jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Oeing | Kevin S. Brandt | Michael Wiedau | Gregor Tolksdorf | Wolfgang Welscher | Norbert Kockmann
Source: Chemie Ingenieur Technik (article, 2023-05-03)
Field: Materials Science — Materials Chemistry

Graph Learning in Machine‐Readable Plant Topology Data Abstract Digitalization shows that data and its exchange are indispensable for a versatile and sustainable process industry. There must be a shift from a document‐oriented to a data‐oriented process industry. Standards for the harmonization of data structures play an essential role in this change. In engineering, DEXPI (Data Exchange in the Process Industry) is already a well‐developed, machine‐readable data standard for describing piping and instrumentation diagrams (P&amp;ID). In this publication, industry, software vendors, and research institutions have joined forces to demonstrate the current developments and potentials of machine‐readable P&amp;IDs in the DEXPI format combined with artificial intelligence. The aim is to use graph neural networks to learn patterns in machine‐readable P&amp;ID data, which results in the efficient engineering and development of new P&amp;IDs. Data exchange ; Harmonization ; Process (computing) ; Computer science ; Piping ; Graph ; Artificial neural network ; Artificial intelligence ; Software ; Software engineering ; Database ; Engineering ; Programming language ; Mechanical engineering ; Theoretical computer science Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Neural Networks and Applications
Authors: Yngve Ögren | Alexey Sepman | Ehsan Fooladgar | Fredrik Weiland | Henrik Wiinikka
Source: Energy and AI (article, 2023-11-04)
Field: Chemistry — Analytical Chemistry

Development and evaluation of a vision driven sensor for estimating fuel feeding rates in combustion and gasification processes A machine vision driven sensor for estimating the instantaneous feeding rate of pelletized fuels was developed and tested experimentally in combustion and gasification processes. The feeding rate was determined from images of the pellets sliding on a transfer chute into the reactor. From the images the apparent area and velocity of the pellets were extracted. Area was determined by a segmentation model created using a machine learning framework and velocities by image registration of two subsequent images. The measured weight of the pelletized fuel passed through the feeding system was in good agreement with the weight estimated by the sensor. The observed variations in the fuel feeding correlated with the variations in the gaseous species concentrations measured in the reactor core and in the exhaust. Since the developed sensor measures the ingoing fuel feeding rate prior to the reactor, its signal could therefore help improve process control. Pellets ; Combustion ; Environmental science ; Process (computing) ; Inlet manifold ; SIGNAL (programming language) ; Core (optical fiber) ; Nuclear engineering ; Process engineering ; Automotive engineering ; Materials science ; Computer science ; Engineering ; Chemistry ; Internal combustion engine Spectroscopy and Chemometric Analyses ; Combustion and flame dynamics ; Thermal and Kinetic Analysis
Authors: Tongyu Liu | Alexander S. Ivanov | Ilja Popovs | Santa Jansone‐Popova | De‐en Jiang
Source: RSC Advances (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

N-oxide ligands for selective separations of lanthanides: insights from computation Preorganized ligands such as bis-lactam-1,10-phenanthroline (BLPhen) show unique selectivity trends across the lanthanide series, indicating the synergistic effects of both N and O donors in complexing with lanthanides. We hypothesize that by replacing amide functional groups with an N-oxide functionality would open the door to new ligand architectures with improved selectivities. To test this idea, we computationally examined mixed N,O-donor ligands containing pyridinic N and N-oxide groups and evaluated their relative aqueous La(iii)/Ln(iii) selectivity by computing free energy changes for the exchange reaction between the designed ligands and a reference ligand. Three novel ligands show promise as excellent extractant agents in selectively separating trivalent lanthanides. The extent of conjugation (and hyperconjugation), the complex geometry, and the electron accumulations on the two O-donors of the N-oxide groups are found to be important factors in dictating the selectivity trends. Lanthanide ; Chemistry ; Selectivity ; Amide ; Ligand (biochemistry) ; Phosphine oxide ; Oxide ; Aqueous solution ; Combinatorial chemistry ; Inorganic chemistry ; Organic chemistry ; Catalysis ; Ion ; Phosphine ; Receptor Radioactive element chemistry and processing ; Extraction and Separation Processes ; Lanthanide and Transition Metal Complexes
Authors: Tasnim Rahman | Brittany L. Bonnett | Derrick Poe | Pubudu N. Wimalasiri | Söenke Seifert | Jyotsana Lal | G. B. Stephenson | Michael J. Servis
Source: Journal of Molecular Liquids (article, 2023-11-23)
Field: Chemistry — Inorganic Chemistry

