jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ning Xu | Liangliang Xu | Yue Wang | Wen Liu | Wenwu Xu | Xiaojuan Hu | Zhongkang Han
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Unraveling the formation of oxygen vacancies on the surface of transition metal-doped ceria utilizing artificial intelligence Ceria has been extensively utilized in different fields, with surface oxygen vacancies playing a central role. However, versatile oxygen vacancy regulation is still in its infancy. In this work, we propose an effective strategy to manipulate the oxygen vacancy formation energy <i>via</i> transition metal doping by combining first-principles calculations and analytical learning. We elucidate the underlying mechanism driving the formation of oxygen vacancies using combined symbolic regression and data analytics techniques. The results show that the Fermi level of the system and the electronegativity of the dopants are the paramount parameters (features) influencing the formation of oxygen vacancies. These insights not only enhance our understanding of the oxygen vacancy formation mechanism in ceria-based materials to improve their functionality but also potentially lay the groundwork for future strategies in the rational design of other transition metal oxide-based catalysts. Doping ; Materials science ; Transition metal ; Oxygen ; Metal ; Nanotechnology ; Chemical engineering ; Chemical physics ; Chemistry ; Metallurgy ; Catalysis ; Optoelectronics ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Bartosz Mazur | Lucyna Firlej | Bogdan Kuchta
Source: ACS Applied Materials & Interfaces (article, 2024-05-06)
Field: Chemistry — Inorganic Chemistry

Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo With the specter of accelerating climate change, securing access to potable water has become a critical global challenge. Atmospheric water harvesting (AWH) through metal-organic frameworks (MOFs) emerges as one of the promising solutions. The standard numerical methods applied for rapid and efficient screening for optimal sorbents face significant limitations in the case of water adsorption (slow convergence and inability to overcome high energy barriers). To address these challenges, we employed grand canonical transition matrix Monte Carlo (GC-TMMC) methodology and proposed an efficient interpolation scheme that significantly reduces the number of required simulations while maintaining accuracy of the results. Through the example of water adsorption in three MOFs: MOF-303, MOF-LA2-1, and NU-1000, we show that the extrapolation of the free energy landscape allows for prediction of the adsorption properties over a continuous range of pressure and temperature. This innovative and versatile method provides rich thermodynamic information, enabling rapid, large-scale computational screening of sorbents for adsorption, applicable for a variety of sorbents and gases. As the presented methodology holds strong applicative potential, we provide alongside this paper a modified version of the RASPA2 code with a ghost swap move implementation and a Python library designed to minimize the user's input for analyzing data derived from the TMMC simulations. Extrapolation ; Python (programming language) ; Monte Carlo method ; Computer science ; Adsorption ; Convergence (economics) ; Computational science ; Materials science ; Mathematical optimization ; Process engineering ; Chemistry ; Mathematics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; High-pressure geophysics and materials
Authors: Laura M. Helleckes | Kira Küsters | Christian Wagner | Rebecca Hamel | Ronja Saborowski | Jan Marienhagen | Wolfgang Wiechert | Marco Oldiges
Source: Microbial Cell Factories (article, 2024-02-24)
Field: Materials Science — Materials Chemistry

High-throughput screening of catalytically active inclusion bodies using laboratory automation and Bayesian optimization At the current state of knowledge, every new enzyme requires screening for different linker/aggregation-inducing tag combinations. For this purpose, the presented CatIB toolbox facilitates fast and simplified construction and screening procedures. The methodology thus assists in finding the best CatIB producer from large libraries in short time, rendering possible automated Design-Build-Test-Learn cycles to generate structure/function learnings. Throughput ; Automation ; Inclusion (mineral) ; Biochemical engineering ; Laboratory automation ; High-throughput screening ; Bayesian optimization ; Bayesian probability ; Computer science ; Engineering ; Chemistry ; Biology ; Artificial intelligence ; Bioinformatics ; Telecommunications Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Electron and X-Ray Spectroscopy Techniques
Authors: David Raciti | Eric Cockayne | John Vinson | Kathleen Schwarz | Angela R. Hight Walker | Thomas P. Moffat
Source: Journal of the American Chemical Society (article, 2024-01-03)
Field: Chemistry — Electrochemistry

