and it is therefore important to include them in the training set; a GPR model trained on a small number of unary compounds is able to extrapolate relatively well to binary and ternary compounds but not vice versa. Kinetic energy ; Group (periodic table) ; Energy (signal processing) ; Chemistry ; Materials science ; Physics ; Mathematics ; Statistics ; Classical mechanics ; Organic chemistry Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Fuel Cells and Related Materials
Authors: Peter G. Mikhael | Itamar Chinn | Regina Barzilay
Source: arXiv (Cornell University) (preprint, 2024-02-09)
Field: Materials Science — Materials Chemistry

CLIPZyme: Reaction-Conditioned Virtual Screening of Enzymes Computational screening of naturally occurring proteins has the potential to identify efficient catalysts among the hundreds of millions of sequences that remain uncharacterized. Current experimental methods remain time, cost and labor intensive, limiting the number of enzymes they can reasonably screen. In this work, we propose a computational framework for in-silico enzyme screening. Through a contrastive objective, we train CLIPZyme to encode and align representations of enzyme structures and reaction pairs. With no standard computational baseline, we compare CLIPZyme to existing EC (enzyme commission) predictors applied to virtual enzyme screening and show improved performance in scenarios where limited information on the reaction is available (BEDROC$_{85}$ of 44.69%). Additionally, we evaluate combining EC predictors with CLIPZyme and show its generalization capacity on both unseen reactions and protein clusters. Enzyme ; Virtual screening ; Computer science ; Chemistry ; Computational biology ; Biochemistry ; Biology Advanced Nanomaterials in Catalysis ; Photosynthetic Processes and Mechanisms ; Amino Acid Enzymes and Metabolism
Authors: Wahid Zamani | Saeedeh Rastgar | Aliakbar Hedayati | M Tajari | Zahra Ghiasvand
Source: Scientific Reports (article, 2024-02-23)
Field: Materials Science — Materials Chemistry