Decomposition of liquid-liquid extraction organic phase structure into critical and pre-peak contributions Small-angle X-ray scattering ; Scattering ; Chemistry ; Chemical physics ; Amphiphile ; Diluent ; Extraction (chemistry) ; Phase (matter) ; Ternary operation ; Organic chemistry ; Optics ; Physics Radioactive element chemistry and processing ; Chemical and Physical Properties in Aqueous Solutions ; NMR spectroscopy and applications
Authors: U Sharin Shana | M. Nirmala Devi | Dharavath Ramesh | Balaji Kannan | M. Djanaguiraman
Source: International Journal of Statistics and Applied Mathematics (review, 2023-09-01)
Field: Chemistry — Analytical Chemistry

Biochemical process optimization via statistical methods: A mini review Biochemical process optimization is now a crucial topic for research and development. Statistical approaches are currently being used by researchers to more effectively optimize the process, reduce waste and unpredictability, improve product quality, and increase process effectiveness. Current advancements in this field include the use of machine learning techniques and the Design of Experiments (DoE). The significance of statistical approaches as useful instruments for process optimization in biochemical research is highlighted in this work. The Taguchi Method, Response Surface Methodology (RSM), and Artificial Neural Networks (ANN) combined with Genetic Algorithm (GA) are three popular approaches that are focused for further comparison. The study presents an overview of each technique, investigates how it might be applied to optimization, examines its benefits and drawbacks, and identifies its main distinctions. Computer science ; Process (computing) ; Taguchi methods ; Artificial neural network ; Field (mathematics) ; Machine learning ; Design of experiments ; Response surface methodology ; Artificial intelligence ; Quality (philosophy) ; Biochemical engineering ; Engineering ; Mathematics Spectroscopy and Chemometric Analyses ; Viral Infectious Diseases and Gene Expression in Insects ; Fault Detection and Control Systems
Authors: Longwen Tang | Morten M. Smedskjær | Mathieu Bauchy
Source: Acta Materialia (article, 2023-02-02)
Field: Materials Science — Ceramics and Composites

The brittle-to-ductile transition in aluminosilicate glasses is driven by topological and dynamical heterogeneity Amorphous materials can exhibit varying degrees of nanoscale ductility depending on their atomic structure. Despite its critical importance for applications, the physical origin that controls ductility remains largely unknown. Here, by using molecular dynamics simulations, we investigate the ductile-to-brittle transition of oxide glasses as a function of the connectivity of the atomic network. Interestingly, based on topological constraint theory, we show that the structural origin of the ductile-to-brittle transition is the rigidity transition caused by the percolation of stressed-rigid atomic clusters. Our further analysis of four-point correlation functions reveals that, similar to the case of supercooled liquids, the plastic dynamics of oxide glasses at room temperature are strongly correlated and spatially heterogeneous. Surprisingly, the dynamical length scale of plastic events significantly decreases when the stressed-rigid cluster percolates, resulting in a narrower transient plastic rearrangement region. These results provide physical insights into the relationship between the topological features of atomic structure, fracture behavior, and stress-induced dynamical heterogeneity of glasses. Materials science ; Brittleness ; Amorphous solid ; Ductility (Earth science) ; Molecular dynamics ; Supercooling ; Fragility ; Atomic units ; Glass transition ; Amorphous metal ; Percolation (cognitive psychology) ; Oxide ; Percolation theory ; Condensed matter physics ; Chemical physics ; Topology (electrical circuits) ; Thermodynamics ; Physics ; Crystallography ; Composite material ; Polymer ; Metallurgy Glass properties and applications ; Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Sreenivasulu Godesi | Hossam Nada | Joohan Lee | Joon-Hee Kang | Soo‐Youl Kim | Yong‐Seok Choi | Kyeong Lee
Source: Molecules (article, 2023-06-23)
Field: Chemistry — Organic Chemistry