SHINERS Study of Chloride Order–Disorder Phase Transition and Solvation of Cu(100) Shell-isolated nanoparticle enhanced Raman spectroscopy (SHINERS) and density functional theory (DFT) are used to probe Cl<sup>-</sup> adsorption and the order-disorder phase transition associated with the c(2 × 2) Cl<sup>-</sup> adlayer on Cu(100) in acid media. A two-component ν(Cu-Cl) vibrational band centered near 260 ± 1 cm<sup>-1</sup> is used to track the potential dependence of Cl<sup>-</sup> adsorption. The potential dependence of the dominant 260 cm<sup>-1</sup> component tracks the coverage of the fluctional c(2 × 2) Cl<sup>-</sup> phase on terraces in good agreement with the normalized intensity of the c(2 × 2) superstructure rods in prior surface X-ray diffraction (SXRD) studies. As the c(2 × 2) Cl<sup>-</sup> coverage approaches saturation, a second ν(Cu-Cl) component mode emerges between 290 and 300 cm<sup>-1</sup> that coincides with the onset and stiffening of step faceting where Cl<sup>-</sup> occupies the threefold hollow sites to stabilize the metal kink saturated Cu <100> step edge. The formation of the c(2 × 2) Cl<sup>-</sup> adlayer is accompanied by the strengthening of ν(O-H) stretching modes in the adjacent non-hydrogen-bonded water at 3600 cm<sup>-1</sup> and an increase in hydronium concentration evident in the flanking H<sub>2</sub>O modes at 3100 cm<sup>-1</sup>. The polarization of the water molecules and enrichment of hydronium arise from the combination of Cl<sup>-</sup> anionic character and lateral templating provided by the c(2 × 2) adlayer, consistent with SXRD studies. At negative potentials, Cl<sup>-</sup> desorption occurs followed by development of a sulfate ν<sub>s</sub>(S═O) band. Below -1.1 V vs Hg/HgSO<sub>4</sub>, a new 200 cm<sup>-1</sup> mode emerges congruent with hydride formation and surface reconstruction reported in electrochemical scanning tunneling microscopy studies. Chemistry ; Hydronium ; Crystallography ; Transition metal ; Adsorption ; Analytical Chemistry (journal) ; Inorganic chemistry ; Molecule ; Physical chemistry Electrochemical Analysis and Applications ; Surface Chemistry and Catalysis ; Spectroscopy and Quantum Chemical Studies
Authors: Prakash Monika | M.N. Chandraprabha | Vivek Radhakrishnan | Prathik Somayaji | Leah Sabu
Source: Wound Repair and Regeneration (review, 2024-09-03)
Field: Materials Science — Biomaterials

Therapeutic potential of silkworm sericin in wound healing applications Chronic wounds are characterised by an imbalance between pro and anti-inflammatory signals, which result in permanent inflammation and delayed re-epithelialization, consequently hindering wound healing. They are associated with bacterial infections, tissue hypoxia, local ischemia, reduced vascularization and MMP-9 upregulation. The global prevalence of chronic wounds has been estimated at 40 million in the adult population, with an alarming annual growth rate of 6.6%, making it an increasingly significant clinical problem. Sericin is a natural hydrophilic protein obtained from the silkworm cocoon. Due to its biocompatibility, biodegradability, non-immunogenicity and oxidation resistance, coupled with its excellent affinity for target biomolecules, it holds great potential in wound healing applications. The silk industry discards 50,000 tonnes of sericin annually, making it a readily available material. Sericin increases cell union sites and promotes cell proliferation in fibroblasts and keratinocytes, thanks to its cytoprotective and mitogenic effects. Additionally, it stimulates macrophages to release more therapeutic cytokines, thus improving vascularization. This review focuses on the biological properties of sericin that contribute towards enhanced wound healing process and its mechanism of interaction with important biological targets involved in wound healing. Emphasis is placed on diverse wound dressing products that are sericin based and the utilisation of nanotechnology to design sericin nanoparticles that aid in chronic wound management. Sericin ; Wound healing ; Biocompatibility ; Inflammation ; Downregulation and upregulation ; Medicine ; Chemistry ; Pharmacology ; Immunology ; Pathology ; Biochemistry Silk-based biomaterials and applications ; Wound Healing and Treatments ; Antimicrobial Peptides and Activities
Authors: Ju Zhou | Feiyi Li | Xinwu Wang | Heng Yin | Wenjing Zhang | Jiaoyang Du | Haibo Pu
Source: Plants (article, 2024-05-03)
Field: Chemistry — Analytical Chemistry