Solvent-thermal approach of MIL-100(Fe)/Cygnea/Fe3O4/TiO2 nanocomposite for the treatment of lead from oil refinery wastewater (ORW) under UVA light The main purpose of this research endeavor is to reduce lead concentrations in the wastewater of an oil refinery through the utilization of a material composed of oyster shell waste (MIL-100(Fe)/Cygnea/Fe<sub>3</sub>O<sub>4</sub>/TiO<sub>2</sub>. Initially, iron oxide nanoparticles (Fe<sub>3</sub>O<sub>4</sub>) were synthesized via solvent-thermal synthesis. It was subsequently coated layer by layer with the organic-metallic framework MIL-100 (Fe) using the core-shell method. Additionally, the solvent-thermal method was utilized to integrate TiO<sub>2</sub> nanoparticles into the magnetic organic-metallic framework's structure. Varieties of analytical analysis were utilized to investigate the physical and chemical properties of the synthetic final photocatalyst. Nitrogen adsorption and desorption technique (BET), scanning electron microscopy (SEM), scanning electron diffraction pattern (XRD), and transmission electron microscopy (TEM). Following the characterization of the final photocatalyst, the physical and chemical properties of the nanoparticles synthesized in each step, several primary factors that significantly affect the removal efficiency in the advanced oxidation system (AOPs) were examined. These variables consist of pH, photocatalyst dosage, lead concentration, and reaction temperature. The synthetic photocatalyst showed optimal performance in the removal of lead from petroleum wastewater under the following conditions: 35 °C temperature, pH of 3, 0.04 g/l photocatalyst dosage, and 100 mg/l wastewater concentration. Additionally, the photocatalyst maintained a significant level of reusability after undergoing five cycles. The findings of the study revealed that the photocatalyst dosage and pH were the most influential factors in the effectiveness of lead removal. According to optimal conditions, lead removal reached a maximum of 96%. The results of this investigation showed that the synthetic photocatalyst, when exposed to UVA light, exhibited an extraordinary capacity for lead removal. Photocatalysis ; Materials science ; Scanning electron microscope ; Nanocomposite ; Adsorption ; Wastewater ; Chemical engineering ; Solvent ; Nuclear chemistry ; Chemistry ; Nanotechnology ; Waste management ; Catalysis ; Organic chemistry ; Composite material Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Gas Sensing Nanomaterials and Sensors
Authors: Sidra Munir | Khoa Minh Ta | Thomas Smith | Lisa J. Gillie | David J. Cooke | Stephen C. Parker | Marco Molinari
Source: The Journal of Physical Chemistry C (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Strain Effects on the Adsorption of Water on Cerium Dioxide Surfaces and Nanoparticles: A Modeling Outlook Nanocrystalline ceria exhibits significant redox activity and oxygen storage capacity. Any factor affecting its morphology can tune such activities. Strain is a promising method for controlling particle morphology, whether as core@shell structures, supported nanoparticles, or nanograins in nanocrystalline ceria. A key challenge is to define routes of controlling strain to enhance the expression of more active morphologies and to maintain their shape during the lifespan of the particle. Here, we demonstrate a computational route to gain insights into the strain effects on particle morphology. We use density functional theory to predict surface composition and particle morphology of strained ceria surfaces, as a function of environmental conditions of temperature and partial pressure of water. We find that adsorbed molecular water is not sufficient to shift stability and as such under all compressive and tensile strains studied, the most stable particle is of octahedral shape, similarly to the unstrained case. When dissociative water is involved at the surfaces of the particle, then the most stable particle morphology changes under high water coverage and tensile strain to cuboidal or truncated cuboidal shapes. This shift in shape is due to strain effects that affect the strength of water adsorption. Cerium ; Adsorption ; Strain (injury) ; Nanoparticle ; Cerium oxide ; Materials science ; Chemistry ; Chemical engineering ; Nanotechnology ; Inorganic chemistry ; Physical chemistry ; Metallurgy ; Engineering Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; ZnO doping and properties
Authors: Beata Kurc | Marita Pigłowska | Paweł Fuć | Natalia Szymlet | Xymena Gross | Adam Piasecki
Source: Ionics (article, 2024-08-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Utilizing kraft lignin–derived hard carbon as an innovative bio-electrode in electrochemical capacitors Abstract In recent years, biomaterials are gaining popularity due to high need to make energy storage devices greener and safer. After the carbonization process, lignin has a slightly larger specific surface and a porous structure, which can provide a significant electrochemical double-layer capacity. When combined with kraft lignin as a binder, which helps maintain the structural integrity of the electrode, the efficient use of the active material can be enhanced. The binder facilitates better dispersion of carbonized lignin particles, reducing agglomeration and ensuring greater availability of active sites for electrolyte ions. The aim of this work is to present a novel lignin-based hard carbon as an electrode material for applications in electrochemical capacitors. To this end, a detailed physicochemical and electrochemical analysis was conducted. Kraft lignin was carbonized at temperatures ranging from 600 to 1000 °C. The resulting material is characterized by thermal stability, a low polydispersion index (PDI), and mesoporosity. Thermogravimetric (TG) analysis was used to determine changes in structure, while functional groups were analyzed using Fourier-transform infrared spectroscopy (FTIR). In electrochemical applications, the material exhibits high cyclic stability and no redox reactions, with the primary mechanism of charge accumulation being based on the electrochemical double layer. Additionally, low resistances contribute to improved charge storage. Graphical abstract Capacitor ; Lignin ; Electrochemistry ; Materials science ; Carbon fibers ; Electrode ; Kraft paper ; Supercapacitor ; Nanotechnology ; Composite material ; Chemistry ; Engineering ; Electrical engineering ; Voltage ; Organic chemistry ; Composite number Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: José Julio Gutiérrez Moreno
Source: Nano Futures (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

<i>Ab initio</i> guided atomistic modelling of nanomaterials on exascale high-performance computing platforms Abstract The continuous development of increasingly powerful supercomputers makes theory-guided discoveries in materials and molecular sciences more achievable than ever before. On this ground, the incoming arrival of exascale supercomputers (running over 10 18 floating point operations per second) is a key milestone that will tremendously increase the capabilities of high-performance computing (HPC). The deployment of these massive platforms will enable continuous improvements in the accuracy and scalability of ab initio codes for materials simulation. Moreover, the recent progress in advanced experimental synthesis and characterisation methods with atomic precision has led ab initio -based materials modelling and experimental methods to a convergence in terms of system sizes. This makes it possible to mimic full-scale systems in silico almost without the requirement of experimental inputs. This article provides a perspective on how computational materials science will be further empowered by the recent arrival of exascale HPC, going alongside a mini-review on the state-of-the-art of HPC-aided materials research. Possible challenges related to the efficient use of increasingly larger and heterogeneous platforms are commented on, highlighting the importance of the co-design cycle. Also, some illustrative examples of materials for target applications, which could be investigated in detail in the coming years based on a rational nanoscale design in a bottom-up fashion, are summarised. Exascale computing ; Nanomaterials ; Computational science ; Ab initio ; Supercomputer ; Computer science ; Nanotechnology ; Parallel computing ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science