Integration of Hybridization Strategies in Pyridine–Urea Scaffolds for Novel Anticancer Agents: Design, Synthesis, and Mechanistic Insights Annually, millions of new cancer cases are reported, leading to millions of deaths worldwide. Among the newly reported cases, breast and colon cancers prevail as the most frequently detected variations. To effectively counteract this rapid increase, the development of innovative therapies is crucial. Small molecules possessing pyridine and urea moieties have been reported in many of the currently available anticancer agents, especially VEGFR2 inhibitors. With this in mind, a rational design approach was employed to create hybrid small molecules combining urea and pyridine. These synthesized compounds underwent in vitro testing against breast and colon cancer cell lines, revealing potent submicromolar anticancer activity. Compound <b>8a</b>, specifically, exhibited an impressive GI<sub>50</sub> value of 0.06 μM against the MCF7 cancer cell line, while compound <b>8h</b> displayed the highest cytotoxic activity against the HCT116 cell line, with a GI<sub>50</sub> of 0.33 ± 0.042 μM. Notably, compounds <b>8a</b>, <b>8h</b>, and <b>8i</b> demonstrated excellent safety profiles when tested on normal cells. Molecular docking, dynamic studies, and free energy calculations were employed to validate the affinity of these compounds as VEGFR2 inhibitors. Pyridine ; Chemistry ; Docking (animal) ; Urea ; Rational design ; In vitro ; Small molecule ; Cancer ; Cell culture ; Cytotoxic T cell ; Combinatorial chemistry ; Cancer research ; Breast cancer ; Colorectal cancer ; Computational biology ; Biochemistry ; Biology ; Medicine ; Nanotechnology ; Materials science ; Internal medicine ; Medicinal chemistry ; Genetics Synthesis and biological activity ; Click Chemistry and Applications ; Computational Drug Discovery Methods
Authors: Raquel Rodríguez‐Fernández | Ángela Fernández-Gómez | Juan C. Mejuto | Gonzalo Astray
Source: Foods (article, 2023-12-14)
Field: Chemistry — Analytical Chemistry

Modelling Polyphenol Extraction through Ultrasound-Assisted Extraction by Machine Learning in Olea europaea Leaves The study of the phenolic compounds present in olive leaves (<i>Olea europaea</i>) is of great interest due to their health benefits. In this research, different machine learning algorithms such as RF, SVM, and ANN, with temperature, time, and volume as input variables, were developed to model the extract yield and the total phenolic content (TPC) from experimental data reported in the literature. In terms of extract yield, the neural network-based ANN<sub>Z-L</sub> model presents the lowest root mean square error (RMSE) value in the validation phase (9.44 mg/g DL), which corresponds with a mean absolute percentage error (MAPE) of 3.7%. On the other hand, the best model to determine the TPC value was the neural network-based model ANN<sub>R</sub>, with an RMSE of 0.89 mg GAE/g DL in the validation phase (MAPE of 2.9%). Both models obtain, for the test phase, MAPE values of 4.9 and 3.5%, respectively. This affirms that ANN models would be good modelling tools to determine the extract yield and TPC value of the ultrasound-assisted extraction (UAE) process of olive leaves under different temperatures, times, and solvents. Olea ; Mean absolute percentage error ; Mean squared error ; Extraction (chemistry) ; Artificial neural network ; Mathematics ; Yield (engineering) ; Support vector machine ; Statistics ; Artificial intelligence ; Polyphenol ; Mean absolute error ; Machine learning ; Biological system ; Horticulture ; Computer science ; Chemistry ; Chromatography ; Biology ; Materials science ; Biochemistry Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Essential Oils and Antimicrobial Activity
Authors: Philip C. Myint | Dane M. Sterbentz | Justin Brown | Brian Stoltzfus | Jean‐Pierre Delplanque | Jonathan L. Belof
Source: Physical Review Letters (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

Scaling Law for the Onset of Solidification at Extreme Undercooling Quasi-isentropic compression enables one to study the solidification of metastable liquid states that are inaccessible through other experimental means. The onset of this nonequilibrium solidification is known to depend on the compression rate and material-specific factors, but this complex interdependence has not been well characterized. In this study, we use a combination of experiments, theory, and computational simulations to derive a general scaling law that quantifies this dependence. One of its applications is a novel means to elucidate melt temperatures at high pressures. Supercooling ; Metastability ; Non-equilibrium thermodynamics ; Scaling ; Scaling law ; Compression (physics) ; Statistical physics ; Materials science ; Thermodynamics ; Isentropic process ; Mechanics ; Physics ; Mathematics Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Crystallization and Solubility Studies
Authors: Susana I.L. Gomes | Carlos P. Roca | Suman Pokhrel | Lutz Mädler | Janeck J. Scott‐Fordsmand | Mónica J.B. Amorim
Source: NanoImpact (article, 2023-02-27)
Field: Materials Science — Materials Chemistry