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture. Hyperspectral imaging ; Mean squared error ; Artificial intelligence ; Computer science ; Feature (linguistics) ; Partial least squares regression ; Pattern recognition (psychology) ; Convolutional neural network ; Feature extraction ; Remote sensing ; Mathematics ; Machine learning ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: Jixin Hou | Xianyan Chen | Taotao Wu | Ellen Kuhl | Xianqiao Wang
Source: Acta Biomaterialia (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Automated data-driven discovery of material models based on symbolic regression: A case study on the human brain cortex Regression analysis ; Regression ; Cortex (anatomy) ; Artificial intelligence ; Computer science ; Materials science ; Neuroscience ; Biomedical engineering ; Machine learning ; Statistics ; Psychology ; Mathematics ; Engineering Machine Learning in Materials Science ; Medical Image Segmentation Techniques ; Image Processing and 3D Reconstruction
Authors: Aldo Marano | Clément Ribart | Henry Proudhon
Source: Materials & Design (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Towards a data platform for multimodal 4D mechanics of material microstructures This paper presents advances in the data management strategy applied to 4D multimodal mechanics of material sample microstructures. Guidelines to build a data platform allowing for complex workflows, involving several high-throughput experimental and numerical techniques, and complying with FAIR data management principles, are discussed. Next, their implementation within the open-source Python package Pymicro is presented, offering a high-level interface to build complex datasets through multimodal methodologies. Its capability to enable and automate complex workflows by building a digital twin of a commercially pure titanium sample under tension are then demonstrated. The digital twin contains microstructural and mechanical data for thousands of grains gathered on the same sample through synchrotron DCT and in-situ SEM experiments, as well as full-field numerical simulation. Finally, a local and statistical comparison between simulation and measurements of plastic slip and crystal rotation in hundreds of grains is shown, as an example of the contribution of this platform to multimodal data convergence and its importance for the development of a new generation of material models. • A data management proposition for multimodal microstructure mechanics. • An open-source Python package implementing a data platform interface. • A 4D multimodal digital twin dataset for a polycrystalline Titanium sample with thousands of grains. • A detailed comparison of plastic activity predicted by crystal plasticity in a 3D microstructure and observed experimentally. Materials science ; Microstructure ; Composite material Machine Learning in Materials Science ; Metallurgy and Material Forming ; Titanium Alloys Microstructure and Properties
Authors: Hadeer M. Nasr El Din | Aly Saeed | Emad A. Salem | R.M. El Shazly | Magd Abdel Wahab
Source: Scientific Reports (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

A multi-function glass shield for neutrons and gamma rays of boron- and bismuth-reinforced silicate glass A successful attempt to produce a multi-function glass shield for attenuating neutrons and gamma rays by reinforcing a silicate glass network with boron and bismuth has been accomplished. A composition of 20SiO<sub>2</sub>-80Na<sub>2</sub>O (BSiBi0) was proposed to be used as a host glass network and prepared using the melt/annealing techniques. The low concentration of SiO<sub>2</sub> in BSiBi0 was not sufficient to form a stable glass network. Then, the proposed BSiBi0 was modified with 10, 20, 30, and 40 mol% of each of B<sub>2</sub>O<sub>3</sub> and Bi<sub>2</sub>O<sub>3</sub> (BSiBi1, BSiBi2, BSiBi3, and BSiBi4) simultaneously. The structural effects of adding B<sup>3+</sup> and Bi<sup>3+</sup> were studied through X-ray diffraction, density, and FTIR, which all showed enhancement of glass forming ability, a former role of Bi<sup>3+</sup> ions, and crowded the glass network by BO<sub>4</sub> units. The derived structural parameters <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>-</mml:mo></mml:math> molar volume, mean silicon - silicon separation, mean boron - boron separation, oxygen packing density, packing density, and number of bridging/non-bridging oxygen <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>-</mml:mo></mml:math> were extensively discussed to explore the impact of B<sup>3+</sup> and Bi<sup>3+</sup> on the formed network. The richness of the proposed host glass network by B<sup>3+</sup> and Bi<sup>3+</sup> enhanced its thermal stability. The obtained elastic properties by ultrasonic measurements reflect the increase of the glass rigidity with increasing concentrations of B<sup>3+</sup> and Bi<sup>3+</sup> ions. The obtained glasses have high visible light transparency and almost complete UV absorption. The measured shielding parameters against two types of neutron energies (total slow and slow) and a wide range of gamma rays' energies showed a significant improvement in the shielding efficiency of the considered glasses. The total slow neutrons, slow neutrons, and gamma rays' attenuation abilities were improved by 22.9, 135.5, and 73.8 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>-</mml:mo></mml:math> 199.5%. High thermal stability, elasticity, visible light transparency, and neutrons and gamma rays' attenuation performance features give the produced glasses, especially BSiBi4 glass, preference as shielding materials in nuclear fields. Boron ; Bismuth ; Materials science ; Analytical Chemistry (journal) ; Silicon ; Silicate ; Mineralogy ; Chemical engineering ; Physics ; Chemistry ; Metallurgy Nuclear materials and radiation effects ; Glass properties and applications ; Radiation Shielding Materials Analysis
Authors: Yafei Wang | Aeli P. Olson | Cody Falconer | Brian Kelleher | Ivan Mitchell | Hongliang Zhang | Kumar Sridharan | Jonathan W. Engle | Adrien Couet
Source: Nature Communications (article, 2024-04-10)
Field: Materials Science — Materials Chemistry