TiO2 nanoparticles' library toxicity (UV and non-UV exposure) – High-throughput in vivo transcriptomics reveals mechanisms The hazards of nanomaterials/nanoparticles (NMs/NPs) are mostly assessed using individual NMs, and a more systematic approach, using many NMs, is needed to evaluate its risks in the environment. Libraries of NMs, with a range of identified different but related characters/descriptors allow the comparison of effects across many NMs. The effects of a custom designed Fe-doped TiO<sub>2</sub> NMs library containing 11 NMs was assessed on the soil model Enchytraeus crypticus (Oligochaeta), both with and without UV (standard fluorescent) radiation. Effects were analyzed at organism (phenotypic, survival and reproduction) and gene expression level (transcriptomics, high-throughput 4x44K microarray) to understand the underlying mechanisms. A total of 48 microarrays (20 test conditions) were done plus controls (UV and non-UV). Unique mechanisms induced by TiO<sub>2</sub> NPs exposure included the impairment in RNA processing for TiO<sub>2</sub>_10nm, or deregulated apoptosis for 2%FeTiO<sub>2</sub>_10nm. Strikingly apparent was the size dependent effects such as induction of reproductive effects via smaller TiO<sub>2</sub> NPs (≤12 nm) - embryo interaction, while larger particles (27 nm) caused reproductive effects through different mechanisms. Also, phagocytosis was affected by 12 and 27 nm NPs, but not by ≤11 nm. The organism level study shows the integrated response, i.e. the result after a cascade of events. While uni-cell models offer key mechanistic information, we here deliver a combined biological system level (phenotype and genotype), seldom available, especially for environmental models. Transcriptome ; Microarray ; In vivo ; Biology ; Toxicity ; Organism ; Model organism ; DNA microarray ; Toxicology ; Chemistry ; Cell biology ; Computational biology ; Gene expression ; Gene ; Genetics Nanoparticles: synthesis and applications ; Environmental Toxicology and Ecotoxicology ; Microplastics and Plastic Pollution
Authors: Jamie W. Gittins | Yuan Chen | Stefanie Arnold | Veronica Augustyn | Andrea Balducci | Thierry Brousse | Elżbieta Frąckowiak | Pedro Gómez‐Romero | Archana Kanwade | Lukas Köps | Plawan Kumar Jha | Michele Meo | Deepak Pandey | Le Pang | Volker Presser | Mario Rapisarda | Daniel Rueda-García | Saeed Saeed | Parasharam M. Shirage | Adam Ślesiński | Francesca Soavi | Jayan Thomas | Maria‐Magdalena Titirici | Hongxia Wang | Zhen Xu | Aiping Yu | Maiwen Zhang | Alexander C. Forse
Source: ChemRxiv (preprint, 2023-05-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interlaboratory Study Assessing the Analysis of Supercapacitor Electrochemistry Data Supercapacitors are fast-charging energy storage devices of great importance for the development of robust and climate-friendly energy infrastructures for the future. Research in this field has seen rapid growth in recent years, hence consistent reporting practices must be implemented to enable reliable comparison of device performance. Although several studies have highlighted the best practices for analysing and reporting data from such energy storage devices, there is yet to be an empirical study that investigates whether researchers in the field are correctly implementing these recommendations, and which assesses the variation in reporting between different laboratories. Here, we address this deficit by carrying out the first interlaboratory study of the analysis of supercapacitor electrochemistry data. We find that the use of incorrect formulae and researchers having different interpretations of key terminologies are the primary causes of variability in data reporting. Furthermore, we highlight the more significant variation in reported results for electrochemical profiles showing non-ideal capacitive behaviour. From the insights gained through this study, we make additional recommendations to the community to help ensure consistent reporting of performance metrics moving forward. Supercapacitor ; Data science ; Computer science ; Field (mathematics) ; Risk analysis (engineering) ; Business ; Electrochemistry Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Asanka Weerasinghe | Enrique Martínez | Brian D. Wirth | Dimitrios Maroudas
Source: ACS Applied Materials & Interfaces (article, 2023-01-31)
Field: Materials Science — Materials Chemistry

Molecular-Dynamics Analysis of the Mechanical Behavior of Plasma-Facing Tungsten We report a systematic computational analysis of the mechanical behavior of plasma-facing component (PFC) tungsten focusing on the impact of void and helium (He) bubble defects on the mechanical response beyond the elastic regime. Specifically, we explore the effects of porosity and He atomic fraction on the mechanical properties and structural response of PFC tungsten, at varying temperature and bubble size. We find that the Young modulus of defective tungsten undergoes substantial softening that follows an exponential scaling relation as a function of matrix porosity and He atomic content. Beyond the elastic regime, our high strain rate simulations reveal that the presence of nanoscale spherical defects (empty voids and He bubbles) reduces the yield strength of tungsten in a monotonically decreasing fashion, obeying an exponential scaling relation as a function of tungsten matrix porosity and He concentration. Our detailed analysis of the structural response of PFC tungsten near the yield point reveals that yielding is initiated by emission of dislocation loops from bubble/matrix interfaces, mainly <sup>1</sup>/<sub>2</sub>⟨111⟩ shear loops, followed by gliding and growth of these loops and reactions to form ⟨100⟩ dislocations. Furthermore, dislocation gliding on the ⟨111⟩{211} twin systems nucleates <sup>1</sup>/<sub>6</sub>⟨111⟩ twin regions in the tungsten matrix. These dynamical processes reduce the stress in the matrix substantially. Subsequent dislocation interactions and depletion of the twin phases via nucleation and propagation of detwinning partials lead the tungsten matrix to a next deformation stage characterized by stress increase during applied straining. Our structural analysis reveals that the depletion of twin boundaries (areal defects) is strongly impacted by the density of He bubbles at higher porosities. After the initial stress relief upon yielding, increase in the dislocation density in conjunction with decrease in the areal defect density facilitates the initiation of dislocation-driven deformation mechanisms in the PFC crystal. Tungsten ; Materials science ; Dislocation ; Porosity ; Molecular dynamics ; Nucleation ; Scaling ; Composite material ; Thermodynamics ; Metallurgy ; Computational chemistry ; Geometry ; Physics ; Chemistry Fusion materials and technologies ; Advanced materials and composites ; Ion-surface interactions and analysis
Authors: Robert Rouš | Joseph Peller | G. Polder | Selwin Hageraats | Thijs Ruigrok | Pieter M. Blok
Source: arXiv (Cornell University) (preprint, 2023-02-17)
Field: Chemistry — Analytical Chemistry

Apple scab detection in orchards using deep learning on colour and multispectral images Apple scab is a fungal disease caused by Venturia inaequalis. Disease is of particular concern for growers, as it causes significant damage to fruit and leaves, leading to loss of fruit and yield. This article examines the ability of deep learning and hyperspectral imaging to accurately identify an apple symptom infection in apple trees. In total, 168 image scenes were collected using conventional RGB and Visible to Near-infrared (VIS-NIR) spectral imaging (8 channels) in infected orchards. Spectral data were preprocessed with an Artificial Neural Network (ANN) trained in segmentation to detect scab pixels based on spectral information. Linear Discriminant Analysis (LDA) was used to find the most discriminating channels in spectral data based on the healthy leaf and scab infested leaf spectra. Five combinations of false-colour images were created from the spectral data and the segmentation net results. The images were trained and evaluated with a modified version of the YOLOv5 network. Despite the promising results of deep learning using RGB images (P=0.8, mAP@50=0.73), the detection of apple scab in apple trees using multispectral imaging proved to be a difficult task. The high-light environment of the open field made it difficult to collect a balanced spectrum from the multispectral camera, since the infrared channel and the visible channels needed to be constantly balanced so that they did not overexpose in the images. Multispectral image ; Hyperspectral imaging ; RGB color model ; Artificial intelligence ; Segmentation ; Computer science ; Pixel ; Computer vision ; Channel (broadcasting) ; Remote sensing ; Pattern recognition (psychology) ; Geography Spectroscopy and Chemometric Analyses ; Plant Disease Management Techniques ; Plant Pathogens and Fungal Diseases