Radionuclide tracing based in situ corrosion and mass transport monitoring of 316L stainless steel in a molten salt closed loop In the study, we report an in situ corrosion and mass transport monitoring method developed using a radionuclide tracing technique for the corrosion study of 316L stainless steel (316L SS) in a NaCl-MgCl<sub>2</sub> eutectic molten salt natural circulation loop. This method involves cyclotron irradiation of a small tube section with 16 MeV protons, later welds at the hot leg of the molten salt flow loop, generating radionuclides <sup>51</sup>Cr, <sup>52</sup>Mn, and <sup>56</sup>Co at the salt-alloy interface. By measuring the activity variations of these radionuclides at different sections along the loop, both the in situ monitoring of the corrosion attack depth of 316L SS and corrosion product transport and its precipitation in flowing NaCl-MgCl<sub>2</sub> molten salt are achieved. While 316L SS is the focus of this study, the technique reported herein can be extended to other structural materials being used in a wide range of industrial applications. Radionuclide ; Corrosion ; In situ ; Tracing ; Molten salt ; Mass transport ; Materials science ; Metallurgy ; Salt (chemistry) ; Loop (graph theory) ; Radiochemistry ; Nuclear engineering ; Chemistry ; Nuclear physics ; Computer science ; Physics ; Engineering Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear reactor physics and engineering
Authors: Kevin M. Brindle
Source: npj Imaging (review, 2024-01-11)
Field: Chemistry — Spectroscopy

Imaging cancer metabolism using magnetic resonance The challenge in clinical oncology is to select the most appropriate treatment for an individual patient. Transcriptome and metabolite profiling have revealed that tumours can display metabolic subtypes with different therapeutic vulnerabilities<sup>1-4</sup>. Metabolic imaging has the potential to distinguish these subtypes and therefore those treatment(s) that should be most effective. Moreover, since changes in tumour metabolism can occur early during treatment, metabolic imaging can also be used subsequently to detect early evidence of treatment response. In this Perspective I briefly review and discuss the relative advantages and disadvantages of magnetic resonance imaging of tumour metabolism using hyperpolarized <sup>13</sup>C- and <sup>2</sup>H-labelled substrates. Magnetic resonance imaging ; Cancer imaging ; Metabolite ; Transcriptome ; Medicine ; Magnetic resonance spectroscopic imaging ; Metabolism ; Metabolomics ; Bioinformatics ; Cancer ; Computational biology ; Cancer research ; Biology ; Internal medicine ; Radiology ; Biochemistry ; Gene Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Aliyu Abubakar | Christos Zachariades
Source: Sensors (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Phase-Resolved Partial Discharge (PRPD) Pattern Recognition Using Image Processing Template Matching This paper proposes a new method for recognizing, extracting, and processing Phase-Resolved Partial Discharge (PRPD) patterns from two-dimensional plots to identify specific defect types affecting electrical equipment without human intervention while retaining the principals that make PRPD analysis an effective diagnostic technique. The proposed method does not rely on training complex deep learning algorithms which demand substantial computational resources and extensive datasets that can pose significant hurdles for the application of on-line partial discharge monitoring. Instead, the developed Cosine Cluster Net (CCNet) model, which is an image processing pipeline, can extract and process patterns from any two-dimensional PRPD plot before employing the cosine similarity function to measure the likeness of the patterns to predefined templates of known defect types. The PRPD pattern recognition capabilities of the model were tested using several manually classified PRPD images available in the existing literature. The model consistently produced similarity scores that identified the same defect type as the one from the manual classification. The successful defect type reporting from the initial trials of the CCNet model together with the speed of the identification, which typically does not exceed four seconds, indicates potential for real-time applications. Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Similarity (geometry) ; Template matching ; Cosine similarity ; Matching (statistics) ; Pipeline (software) ; Process (computing) ; Image processing ; Partial discharge ; Computer vision ; Image (mathematics) ; Data mining ; Mathematics ; Engineering ; Voltage High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics